Natural product drupacine acting on a novel herbicidal target shikimate dehydrogenase.

Natural products are one of the important sources for the creation of new pesticides. Drupacine ((1R,11S,12S,13R,15S)-13-methoxy-5,7,21-trioxa-19-azahexacyclo[11.7.1.02,10.04,8.011,15.015,19]henicosa-2,4(8),9-trien-12-ol), isolated from Cephalotaxus sinensis (Chinese plum-yew), is a potent herbicidal compound containing an oxo-bridged oxygen bond structure. However, its molecular target still remains unknown. In this study, the targets of drupacine in Amaranthus retroflexus were identified by combining drug affinity responsive target stability (DARTS), cellular thermal shift assay coupled with mass spectrometry (CETSA MS), RNA-seq transcriptomic, and TMT proteomic analyses. Fifty-one and sixty-eight main binding proteins were identified by DARTS and CETSA MS, respectively, including nine co-existing binding proteins. In drupacine-treated A. retroflexus seedlings we identified 1389 up-regulated genes and 442 down-regulated genes, 34 up-regulated proteins, and 194 down-regulated proteins, respectively. Combining the symptoms and the biochemical profiles, Profilin, Shikimate dehydrogenase (SkDH), and Zeta-carotene desaturase were predicted to be the drupacine potential target proteins. At the same time, drupacine was found to bind SkDH stronger by molecular docking, and its inhibition on ArSkDH increased with the treatment concentration increase. Our results suggest that the molecular target of drupacine is SkDH, a new herbicide target, which lay a foundation for the rational design of herbicides based on new targets from natural products and enrich the target resources for developing green herbicides.

A Clinician-Led, Experience-Based Co-Design Approach for Developing mHealth Services to Support the Patient Self-management of Chronic Conditions: Development Study and Design Case.

BACKGROUND: Despite the increasing use of mobile health (mHealth) services, such as mHealth apps or SMS text messaging services, that support the patient self-management of chronic conditions, many existing mHealth services lack theoretical guidance. In addition, although often the target audience for requirement acquisition at the initial mHealth app design stage, it is a common challenge for them to fully conceptualize their needs for mHealth services that help self-manage chronic conditions. OBJECTIVE: This study proposes a novel co-design approach with the initial requirements for mHealth services proposed by clinicians based on their experiences in guiding patients to self-manage chronic conditions. A design case is presented to illustrate our innovative approach to designing an mHealth app that supports the self-management of patients with obesity in their preparation for elective surgery. METHODS: We adopted a clinician-led co-design approach. The co-design approach consisted of the following four cyclic phases: understanding user needs, identifying an applicable underlying theory, integrating the theory into the prototype design, and evaluating and refining the prototype mHealth services with patients. Expert panel discussions, a literature review, intervention mapping, and patient focus group discussions were conducted in these four phases. RESULTS: In stage 1, the expert panel proposed the following three common user needs: motivational, educational, and supportive needs. In stage 2, the team selected the Social Cognitive Theory to guide the app design. In stage 3, the team designed and developed the key functions of the mHealth app, including automatic push notifications; web-based resources; goal setting and monitoring; and interactive health-related exchanges that encourage physical activity, healthy eating, psychological preparation, and a positive outlook for elective surgery. Push notifications were designed in response to a patient's risk level, as informed by the person's response to a baseline health survey. In stage 4, the prototype mHealth app was used to capture further requirements from patients in the two focus group discussions. Focus group participants affirmed the potential benefits of the app and suggested more requirements for the function, presentation, and personalization needs. The app was improved based on these suggestions. CONCLUSIONS: This study reports an innovative co-design approach that was used to leverage the clinical experiences of clinicians to produce the initial prototype app and the approach taken to allow patients to effectively voice their needs and expectations for the mHealth app in a focus group discussion. This approach can be generalized to the design of any mHealth service that aims to support the patient self-management of chronic conditions.

Measuring Success of Patients' Continuous Use of Mobile Health Services for Self-management of Chronic Conditions: Model Development and Validation.

