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OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.
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Inteligência Artificial , Biomarcadores Tumorais , Neoplasias Ósseas , Neoplasias da Mama , Humanos , Neoplasias da Mama/patologia , Feminino , Neoplasias Ósseas/secundário , Neoplasias Ósseas/sangue , Pessoa de Meia-Idade , Biomarcadores Tumorais/sangue , Adulto , Idoso , Curva ROC , Aprendizado de Máquina , Valor Preditivo dos TestesRESUMO
Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.
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Importance: The long-term estimated risk of development of cataracts among pediatric patients with uveitis is not clear. Objective: To describe factors associated with the development of cataracts among pediatric patients with uveitis. Design, Setting, and Participants: This cohort study used the international TriNetX database to enroll pediatric patients with and without uveitis from January 1, 2002, to December 31, 2022. The nonuveitis cohort consisted of randomly selected control patients matched by age, sex, race and ethnicity, and specific comorbidities. Exposure: Diagnosis of uveitis, identified using diagnostic codes. Main Outcomes and Measures: The primary outcome was the risk of developing cataracts among the uveitis group compared with the nonuveitis comparison group, with hazard ratios (HRs) and 95% CIs reported. Results: A total of 22 687 pediatric patients with uveitis (mean [SD] age, 10.3 [5.6] years; 54.2% male) and 22 687 comparators without uveitis (mean [SD] age, 10.3 [5.6] years; 54.5% male) were enrolled in the study. The risk of cataracts was increased among pediatric patients with uveitis up to a follow-up duration of 20 years (HR, 17.17; 95%CI, 12.90-22.80) from the index date. Subgroup analyses revealed an elevated cataract risk across age groups: 0 to 6 years (HR, 19.09; 95% CI, 10.10-36.00), 7 to 12 years (HR, 27.16; 95% CI, 15.59-47.20), and 13 to 18 years (HR, 13.39; 95% CI, 8.84-20.30); both female sex (HR, 13.76; 95% CI, 9.60-19.71) and male sex (HR, 11.97; 95% CI, 8.47-16.91); and Asian (HR, 13.80; 95% CI, 3.28-58.07), Black or African American (HR, 10.41; 95% CI, 5.60-19.36), and White (HR, 15.82; 95% CI, 11.05-22.60) race. Furthermore, increased cataract risks were also observed among those with and without a history of immunosuppressive agents (with: HR, 26.52 [95% CI, 16.75-41.90]; without: HR, 17.69 [95% CI: 11.39-27.40]), a history of steroid eye drop use (with: HR, 29.51 [95% CI, 14.56-59.70]; without: HR, 16.49 [95% CI, 11.92-22.70]), and a history of intraocular procedures (with: HR, 11.07 [95%CI, 4.42-27.71]; without: HR, 14.49 [95% CI, 10.11-20.70]). Conclusions and Relevance: In this cohort study of pediatric patients with uveitis, an elevated risk of cataracts following a uveitis diagnosis was found compared with pediatric patients without uveitis. The findings suggest that pediatric patients with uveitis should be monitored for cataract development.
