Enhancing intraneural revascularization following peripheral nerve injury through hypoxic Schwann-cell-derived exosomes: an insight into endothelial glycolysis.

BACKGROUND: Endothelial cell (EC)-driven intraneural revascularization (INRV) and Schwann cells-derived exosomes (SCs-Exos) both play crucial roles in peripheral nerve injury (PNI). However, the interplay between them remains unclear. We aimed to elucidate the effects and underlying mechanisms of SCs-Exos on INRV following PNI. RESULTS: We found that GW4869 inhibited INRV, as well as that normoxic SCs-Exos (N-SCs-Exos) exhibited significant pro-INRV effects in vivo and in vitro that were potentiated by hypoxic SCs-Exos (H-SCs-Exos). Upregulation of glycolysis emerged as a pivotal factor for INRV after PNI, as evidenced by the observation that 3PO administration, a glycolytic inhibitor, inhibited the INRV process in vivo and in vitro. H-SCs-Exos more significantly enhanced extracellular acidification rate/oxygen consumption rate ratio, lactate production, and glycolytic gene expression while simultaneously suppressing acetyl-CoA production and pyruvate dehydrogenase E1 subunit alpha (PDH-E1α) expression than N-SCs-Exos both in vivo and in vitro. Furthermore, we determined that H-SCs-Exos were more enriched with miR-21-5p than N-SCs-Exos. Knockdown of miR-21-5p significantly attenuated the pro-glycolysis and pro-INRV effects of H-SCs-Exos. Mechanistically, miR-21-5p orchestrated EC metabolism in favor of glycolysis by targeting von Hippel-Lindau/hypoxia-inducible factor-1α and PDH-E1α, thereby enhancing hypoxia-inducible factor-1α-mediated glycolysis and inhibiting PDH-E1α-mediated oxidative phosphorylation. CONCLUSION: This study unveiled a novel intrinsic mechanism of pro-INRV after PNI, providing a promising therapeutic target for post-injury peripheral nerve regeneration and repair.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Multiple on-line HPLC coupled with biochemical detection methods to evaluate bioactive compounds in Danshen injection.

On-line high performance liquid chromatography (HPLC) coupled with three biochemical detection (BCD) methods was applied to evaluate bioactive components in Danshen injection. On-line HPLC-photo-diode array-fluorescence detection based on the fluorogenic substrate 7-acetoxy-1-methyl quinolinium iodide, was built to search acetylcholinesterase (AChE) inhibitors in Danshen injection. On-line HPLC coupled with the scavenging assay of 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radicals was developed to screen antioxidants. The three active profiles were obviously different. Radical scavenging profiles revealed seven strong peaks in the chromatographic fingerprint possessing obvious free radical inhibition effects, while some minor peaks exhibited stronger AChE inhibition activities. The main radical scavengers and AChE inhibitors were identified by HPLC-MS. Several unknown ingredients showing strong AChE inhibition activities needed further identification except protocatechuic aldehydrate, salvianolic acid H or I and lithospermic acid. The on-line multiple on-line HPLC-BCD methods will provide powerful tools in the field of pharmacognosy for fast-track identification of interesting and/or novel bioactive compounds.

Exploration of the risk factor for infarction after revascularization in moyamoya disease.

RESULTS: Eventually, 108 consecutive patients received 174 surgeries were enrolled, experienced new or expanded infarction occured in 13 (7.47%) surgeries, which showed higher Suzuki stage on the non-operative side, more posterior cerebral artery (PCA) involvement, and more intraoperative hypotension compared to those without infarction(p < .05). The Suzuki stage on the non-operative side had the highest area under the curve (AUC) of 0.737, with a sensitivity of 0.692 and specificity of 0.783. Combination of the three factors showed better efficiency, with an AUC of 0.762, a sensitivity of 0.692, and a specificity of 0.907. CONCLUSIONS: Revascularization was a safe option for patients with MMD, higher Suzuki stage on the non-operative side, PCA involvement, and intraoperative hypotension might be the risk factors for new or expanded infarction after revascularization in patients with MMD.

Elevated triglyceride-glucose index predicts poor outcome in patients with intracranial atherosclerotic stenosis after extracranial and intracranial bypass.

