Pharmacogenetics Clinical Decision Support Systems for Primary Care in England: Co-Design Study.

BACKGROUND: Pharmacogenetics can impact patient care and outcomes through personalizing the selection of medicines, resulting in improved efficacy and a reduction in harmful side effects. Despite the existence of compelling clinical evidence and international guidelines highlighting the benefits of pharmacogenetics in clinical practice, implementation within the National Health Service in the United Kingdom is limited. An important barrier to overcome is the development of IT solutions that support the integration of pharmacogenetic data into health care systems. This necessitates a better understanding of the role of electronic health records (EHRs) and the design of clinical decision support systems that are acceptable to clinicians, particularly those in primary care. OBJECTIVE: Explore the needs and requirements of a pharmacogenetic service from the perspective of primary care clinicians with a view to co-design a prototype solution. METHODS: We used ethnographic and think-aloud observations, user research workshops, and prototyping. The participants for this study included general practitioners and pharmacists. In total, we undertook 5 sessions of ethnographic observation to understand current practices and workflows. This was followed by 3 user research workshops, each with its own topic guide starting with personas and early ideation, through to exploring the potential of clinical decision support systems and prototype design. We subsequently analyzed workshop data using affinity diagramming and refined the key requirements for the solution collaboratively as a multidisciplinary project team. RESULTS: User research results identified that pharmacogenetic data must be incorporated within existing EHRs rather than through a stand-alone portal. The information presented through clinical decision support systems must be clear, accessible, and user-friendly as the service will be used by a range of end users. Critically, the information should be displayed within the prescribing workflow, rather than discrete results stored statically in the EHR. Finally, the prescribing recommendations should be authoritative to provide confidence in the validity of the results. Based on these findings we co-designed an interactive prototype, demonstrating pharmacogenetic clinical decision support integrated within the prescribing workflow of an EHR. CONCLUSIONS: This study marks a significant step forward in the design of systems that support pharmacogenetic-guided prescribing in primary care settings. Clinical decision support systems have the potential to enhance the personalization of medicines, provided they are effectively implemented within EHRs and present pharmacogenetic data in a user-friendly, actionable, and standardized format. Achieving this requires the development of a decoupled, standards-based architecture that allows for the separation of data from application, facilitating integration across various EHRs through the use of application programming interfaces (APIs). More globally, this study demonstrates the role of health informatics and user-centered design in realizing the potential of personalized medicine at scale and ensuring that the benefits of genomic innovation reach patients and populations effectively.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation.

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Systematic study on expression and prognosis of E2Fs in human colorectal cancer.

BACKGROUND: E2Fs are important components of transcription factors and play key roles in occurrence or advancement of various cancers, but the expression and exact roles of each E2F in colorectal cancer (CRC) are rarely known. METHODS: To address this issue, we investigated the roles and prognostic values of E2Fs expressions in CRC patients by searching ONCOMINE, cBioPortal, GEPIA, Matascape and UALCAN. RESULTS: E2F1, 3-8 were upregulated at the mRNA level and E2F2 was less expressed in CRC tissues than in normal tissues. The eight E2Fs were correlated with tumor stages of CRC. Survival analysis using GEPIA revealed that high expressions of E2F3, 4 were related with short overall survival in all CRC patients. The mutation rate of E2Fs (60%) was high and genetic alteration in E2Fs was linked with longer overall survival in CRC patients. Functional analysis implied that E2Fs and their 50 nearby genes were concentrated in tumor-related pathways. CONCLUSIONS: E2Fs may be candidate biomarkers for CRC diagnosis and E2F3, 4 are potential prognosis biomarkers of CRC. Nevertheless, our findings must be validated in the future to popularize the clinical application of E2Fs in CRC.

Physician Burnout and the Electronic Health Record Leading Up to and During the First Year of COVID-19: Systematic Review.