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Catarata , Uveíte , Humanos , Uveíte/epidemiologia , Uveíte/etiologia , Catarata/epidemiologia , Catarata/complicações , Catarata/etiologia , Masculino , Feminino , Criança , Adolescente , Pré-Escolar , Fatores de Risco , Estudos de Coortes , Lactente , Modelos de Riscos ProporcionaisRESUMO
OBJECTIVES: The impact of rheumatic diseases, long-term medication, and vaccination on COVID-19 severity remain insufficiently understood, hindering effective patient management. This study aims to investigate factors influencing COVID-19 severity in Chinese rheumatic patients and to provide real-world evidence for patient care. METHODS: We conducted a retrospective observational study consisting of two cohorts, followed by a nested case-control analysis. The outpatient cohort included non-severe COVID-19 patients, while the inpatient cohort included consecutive severe COVID-19 inpatients. Additionally, rheumatic patients from both cohorts were included for the nested case-control study. Clinical information was obtained from electronic medical records and surveys. RESULTS: A total of 749 outpatients and 167 inpatients were enrolled. In the outpatient cohort, rheumatic diseases were identified as a risk factor for the severity of dyspnea (No rheumatic disease: OR = 0.577, 95% CI = 0.396-0.841, p = .004), but not for mortality, length of hospitalization, or hospitalization costs in the inpatient cohort. Long-term glucocorticoids use was identified as an independent risk factor for severity of dyspnea in rheumatic patients (OR = 1.814, 95% CI = 1.235-2.663, p = .002), while vaccination and immunosuppressant treatment showed no association. Vaccination was identified as a protective factor against hospitalization due to COVID-19 in patients with rheumatic diseases (OR = 0.031, 95% CI = 0.007-0.136, p < .001), whereas long-term glucocorticoids and immunosuppressant treatment showed no association. CONCLUSIONS: Rheumatic diseases and long-term glucocorticoids use are significant risk factors for COVID-19 severity in the Chinese population, whereas emphasizing the protective effects of vaccines against COVID-19 severity is crucial. Additionally, the investigation provides preliminary support for the concept that long-term immunosuppressant therapy does not necessarily require additional prescription adjustments.
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COVID-19 , Glucocorticoides , Imunossupressores , Doenças Reumáticas , SARS-CoV-2 , Índice de Gravidade de Doença , Humanos , COVID-19/prevenção & controle , COVID-19/epidemiologia , Doenças Reumáticas/tratamento farmacológico , Doenças Reumáticas/epidemiologia , Masculino , Feminino , Pessoa de Meia-Idade , Estudos Retrospectivos , Glucocorticoides/uso terapêutico , Glucocorticoides/efeitos adversos , Imunossupressores/efeitos adversos , Imunossupressores/uso terapêutico , Fatores de Risco , Idoso , Adulto , China/epidemiologia , Estudos de Casos e Controles , Vacinas contra COVID-19/efeitos adversos , Vacinação , Fatores de Tempo , Hospitalização/estatística & dados numéricosRESUMO
The current polyvinyl chloride (PVC) gel flexible actuators are facing challenges of high input voltage and an insufficient elastic modulus. In this study, we conducted a detailed study on the properties of PVC gel prepared by introducing the modifier polyvinyl chloride-vinyl acetate (P(VC-VA)). We compared a modified PVC gel with the traditional one in terms of the relative dielectric constant, mechanical modulus, and electromechanical actuation performance. Experimental results demonstrated that the introduction of P(VC-VA) enhanced the dielectric constant and reduced the driving electric field strength of PVC gels. The dielectric constant increased from 4.77 to 7.3. The electromechanical actuation performance increased by 150%. We employed the Gent model to fit the experimental results, and the actual experimental data aligned well with the expectations of the Gent model. The research results show that this type of plasticizing method effectively balanced the mechanical and electrical performance of PVC gels. This study summarizes the experimental results and performance analysis of PVC gels prepared using innovative plasticization methods, revealing the potential engineering applications of polymeric gels.
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Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.
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Biomarcadores Tumorais , Neoplasias da Mama , Metástase Neoplásica , Humanos , Neoplasias da Mama/sangue , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico por imagem , Feminino , Biomarcadores Tumorais/sangue , Pessoa de Meia-Idade , Adulto , Ultrassonografia/métodos , IdosoRESUMO
Iron in the leaching solution with high K+ and Na+ concentrations was usually precipitated as the typical hazardous and toxic jarosite residues. However, this method of treatment has been greatly restricted by increasingly strict environmental regulations. Here we propose that iron can be precipitated from the solution with high K+ and Na+ concentrations as recyclable hematite products by adjusting the concentration ratio of sodium and potassium ions in the solution. The transformation behavior of jarosite into hematite in high concentration potassium ion and sodium ion solution was explained based on collision theory. The results indicated that in instances where the concentration ratio of Na+/K+ is ≥ 4:1, the iron present in the solution can be effectively precipitated as a recyclable hematite product, as opposed to forming the conventional hazardous jarosite residue, even under conditions where the potassium ion concentration reaches levels as high as 4 g/L. On the other hand, thermodynamic and molecular dynamics simulations indicate that at a temperature of 185 °C, the decomposition transformation of Na-jarosite (32.64 kJ and 7.25 eV) is more energetically advantageous compared to that of K-jarosite (61.07 kJ and 15.31 eV). The results were verified by the leaching solution from smelting industry. The iron content in the residues is above 58%, the sulfur content is below 4%, the zinc content is below 1%, and the total iron concentration in the supernatant is about 4 g/L, reaching the production index of the smelting industry. The green, environmentally friendly, and recyclable separation of iron in a solution with high concentrations of potassium and sodium ions is achieved, which is of great significance for the treatment of iron-containing solution and wastewater in the chemical industry and metallurgy fields.
