Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

[This corrects the article DOI: 10.2196/27806.].

Identify the Characteristics of Metabolic Syndrome and Non-obese Phenotype: Data Visualization and a Machine Learning Approach.

Introduction: A third of the world's population is classified as having Metabolic Syndrome (MetS). Traditional diagnostic criteria for MetS are based on three or more of five components. However, the outcomes of patients with different combinations of specific metabolic components are undefined. It is challenging to be discovered and introduce treatment in advance for intervention, since the related research is still insufficient. Methods: This retrospective cohort study attempted to establish a method of visualizing metabolic components by using unsupervised machine learning and treemap technology to discover the relations between predicting factors and different metabolic components. Several supervised machine-learning models were used to explore significant predictors of MetS and to construct a powerful prediction model for preventive medicine. Results: The random forest had the best performance with accuracy and c-statistic of 0.947 and 0.921, respectively, and found that body mass index, glycated hemoglobin, and controlled attenuation parameter (CAP) score were the optimal primary predictors of MetS. In treemap, high triglyceride level plus high fasting blood glucose or large waist circumference group had higher CAP scores (>260) than other groups. Moreover, 32.2% of patients with high CAP scores during 3 years of follow-up had metabolic diseases are observed. This reveals that the CAP score may be used for detecting MetS, especially for the non-obese MetS phenotype. Conclusions: Machine learning and data visualization can illustrate the complicated relationships between metabolic components and potential risk factors for MetS.

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

BACKGROUND: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country's policy measures. OBJECTIVE: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Machine-Learning Monitoring System for Predicting Mortality Among Patients With Noncancer End-Stage Liver Disease: Retrospective Study.

BACKGROUND: Patients with end-stage liver disease (ESLD) have limited treatment options and have a deteriorated quality of life with an uncertain prognosis. Early identification of ESLD patients with a poor prognosis is valuable, especially for palliative care. However, it is difficult to predict ESLD patients that require either acute care or palliative care. OBJECTIVE: We sought to create a machine-learning monitoring system that can predict mortality or classify ESLD patients. Several machine-learning models with visualized graphs, decision trees, ensemble learning, and clustering were assessed. METHODS: A retrospective cohort study was conducted using electronic medical records of patients from Wan Fang Hospital and Taipei Medical University Hospital. A total of 1214 patients from Wan Fang Hospital were used to establish a dataset for training and 689 patients from Taipei Medical University Hospital were used as a validation set. RESULTS: The overall mortality rate of patients in the training set and validation set was 28.3% (257/907) and 22.6% (145/643), respectively. In traditional clinical scoring models, prothrombin time-international normalized ratio, which was significant in the Cox regression (P<.001, hazard ratio 1.288), had a prominent influence on predicting mortality, and the area under the receiver operating characteristic (ROC) curve reached approximately 0.75. In supervised machine-learning models, the concordance statistic of ROC curves reached 0.852 for the random forest model and reached 0.833 for the adaptive boosting model. Blood urea nitrogen, bilirubin, and sodium were regarded as critical factors for predicting mortality. Creatinine, hemoglobin, and albumin were also significant mortality predictors. In unsupervised learning models, hierarchical clustering analysis could accurately group acute death patients and palliative care patients into different clusters from patients in the survival group. CONCLUSIONS: Medical artificial intelligence has become a cutting-edge tool in clinical medicine, as it has been found to have predictive ability in several diseases. The machine-learning monitoring system developed in this study involves multifaceted analyses, which include various aspects for evaluation and diagnosis. This strength makes the clinical results more objective and reliable. Moreover, the visualized interface in this system offers more intelligible outcomes. Therefore, this machine-learning monitoring system provides a comprehensive approach for assessing patient condition, and may help to classify acute death patients and palliative care patients. Upon further validation and improvement, the system may be used to help physicians in the management of ESLD patients.

Molecular engineering of thixotropic, sprayable fluids with yield stress using associating polysaccharides.