BACKGROUND: Physician burnout was first documented in 1974, and the electronic health record (EHR) has been known to contribute to the symptoms of physician burnout. Authors pondered the extent of this effect, recognizing the increased use of telemedicine during the first year of COVID-19. OBJECTIVE: The aim of this review was to objectively analyze the literature over the last 5 years for empirical evidence of burnout incident to the EHR and to identify barriers to, facilitators to, and associated patient satisfaction with using the EHR to improve symptoms of burnout. METHODS: No human participants were involved in this review; however, 100% of participants in studies analyzed were adult physicians. We queried 4 research databases and 1 targeted journal for studies commensurate with the objective statement from January 1, 2016 through January 31, 2021 (n=25). RESULTS: The hours spent in documentation and workflow are responsible for the sense of loss of autonomy, lack of work-life balance, lack of control of one's schedule, cognitive fatigue, a general loss of autonomy, and poor relationships with colleagues. Researchers have identified training, local customization of templates and workflow, and the use of scribes as strategies to alleviate the administrative burden of the EHR and decrease symptoms of burnout. CONCLUSIONS: The solutions provided in the literature only addressed 2 of the 3 factors (workflow and documentation time) but not the third factor (usability). Practitioners and administrators should focus on the former 2 factors because they are within their sphere of control. EHR vendors should focus on empirical evidence to identify and improve the usability features with the greatest impact. Researchers should design experiments to explore solutions that address all 3 factors of the EHR that contribute to burnout. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020201820; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=201820. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/15490.

A Cloud-connected NO2 and Ozone Sensor System for Personalized Pediatric Asthma Research and Management.

This paper presents a cloud-connected indoor air quality sensor system that can be deployed to patients' homes to study personal microenvironmental exposure for asthma research and management. The system consists of multiple compact sensor units that can measure residential NO2, ozone, humidity, and temperature at one-minute resolution and a cloud-based informatic system that acquires, stores, and visualizes the microenvironmental data in real-time. The sensor hardware can measure NO2 as low as 10 ppb and ozone at 15 ppb. The cloud informatic system is implemented using open-source software on Amazon Web Service for easy deployment and scalability. This system was successfully deployed to pediatric asthma patients' homes in a pilot study. In this study, we discovered that some families had short-term NO2 exposure higher than EPA's one-hour exposure limit (100 ppb), and NO2 micro-pollution episodes often arise from natural gas appliance usage such as gas stove burning during cooking. By combining the personalized air pollutant exposure measurements with the physiological responses from monitoring devices, patient diaries, or medical records, this system can potentially enable novel asthma research and personalized asthma management.

Peptidomic Approach for the Identification of Peptides with Potential Antioxidant and Anti-Hyperthensive Effects Derived From Asparagus By-Products.

Asparagus waste represents products of great interest since many compounds with high biological value are located in the lower portion of the spears. The extraction of bioactive compounds from asparagus by-products is therefore crucial for the purpose of adding value to these by-products. In this paper, bioactive peptides from asparagus waste were extracted, digested, purified and identified. In particular, Alcalase® was chosen as the enzyme to use to obtain protein hydrolysate due to its low cost and, consequently, the possibility of implementing the method on a large scale. In order to simplify the peptide extract to reach better identification, the hydrolysate was fractionated by reversed-phase chromatography in 10 fractions. Two tests were carried out for antioxidant activity (ABTS-DPPH) and one for antihypertensive activity (ACE). Fractions with a higher bioactivity score were identified by peptidomics technologies and screened for bioactivity with the use of bioinformatics. For ACE-inhibitor activity, two peptides were synthetized, PDWFLLL and ASQSIWLPGWL, which provided an EC50 value of 1.76 µmol L-1 and 4.02 µmol L-1, respectively. For the antioxidant activity, by DPPH assay, MLLFPM exhibited the lowest EC50 value at 4.14 µmol L-1, followed by FIARNFLLGW and FAPVPFDF with EC50 values of 6.76 µmol L-1 and 10.01 µmol L-1, respectively. A validation of the five identified peptides was also carried out. The obtained results showed that peptides obtained from asparagus by-products are of interest for their biological activity and are suitable for being used as functional ingredients.

Informatic deconvolution of biased GPCR signaling mechanisms from in vivo pharmacological experimentation.

Ligands possessing different physico-chemical structures productively interact with G protein-coupled receptors generating distinct downstream signaling events due to their abilities to activate/select idiosyncratic receptor entities ('receptorsomes') from the full spectrum of potential receptor partners. We have employed multiple novel informatic approaches to identify and characterize the in vivo transcriptomic signature of an arrestin-signaling biased ligand, [D-Trp(12),Tyr(34)]-bPTH(7-34), acting at the parathyroid hormone type 1 receptor (PTH1R), across six different murine tissues after chronic drug exposure. We are able to demonstrate that [D-Trp(12),Tyr(34)]-bPTH(7-34) elicits a distinctive arrestin-signaling focused transcriptomic response that is more coherently regulated, in an arrestin signaling-dependent manner, across more tissues than that of the pluripotent endogenous PTH1R ligand, hPTH(1-34). This arrestin-focused response signature is strongly linked with the transcriptional regulation of cell growth and development. Our informatic deconvolution of a conserved arrestin-dependent transcriptomic signature from wild type mice demonstrates a conceptual framework within which the in vivo outcomes of biased receptor signaling may be further investigated or predicted.