BACKGROUND AND PURPOSE: The triglyceride-glucose (TyG) index, a novel reliable biomarker for IR that incorporates blood glucose and triglyceride, is linked to intracranial atherosclerotic stenosis (ICAS). In this study, we aimed to further investigate the association between the TyG index and the outcomes of ICAS patients following extracranial-to-intracranial (EC-IC) bypass grafting. METHODS: 489 ICAS patients who underwent EC-IC bypass between Jan 2009 and Jan 2022 at our hospital were retrospectively collected. The major adverse cardiac and cerebrovascular events (MACCEs), and anastomotic restenosis, both of which are critical factors leading to poor prognosis of ICAS patients after EC-IC bypass, were mainly recorded and analyzed. Kaplan-Meier survival curve and Log-rank tests were sequentially conducted. Cox regression model was used to investigate the association between the TyG index and MACCEs & anastomotic stenosis. C-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) evaluated the incremental predictive value of the TyG index. RESULTS: A higher incidence of MACCEs and anastomotic stenosis was found in higher-tertile TyG index group. The TyG index was significantly associated with an increased risk of MACCEs and anastomotic stenosis, independent of confounding factors, with a value of HR (1.30, 95%CI 1.10-1.51, p < 0.001) and (1.27, 95%CI 1.16-1.40, p < 0.001) respectively. The area under the curve (AUC) in the model with the TyG index for predicting the occurrence of MACCEs and anastomotic stenosis were 0.708 (95%CI 0.665-0.748) and 0.731 (95%CI 0.689-0.770) respectively. The addition of the TyG index significantly improved the global performance of the baseline model according to the C-statistics, NRI, and IDI (All p < 0.05). CONCLUSIONS: Higher TyG levels were associated with poorer outcomes in ICAS patients after EC-IC bypass. TyG could be a key factor in managing ICAS risk and standardizing the indications for EC-IC bypass.

Berberine inhibits NLRP3 inflammasome activation and proinflammatory macrophage M1 polarization to accelerate peripheral nerve regeneration.

Berberine (BBR) has demonstrated potent anti-inflammatory effects by modulating macrophage polarization. Nevertheless, the precise mechanisms through which berberine regulates post-injury inflammation within the peripheral nerve system remain elusive. This study seeks to elucidate the role of BBR and its underlying mechanisms in inflammation following peripheral nerve injury (PNI). Adult male C57BL/6J mice subjected to PNI were administered daily doses of berberine (0, 60, 120, 180, 240 â€‹mg/kg) via gavage from day 1 through day 28. Evaluation of the sciatic function index (SFI) and paw withdrawal threshold revealed that BBR dose-dependently enhanced both motor and sensory functions. Immunofluorescent staining for anti-myelin basic protein (anti-MBP) and anti-neurofilament-200 (anti-NF-200), along with histological staining comprising hematoxylin-eosin (HE), luxol fast blue (LFB), and Masson staining, demonstrated that BBR dose-dependently promoted structural regeneration. Molecular analyses including qRT-PCR, Western blotting, enzyme-linked immunosorbent assay (ELISA), and immunofluorescence confirmed that inactivation of the NLRP3 inflammasome by MCC950 shifted macrophages from the pro-inflammatory M1 phenotype to the anti-inflammatory M2 phenotype, while also impeding macrophage infiltration. Furthermore, BBR significantly downregulated the expression of the NLRP3 inflammasome and its associated molecules in macrophages, thereby mitigating NLRP3 inflammasome activation-induced macrophage M1 polarization and inflammation. In summary, BBR's neuroprotective effects were concomitant with the suppression of inflammation after PNI, achieved through the inhibition of NLRP3 inflammasome activation-induced macrophage M1 polarization.

Myosteatosis predicting risk of transition to severe COVID-19 infection.

BACKGROUND: About 10-20% of patients with Coronavirus disease 2019 (COVID-19) infection progressed to severe illness within a week or so after initially diagnosed as mild infection. Identification of this subgroup of patients was crucial for early aggressive intervention to improve survival. The purpose of this study was to evaluate whether computer tomography (CT) - derived measurements of body composition such as myosteatosis indicating fat deposition inside the muscles could be used to predict the risk of transition to severe illness in patients with initial diagnosis of mild COVID-19 infection. METHODS: Patients with laboratory-confirmed COVID-19 infection presenting initially as having the mild common-subtype illness were retrospectively recruited between January 21, 2020 and February 19, 2020. CT-derived body composition measurements were obtained from the initial chest CT images at the level of the twelfth thoracic vertebra (T12) and were used to build models to predict the risk of transition. A myosteatosis nomogram was constructed using multivariate logistic regression incorporating both clinical variables and myosteatosis measurements. The performance of the prediction models was assessed by receiver operating characteristic (ROC) curve including the area under the curve (AUC). The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. RESULTS: A total of 234 patients were included in this study. Thirty-one of the enrolled patients transitioned to severe illness. Myosteatosis measurements including SM-RA (skeletal muscle radiation attenuation) and SMFI (skeletal muscle fat index) score fitted with SMFI, age and gender, were significantly associated with risk of transition for both the training and validation cohorts (P < 0.01). The nomogram combining the SM-RA, SMFI score and clinical model improved prediction for the transition risk with an AUC of 0.85 [95% CI, 0.75 to 0.95] for the training cohort and 0.84 [95% CI, 0.71 to 0.97] for the validation cohort, as compared to the nomogram of the clinical model with AUC of 0.75 and 0.74 for the training and validation cohorts respectively. Favorable clinical utility was observed using decision curve analysis. CONCLUSION: We found CT-derived measurements of thoracic myosteatosis to be associated with higher risk of transition to severe illness in patients affected by COVID-19 who presented initially as having the mild common-subtype infection. Our study showed the relevance of skeletal muscle examination in the overall assessment of disease progression and prognosis of patients with COVID-19 infection.

Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study.

Coronavirus disease 2019 (COVID-19) is a global pandemic associated with a high mortality. Our study aimed to determine the clinical risk factors associated with disease progression and prolonged viral shedding in patients with COVID-19. Consecutive 564 hospitalized patients with confirmed COVID-19 between January 17, 2020 and February 28, 2020 were included in this multicenter, retrospective study. The effects of clinical factors on disease progression and prolonged viral shedding were analyzed using logistic regression and Cox regression analyses. 69 patients (12.2%) developed severe or critical pneumonia, with a higher incidence in the elderly and in individuals with underlying comorbidities, fever, dyspnea, and laboratory and imaging abnormalities at admission. Multivariate logistic regression analysis indicated that older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.06), hypertension without receiving angiotensinogen converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB) therapy (OR, 2.29; 95% CI, 1.14-4.59), and chronic obstructive pulmonary disease (OR, 7.55; 95% CI, 2.44-23.39) were independent risk factors for progression to severe or critical pneumonia. Hypertensive patients without receiving ACEI/ARB therapy showed higher lactate dehydrogenase levels and computed tomography (CT) lung scores at about 3 days after admission than those on ACEI/ARB therapy. Multivariate Cox regression analysis revealed that male gender (hazard ratio [HR], 1.22; 95% CI, 1.02-1.46), receiving lopinavir/ritonavir treatment within 7 days from illness onset (HR, 0.75; 95% CI, 0.63-0.90), and receiving systemic glucocorticoid therapy (HR, 1.79; 95% CI, 1.46-2.21) were independent factors associated with prolonged viral shedding. Our findings presented several potential clinical factors associated with developing severe or critical pneumonia and prolonged viral shedding, which may provide a rationale for clinicians in medical resource allocation and early intervention.

[Expression of hCTLA4-Ig mediated by adeno-associated virus in porcine islets and their biological activity].

OBJECTIVE: To evaluate the expression of hCTLA4-Ig and their biological function in newborn porcine islets (NPIs) transfected with AAV-hCTLA4-Ig. METHODS: Cultured NPIs were transfected with AAV-hCTLA4-Ig. The expression of CTLA4-Ig in these NPIs was assayed by RT-PCR and immunocytochemistry. The levels of IL-2, IFN-gamma, and TNF-alpha in the culture medium were assayed by ELISA after these cells the co-cultured with human. The response of glucose-stimulated insulin secretion was observed in the transgene group and the control group. RESULTS: The expressions of CTLA4-Ig mRNA and protein were detected in the transgene group. The levels of cytokines were obviously lower in the transgene group than those in the control group (P<0.01). There was no significant difference in the response of glucose-stimulated insulin release between the transgene group and the control group (P>0.05). CONCLUSION: AAV mediated hCTLA4-Ig expression in NPIs could inhibit T lymphocyte to produce cytokines, while the endocrine functions of the NPIs were not significantly affected.

[Human peripheral blood CD4+ T-lymphocytes reconstituting severe combined immunodeficient mouse-human cell immune system and its immunological function].

OBJECTIVE: To observe whether severe combined immunodeficiency disease (SCID) mice can reconstitute human cell immune system by adoptive transferring of human peripheral blood CD4+ T-lymphocytes to the peritoneal cavity in SCID mice, and to determine the characteristics and function of SCID mice immune system after the reconstitution. METHODS: SCID mice were injected mature human CD4+ T-lymphocytes to the peritoneal cavity, accompanied with the stimulation of rIL-2 after the injection. Six weeks after the injection, mice were killed in batch, the form and dimension of liver and spleen were observed. The DNA of human lymphocytes was detected in SCID mouse peripheral blood by PCR amplification. The lymphocytes phenotype of SCID mouse immune organs were assayed with immunohistochemistry. The concentration of human cytokines in SCID mouse blood serum was assayed with ELISA after transplanting xenografts. RESULTS: Intraperitoneal injection of SCID mice with mature human peripheral blood CD4+ T lymphocytes could graft human cell immune system to SCID mice. Human CD4+ T lymphocytes were found in the liver and spleen, and the immunological function of lymphocytes was normal. The HLA-II constant region segment of human lymphocytes was found in hu-CD4+ T-SCID mouse peripheral blood by PCR amplification. Human IL-2, TNF-alpha, and INF-gamma were found in the serum of hu-CD4+ T-SCID mice. CONCLUSION: Intraperitoneal injection of SCID mice with mature human peripheral blood CD4+ T lymphocytes can result in a human cell immune system. The method is simple, quick and has abundant donors.