Robust, scalable, and highly selective spirocyclic catalysts for industrial hydroformylation and isomerization-hydroformylation.

Hydroformylation (HF) or isomerization-hydroformylation (ISO-HF) represents the most direct and practical route for producing aldehydes on an industrial scale. To resolve the issues of low activity, low linear/branched (l/b) ratio, and low stability in HF and ISO-HF, we herein reported a class of spirocyclic diphosphites. Notably, the ligand termed O-SDPhite afforded excellent catalytic activity and regioselectivity for the HF of various olefins. Excellent l/b ratio and an unprecedented turnover number of up to 17,620,000 were achieved. O-SDPhite was also found to be effective in the regioselective ISO-HF of the industrially related cheap and abundant C4 Raffinates to n-valeraldehyde produced on a multimillion-ton scale. The reaction with O-SDPhite, superior to that of benchmark Biphephos, was continuously operated for 41 days and afforded an average 38.6 l/b ratio (31 days and 14.7 l/b ratio for Biphephos).

Optimisation of hypocrellin production in Shiraia-like fungi via genetic modification involving a transcription factor gene and a putative monooxygenase gene.

Shiraia-like fungi, which are rare parasitic fungi found around bamboo, play an important role in traditional medicine. Their main active component, hypocrellin, is widely used in medicine, food, and cosmetics. By comparing strains with different hypocrellin yields, we identified a transcription factor (SbTF) in the hypocrellin biosynthesis pathway. SbTF from high-yielding zzz816 and low-yielding CNUCC C72 differed in its protein structure. Subsequently, SbTF from high-yielding zzz816 was overexpressed in several strains. This stabilised the yield in zzz816 and significantly increased the yield in low-yielding CNUCC C72. Comparing downstream non-essential genes between wild type and SbTF-overexpressing CNUCC C72 showed that SbMNF was significantly up-regulated. Therefore, it was selected for further study. SbMNF overexpression increased the hypocrellin yield in low-yielding CNUCC C72 and altered the composition of compounds in high-yielding CNUCC 1353PR and zzz816. This involved an increased elsinochrome C yield in CNUCC 1353PR and an increased hypocrellin B yield in zzz816 (by 2 and 70.3 times that in the corresponding wild type, respectively). This study is the first to alter hypocrellin synthesis to alter the levels of one bioactive agent compared to another. The results provide new insights regarding genetic modification and will help to optimise fungal fermentation.

Medical knowledge graph completion via fusion of entity description and type information.

Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors.

PPM Ir-f-phamidol-Catalyzed Asymmetric Hydrogenation of γ-Amino Ketones Followed by Stereoselective Cyclization for Construction of Chiral 2-Aryl-pyrrolidine Pharmacophores.

Transition metal catalysts with a million turnovers and excellent selectivity are rarely reported but are crucial for the industrial manufacture of optical pure pharmaceuticals, natural products, and fine chemicals. In this paper, we report an unprecedented aninoic Ir-f-phamidol catalyst for asymmetric hydrogenation of γ-amino ketones followed by stereoselective cyclization for construction of valuable chiral 2-aryl-pyrrolidine pharmacophores. The Ir-f-phamidol catalyst showed up to 1,000,000 TON and >99% ee, as well as excellent tolerance of substrates and protecting groups, providing various chiral amino alcohol intermediates. Upon optimization of the conditions, the stereoselective cyclization reaction was highly smooth and efficient (quantitative conversions, 92 to >99% ee). Finally, this solution was applied in the preparation of high-value chiral entities containing such chiral 2-aryl-pyrrolidine pharmacophores.

A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports.

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.

The effect of two facets of physicians' environmental stress on patients' compliance with COVID-19 guidelines: moderating roles of two types of ego network.

Drawing upon the Conservation of Resources Theory, this study seeks to examine the association between two dimensions of environmental stress experienced by physicians and patients' adherence to COVID-19 guidelines, within the context of a social network framework. A third-wave longitudinal study was employed to gather 439 valid data points in China. Social network analysis and structural equation model were used to test the conceptual model. The results reveal the pivotal role of physicians' environmental stress related to their work and family contexts in influencing patients' adherence to COVID-19 guidelines through the mediation of physicians' information sharing. The ego networks of physicians, encompassing both advice-seeking and friendship ties, were observed to negatively moderate the relationship between stress and resource depletion. Broadly, our study shows the importance of understanding physicians' stress caused by the working and family environments, as these factorsnot only impact the psychological well-being of physicians but also significantly affect patients' compliance with COVID-19 guidelines. In addition, the work offers a framework for understanding the impact of the ego advice-seeking network and the ego friend network.