HYPOTHESIS: Molecular engineering facilitates the development of a complex fluid with contradictory requirements of yield stress and sprayability, while minimizing the amount of structuring material (<0.05 wt%). This unique system can be achieved by a biopolymer hydrogel with tunable inter- and intra-molecular interactions for microstructural robustness and molecular extensibility by the variation of chemical conformations that microstructure breaks up under shear followed by a low molecularly extensible response. EXPERIMENTS: Blends of xanthan and konjac glucomannan containing 99.95 wt% water are demonstrated to satisfy these contradictory requirements and formulated as a function of KCl concentrations. A systematic study was performed using shear and extensional rheology and compared to a reference solution of polyethylene oxide (PEO), a well-known, Boger fluid, highlights the role of molecular elasticity in controlling critical rheological properties. Static light scattering (SLS) and simultaneous rheology and small-angle neutron scattering (RheoSANS) are also used to elucidate the equilibrium structure and flow dynamics. FINDINGS: The blends exhibit a lower yield stress and extensional resistance with added KCl, which leads to good spray characteristics in contrast to strain-hardening PEO. The results suggest that the inter-molecular attractions between the two gums leading to network formation with appropriate stiffness, that break up readily under shear, and low molecular elasticity are critical molecular design parameters necessary to achieve sprayable, yields stress fluids.

Correction: Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

[This corrects the article DOI: 10.2196/18585.].

Comparing the Coalescence Rate of Water-in-Oil Emulsions Stabilized with Asphaltenes and Asphaltene-like Molecules.

Asphaltenes are a significant contributor to flow assurance problems related to crude oil production. Because of their polydispersity, model molecules such as coronene and violanthrone-79 (VO-79) have been used as mimics to represent the physiochemical properties of asphaltenes. This work aims to evaluate the emulsion-stabilization characteristics of fractionated asphaltenes and these two model molecules. Such evaluation is expected to better characterize the stabilizing mechanisms of asphaltenes on water-in-oil emulsions. The coalescence process of water-in-oil emulsion droplets is visualized using a microfluidic flow-focusing geometry. The rate of coalescence events is used as the parameter to assess emulsion stability. Interfacial tension (IFT) and oil/brine zeta potential are measured to help explain the differences in the rates of coalescence. VO-79 is found to be better at stabilizing emulsions as compared to coronene. Although VO-79 and asphaltenes have similar interfacial tension and oil/brine zeta potential values, the rate of coalescence differs significantly. This highlights the difficulty in using model molecules to mimic the transport dynamics of asphaltenes.

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach.

BACKGROUND: In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. OBJECTIVE: In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. METHODS: We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. RESULTS: The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. CONCLUSIONS: Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study.

BACKGROUND: Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE: We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS: Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS: Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS: Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Clustering Heatmap for Visualizing and Exploring Complex and High-dimensional Data Related to Chronic Kidney Disease.

BACKGROUND: Preventive medicine and primary health care are essential for patients with chronic kidney disease (CKD) because the symptoms of CKD may not appear until the renal function is severely compromised. Early identification of the risk factors of CKD is critical for preventing kidney damage and adverse outcomes. Early recognition of rapid progression to advanced CKD in certain high-risk populations is vital. METHODS: This is a retrospective cohort study, the population screened and the site where the study has been performed. Multivariate statistical analysis was used to assess the prediction of CKD as many potential risk factors are involved. The clustering heatmap and random forest provides an interactive visualization for the classification of patients with different CKD stages. RESULTS: uric acid, blood urea nitrogen, waist circumference, serum glutamic oxaloacetic transaminase, and hemoglobin A1c (HbA1c) were significantly associated with CKD. CKD was highly associated with obesity, hyperglycemia, and liver function. Hypertension and HbA1c were in the same cluster with a similar pattern, whereas high-density lipoprotein cholesterol had an opposite pattern, which was also verified using heatmap. Early staged CKD patients who are grouped into the same cluster as advanced staged CKD patients could be at high risk for rapid decline of kidney function and should be closely monitored. CONCLUSIONS: The clustering heatmap provided a new predictive model of health care management for patients at high risk of rapid CKD progression. This model could help physicians make an accurate diagnosis of this progressive and complex disease.

Evaluation of urate-lowering therapy in hyperuricemia patients: a systematic review and Bayesian network meta-analysis of randomized controlled trials.

