Reduction of Superoxide Radical Intermediate by Polydopamine for Efficient Hydrogen Peroxide Photosynthesis.

The synthesis of hydrogen peroxide through artificial photosynthesis is a green and promising technology with advantages in sustainability, economy and safety. However, superoxide radical (⋅O2 -), an important intermediate in photocatalytic oxygen reduction to H2O2 production, has strong oxidizing properties that potentially destabilize the catalyst. Therefore, avoiding the accumulation of ⋅O2 - for its rapid conversion to H2O2 is of paramount significance in improving catalyst stability and H2O2 yield. In this work, a strategy was developed to utilize protonated groups for the rapid depletion of converted ⋅O2 -, thereby the efficiency of photocatalytic synthesis of H2O2 from CN was successfully enhanced by 47-fold. The experimental findings demonstrated that polydopamine not only improved carrier separation efficiency, and more importantly, provided the adsorption reduction active site for ⋅O2 - for efficient H2O2 production. This work offers a versatile approach for synthesizing efficient and stable photocatalysts.

Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier.

The Swiss classification of surgical interventions (CHOP) has to be used in daily practice by physicians to classify clinical procedures. Its purpose is to encode the delivered healthcare services for the sake of quality assurance and billing. For encoding a procedure, a code of a maximal of 6-digits has to be selected from the classification system, which is currently realized by a rule-based system composed of encoding experts and a manual search in the CHOP catalog. In this paper, we will investigate the possibility of automatic CHOP code generation based on a short query to enable automatic support of manual classification. The wide and deep hierarchy of CHOP and the differences between text used in queries and catalog descriptions are two apparent obstacles for training and deploying a learning-based algorithm. Because of these challenges, there is a need for an appropriate classification approach. We evaluate different strategies (multi-class non-terminal and per-node classifications) with different configurations so that a flexible modular solution with high accuracy and efficiency can be provided. The results clearly show that the per-node binary classification outperforms the non-terminal multi-class classification with an F1-micro measure between 92.6 and 94%. The hierarchical prediction based on per-node binary classifiers achieved a high exact match by the single code assignment on the 5-fold cross-validation. In conclusion, the hierarchical context from the CHOP encoding can be employed by both classifier training and representation learning. The hierarchical features have all shown improvement in the classification performances under different configurations, respectively: the stacked autoencoder and training examples aggregation using true path rules as well as the unified vocabulary space have largely increased the utility of hierarchical features. Additionally, the threshold adaption through Bayesian aggregation has largely increased the vertical reachability of the per node classification. All the trainable nodes can be triggered after the threshold adaption, while the F1 measures at code levels 3-6 have been increased from 6 to 89% after the threshold adaption.

Examining the association between urban green space and viral transmission of COVID-19 during the early outbreak.

Even though exposure to urban green spaces (UGS) has physical and mental health benefits during COVID-19, whether visiting UGS will exacerbate viral transmission and what types of counties would be more impacted remain to be answered. In this research, we adopted mobile phone data to measure the county-level UGS visitation across the United States. We developed a Bayesian model to estimate the effective production number of the pandemic. To consider the spatial dependency, we applied the geographically weighted panel regression to estimate the association between UGS visitation and viral transmission. We found that visitations to UGS may be positively correlated with the viral spread in Florida, Idaho, New Mexico, Texas, New York, Ohio, and Pennsylvania. Especially noteworthy is that the spread of COVID-19 in the majority of counties is not associated with green space visitation. Further, we found that when people visit UGS, there may be a positive association between median age and viral transmission in New Mexico, Colorado, and Missouri; a positive association between concentration of blacks and viral transmission in North Dakota, Minnesota, Wisconsin, Michigan, and Florida; and a positive association between poverty rate and viral transmission in Iowa, Missouri, Colorado, New Mexico, and the Northeast United States.

MiRNA-671-5p Promotes prostate cancer development and metastasis by targeting NFIA/CRYAB axis.