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BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. METHODS: This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. RESULTS: The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-ß-D-glucan test (G test). CONCLUSION: We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.
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Infecções Comunitárias Adquiridas , Doenças do Tecido Conjuntivo , Unidades de Terapia Intensiva , Aprendizado de Máquina , Admissão do Paciente , Pneumonia , Humanos , Infecções Comunitárias Adquiridas/diagnóstico , Infecções Comunitárias Adquiridas/epidemiologia , Masculino , Doenças do Tecido Conjuntivo/diagnóstico , Doenças do Tecido Conjuntivo/epidemiologia , Feminino , Pessoa de Meia-Idade , Estudos Retrospectivos , Unidades de Terapia Intensiva/tendências , Idoso , Admissão do Paciente/tendências , Pneumonia/diagnóstico , Pneumonia/epidemiologia , Valor Preditivo dos Testes , China/epidemiologia , AdultoRESUMO
In this article, we propose two methods for designing higher Chern number models from the topological defect perspective. Based on the fact that the Chern number is equal to a summation of the charges of meron defects, we show that the higher Chern number structures can be realized by either moving the positions of merons or increasing the amount of them. The combination of the two methods is also verified to be a viable approach. We shall construct several models and investigate their energy spectrum. More than one gapless state can be observed on the edges of these models. Expectedly, our theory promises to provide not only a simple approach to obtain the Chern number without computing any integrals, but also a practical technique for new material design.
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Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet's 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F's superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.
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BACKGROUND: Myositis ossificans (MO) is a rare disease involving the formation of bone outside the musculoskeletal system. While surgical intervention is the main treatment approach, preventing recurrence and standardized rehabilitation are also crucial. Here, we present a surgical strategy to prevent the recurrence of MO. CASE SUMMARY: A 28-year-old female patient was admitted for the first time for a comminuted fracture of the left olecranon. However, incorrect postoperative rehabilitation resulted in the development of elbow joint stiffness with ectopic ossification, causing a loss of normal range of motion. The patient was diagnosed with MO based on physical examination, X-ray findings, and clinical presentation. We devised a surgical strategy to remove MO, followed by fixation with an Ilizarov frame, and implemented a scientifically reasonable rehabilitation plan. The surgery lasted for 3 h with an estimated blood loss of 45 mL. A drainage tube was placed after surgery, and fluid was aspirated through ultrasound-guided puncture. The patient experienced a significant reduction in joint stiffness after surgery. In the final follow-up at 9 mouths, there was evident improvement in the range of motion of the elbow joint, and no other symptoms were reported. CONCLUSION: The Ilizarov frame is an advantageous surgical technique for facilitating rehabilitation after MO removal. It offers benefits such as passive recovery, individualized treatment, and prompt recovery.
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OBJECTIVES: Predicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing. METHODS: We analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with 5-fold cross validation. RESULTS: Disease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the ß-diversity between groups (p=0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871. CONCLUSIONS: Identification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.