Informatic search strategies to discover analogues and variants of natural product archetypes.

Natural products are a crucial source of antimicrobial agents, but reliance on low-resolution bioactivity-guided approaches has led to diminishing interest in discovery programmes. Here, we demonstrate that two in-house automated informatic platforms can be used to target classes of biologically active natural products, specifically, peptaibols. We demonstrate that mass spectrometry-based informatic approaches can be used to detect natural products with high sensitivity, identifying desired agents present in complex microbial extracts. Using our specialised software packages, we could elaborate specific branches of chemical space, uncovering new variants of trichopolyn and demonstrating a way forward in mining natural products as a valuable source of potential pharmaceutical agents.

Informatic application to characterise and identify small mammal species: Arvicolinae (Cricetidae, Rodentia, Mammalia).

The classification of rodent species can be challenging due to high morphological similarities observed among them. This problem is further increased in palaeontological systematics, where classification is traditionally based on the molar morphology. The subfamily Arvicolinae (Rodentia, Mammalia) is one of these rodent groups, whose classification being important for biostratigraphic and climatic studies of the Quaternary period is challenging. We present an application developed using the MatLab informatic algorithm, designed to classify the Arvicolinae species using Geometric Morphometrics (GMM) analyses of the first lower molar. Moreover, the application includes an option to automatically obtain the linear measurements that are commonly used for the identification of these species. This method shows a high degree of accuracy in the species classification, which is expected to increase as the reference database is further developed. This application can serve as an alternative tool for the classification of specimens with unclear morphologies. It can also be used to reduce the time required to manually obtain the linear indices necessary for their classification.

The quality and reliability of patient education regarding sound therapy videos for tinnitus on YouTube.

Background: Numerous online videos are available on sound therapy as a treatment modality for tinnitus, but it is uncertain if these videos are adequate for patient education. This study aims to evaluate the quality and reliability of tinnitus sound therapy videos on YouTube for patient education. Methods: YouTube videos were searched using keywords related to "tinnitus sound therapy". The top 100 videos were analyzed after excluding those were repetitive, irrelevant, less than 3 min, or not in English. After categorising the videos based on their authorship and content, the video power index (VPI) was relied to determine their popularity. The DISCERN questionnaire (DISCERN), the Global Quality Score (GQS), the Journal of the American Medical Association benchmark criteria (JAMA), and the Patient Education Materials Assessment Tool (PEMAT) were utilized to evaluate the quality, transparency, and patient education. Results: Over half (56%) of the videos were published by professional organizations. A total of 93% of them contained sound only. Only 17% followed the recommendations of the Clinical Management of Tinnitus Guidelines, and 3% provided literature referenced by the video. A variety types of sound were used, among which music accounting for 35%. The videos were highly popular with an average views of 7,335,003.28 ± 24,174,764.02 and an average VPI of 4,610.33 ± 11,531.10. However, their quality was poor (the median scores: 38/80 for DISCERN, 2/5 for GQS, 1/4 for JAMA, and 50%/100% for PEMAT). There was a negative correlation between the popularity of the videos and their quality, indicated by PEMAT: -0.207, DISCERN: -0.307, GQS: -0.302, and JAMA: -0.233. Several dimensions of the videos require improvement, especially actionability, treatment options, and transparency with lacks of 100%, 63%, and 75% respectively. Conclusion: The tinnitus sound therapy videos available on YouTube exhibit low quality. Nevertheless, they also hold potential for health education if refined and utilized suitably.

Next-Generation Sequencing Informatic Architecture Considerations.

Clinically relevant sequencing methodologies continue to expand in number, diversity, complexity, and scale. This evolving and varied landscape requires unique implementations in all aspects of the assay, including the wet bench, bioinformatics, and reporting. Following implementation, the informatics of many of these tests continue to change over time, from software and annotation source updates, guidelines, and knowledgebase changes to changes in underlying information technology (IT) infrastructure. Key principles can be applied when implementing the informatics of a new clinical test which can greatly improve the lab's ability to deal with these updates rapidly and reliably. In this chapter, we discuss a variety of informatics issues which span all NGS applications. In particular, there is the need for implementing a reliable, repeatable, redundant, and version-controlled bioinformatics pipeline and architecture and a discussion of common methodologies to address these needs.