Prostate cancer (PCa) is the second cause of death due to malignancy among men, and metastasis is the leading cause of mortality in patients with PCa. MicroRNAs (miRNAs) play important regulatory roles in tumor development and metastasis. Here, we identified 13 miRNAs related to PCa metastasis by bioinformatics analysis. Moreover, we found that miR-671-5p was increased in metastatic PCa tissues, and its high expression indicated poor prognosis of PCa. MiR-671-5p could facilitate PCa cells proliferation, migration, and invasion in vitro and vivo. We confirmed that miR-671-5p directly bound to the 3' untranslated regions of NFIA mRNA, and NFIA directly bound to the CRYAB promoter. High expression of NFIA and CRYAB negatively correlated with the advanced clinicopathological characteristics and metastasis status of PCa patients. Our study demonstrated that miR-671-5p promoted PCa development and metastasis by suppressing NFIA/ CRYAB axis.

Significant Increase of Sexual Dysfunction in Patients With Renal Failure Receiving Renal Replacement Therapy: A Systematic Review and Meta-Analysis.

BACKGROUND: It has been shown that sexual dysfunction (SD) is highly prevalent among patients with chronic renal failure (CRF), and starting renal replacement therapy may even increase it. However, SD is an infrequently reported problem in these treated patients. AIM: To investigate the prevalence of SD among patients with CRF undergoing renal replacement therapy, by a meta-analysis method. METHODS: PubMed, Embase, and the Cochrane Library were systematically searched for all studies assessing sexual function in patients with CRF receiving renal replacement therapy from January 2000 to April 2020. Relative risk (RR) with 95% CIs was used for analysis to assess the risk of SD in patients with CRF receiving renal replacement therapy. The cross-sectional study quality methodology checklist was used for the cross-sectional study. The methodologic quality of the case-control and cohort studies was assessed with the Newcastle-Ottawa Scale. Data were pooled for the random-effect model. Sensitivity analyses were conducted to assess potential bias. The Begg and Egger tests were used for publication bias analysis. OUTCOMES: The prevalence of SD among patients with CRF receiving renal replacement therapy was summarized using pooled RR and 95% CI. RESULTS: This meta-analysis included 3,725 participants from 10 studies. Of these, 737 were patients with CRF receiving renal replacement therapy. The mean age of participants ranged from 32.75 to 56.1 years. Based on the random-effect model, synthesis of results demonstrated that the prevalence of SD was significantly increased among patients with CRF receiving renal replacement therapy in women (RR = 2.07, 95% CI: 1.47-2.91, P = .000; heterogeneity: I2 = 78.7%, P = .000) and in men (RR = 2.95, 95% CI: 2.16-4.02, P = .000; heterogeneity: I2 = 86.1%, P = .000). Estimates of the total effects were generally consistent in the sensitivity analysis. No evidence of publication bias was observed. CLINICAL IMPLICATIONS: Patients with CRF receiving renal replacement therapy had a significantly increased risk of SD, which suggests that clinicians should evaluate sexual function, when managing patients with CRF receiving renal replacement therapy. STRENGTHS AND LIMITATIONS: This is the first study to explore the prevalence of SD among patients with CRF undergoing renal replacement therapy based on all available epidemiologic studies. However, all included studies were an observational design, which may downgrade this evidence. CONCLUSION: The prevalence of SD is significantly increased among patients with CRF receiving renal replacement therapy. More research studies are warranted to clarify the relationship. Luo L, Xiao C, Xiang Q, et al. Significant Increase of Sexual Dysfunction in Patients With Renal Failure Receiving Renal Replacement Therapy: A Systematic Review and Meta-Analysis. J Sex Med 2020;17:2382-2393.

The Correlation between PSCA Expression and Neuroendocrine Differentiation in Prostate Cancer.