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Bacteriophages (phages) are viruses that specifically target and kill bacteria, serving as a promising therapeutic to combat multidrug-resistant (MDR) pathogens such as Pseudomonas aeruginosa (Pa). However, delivering adequate concentrations of active phages directly to the infection site over sufficient times to eradicate infections remains an outstanding challenge to phage therapy (PT). Here we present "HydroPhage", a biocompatible hydrogel system for the sustained release of high-titre phages to effectively treat infections caused by MDR pathogens. We develop injectable hydrogels comprised of hyaluronic acid (HA) and polyethylene glycol (PEG) crosslinked through static covalent thioether bonds and hemithioacetal-based dynamic covalent crosslinks (DCC), which encapsulate phages at concentration up to 1011 PFU per mL gel, and achieve sustained release over a week with more than 60% total phage recovery. In a preclinical mouse model of extended wound infection, we demonstrate enhanced bacterial clearance compared to intravenous treatment. Thus, using hydrogels for local and sustained delivery of phage may represent an effective approach to eradicating MDR infections broadly.
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The purpose of this study is to develop a smart training and assessment system called SmartCPR, for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee's compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer's teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.
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Corticosteroides , Infecções Comunitárias Adquiridas , Pneumonia , Ensaios Clínicos Controlados Aleatórios como Assunto , Humanos , Infecções Comunitárias Adquiridas/tratamento farmacológico , Pneumonia/tratamento farmacológico , Corticosteroides/uso terapêutico , Corticosteroides/efeitos adversosRESUMO
Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.
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Artefatos , Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Movimento (Física) , Encéfalo/diagnóstico por imagem , Córtex Cerebral , Processamento de Imagem Assistida por Computador/métodosRESUMO
BACKGROUND: Endovascular thrombectomy (EVT) is a standard treatment for acute ischemic stroke (AIS) with large vessel occlusion. Hypertension and increased blood pressure variability within the first 24 h after successful reperfusion are related to a higher risk of symptomatic intracerebral hemorrhage and higher mortality. AIS patients might suffer from ischemia-reperfusion injury following reperfusion, especially within 24 h. Dexmedetomidine (DEX), a sedative commonly used in EVT, can stabilize hemodynamics by inhibiting the sympathetic nervous system and alleviate ischemia-reperfusion injury through anti-inflammatory and antioxidative properties. Postoperative prolonged sedation for 24 h with DEX might be a potential pharmacological approach to improve long-term prognosis after EVT. METHODS: This single-center, open-label, prospective, randomized controlled trial will include 368 patients. The ethics committee has approved the protocol. After successful reperfusion (modified thrombolysis in cerebral infarction scores 2b-3, indicating reperfusion of at least 50% of the affected vascular territory), participants are randomly assigned to the intervention or control group. In the intervention group, participants will receive 0.1~1.0 µg/kg/h DEX for 24 h. In the control group, participants will receive an equal dose of saline for 24 h. The primary outcome is the functional outcome at 90 days, measured with the categorical scale of the modified Rankin Scale, ranging from 0 (no symptoms) to 6 (death). The secondary outcome includes (1) the changes in stroke severity between admission and 24 h and 7 days after EVT, measured by the National Institute of Health Stroke Scale (ranging from 0 to 42, with higher scores indicating greater severity); (2) the changes in ischemic penumbra volume/infarct volume between admission and 7 days after EVT, measured by neuroimaging scan; (3) the length of ICU/hospital stay; and (4) adverse events and the all-cause mortality rate at 90 days. DISCUSSION: This randomized clinical trial is expected to verify the hypothesis that postoperative prolonged sedation with DEX after successful reperfusion may promote the long-term prognosis of patients with AIS and may reduce the related socio-economic burden. TRIAL REGISTRATION: ClinicalTrials.gov NCT04916197. Prospectively registered on 7 June 2021.