Identification of Zip8-correlated hub genes in pulmonary hypertension by informatic analysis.

Background: Pulmonary hypertension (PH) is a syndrome characterized by marked remodeling of the pulmonary vasculature and increased pulmonary vascular resistance, ultimately leading to right heart failure and even death. The localization of Zrt/Irt-like Protein 8 (ZIP8, a metal ion transporter, encoded by SLC39A8) was abundantly in microvasculature endothelium and its pivotal role in the lung has been demonstrated. However, the role of Zip8 in PH remains unclear. Methods: Bioinformatics analysis was employed to identify SLC39A8 expression patterns and differentially expressed genes (DEGs) between PH patients and normal controls (NC), based on four datasets (GSE24988, GSE113439, GSE117261, and GSE15197) from the Biotechnology Gene Expression Omnibus (NCBI GEO) database. Gene set enrichment analysis (GSEA) was performed to analyze signaling pathways enriched for DEGs. Hub genes were identified by cytoHubba analysis in Cytoscape. Reverse transcriptase-polymerase chain reaction was used to validate SLC39A8 and its correlated metabolic DEGs expression in PH (SU5416/Hypoxia) mice. Results: SLC39A8 expression was downregulated in PH patients, and this expression pattern was validated in PH (SU5416/Hypoxia) mouse lung tissue. SLC39A8-correlated genes were mainly enriched in the metabolic pathways. Within these SLC39A8-correlated genes, 202 SLC39A8-correlated metabolic genes were screened out, and seven genes were identified as SLC39A8-correlated metabolic hub genes. The expression patterns of hub genes were analyzed between PH patients and controls and further validated in PH mice. Finally, four genes (Fasn, Nsdhl, Acat2, and Acly) were downregulated in PH mice. However, there were no significant differences in the expression of the other three hub genes between PH mice and controls. Of the four genes, Fasn and Acly are key enzymes in fatty acids synthesis, Nsdhl is involved in cholesterol synthesis, and Acat2 is implicated in cholesterol metabolic transformation. Taken together, these results provide novel insight into the role of Zip8 in PH.

Optimizing Voice Recognition Informatic Robots for Effective Communication in Outpatient Settings.

Aim/Objective Within the dynamic healthcare technology landscape, this research aims to explore patient inquiries within outpatient clinics, elucidating the interplay between technology and healthcare intricacies. Building upon the initial intelligent guidance robot implementation shortcomings, this investigation seeks to enhance informatic robots with voice recognition technology. The objective is to analyze users' vocal patterns, discern age-associated vocal attributes, and facilitate age differentiation through subtle vocal nuances to enhance the efficacy of human-robot communication within outpatient clinical settings. Methods This investigation employs a multi-faceted approach. It leverages voice recognition technology to analyze users' vocal patterns. A diverse dataset of voice samples from various age groups was collected. Acoustic features encompassing pitch, formant frequencies, spectral characteristics, and vocal tract length are extracted from the audio samples. The Mel Filterbank and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for speech and audio processing tasks alongside machine learning algorithms to assess and match vocal patterns to age-related traits. Results The research reveals compelling outcomes. The incorporation of voice recognition technology contributes to a significant improvement in human-robot communication within outpatient clinical settings. Through accurate analysis of vocal patterns and age-related traits, informatic robots can differentiate age through nuanced verbal cues. This augmentation leads to enhanced contextual understanding and tailored responses, significantly advancing the efficiency of patient interactions with the robots. Conclusion Integrating voice recognition technology into informatic robots presents a noteworthy advancement in outpatient clinic settings. By enabling age differentiation through vocal nuances, this augmentation enhances the precision and relevance of responses. The study contributes to the ongoing discourse on the dynamic evolution of healthcare technology, underscoring the complex synergy between technological progression and the intricate realities within healthcare infrastructure. As healthcare continues to metamorphose, the seamless integration of voice recognition technology marks a pivotal stride in optimizing human-robot communication and elevating patient care within outpatient settings.

Online Information on Painful Sexual Dysfunction in Women: Quality Analysis of Websites in SPANISH about Dyspareunia, Vaginismus and Vulvodynia.