The prostate stem cell antigen (PSCA), as a predominantly prostate-specific marker, is overexpressed in most prostate cancer specimens, is positively correlated with prostate cancer androgen independence, and has the potential to be treated with castration-resistant prostate cancer (CRPC) as a gene therapy target. Using the typical androgen deprivation therapy, most tumors will progress to CRPC, as well as develop into neuroendocrine prostate cancer (NEPC) characterized by the expression of neuroendocrine markers such as enolase 2 (NSE). Our study was aimed at investigating the expressions of PSCA and NSE and the relationship between the two markers, as well as the correlation between the PSCA and NSE expressions and the clinicopathological parameters in prostate cancer specimens from 118 patients by using immunohistochemistry. Our results demonstrated that the PSCA and NSE protein expressions did not correlate with the prostate cancer patients' age or the hormone therapy but showed a significant correlation with the pathological tumor stage of prostate cancer, the Gleason score, and the presence of metastasis. There is a positive association between PSCA and NSE but a negative one between the prostate-specific antigen (PSA) and PSCA or between PSA and NSE. High PSCA and NSE expressions correlated with a poor prognosis in prostate cancer patients. PSCA may play an important role in the progression of neuroendocrine prostate cancer (NEPC).

The m6A methylation regulator-based signature for predicting the prognosis of prostate cancer.

Aim: To construct a survival prediction signature for prostate cancer (PC) based on the RNA N6-methyladenosine (m6A) methylation regulator. Materials & methods: This paper explores the interaction network of differentially expressed m6A RNA methylation regulators in PC by Pearson correlation analysis. Univariate Cox risk regression and LASSO regression analysis were used to construct a predictive signature of PC. Kaplan-Meier survival analysis compared the overall survival of the high- and low-risk groups. Results & Conclusion: We first constructed a prognostic two gene signature for PC based on the m6A RNA methylation regulators MRTTL14 and YTHDF2. The interaction network of m6A RNA methylation regulators in PC was also established.

Identifying the key genes and microRNAs in prostate cancer bone metastasis by bioinformatics analysis.

Prostate adenocarcinoma (PCa) is the most common cause of death due to malignancy among men, and bone metastasis is the leading cause of mortality in patients with PCa. Therefore, identifying the causes and molecular mechanism of bone metastasis is important for early detection, diagnosis and personalized therapy. In this study, we systematically analyzed molecular correlates of bone metastasis by bioinformatics analysis. A total of 12 differentially expressed microRNAs (miRNAs) and 102 differentially expressed genes were identified. Five miRNAs had prognostic significance in biochemical recurrence-free survival (miR-636, miR-491-5p, miR-199b-5p, miR-199b-3p, miR-28-3p). The differentially expressed genes were significantly enriched in extracellular matrix, cell-substrate adhesion, collagen and integrin. Seven hub genes (VCAN, COL3A1, COL1A1, APOE, COL1A2, SDC1, THY1) with worse biochemical recurrence-free survival and one hub gene (MMP9) with worse overall survival were detected. miR-636, a novel oncogene, was found to be up-regulated in bone metastatic PCa tissues and also predominately up-regulated in human PCa cell lines. miR-636 promoted cellular invasion and migration, and may promote bone metastasis via targeting MBNL2, TNS1 and STAB1. In conclusion, we have successfully defined molecular signatures of bone metastasis in PCa.

Mesenchymal stem cell-derived exosomes ameliorate erection by reducing oxidative stress damage of corpus cavernosum in a rat model of artery injury.

Erectile dysfunction (ED) is a common ageing male's disease, and vascular ED accounts for the largest proportion of all types of ED. One of the mechanisms of vascular ED in the clinic is arterial insufficiency, which mainly caused by atherosclerosis, trauma and surgical. Moreover, oxidative stress damage after tissue ischemia usually aggravated the progress of ED. As a new way of acellular therapy, mesenchymal stem cell-derived exosomes (MSC-Exos) have great potential in ED treatment. In the current study, we have explored the mechanism of MSC-Exos therapy in a rat model of internal iliac artery injury-induced ED. Compared with intracavernous (IC) injection of phosphate-buffered saline after artery injury, of note, we observed that both mesenchymal stem cells (MSCs) and MSC-Exos through IC injection could improve the erectile function to varying degrees. More specifically, IC injection MSC-Exos could promote cavernous sinus endothelial formation, reduce the organization oxidative stress damage, and improve the nitric oxide synthase and smooth muscle content in the corpus cavernosum. With similar potency compared with the stem cell therapy and other unique advantages, IC injection of MSC- Exos could be an effective treatment to ameliorate erectile function in a rat model of arterial injury.

