[Using Social Prescribing to Practice Social Responsibility: A Case Study in the Taipei Medical University System].

The Ministry of Education launched the University Social Responsibility (USR) Program in 2018. Since then, universities have actively promoted inter-disciplinary cooperation and implemented high-end knowledge to meet community needs. Universities are using social responsibility to enhance the level of social participation engaged in by their students to achieve the sustainable development goals set by the United Nations. The aim of this paper was to share the experience of the Taipei Medical University (TMU) system in practicing social responsibility by integrating TMU resources with those of TMU-affiliated hospitals. The USR program at Taipei Medical University promotes the reform of school governance through encouraging university faculties to collaborate with hospital physicians to develop a specific curriculum that implements an innovative social-prescribing model. This curriculum is designed to provide opportunities for students to step out the classroom and into the community. Through clinical observations, home visits, and community service, students gain a deep understanding of community health issues from residents in the surrounding neighborhoods. Medical humanities education is achieved through students' involvement in both patient individual care and community health issues. In addition, faculties guide students in reflective learning from holistic healthcare perspectives. Students are encouraged to use evidence-based methods to develop innovative models of care in the community. Medical staff participation in this program helps ensure that social prescribing to community residents accurately reflects professional assessments. These prescriptions refer patients to local health promotion units involved in community collaboration. The medical university students build a bridge between the medical system and the community to implement the goals of health education. The impact of social prescribing on the social determinants of health is known to be significantly greater than that of medicine prescriptions. The expected outcomes of this project are to reduce the health inequalities experienced by vulnerable groups in the community and to share Taiwan's experiences with the academic community worldwide.

Structural basis for DNA-mediated allosteric regulation facilitated by the AAA+ module of Lon protease.

Lon belongs to a unique group of AAA+ proteases that bind DNA. However, the DNA-mediated regulation of Lon remains elusive. Here, the crystal structure of the α subdomain of the Lon protease from Brevibacillus thermoruber (Bt-Lon) is presented, together with biochemical data, and the DNA-binding mode is delineated, showing that Arg518, Arg557 and Arg566 play a crucial role in DNA binding. Electrostatic interactions contributed by arginine residues in the AAA+ module are suggested to be important to DNA binding and allosteric regulation of enzymatic activities. Intriguingly, Arg557, which directly binds DNA in the α subdomain, has a dual role in the negative regulation of ATPase stimulation by DNA and in the domain-domain communication in allosteric regulation of Bt-Lon by substrate. In conclusion, structural and biochemical evidence is provided to show that electrostatic interaction in the AAA+ module is important for DNA binding by Lon and allosteric regulation of its enzymatic activities by DNA and substrate.

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning.

Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.

Crystallization and preliminary X-ray diffraction analysis of the α subdomain of Lon protease from Brevibacillus thermoruber.

DNA-binding ability has previously been reported as a novel function for the thermostable Lon protease from Brevibacillus thermoruber WR-249 (Bt-Lon), and the α subdomain (amino acids 491-605) of Bt-Lon has been identified as being responsible for DNA binding. However, the physiological role and DNA-recognition mode of Bt-Lon still remain unclear. In this study, the crystallization and preliminary crystallographic analysis of the Bt-Lon α subdomain are presented. Native diffraction data to 2.88 Å resolution were obtained from a vitrified crystal at 100 K on the BL13C1 beamline at the NSRRC (National Synchrotron Radiation Research Center), Taiwan. The crystals belonged to space group P23, with unit-cell parameters a = b = c = 94.28 Å. Solvent-content calculations and molecular-replacement results suggest that there are two molecules of Bt-Lon α subdomain per asymmetric unit.

Exploring the Potential Performance of Fibroscan for Predicting and Evaluating Metabolic Syndrome using a Feature Selected Strategy of Machine Learning.

Metabolic syndrome (MetS) includes several conditions that can increase an individual's predisposition to high-risk cardiovascular events, morbidity, and mortality. Non-alcoholic fatty liver disease (NAFLD) is a predominant cause of cirrhosis, which is a global indicator of liver transplantation and is considered the hepatic manifestation of MetS. FibroScan® provides an accurate and non-invasive method for assessing liver steatosis and fibrosis in patients with NAFLD, via a controlled attenuation parameter (CAP) and liver stiffness measurement (LSM or E) scores and has been widely used in current clinical practice. Several machine learning (ML) models with a recursive feature elimination (RFE) algorithm were applied to evaluate the importance of the CAP score. Analysis by ANOVA revealed that five symptoms at different CAP and E score levels were significant. All eight ML models had accuracy scores > 0.9, while treebags and random forest had the best kappa values (0.6439 and 0.6533, respectively). The CAP score was the most important variable in the seven ML models. Machine learning models with RFE demonstrated that using the CAP score to identify patients with MetS may be feasible. Thus, a combination of CAP scores and other significant biomarkers could be used for early detection in predicting MetS.