OBJECTIVE: Hyperuricemia is a strong precursor of gout, which deteriorates patients' health and quality of life. Sustained adherence to urate-lowering therapies (ULTs) is crucial for efficacy and therapeutic cost-effectiveness. Recently, several new ULTs have been proposed. We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to reassess the efficacy and safety of the current ULTs, focusing on adherence attrition-related adverse event reporting. METHOD: The Bayesian network meta-analysis was applied to compare ULTs. Drug efficacy and safety were measured by whether the target level of serum urate acid was achieved and whether any adverse events occurred. The results were summarized using the pooled estimates of effect sizes (odds ratios), their precisions (95% credible interval), and the ranking probabilities. RESULTS AND CONCLUSIONS: Thirty-nine RCTs were identified, accumulating 19,401 patients. Consistent with previous studies, febuxostat (≥ 40 mg/day) was superior to other monoagent ULTs. The new findings were as follows: (i) dual-agent ULTs were superior to febuxostat alone, and further surveillance on the adverse effects when lesinurad is uptitrated is needed, and (ii) terminalia bellerica 500 mg/day, a novel xanthine oxidase inhibitor (XOI) made of natural fruit extracts, and topiroxostat ≥ 80 mg/day, an XOI used mostly in Japan, could be new effective options for lowering the occurrence of adherence attrition events. Evidence from RCTs regarding second-line agents, such as probenecid and pegloticase, remains insufficient for clinical decision-making.Key Points• Dual-agent ULTs were superior to febuxostat alone, and further surveillance on the adverse-effects when lesinurad is uptitrated is needed.• Terminalia bellerica 500 mg/day, a novel xanthine oxidase inhibitor (XOI) made of natural fruit extracts, and topiroxostat 80 mg/day, an XOI used mostly in Japan, could be new effective options for lowering the occurrence of adherence attrition events.

Noninvasive and Convenient Screening of Metabolic Syndrome Using the Controlled Attenuation Parameter Technology: An Evaluation Based on Self-Paid Health Examination Participants.

BACKGROUND: There is a medical need for an easy, fast, and non-invasive method for metabolic syndrome (MetS) screening. This study aimed to assess the ability of FibroScan to detect MetS, in participants who underwent a self-paid health examination. METHODS: A retrospective cohort study was conducted on all adults who underwent a self-paid health examination comprising of an abdominal transient elastography inspection using FibroScan 502 Touch from March 2015 to February 2019. FibroScan can assess the level of liver fibrosis by using a liver stiffness score, and the level of liver steatosis by using the controlled attenuation parameter (CAP) score. The logistic regression analysis and receiver operating characteristic curve were applied to select significant predictors and assess their predictability. A final model that included all significant predictors that are found by univariate analysis, and a convenient model that excluded all invasive parameters were created. RESULTS: Of 1983 participants, 13.6% had a physical status that fulfilled MetS criteria. The results showed that the CAP score solely could achieve an area under the curve (AUC) of 0.79 (0.76-0.82) in predicting MetS, and the AUC can be improved to 0.88 (0.85-0.90) in the final model. An AUC of 0.85 (0.83-0.88) in predicting MetS was obtained in the convenient model, which includes only 4 parameters (CAP score, gender, age, and BMI). A panel of predictability indices (the ranges of sensitivity, specificity, positive and negative likelihood ratio: 0.78-0.89, 0.66-0.82, 2.64-4.47, and 0.17-0.26) concerning gender- and BMI-specific CAP cut-off values (range: 191.65-564.95) were presented for practical reference. CONCLUSIONS: Two prediction systems were proposed for identifying individuals with a physical status that fulfilled the MetS criteria, and a panel of predictability indices was presented for practical reference. Both systems had moderate predictive performance. The findings suggested that FibroScan evaluation is appropriate as a first-line MetS screening; however, the variation in prediction performance of such systems among groups with varying metabolic derangements warrants further studies in the future.

Epidemiology of Emergency Department Sepsis: A National Cohort Study Between 2001 and 2012.