Towards Emotion-Sensitive Conversational User Interfaces in Healthcare Applications.

Perception of emotions and adequate responses are key factors of a successful conversational agent. However, determining emotions in a healthcare setting depends on multiple factors such as context and medical condition. Given the increase of interest in conversational agents integrated in mobile health applications, our objective in this work is to introduce a concept for analyzing emotions and sentiments expressed by a person in a mobile health application with a conversational user interface. The approach bases upon bot technology (Synthetic intelligence markup language) and deep learning for emotion analysis. More specifically, expressions referring to sentiments or emotions are classified along seven categories and three stages of strengths using treebank annotation and recursive neural networks. The classification result is used by the chatbot for selecting an appropriate response. In this way, the concerns of a user can be better addressed. We describe three use cases where the approach could be integrated to make the chatbot emotion-sensitive.

Gout is associated with elevated risk of erectile dysfunction: a systematic review and meta-analysis.

Previous studies investigating the risk of erectile dysfunction (ED) among patients with gout have produced inconsistent evidence. Therefore, the aim of this meta-analysis was to investigate the relationship between gout and the risk of ED. The Embase, Medline, Scopus, Web of Science and Cochrane Library databases were searched for all studies assessing the risk of ED in patients with gout. Relative risks (RR) and corresponding 95% confidence intervals (CI) were adopted to estimate the association between gout and the risk of ED. Sensitivity analyses were applied to evaluate the robustness of results. Overall, 355,761 participants were included from 8 studies (3 cross-sectional and 5 cohort studies). Of these, 85,067 were patients with gout. Synthesis results showed patients with gout had a 1.2-fold higher risk of ED than individual without gout (RR 1.20, 95% CI 1.10-1.31, P < 0.001). The results of sensitivity analysis are consistent with the trend of synthesis results. The present meta-analysis revealed that the risk of ED in patients with gout was dramatically increased when compared with the general population, which suggests that clinicians should assess erectile function when treating an individual who suffers from gout.

Elucidating Mechanisms of Long-Term Gasoline Vehicle Exhaust Exposure-Induced Erectile Dysfunction in a Rat Model.

INTRODUCTION: Exposure to air pollution poses a risk for morbidity in multiple diseases. However, the role of ambient air pollutant emissions in public sexual health is just beginning to be understood and remains controversial. AIM: We have determined to elucidate the specific role of gasoline vehicle exhaust (VE), a crucial source and toxicant of air pollution, in the penile erectile function via a rat model. METHODS: 40 male Sprague Dawley rats, 12 weeks of age, were used in this experiment. Except for the control group (10 rats), rats were equally exposed to VE for total 2 hours, 4 hours, and 6 hours daily for 3 months consecutively. During each VE exposure periods, particulate matter (PM) mass concentrations of PM1, PM2.5, and PM10 were 1.43 ± 0.036, 1.45 ± 0.033, and 1.47 ± 0.037 mg/m3, respectively. MAIN OUTCOME MEASURE: Erectile function, pulmonary function, serum inflammatory factors, and histologic examinations of the lung and penile tissues were evaluated. RESULTS: Our study indicates that in vivo, 4-hour, and 6-hour daily exposure to VE causes significant reduction of erectile function, as judged by intracavernous pressure measurement. Meanwhile, we have observed that the 4-hour and 6-hour VE exposure rats exhibited remarkable increased levels of serum inflammatory factors, decreased total lung capacity and chord compliance, thickened alveoli septum, destroyed alveoli, pulmonary fibrosis, as well as down-regulation of the messenger RNA and protein expression of endothelial and neuronal nitric oxide synthase in the penile tissue when compared with normal control rats. CLINICAL IMPLICATIONS: We speculated that the underlying mechanisms of VE inducing erectile dysfunction could be attributed to systemic inflammation, pulmonary dysfunction, and the reduction of nitric oxide synthase activity in the corpus cavernosum. STRENGTH & LIMITATIONS: For the first time, our study revealed the deleterious effect of VE on penile erection in vivo. However, the VE exposure model might not entirely mimic the natural condition of ambient air pollution. CONCLUSION: Our results raise concerns about the potential role played by long-term exposure to gasoline VE in the development of erectile dysfunction. Zhao S, Wang J, Xie Q, et al. Elucidating Mechanisms of Long-Term Gasoline Vehicle Exhaust Exposure-Induced Erectile Dysfunction in a Rat Model. J Sex Med 2019;16:155-167.