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Dexmedetomidina , AVC Isquêmico , Traumatismo por Reperfusão , Acidente Vascular Cerebral , Humanos , AVC Isquêmico/diagnóstico , AVC Isquêmico/cirurgia , Dexmedetomidina/efeitos adversos , Estudos Prospectivos , Reperfusão , Trombectomia/efeitos adversos , Acidente Vascular Cerebral/diagnóstico , Acidente Vascular Cerebral/terapia , Traumatismo por Reperfusão/etiologia , Traumatismo por Reperfusão/prevenção & controle , Prognóstico , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
To explore the effects of different concentrations of zinc ï¼Znï¼ on the growth and root architecture classification of maize seedlings under cadmium ï¼Cdï¼ stressï¼ a hydroponic experiment was conducted to study the effects of different concentrations of Zn ï¼0ï¼ 10ï¼ 25ï¼ 50ï¼ 100ï¼ 200ï¼ and 400 µmol·L-1ï¼ on the growthï¼ root architecture and classification characteristicsï¼ Cd contentï¼ root Cd uptake capacityï¼ and photosynthetic system of maize seedlings under Cd stress ï¼50 µmol·L-1ï¼ by using Zhengdan 958 as the experimental material. Principal component analysis and the membership function method were used for comprehensive evaluation. The results showed that the 50 µmol·L-1 Cd stress had a significant toxic effect on maize seedlingsï¼ which significantly reduced chlorophyll content and photosynthetic parameters. The main root lengthï¼ plant heightï¼ biomassï¼ root forksï¼ and root tipsï¼ including the root length and root surface area of the grade â -â ¢ diameter range and the root volume of the grade â -â ¡ diameter rangeï¼ decreased significantlyï¼ which hindered the normal growth and development of maize seedlings. Compared with that under no Zn applicationï¼ 100 µmol·L-1 and 200 µmol·L-1 Zn application reduced the uptake of Cd by maize seedlingsï¼ significantly reduced the Cd content in shoots and roots and the Cd uptake efficiency. The toxic effect on maize seedlings was alleviatedï¼ and the fresh weightï¼ dry weightï¼ tolerance indexï¼ and root forks of shoots and roots were significantly increased. The photosynthesis of maize seedlings was significantly enhancedï¼ and the photosynthetic rate and the total chlorophyll content was significantly increased. The RLï¼ SAï¼ and RV in the â -â ¡ diameter range reached the maximum at 100 µmol·L-1 Znï¼ and the RLï¼ SAï¼ and RV in the â ¢ diameter range reached the maximum at 200 µmol·L-1 Znï¼ which were significantly higher than those without Zn treatment. The comprehensive evaluation of the growth tolerance of maize seedlings showed that 100 µmol·L-1 and 200 µmol·L-1 Zn had better effects on alleviating Cd toxicity. Comprehensive analysis showed that the application of appropriate concentration of Zn could reduce the Cd content in maize seedlingsï¼ the Cd uptake capacityï¼ and Cd uptake efficiency of rootsï¼ increase the biomass accumulation of maize seedlingsï¼ reduce the effect of Cd toxicity on root architectureï¼ reduce the effect on the light and systemï¼ and improve the tolerance of maize seedlings to Cd.
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Plântula , Poluentes do Solo , Zinco , Cádmio , Zea mays , Raízes de Plantas , ClorofilaRESUMO
Coronavirus disease 2019 (COVID-19) vaccination is essential for patients with autoimmune inflammatory rheumatic diseases (AIIRD) to reduce the risk of morbidity and mortality associated with serious COVID-19 infection. With endemicity, waning of vaccine- and infection-acquired immunity, and development of SARS-CoV-2 variants, the need for additional doses of vaccines against serious illness in high-risk immunocompromised persons remains imperative. This review examines how immunomodulatory therapies affect vaccine-induced immune response in patients with AIIRD. Glucocorticoids, methotrexate, azathioprine, calcineurin inhibitors, mycophenolate mofetil, tumor necrosis factor inhibitors, and abatacept have been shown to variably attenuate both humoral and cellular immune responses to vaccination. Janus kinase inhibitors reduce humoral immune response. In contrast, sulfasalazine, leflunomide, belimumab, interleukin (IL)-17, IL-12/23, IL-6, and IL-1 inhibitors appear favorable, with mild or no impact on vaccine response. Although rituximab is known to profoundly diminish humoral immune response, cellular immunity is relatively preserved. Administering a third and subsequent vaccine dose or temporally coordinating the dosing of immunomodulatory drugs may improve vaccine effectiveness. Further research is needed to personalise vaccination strategies for AIIRD patients, considering their specific immunomodulatory treatments.