The aim of this study is to examine the incidence trend of sepsis over 11 years and compared mortality outcomes among Taiwanese patients with sepsis admitted from emergency department (ED) and non-ED routes. We used a nationwide health insurance database from Taiwan, which comprise of 23 million beneficiaries. Patients with sepsis were identified by ICD-9 CM codes for infection and organ dysfunction from 2001 to 2012. We performed propensity score matching and compared mortality rates between ED-admitted and non ED-admitted patients.During the 11-year study period, we identified 1,256,684 patients with sepsis. 493,397 (29.3%) were admitted through the ED, and 763,287 (70.7%) were admitted directly to the floor. For patients with sepsis, mortality in ED-admitted patients decreased from 27.2% in 2002 to 21.1% in 2012 while that in non-ED admitted patients decreased from 35.3% in 2002 to 30.7% in 2012. Although patients with sepsis admitted through the ED had a higher incidence of organ dysfunction than patients who were directly admitted, they had more favorable outcomes in mortality, length of intensive care unit stay, and hospital stay. After propensity score matching, ED-admitted patients had a 7% lower risk of 90-day mortality (HR, 0.93, 95% CI, 0.89-0.97) compared with directly admitted patients. During the study period, mortality declined faster among ED admitted sepsis patients than directly admitted sepsis patients. Results of this study should be interpreted in light of limitations. Like other administrative database studies, treatment details are not available. Further clinical studies evaluating the treatment and outcome difference between ED and non-ED admitted sepsis patients are warranted.

Examining Asphaltene Solubility on Deposition in Model Porous Media.

Asphaltenes are known to cause severe flow assurance problems in the near-wellbore region of oil reservoirs. Understanding the mechanism of asphaltene deposition in porous media is of great significance for the development of accurate numerical simulators and effective chemical remediation treatments. Here, we present a study of the dynamics of asphaltene deposition in porous media using microfluidic devices. A model oil containing 5 wt % dissolved asphaltenes was mixed with n-heptane, a known asphaltene precipitant, and flowed through a representative porous media microfluidic chip. Asphaltene deposition was recorded and analyzed as a function of solubility, which was directly correlated to particle size and Péclet number. In particular, pore-scale visualization and velocity profiles, as well as three stages of deposition, were identified and examined to determine the important convection-diffusion effects on deposition.

Patient empowerment in a hand hygiene program: differing points of view between patients/family members and health care workers in Asian culture.

BACKGROUND: "Patient empowerment" is an important component of World Health Organization hand hygiene program, but little is known about the intentions and attitude of patients/families and health care workers (HCWs) regarding this. METHODS: A cross-sectional survey using questionnaires was conducted in a tertiary teaching hospital in Taiwan to assess hand hygiene knowledge and the attitudes and intentions regarding patient empowerment among patients/families and HCWs. RESULTS: Among patients/families, 95.4% (329/345) had positive attitudes regarding patient empowerment; however, only 67.2% (232/345) had the positive intention to remind HCWs about hand hygiene (P < .001). Risk factors for negative intention were being female (odds ratio [OR], 1.82; 95% confidence interval [CI]: 1.08-3.03), illiteracy (OR, 3.18; 95% CI: 0.86-11.7), and being patients/families in the pediatric department (OR, 1.86; 95% CI: 0.93-3.64). Among HCWs, the difference between positive attitude (81.1%; 714/880) and positive intention regarding being reminded about hand hygiene (62.8%; 553/880) was significant (P < .001). Risk factors for negative intention were age > 25 years (OR, 3.20; 95% CI: 1.51-6.81) and a negative attitude toward patient empowerment (OR, 10.00; 95% CI: 5.88-16.67). CONCLUSION: There were significant gaps between attitude and intention regarding patient empowerment both among patients/families and HCWs. Special strategies targeting women, the pediatric population, or illiterate people may help improve patient/family participation. Additionally, hand hygiene education should be incorporated into early-stage medical/nursing education to create a facilitating environment. Patients/families and HCWs cooperation is needed to promote the hand hygiene program further.

MALDI mechanism of dihydroxybenzoic acid isomers: desorption of neutral matrix and analyte.

Angular resolved velocity distributions of laser desorbed neutral matrices (dihydroxybenzoic acids, DHB) and analytes (tryptophan) embedded in these matrices were investigated at 322 nm by a modified crossed molecular beam apparatus. Desorbed ions generated from MALDI were measured by a time-of-flight mass spectrometer. Desorptions of neutral matrix and analyte from 2,3-DHB, 2,4-DHB, 2,5-DHB, 2,6-DHB, and 3,5--DHB at 322 nm have similar properties, but the ion intensities are in the order 2,3DHB ≅ 2,6-DHB > 2,5-DHB ≅ 2,4-DHB > 3,5-DHB. It indicates that the combination of various parameters related to neutral species, including absorption coefficient, sublimation energy, contact of analyte and matrix in crystal, and plume dynamics of desorbed species are not crucial in the determination of MALDI process for DHB isomers. The difference of matrix activity of DHB isomers at this wavelength must result from the other properties, like the excited state lifetime, proton affinity, gas-phase basicity, acidity, ionization energy, or the other properties related to the primary reactions in ion generation.