Towards automatic encoding of medical procedures using convolutional neural networks and autoencoders.

Classification systems such as ICD-10 for diagnoses or the Swiss Operation Classification System (CHOP) for procedure classification in the clinical treatment are essential for clinical management and information exchange. Traditionally, classification codes are assigned manually or by systems that rely upon concept-based or rule-based classification methods. Such methods can reach their limit easily due to the restricted coverage of handcrafted rules and of the vocabulary in underlying terminological systems. Conventional machine learning approaches normally depend on selected features within a human annotated training set. However, it is quite laborious to obtain a well labeled data set and its generation can easily be influenced by accumulative errors caused by human factors. To overcome this, we will present our processing pipeline for query matching realized through neural networks within the task of medical procedure classification. The pipeline is built upon convolutional neural networks (CNN) and autoencoder with logistic regression. On the task of relevance determination between query and category text, the autoencoder based method has achieved a micro F1 score of 70.29%, while the convolutional based method has reached a micro F1 score of 60.86% with high efficiency. These two algorithms are compared in experiments with different configurations and baselines (SVM, logistic regression) with respect to their suitability for the task of automatic encoding. Advantages and limitations are discussed.

A Concept for Improving Cross-Sector Care by a Mobile Patient Navigator App.

Hip and knee joint prosthetic implants are common surgical interventions in Switzerland, as well as in other countries. Different care providers are involved in the care process which constitutes a potential risk of inefficiency and communication deficits. A patient can get lost on his cross-sectoral clinical pathway, miss appointments or underestimate the relevance of certain control visits or examinations. To address this, we introduce a concept for a mobile application that guides patients along the cross-sectoral clinical pathway. The information on appointments is consistently retrieved from information systems at the care provider's side. This facilitates the coordination of activities and appointments not only for the patient, but also for the care providers. The aim is to promote the patients' autonomy, to support their management of appointments, to increase the understanding for their clinical condition and to improve their adherence to the prescribed measures - all of which lead to an improved outcome. Another aim is to increase patient satisfaction in terms of adequate information and quality of treatment. The advantages and disadvantages of the concept will be discussed. goes here.

Last Mile Towards Efficient Healthcare Delivery in Switzerland: eHealth Enabled Applications Could Speed Up the Care Process.

A precise and timely care delivery depends on an efficient triage performed by primary care providers and smooth collaboration with other medical specialities. In recent years telemedicine gained increasing importance for efficient care delivery. It's use, however, has been limited by legal issues, missing digital infrastructures, restricted support from health insurances and the digital divide in the population. A new era towards eHealth and telemedicine starts with the establishment of national eHealth regulations and laws. In Switzerland, a nation-wide digital infrastructure and electronic health record will be established. But appropriate healthcare apps to improve patient care based on this infrastructure remain rare. In this paper, we present two applications (self-anamnesis and eMedication assistant) for eHealth enabled care delivery which have the potential to speed up diagnosis and treatment.

Concept Embedding for Relevance Detection of Search Queries Regarding CHOP.

Automatic encoding of diagnosis and procedures can increase the interoperability and efficacy of the clinical cooperation. The concept, rule-based and machine learning classification methods for automatic code generation can easily reach their limit due to the handcrafted rules and a limited coverage of the vocabulary in a concept library. As the first step to apply deep learning methods in automatic encoding in the clinical domain, a suitable semantic representation should be generated. In this work, we will focus on the embedding mechanism and dimensional reduction method for text representation, which mitigate the sparseness of the data input in the clinical domain. Different methods such as word embedding and random projection will be evaluated based on logs of query-document matching.