COVID-19 Vaccine Adverse Event Detection Based on Multi-Label Classification With Various Label Selection Strategies.

Analyzing massive VAERS reports without medical context may lead to incorrect conclusions about vaccine adverse events (VAE). Facilitating VAE detection promotes continual safety improvement for new vaccines. This study proposes a multi-label classification method with various term-and topic-based label selection strategies to improve the accuracy and efficiency of VAE detection. Topic modeling methods are first used to generate rule-based label dependencies from Medical Dictionary for Regulatory Activities terms in VAE reports with two hyper-parameters. Multiple label selection strategies, namely one-vs-rest (OvsR), problem transformation (PT), algorithm adaption (AA), and deep learning (DL) methods, are used in multi-label classification to examine the model performance, respectively. Experimental results indicated that the topic-based PT methods improve the accuracy by up to 33.69% using a COVID-19 VAE reporting data set, which improves the robustness and interpretability of our models. In addition, the topic-based OvsR methods achieve an optimal accuracy of up to 98.88%. The accuracy of the AA methods with topic-based labels increased by up to 87.36%. By contrast, the state-of-art LSTM- and BERT-based DL methods have relatively poor performance with accuracy rates of 71.89% and 64.63%, respectively. Our findings reveal that the proposed method effectively improves the model accuracy and strengthens VAE interpretability by using different label selection strategies and domain knowledge in multi-label classification for VAE detection.

The effects of factors on the motivations for knowledge sharing in online health communities: A benefit-cost perspective.

Online health communities (OHCs) provide knowledge for users, enabling conversations across a broad range of health topics. The development of OHCs depends on users' motivations to share health knowledge. Yet little literature has explored how perceived benefits and costs affect users' motivations for sharing both general and specific knowledge. Based on social exchange theory, we propose a research model that comprises intrinsic benefits (sense of self-worth, satisfaction), extrinsic benefits (social support, reputation, and online attention), cognitive cost, and executional cost to investigate the effects of these factors on users' motivations for general and specific knowledge sharing. We compare the different effects of these factors on users' motivations for knowledge sharing. Results demonstrate positive effects of intrinsic and extrinsic benefits on users' motivations for general and specific knowledge sharing. Differences exist in the negative effects of cognitive and executional costs on users' motivations for general and specific knowledge sharing. This study contributes to promoting the enrichment of online health knowledge and provides implications for the development of OHCs.

Impact of social media news on COVID-19 vaccine hesitancy and vaccination behavior.

In order to take advantage of the power of social media to promote vaccination, this study reveals the mechanisms of positive and negative impacts of social media news on vaccine hesitancy and vaccination behavior. Based on the stimulus-organism-response (S-O-R) framework, we developed a research model to understand the effects of vaccine safety news and risk news from social media (external stimuli) on individuals' psychological organism (i.e., safety perception and risk perception) and consequent behavioral response, vaccine hesitancy and vaccination behavior. The proposed model was tested by partial least square structural equation modeling (PLS-SEM) on a sample gathered in China from September 2021 to November 2021 and from February 2022 to April 2022 (valid responses = 1579). The results found that the relationship between vaccine risk news from social media and risk perception was higher than the relationship between vaccine safety news from social media and safety perception. Individuals are more sensitive to vaccine risk news than safety news on social media. Moreover, both safety perception and risk perception explained the critical psychological mechanisms behind vaccine hesitancy. Interestingly, ego network density mitigated the effect of safety news on safety perception and the effect of risk news on risk perception. The findings contribute to the S-O-R model, the research on social media effects, and the literature on vaccination attitudes and behaviors. This study also informs public health officials about leveraging the power of social media to motivate the public to accept the COVID-19 vaccines.

What drives the adoption of online health communities? An empirical study from patient-centric perspective.