The objective of this study was to evaluate the content, quality, and readability of websites containing information on dyspareunia, vaginismus, and vulvodynia in Spanish. Web pages were retrieved entering the terms "dyspareunia", "vaginismus", and "vulvodynia" in Google, Yahoo!, and Bing search engines. Two researchers employed the DISCERN and Bermúdez-Tamayo questionnaires to analyze the content and quality of the websites, and the INFLESZ scale to evaluate their readability. IBM SPSS® version 25 statistical software was employed for data analysis. The internet search yielded 262 websites, 91 of which were included after applying the selection criteria. Websites with information on dyspareunia obtained median scores of 24 (30-21) in the DISCERN, 38 (41.0-35.5) in the Bermúdez-Tamayo, and 55.3 (57.2-50.9) in the INFLESZ tools. The results for websites on vaginismus revealed median scores of 23.5 (30-20) in the DISCERN, 37 (42-35) in the Bermúdez-Tamayo, and 52.9 (55.6-46.4) in the INFLESZ. Finally, the median scores for vulvodynia sites was 25.5 (30-20) in the DISCERN, 38 (43-33.7) in the Bermúdez-Tamayo, and 54.2 (57.3-47.2) in the INFLESZ. These outcomes indicate that the quality of information in these websites is very low, while the overall quality of the web pages is moderate. Sites on vaginismus and vulvodynia were "somewhat difficult" to read, while readability was "normal" for websites on dyspareunia. Healthcare professionals should be aware of the shortcomings of these websites and address them through therapeutic education with resources containing updated, quality information. This raises the need for health professionals to generate these resources themselves or for experts and/or scientific societies in the field to check the quality and timeliness of the contents, regardless of whether or not the websites are endorsed with quality seals.

How Internet Websites Portray Herbal Vitality Products Containing Eurycoma longifolia Jack: An Evaluation of the Quality and Risks of Online Information.

BACKGROUND: Tongkat Ali (TA) or Eurycoma longifolia is a herbal medicine (HM) plant traditionally used to treat sexual dysfunction and enhance libido in men. Websites containing information about HM are abundant. However, studies have shown that in general the quality of websites containing information on HM is low. The present study aims to assess the quality and risks of websites containing information about TA supplements and to identify the health claims for TA. METHODOLOGY: A cross-sectional study to evaluate the quality and risks of websites discussing TA supplements was conducted. Online marketing websites, research articles, news articles, personal opinions, and those restricted by password were excluded. The quality and risks of websites were assessed using a modified DISCERN tool and a set of risk assessment criteria, respectively. The health claims for TA were identified and analyzed using content analysis. RESULTS: Overall, 321 websites met the inclusion criteria and were further evaluated. The overall rating of the quality of the websites was low, with a mean score ± standard deviation of 1.07 ± 0.51. Most websites lacked information that there may be more than one possible treatment choice and did not discuss areas of uncertainty. However, 67.9% (218/321) of the websites received a risk score of zero. A minority of websites (5/321, 1.6%) discouraged the use of conventional medicines. The most common health claims for TA included in the websites related to the enhancement of testosterone level (121/321, 37.7%), treatment of malaria (112/321, 34.9%), and improvement in libido (108/321, 33.6%). CONCLUSIONS: Websites containing information about TA supplements generally have a low-quality rating based on a modified DISCERN tool despite having a low-risk score. Government agencies and healthcare professionals (HCPs) must be more proactive in the critique and dissemination of information relating to HM, and in ensuring the safe use of HM among the public and patients.

Interactive Versus Static Decision Support Tools for COVID-19: Randomized Controlled Trial.

BACKGROUND: During the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs' underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree's nodes step-by-step in an interactive manner. OBJECTIVE: This study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts. METHODS: We developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention's guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants' accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants' ratings of the DSTs' usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants' perception of the tools. We used ANOVAs and t tests to assess statistical significance. RESULTS: Our survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7%, SD 14.1%; static DST group: mean 80.5%, SD 15.8%) compared to the control group (mean 65.8%, SD 20.8%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants' perception of the DSTs. CONCLUSIONS: When the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered).

[Spatial and temporal variations of vegetation coverage in the middle section of Yellow River Basin based on terrain gradient:Taking Yan'an City as an example.] / 基于地形梯度的黄河流域中段植被覆盖时空分异特征­­ä»¥å»¶å®‰å¸‚为例.