Exploring and predicting mortality among patients with end-stage liver disease without cancer: a machine learning approach.

OBJECTIVE: End-stage liver disease is a global public health problem with a high mortality rate. Early identification of people at risk of poor prognosis is fundamental for decision-making in clinical settings. This study created a machine learning prediction system that provides several related models with visualized graphs, including decision trees, ensemble learning and clustering, to predict mortality in patients with end-stage liver disease. METHODS: A retrospective cohort study was conducted: the training data were from patients enrolled from January 2009 to December 2010 and followed up to December 2014; validation data were from patients enrolled from January 2015 to December 2016 and followed up to January 2019. Hospitalized patients with noncancer-related chronic liver disease were identified from the hospital's electrical medical records. RESULTS: In traditional multivariable logistic regression and Cox proportional hazard model, prothrombin time of international normalized ratio, which was significant with P value = 0.002, odds ratio = 2.790 and hazard ratio 1.363. Besides, blood urea nitrogen and C-reactive protein were also significant, with P value <0.001 and 0.026. The area under the curve was 0.771 in the receiver operating characteristic curve. In machine learning, blood urea nitrogen and age were regarded as the primary factors for predicting mortality. Creatinine, prothrombin time of international normalized ratio and bilirubin were also significant mortality predictors. The area under the curve of the random forest and AdaBoost was 0.838 and 0.792. CONCLUSION: The machine learning techniques provided a comprehensive assessment of patient conditions; it could help physicians make an accurate diagnosis of chronic liver disease and improve healthcare management.

Sensing of Streptococcus mutans by microscopic imaging ellipsometry.

Microscopic imaging ellipsometry is an optical technique that uses an objective and sensing procedure to measure the ellipsometric parameters ? and ? in the form of microscopic maps. This technique is well known for being noninvasive and label-free. Therefore, it can be used to detect and characterize biological species without any impact. Microscopic imaging ellipsometry was used to measure the optical response of dried Streptococcus mutans cells on a glass substrate. The ellipsometric ? and ? maps were obtained with the Optrel Multiskop system for specular reflection in the visible range ( ? = 450 to 750 nm). The ? and ? images at 500, 600, and 700 nm were analyzed using three different theoretical models with single-bounce, two-bounce, and multibounce light paths to obtain the optical constants and height distribution. The obtained images of the optical constants show different aspects when comparing the single-bounce analysis with the two-bounce or multibounce analysis in detecting S. mutans samples. Furthermore, the height distributions estimated by two-bounce and multibounce analyses of S. mutans samples were in agreement with the thickness values measured by AFM, which implies that the two-bounce and multibounce analyses can provide information complementary to that obtained by a single-bounce light path.

Backbone resonance assignments of the α sub-domain of Brevibacillus thermoruber Lon protease.

Lon is an ATPases associated with diverse cellular activities protease and belongs to a unique group that binds DNA. The α sub-domain of Lon protease is responsible for DNA-binding, but the structural information for its DNA-recognition mode is still limited. Here, we report (1)H, (15)N and (13)C backbone assignment for the α sub-domain from Brevibacillus thermoruber Lon protease as the basis for the elucidation of its structure and interactions with DNA, necessary for understanding the allosteric regulatory mechanism of the enzymatic function.

Crystal engineering for pi-pi stacking via interaction between electron-rich and electron-deficient heteroaromatics.

New dipolar compounds containing alternating electron-rich thieno[3,2-b]thiophene units and electron deficient units have been synthesized. Compounds with 5-pyrimidinyl (compound 2) or benzothiazole (compound 5) as the electron-deficient unit were structurally characterized by the single-crystal X-ray diffraction method. The arrangement of the molecules is found to be one-dimensional slipped-pi-stack for 2. That of 5 is of slipped-pi-stack, albeit with a tilt angle between neighboring pi-stacks. The pi-pi interfacial distances of the molecules in the crystal lattice are 3.47 and 3.59 A for 2 and 5, respectively. On the basis of the crystal structure, compound 2, with negligible pi-pi slip along the short axis of the molecules, has a calculated electronic coupling value (0.153 eV) twice as large as that of the largest coupling of pentacene. Accordingly, the theoretically estimated hole mobility (mu(+)) for 2 (2.32 cm(2) s(-1) V(-1)) compares favorably with that of pentacene (1.93-5.43 cm(2) s(-1) V(-1)), despite of the larger reorganization energy for hole transport in 2. The symmetric intrastack S...C contacts found between the thieno[3,2-b]thiophene and pyrimidinyl units explain the unique features of the crystal structure of 2 and the resulting large electronic coupling.