BACKGROUND: Online health communities (OHCs) provide platforms for patients to seek advice from physicians and receive professional suggestions online. It can improve the efficiency of patients' diagnosis of simple diseases and alleviate hospital congestion. However, few empirical studies have comprehensively explored the factors influencing patients' intention to use OHCs through objective data. This study aims to fill this gap by identifying key factors that influence patients' acceptance of OHCs and proposing effective ways to promote the applications of OHCs in China. METHODS: Based on the Unified Theory of Acceptance and Usage of Technology (UTAUT), extended with additional constructs identified with patients' information demands in OHCs, this study developed a research model and proposed nine hypotheses. An online survey involving 783 valid responses was conducted in China to collect data to validate the proposed model. Confirmatory factor analysis and partial least squares (PLS) path model were conducted for instrument validation and hypothesis testing. RESULTS: Price value, eHealth literacy, and performance expectancy are the most prominent constructs in the study context. Interestingly, relation quality was also found to have a significant positive relationship with behavioral intention. CONCLUSIONS: Based on these findings, OHC operators need to create a user-friendly platform, improve information quality, set reasonable prices, and establish consummate security systems. Physicians and related organizations can raise awareness and assist patients in developing the skills to appropriately comprehend and utilize information in OHCs. This study contributes to both technology adoption theory and practice.

Fast Isomerization Before Isomerization-Hydroformylation: Probing the Neglected Period with A Novel Microfluidic Device.

By combining the concept of flash chemistry and radial synthesis, a novel microreactor (Flashstop reactor) was designed to study isomerization process of hydroformylation by a Rh/tetraphosphite catalyst in a time scale of seconds. It was found that in the initial 313 seconds, 60-99 % of 1-octene was isomerized to 2- and 3-octenes before the formation of aldehydes. Within this period, two different types of isomerization reactions were observed. It was proposed that a monohydride complex without CO ligand accounts for the ultrafast isomerization in the initial 30 seconds. The isomerization rate with such monohydride species was calculated much faster than that with the well-known H(CO)Rh(P-P) species. Both experimental and DFT computational studies were carried out to support this assumption. Fast transformations early on in catalytic cycles have been rarely studied due to the lack of proper tools. We believe that the Flashstop reactor is a powerful tool for analysis of kinetics in gas-liquid biphasic reactions within a time scale of seconds to minutes.

Sonodynamic therapy reduces cardiomyocyte apoptosis through autophagy activated by reactive oxygen species in myocardial infarction.

Myocardial infarction (MI) is lethal to patients because of acute ischemia and hypoxia leading to cardiac tissue apoptosis. Autophagy played a key role in MI through affecting the survival of cardiomyocytes. LncRNA-MHRT (myosin heavy-chain-associated RNA transcripts) was specific to the heart and cardiomyocytes, and inhibition of lncRNA-MHRT transcription under pathological stimuli could cause cardiac hypertrophy and even heart failure (HF). Sonodynamic therapy (SDT) is a new and developing medical technique that utilizes low-intensity ultrasound to locally activate a preloaded sonosensitizer. Our group previously reported that SDT could regulate autophagy. In this study, we investigated whether SDT could reduce MI-induced cardiomyocyte apoptosis via activating autophagy pathway. SDT improved cardiac function and suppresses MI-induced cardiomyocyte apoptosis. SDT alleviated MI-induced cardiomyocyte apoptosis by improving autophagy. MHRT mediated the inhibiting effect of SDT on cardiomyocyte apoptosis via activating autophagy pathway. Our data reveal a novel effect that SDT protects against MI and confirm that SDT reduces MI-induced cardiomyocyte apoptosis via activating MHRT-mediated-autophagy. Thus, our findings also indicate that SDT may be used as a potential method for treatment of post-myocardial infarction heart failure.

Integration of Milstein Ru-PNN and Rh-Tribi/Tetrabi for Isomerization Linear Selective Hydroformylation of Far Internal Alkenes.

Converting cheap and abundant internal alkenes to value-added linear aldehydes is of great importance but not an addressed issue. In this paper, an integration of a Milstein-type Ru-PNN catalyst and our Rh-Tribi/Tetrabi catalyst was first demonstrated in highly improved isomerization linear selective hydroformylation of 2-, 3-, and 4-alkenes, yielding excellent linear selectivities and activities (linear selectivity improvements of 2.2-58%, up to 94.2-98.6%, and turnover numbers improvements of 61-335 TON, up to 385-851) compared to the Ru-PNN/Rh-Bisbi system.