Compliance of health care workers with hand hygiene practices: independent advantages of overt and covert observers.

BACKGROUND: Evaluation and feedback of hand hygiene (HH) compliance are important elements of the WHO multimodal strategy for hospital infection control. Overt observation is recommended, but it may be confounded by Hawthorne effect. Covert observation offers the opportunity to decrease observer bias. In this study we conducted a one year hospital-wide HH promotion program that included medical students (MS) as covert observers. METHODS: HH compliance for the five WHO indications was determined by trained and validated observers. The overt observers consisted of eleven infection control nurses (ICNs) and two unit HH ambassadors (UAs) in each of 83 wards. The covert observers consisted of nine MS during their rotating clinical clerkships. Feedback was provided to department heads and staff each quarter. RESULTS: Of the 23,333 HH observations 76.0% were by MS, 5.3% by ICNs and 18.7% by UAs. The annual compliance rates were MS 44.1%, ICNs 74.4% and UAs 94.1%; P<0.001. The MS found significantly lower annual compliance rates for 4/5 HH indications compared to ICNs and UAs; P<0.05. The ICNs reported significantly improvement from the first to the fourth quarter; P<0.001. This was associated with feedback from the MS of very poor compliance by nurses during the first quarter. CONCLUSIONS: Based on these findings we recommend a two-pronged approach to HH programs. The role of ICNs and UAs is to educate, serve as role models, establish, sustain good HH practices and provide direct feedback. The role of the covert observers is to measure compliance and provide independent feedback.

High ion yields of carbohydrates from frozen solution by UV-MALDI.

An aqueous acetonitrile solution containing oligosaccharides (maltopentaose and polysaccharides) and a matrix (2,5-dihydroxybenzoic acid) was frozen at 100 K for mass analysis using ultraviolet matrix-assisted laser desorption/ionization (UV-MALDI). Compared with conventional UV-MALDI (i.e., using a dry analyte/matrix mixture), a frozen solution generates more oligosaccharide ions and less fragments from postsource decay. Furthermore, the ion signal is long-lasting, and the analyte distribution features enhanced homogeneity. The ion generation efficiency for this procedure is 20-30 times greater than that for a conventional dried mixture. Interestingly, the percentages for maltopentaose fragmentation from postsource decay for the frozen samples are close to zero (<2%), as compared with the 17% and 40% values found for dried samples at low and high laser fluences, respectively. Comparisons with other UV matrixes (α-cyano-4-hydroxycinnamic acid and sinapinic acid) and ionic liquids (2,5-dihydroxybenzoic acid + pyridine and α-cyano-4-hydroxycinnamic acid + butylamine) were investigated, and possible mechanisms are discussed.

Effects of copolypeptides on amyloid fibrillation of hen egg-white lysozyme.

The fibrillation of hen egg-white lysozyme (HEWL) in the absence and presence of simple, unstructured D,L-lysine-co-glycine (D,L-Lys-co-gly) and D,L-lysine-co-L-phenylalanine (D,L-Lys-co-Phe) copolypeptides was studied by using a variety of analytical techniques. The attenuating and decelerating effects on fibrillation are significantly dependent on the polypeptide concentration and the composition ratios in the polypeptide chain. Interestingly, D,L-Lys-co-gly and D,L-Lys-co-Phe copolypeptides with the same composition ratio have comparable attenuating effects on fibrillation. The copolypeptide with highest molar fraction of glycine residue exhibits the strongest suppression of HEWL fibrillation. The copolypeptide has the highest hydrophobic interacting capacity due to the more molar ratio of apolar monomer in the polymer backbone. The major driving forces for the association of HEWL and copolypeptides are likely to be hydrogen bonding and hydrophobic interactions, and these interactions reduce the concentration of free protein in solution available to proceed to fibrillation, leading to the increase of lag time and attenuation of fibrillation. The results of this work may contribute to the understanding of the molecular factors affecting amyloid fibrillation and the molecular mechanism(s) of the interactions between the unstructured polypeptides and the amyloid proteins.