Aspect-Oriented Visualization of the Health Status: An Example in Treatment of Cervical Spine Defect.

Clinical data is often captured in unstructured texts and scattered in different health information systems. This complicates the aggregation of information in the process of clinical decision making. However, having a quick overview and an efficient representation of relevant aspects of a patient's health status are crucial for this process. While accessing patient data and perusing clinical documents, relevant details need to be discovered quickly. In this paper, we introduce an approach to visualize relevant information from clinical documents by tag clouds. The conventional tag clouds visualize the content of a document using the terms they are containing shown in different sizes with the size calculated based on the term frequency. Important facts and diagnostic results with low occurrence in a text may be ignored by this naïve method. In this paper, we therefore adapt the conventional tag clouds by information extraction and a guidelines-based classification schema, so that the clinical concerns can be visualized more correctly. The aspects are extracted according to a classification schema developed by clinical experts. We evaluate the approach on a set of radiology reports for cervical spine treatment.

Patient Centered Event Representation for the Treatment of Multifactorial Diseases: Current Progress and Challenges.

Events such as clinical interventions, adverse drug events are one of the basic semantic units in the clinical workflow and are the foundation of the pathway representation. Current research has mainly concentrated on event recognition through concept mapping using from medical ontologies (UMLS, SNOMED CT) and gene relation detection in the biological context. However, the analysis of the patient status and the interaction between the patient status and a context event is still at the primary stage. In order to realize an efficient personalized treatment design and pathway planning, the correlation between a patient status and different types of clinical events should be analyzed. In this paper, we will provide a summary of the current research progress in clinical event detection in the biomedical domain and compare two approaches of event acquisition: an event schema produced using a guideline-based method and an expert-based annotation. We will apply the approaches to generate a structured annotation corpus and a special case of an event schema based on the complication classification and risk management in treatment of laryngeal cancer.

Rule-based Cervical Spine Defect Classification Using Medical Narratives.

Classifying the defects occurring at the cervical spine provides the basis for surgical treatment planning and therapy recommendation. This process requires evidence from patient records. Further, the degree of a defect needs to be encoded in a standardized from to facilitate data exchange and multimodal interoperability. In this paper, a concept for automatic defect classification based on information extracted from textual data of patient records is presented. In a retrospective study, the classifier is applied to clinical documents and the classification results are evaluated.

Sentiment analysis in medical settings: New opportunities and challenges.

OBJECTIVE: Clinical documents reflect a patient's health status in terms of observations and contain objective information such as descriptions of examination results, diagnoses and interventions. To evaluate this information properly, assessing positive or negative clinical outcomes or judging the impact of a medical condition on patient's well being are essential. Although methods of sentiment analysis have been developed to address these tasks, they have not yet found broad application in the medical domain. METHODS AND MATERIAL: In this work, we characterize the facets of sentiment in the medical sphere and identify potential use cases. Through a literature review, we summarize the state of the art in healthcare settings. To determine the linguistic peculiarities of sentiment in medical texts and to collect open research questions of sentiment analysis in medicine, we perform a quantitative assessment with respect to word usage and sentiment distribution of a dataset of clinical narratives and medical social media derived from six different sources. RESULTS: Word usage in clinical narratives differs from that in medical social media: Nouns predominate. Even though adjectives are also frequently used, they mainly describe body locations. Between 12% and 15% of sentiment terms are determined in medical social media datasets when applying existing sentiment lexicons. In contrast, in clinical narratives only between 5% and 11% opinionated terms were identified. This proves the less subjective use of language in clinical narratives, requiring adaptations to existing methods for sentiment analysis. CONCLUSIONS: Medical sentiment concerns the patient's health status, medical conditions and treatment. Its analysis and extraction from texts has multiple applications, even for clinical narratives that remained so far unconsidered. Given the varying usage and meanings of terms, sentiment analysis from medical documents requires a domain-specific sentiment source and complementary context-dependent features to be able to correctly interpret the implicit sentiment.