Exploring the effects of health information seeking on e-satisfaction in online health communities: an empirical investigation.

BACKGROUND: Online health communities (OHCs) are becoming effective platforms for people to seek health information. Existing studies divide health information into general and specific information in OHCs. However, few studies discuss the effects of different types of information seeking in OHCs on users' electronic satisfaction (e-satisfaction). OBJECTIVE: This study explores the effects of general and specific information seeking on users' e-satisfaction with OHCs through the mediating roles of perceived benefits and costs drawing on the social information processing theory and the social exchange theory. METHODS: This study conducted an online survey to collected data from individuals who used OHCs to seek information. The structural equation model was used to analyze the collect data and the research model. Specifically, this study examined the common method bias and conducted a robustness check. RESULTS: Results show that general and specific information seeking affect e-satisfaction through the mediating roles of perceived benefits and costs. An interesting result is that general information seeking has a stronger effect on e-satisfaction than specific information seeking. CONCLUSIONS: This study suggests that e-satisfaction should be further enhanced by information seeking as online healthcare practices evolve and change. Managers of OHCs should focus on increasing users' perceived benefits, thereby increasing their e-satisfaction. Besides, this study discusses implications, limitations, and future research directions.

Effects of traditional Chinese exercises on insomnia after coronavirus disease 2019: A protocol of systematic review and meta-analysis.

BACKGROUND: There is growing evidence that coronavirus disease 2019 (COVID-19) can trigger acute episodes of insomnia. Reports on the treatment of COVID-19-related insomnia are limited. Therefore, our study aims to perform a systematic evaluation and meta-analysis of the effects of traditional Chinese exercises (TCEs) in patients with insomnia due to COVID-19. METHODS: We will search the Embase, PubMed, Cochrane Library, Web of Science, MEDLINE, Scopus, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure Database, and Wan Fang Database from December 1, 2019 to October 2, 2022 to identify all articles on treatment of COVID-19-related insomnia using TCEs. Two researchers will screen the articles and extract the relevant information. RESULTS: The results will provide a systematic overview of the current evidence on the use of TCE to treat patients with insomnia after COVID-19. CONCLUSIONS: The conclusions of this study will help clarify the effects of TCEs on patients with insomnia after COVID-19.

Factors influencing patients' opt-in intention of exchanging health information.

Introduction: Health information exchange (HIE) exhibits tremendous benefits in improving the quality of healthcare and reducing healthcare costs. However, it also poses challenges related to data security, data privacy, patient engagement, etc. Objective: This study aimed to explore the factors affecting patients' opt-in intention to HIE by using an empirical study based on the theory of planned behavior. Methods: A Web-based survey was conducted involving 501 valid participants in China (69% validity rate). Results: Information sensitivity and perceived HIE transparency affected the patients' opt-in intention to HIE through the mediation of perceived behavior control and trust in HIE. Information sensitivity negatively influenced perceived behavior control (-0.551, P < 0.001) and trust in HIE (-0.489, P < 0.001). Perceived transparency of HIE positively influenced perceived behavior control (0.396, P < 0.001) and trust in HIE (0.471, P < 0.001). Moreover, patients' opt-in intention to HIE can be positively affected by perceived HIE transparency (0.195, P < 0.001) and trust in HIE (0.294, P < 0.001). In addition, the moderating effect of health status was positive and significant between trust in HIE and opt-in intention to HIE but not between the perceived behavior control and opt-in intention to HIE. Conclusion: This study contributes to the theory of planned behavior and enriches the literature on HIE efforts. HIE administrators should design personalized health services on the basis of these different health statuses to successfully achieve patients' opt-in intention to HIE.

Rhodium-Catalyzed Chemo-, Regio- and Enantioselective Hydroformylation of Cyclopropyl-Functionalized Trisubstituted Alkenes.