BACKGROUND: Mobile health services are gradually being introduced to support patients' self-management of chronic conditions. The success of these services is contingent upon patients' continuous use of them. OBJECTIVE: This study aims to develop a model to measure the success of patients' continuous use of mobile health services for the self-management of chronic conditions. METHODS: The proposed model was derived from the information systems continuance model and the information systems success model. This model contains 7 theoretical constructs: information quality, system quality, service quality, perceived usefulness, user satisfaction, perceived health status, and continuous use intention. A web-based questionnaire survey instrument was developed to test the model. The survey was conducted to collect data from 129 patients who used a mobile health app for hypertension management from 2017 to 2019. The questionnaire items were derived from validated instruments and were measured using a 5-point Likert scale. The partial least squares modelling method was used to test the theoretical model. RESULTS: The model accounted for 58.5% of the variance in perceived usefulness (R2=0.585), 52.3% of the variance in user satisfaction (R2=0.523), and 41.4% of the variance in patients' intention to make continuous use of mobile health services (R2=0.414). The continuous use intention was significantly influenced by their perceived health status (ß=.195, P=.03), perceived usefulness (ß=.307, P=.004), and user satisfaction (ß=.254, P=.04) with the mobile health service. Information quality (ß=.235, P=.005), system quality (ß=.192, P=.02), and service quality (ß=.494, P<.001) had a significantly positive influence on perceived usefulness but not on user satisfaction. Perceived usefulness had a significantly positive influence on user satisfaction (ß=.664, P<.001). In a result opposite to the original hypothesis, perceived health status did not negatively influence patients' intention to continue using the mobile health service but showed a significantly positive correlation. CONCLUSIONS: This study developed a theoretical model to predict and explain patients' continuous use of mobile health services for self-management of chronic conditions and empirically tested the model. Perceived usefulness, user satisfaction, and health status contributed to patients' intention to make continuous use of mobile health services for self-managing their chronic conditions.

A Comprehensive 6A Framework for Improving Patient Self-Management of Hypertension Using mHealth Services: Qualitative Thematic Analysis.

BACKGROUND: Hypertension affects over 15% of the world's population and is a significant global public health and socioeconomic challenge. Mobile health (mHealth) services have been increasingly introduced to support hypertensive patients to improve their self-management behaviors, such as adherence to pharmacotherapy and lifestyle modifications. OBJECTIVE: This study aims to explore patients' perceptions of mHealth services and the mechanisms by which the services support them to self-manage their hypertension. METHODS: A semistructured, in-depth interview study was conducted with 22 outpatients of the General Hospital of Ningxia Medical University from March to May 2019. In 2015, the hospital introduced an mHealth service to support community-dwelling outpatients with self-management of hypertension. Content analysis was conducted by following a grounded theory approach for inductive thematic extraction. Constant comparison and categorization classified the first-level codes with similar meanings into higher-level themes. RESULTS: The patient-perceived mechanisms by which the mHealth service supported their self-management of hypertension were summarized as 6A: access, assessment, assistance, awareness, ability, and activation. With the portability of mobile phones and digitization of information, the mHealth service provided outpatients with easy access to assess their vital signs and self-management behaviors. The assessment results gave the patients real-time awareness of their health conditions and self-management performance, which activated their self-management behaviors. The mHealth service also gave outpatients access to assistance, which included health education and self-management reminders. Both types of assistance could also be activated by abnormal assessment results, that is, uncontrolled or deteriorating blood pressure values, discomfort symptoms, or not using the service for a long period. With its scalable use to handle any possible information and services, the mHealth service provided outpatients with educational materials to learn at their own pace. This led to an improvement in self-management awareness and ability, again activating their self-management behaviors. The patients would like to see further improvements in the service to provide more useful, personalized information and reliable services. CONCLUSIONS: The mHealth service extended the traditional hypertension care model beyond the hospital and clinician's office. It provided outpatients with easy access to otherwise inaccessible hypertension management services. This led to process improvement for outpatients to access health assessment and health care assistance and improved their awareness and self-management ability, which activated their hypertension self-management behaviors. Future studies can apply the 6A framework to guide the design, implementation, and evaluation of mHealth services for outpatients to self-manage chronic conditions.

Validation of 4D Components for Measuring Quality of the Public Health Data Collection Process: Elicitation Study.