Vegetation restoration is an important requirement for ecological protection and high-quality development in the Yellow River Basin. In-depth understanding the temporal and spatial differentiation of vegetation has important practical significance. With the four Landsat TM/OLI remote sensing images as the data source, the pixel binary model was used to estimate vegetation coverage. The transfer matrix, geological information map, and the center of gravity migration model were used to analyze the temporal and spatial variations of vegetation coverage in Yan'an City in the middle of the Yellow River Basin from 1988 to 2018. The spatial variation of vegetation cover to elevation and slope was explored using terrain distribution index combined with terrain data. The results showed that vegetation coverage in Yan'an was low in the north region and high in the south region. The vegetation coverage increased significantly due to policy changes. From 1988 to 2018, the vegetation change pattern in Yan'an was dominated by continuous improvement and stability. Vegetation coverage increased in about 50% of the area. 83% of the study area with high vegetation coverage remained stable. At each elevation and slope level, the advantage of high vegetation cove-rage increased with time. At each slope level, vegetation cover increased with slope. The transition direction of vegetation cover of different levels in Yan'an was basically consistent with the overall spatial change trend of vegetation cover, with the overall shift being from north to west. The vegetation construction in Yan'an has achieved remarkable results, though the vegetation coverage in the north still needs to be improved. Optimizing vegetation types and structures is an important direction for future vegetation construction.

The molecular landscape of neural differentiation in the developing Drosophila brain revealed by targeted scRNA-seq and multi-informatic analysis.

The Drosophila type II neuroblast lineages present an attractive model to investigate the neurogenesis and differentiation process as they adapt to a process similar to that in the human outer subventricular zone. We perform targeted single-cell mRNA sequencing in third instar larval brains to study this process of the type II NB lineage. Combining prior knowledge, in silico analyses, and in situ validation, our multi-informatic investigation describes the molecular landscape from a single developmental snapshot. 17 markers are identified to differentiate distinct maturation stages. 30 markers are identified to specify the stem cell origin and/or cell division numbers of INPs, and at least 12 neuronal subtypes are identified. To foster future discoveries, we provide annotated tables of pairwise gene-gene correlation in single cells and MiCV, a web tool for interactively analyzing scRNA-seq datasets. Taken together, these resources advance our understanding of the neural differentiation process at the molecular level.

SSP: Early prediction of sepsis using fully connected LSTM-CNN model.

BACKGROUND: Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote the survival of suspected patients. However, reliable and intelligent systems for predicting sepsis are scarce. METHODS: This paper presents a novel technique called Smart Sepsis Predictor (SSP) to predict sepsis onset in patients admitted to an intensive care unit (ICU). SSP is a deep neural network architecture that encompasses long short-term memory (LSTM), convolutional, and fully connected layers to achieve early prediction of sepsis. SSP can work in two modes; Mode 1 uses demographic data and vital signs, and Mode 2 uses laboratory test results in addition to demographic data and vital signs. To evaluate SSP, we have used the 2019 PhysioNet/CinC Challenge dataset, which includes the records of 40,366 patients admitted to the ICU. RESULTS: To compare SSP with existing state-of-the-art methods, we have measured the accuracy of the SSP in 4-, 8-, and 12-h prediction windows using publicly available data. Our results show that the SSP performance in Mode 1 and Mode 2 is much higher than existing methods, achieving an area under the receiver operating characteristic curve (AUROC) of 0.89 and 0.92, 0.88 and 0.87, and 0.86 and 0.84 for 4 h, 8 h, and 12 h before sepsis onset, respectively. CONCLUSIONS: Using ICU data, sepsis onset can be predicted up to 12 h in advance. Our findings offer an early solution for mitigating the risk of sepsis onset.

The effect of evidence-based practice perception, information literacy self-efficacy, and academic motivation on nursing students' future implementation of evidence-based practice.

BACKGROUND: Evidence-based practice (EBP) in nursing is an important tool for promoting quality care and improving patient outcomes. Global evidence suggests that the rate of EBP implementation among nurses and nursing students is low. The effects of EBP perception, information literacy self-efficacy, and academic motivation on nursing students' future implementation of EBP have not been fully explored. PURPOSE: To examine the effect of EBP perception, information literacy self-efficacy, and academic motivation on future implementation of EBP among pre-registration nursing students. METHOD: A cross-sectional study used a self-administered structured questionnaire and analyzed data from 148 pre-registration nursing students at a university in central Israel. A hierarchical forced steps regression analysis was conducted to predict future implementation of EBP. RESULTS: Information literacy self-efficacy, EBP perception, and intrinsic academic motivation to experience stimulation, may predict EBP future implementation. The model explained 46.3% of the variance. CONCLUSION: The results indicate the importance of information literacy skills and positive attitudes to EBP, as well as of intrinsic academic motivation, for enhancing future implementation of EBP among nursing students.