The first rhodium-catalyzed highly chemo-, regio- and enantioselective hydroformylation of cyclopropyl-functionalized trisubstituted alkenes affording useful chiral cyclopropyl entities is reported. Compared to generally used diphosphine ligands for asymmetric catalysis, the modified hybrid phosphorus ligand, named (R,S)-DTBM-Yanphos, can convert a series of readily available cyclopropyl-functionalized trisubstituted alkenes into high-value chiral cyclopropyl-functionalized aldehydes with high selectivities (81-98 % ee). Gram-scale reactions (TON up to 1500) and follow-up transformations to the corresponding alcohol, acid, esters and nitrile are also presented. Finally, a possible hydroformylation mechanism involving ring-open-hydroformylation pathways is proposed based on control and deuteroformylation reactions.

Factors Influencing Continued Usage Behavior on Mobile Health Applications.

(1) Background: As people pay more attention to health, mobile health applications (mHealth apps) are becoming popular. These apps offer health services that run on mobile devices to help improve users' health behaviors. However, few studies explore what motivates users to continue to use these apps. This study proposes antecedents influencing users' electronic satisfaction (e-satisfaction) and their continued behaviors of using mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), this study constructs a research model including perceived reliability and online review to predict the continued usage behavior on mHealth apps in China; (2) Methods: We conduct an online survey to collect data from participants who have used mHealth apps. This study receives 327 valid responses and tests the research model using the partial least squares structural equation model approach; (3) Results: Our results find that antecedents positively affect continued usage intention through the mediation role of e-satisfaction with mHealth apps. Interestingly, this study reveals that habit positively affects the continued usage behavior and moderates the effect of e-satisfaction and continued intention of using mHealth apps; (4) Conclusions: This study presents theoretical implications on the extended UTAUT2 and provides practical implications understanding of managing mHealth apps in China.

Factors affecting physicians using mobile health applications: an empirical study.

BACKGROUND: Mobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians' participation in mHealth apps. OBJECTIVE: This study aims to empirically explore factors affecting physicians' usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings. METHODS: We collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians' usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias. RESULTS: Our results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians' cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention. CONCLUSIONS: This study contributes to the existing literature on UTAUT2 extension of physicians' acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians' usage behaviors on mHealth apps in China and provide such apps' managers with an insight into the promotion of physicians' active acceptance and usage behaviors.

Association Between eHealth Literacy in Online Health Communities and Patient Adherence: Cross-sectional Questionnaire Study.

BACKGROUND: eHealth literacy is significantly associated with patients' online information behavior, physician-patient relationship, patient adherence, and health outcomes. As an important product of the internet, online health communities (OHCs) can help redistribute idle medical resources, increase medical resource utilization, and improve patient adherence. However, studies on eHealth literacy in OHCs are limited. Therefore, this study examined patients' eHealth literacy regarding health information-seeking behavior and physician-patient communication in OHCs. OBJECTIVE: This study aimed to investigate the association between eHealth literacy in OHCs and patient adherence by employing social cognitive theory. METHODS: This was an empirical study, in which a research model consisting of 1 independent variable (patients' eHealth literacy), 3 mediators (physician-patient communication in OHCs, patient health information-seeking behavior in OHCs, and patients' perceived quality of health information in OHCs), 1 dependent variable (patient adherence), and 4 control variables (age, gender, living area, and education level) was established to examine the associations. Multi-item scales were used to measure variables. An anonymous online survey involving 560 participants was conducted through Chinese OHCs in July 2018 to collect data. Partial least squares and structural equation modeling were adopted to analyze data and test hypotheses. RESULTS: The survey response rate was 79.6% (446/560). The reliability, convergent validity, and discriminant validity were acceptable. Age, gender, living area, and education level were positively associated with patient adherence, and gender was positively associated with physician-patient communication and patients' perceived quality of internet health information in OHCs. Patients' eHealth literacy was positively associated with patient adherence through the mediations of physician-patient communication, internet health information-seeking behavior, and perceived quality of internet health information in OHCs. CONCLUSIONS: Results indicate that physician-patient communication, internet health information-seeking behavior, and the perceived quality of internet health information are significantly associated with improving patient adherence via a guiding of eHealth literacy in OHCs. These findings suggest that physicians can understand and guide their patients' eHealth literacy to improve treatment efficiency; OHCs' operators should this strengthen the management of information quality, develop user-friendly features, and minimize the gap between the actual and perceived information quality.