BACKGROUND: Identification of the essential components of the quality of the data collection process is the starting point for designing effective data quality management strategies for public health information systems. An inductive analysis of the global literature on the quality of the public health data collection process has led to the formation of a preliminary 4D component framework, that is, data collection management, data collection personnel, data collection system, and data collection environment. It is necessary to empirically validate the framework for its use in future research and practice. OBJECTIVE: This study aims to obtain empirical evidence to confirm the components of the framework and, if needed, to further develop this framework. METHODS: Expert elicitation was used to evaluate the preliminary framework in the context of the Chinese National HIV/AIDS Comprehensive Response Information Management System. The research processes included the development of an interview guide and data collection form, data collection, and analysis. A total of 3 public health administrators, 15 public health workers, and 10 health care practitioners participated in the elicitation session. A framework qualitative data analysis approach and a quantitative comparative analysis were followed to elicit themes from the interview transcripts and to map them to the elements of the preliminary 4D framework. RESULTS: A total of 302 codes were extracted from interview transcripts. After iterative and recursive comparison, classification, and mapping, 46 new indicators emerged; 24.8% (37/149) of the original indicators were deleted because of a lack of evidence support and another 28.2% (42/149) were merged. The validated 4D component framework consists of 116 indicators (82 facilitators and 34 barriers). The first component, data collection management, includes data collection protocols and quality assurance. It was measured by 41 indicators, decreased from the original 49% (73/149) to 35.3% (41/116). The second component, data collection environment, was measured by 37 indicators, increased from the original 13.4% (20/149) to 31.9% (37/116). It comprised leadership, training, funding, organizational policy, high-level management support, and collaboration among parallel organizations. The third component, data collection personnel, includes the perception of data collection, skills and competence, communication, and staffing patterns. There was no change in the proportion for data collection personnel (19.5% vs 19.0%), although the number of its indicators was reduced from 29 to 22. The fourth component, the data collection system, was measured using 16 indicators, with a slight decrease in percentage points from 18.1% (27/149) to 13.8% (16/116). It comprised functions, system integration, technical support, and data collection devices. CONCLUSIONS: This expert elicitation study validated and improved the 4D framework. The framework can be useful in developing a questionnaire survey instrument for measuring the quality of the public health data collection process after validation of psychometric properties and item reduction.

Application of a four-dimensional framework to evaluate the quality of the HIV/AIDS data collection process in China.

OBJECTIVE: To qualitatively evaluate the quality of the data collection process used by the Chinese national HIV/AIDS data repository (CRIMS), using a four-dimensional (4D) framework. The process is vital for the acquisition of high-quality data for ending the HIV/AIDS epidemic in China. METHODS: The study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was conducted to recruit 28 study participants including health administrators, public health professionals and clinicians. Data were collected through semi-structured interviews with the participants and from field observations in six hospitals. Content analysis was conducted following the 4D Framework. RESULTS: 61 percent of the facilitators and 74 percent of the barriers of the 4D Framework were confirmed in the CRIMS data collection process. The CRIMS achieved better-quality data collection management. The perceived gaps primarily included: impractical data collection protocol and invalid quality assessment mechanism for data collection management; weak leadership and unsupportive organizational policy for data collection environment; poor communication and job fatigue for data collection personnel; and inflexibility and inaccessibility of data collection system. Areas for improvement included: engaging frontline staff in the design of data collection protocol, standardizing quality assurance procedures, strengthening leadership, recognizing data collector's contributions, and meeting end-users' needs for the CRIMS. CONCLUSION: The findings generated knowledge about the quality of the CRIMS data collection process. The 4D Framework has potential as an evaluation tool for decision-makers on the improvement of the public health data collection process.

Identification of the essential components of quality in the data collection process for public health information systems.

This study identifies essential components in the data collection process for public health information systems based on appraisal and synthesis of the reported factors affecting this process in the literature. Extant process assessment instruments and studies of public health data collection from electronic databases and the relevant institutional websites were reviewed and analyzed following a five-stage framework. Four dimensions covering 12 factors and 149 indicators were identified. The first dimension, data collection management, includes data collection system and quality assurance. The second dimension, data collector, is described by staffing pattern, skill or competence, communication and attitude toward data collection. The third, information system, is assessed by function and technology support, integration of different data collection systems, and device. The fourth dimension, data collection environment, comprises training, leadership, and funding. With empirical testing and contextual analysis, these essential components can be further used to develop a framework for measuring the quality of the data collection process for public health information systems.

Using Inpatient Portals to Engage Family Caregivers in Acute Care Setting: A Literature Review.

This study aims to review the literature evaluating the design, acceptance, use and usability of inpatient portals for family caregivers. Fourteen articles were included. Information about patient medication, lab results and healthcare team was most important. Most caregivers were satisified and perceived the ease of use and usefulnesss of portals. Further research is needed to collect sufficient evidence with regard to the impact of inpatient portals on patient engagement and quality of care.

The Use of Theory in Mobile Health Interventions for Patient Self-Management of Chronic Diseases.

This study aims to investigate the use of theory in mobile health interventions for patient self-management of chronic conditions, with a focus on hypertension. We identified six theories in ten studies that were reviewed. These included Health Belief Model, Social Cognitive Theory, Self-Determination Theory, Theory of Planned Behavior, Transtheoretic Model, and Technology Acceptance Model. The findings are useful for further advancement of mobile health interventions to support chronic disease management.

Data Quality of the Chinese National AIDS Information System: A Critical Review.

Thirty-nine electronic English and Chinese articles on data quality assessment of the Chinese AIDS information system were critically reviewed. Some performance assessment related indicators of data quality have improved since the system was launched in 2008. After a thematic analysis of the factors that may affect data quality, four domains were identified. They are data management, data collector, information system, and data collection environment. The findings are useful to guide data quality improvement effort.