METHODS: A scoping review was carried out using the Arksey and O'Malley methodological framework. The search strategy was developed iteratively, with three main aspects: general practice/primary care contexts, risk assessment/decision support tools, and workload-related factors. Three databases were searched in 2019, and updated in 2021, covering articles published since 2009: Medline (Ovid), HMIC (Ovid) and Web of Science (TR). Double screening was completed by two reviewers, and data extracted from included articles were analysed.
RESULTS: The search resulted in 5,594 references, leading to 95 full articles, referring to 87 studies, after screening. Of these, 36 studies were based in the USA, 21 in the UK and 11 in Australia. A further 18 originated from Canada or Europe, with the remaining studies conducted in New Zealand, South Africa and Malaysia. Studies examined the use of eCDS tools and reported some findings related to their impact on workload, including on consultation duration. Most studies were qualitative and exploratory in nature, reporting health professionals' subjective perceptions of consultation duration as opposed to objectively-measured time spent using tools or consultation durations. Other workload-related findings included impacts on cognitive workload, "workflow" and dialogue with patients, and clinicians' experience of "alert fatigue".
CONCLUSIONS: The published literature on the impact of eCDS tools in general practice showed that limited efforts have focused on investigating the impact of such tools on workload and workflow. To gain an understanding of this area, further research, including quantitative measurement of consultation durations, would be useful to inform the future design and implementation of eCDS tools.
METHOD: Medical image data for five types of defects were selected, segmented, converted and decimated to 3D polygon models on a personal computer. The models were transferred to a computer aided design (CAD) software which aided in designing the prosthesis according to the virtual models. Two templates were designed for each defect, one by an OS (free) system and one by CS. The parameters for analyses were the virtual volume, Dice similarity coefficient (DSC) and Hausdorff's distance (HD) and were executed by the OS point cloud comparison tool.
RESULT: There was no significant difference (p > 0.05) between CS and OS when comparing the volume of the template outputs. While HD was within 0.05-4.33 mm, evaluation of the percentage similarity and spatial overlap following the DSC showed an average similarity of 67.7% between the two groups. The highest similarity was with orbito-facial prostheses (88.5%) and the lowest with facial plate prosthetics (28.7%).
CONCLUSION: Although CS and OS pipelines are capable of producing templates which are aesthetically and volumetrically similar, there are slight comparative discrepancies in the landmark position and spatial overlap. This is dependent on the software, associated commands and experienced decision-making. CAD-based templates can be planned on current personal computers following appropriate decimation.
Materials and Methods: The present systematic review was carried out according to PRISMA guidelines. The search was carried out on PubMed/MEDLINE, Cochrane collaboration, Science direct, and Scopus scientific engines using selected MeSH keywords. The articles fulfilling the predefined selection criteria based on the fit and accuracy of removable partial denture (RPD) frameworks constructed from digital workflow (CAD/CAM; rapid prototyping) and conventional techniques were included.
Results: Nine full-text articles comprising 6 in vitro and 3 in vivo studies were included in this review. The digital RPDs were fabricated in all articles by CAD/CAM selective laser sintering and selective laser melting techniques. The articles that have used CAD/CAM and rapid prototyping technique demonstrated better fit and accuracy as compared to the RPDs fabricated through conventional techniques. The least gaps between the framework and cast (41.677 ± 15.546 μm) were found in RPDs constructed through digital CAD/CAM systems.
Conclusion: A better accuracy was achieved using CAD/CAM and rapid prototyping techniques. The RPD frameworks fabricated by CAD/CAM and rapid prototyping techniques had clinically acceptable fit, superior precision, and better accuracy than conventionally fabricated RPD frameworks.
OBJECTIVE: In this study, we report a rapid method for the residue analysis of IND and its metabolites, viz., IND-carboxylic acid, diaminotriazine, and triazine indanone in a wide range of palm oil matrices using liquid chromatography-tandem mass spectrometry (LC-MS/MS).
METHOD: The optimized sample preparation workflows included two options: (1) acetonitrile extraction (QuEChERS workflow), followed by freezing at -80°C and (2) acetonitrile extraction, followed by cleanup through a C18 solid phase extraction (SPE) cartridge. The optimized LC runtime was 7 min. All these analytes were estimated by LC-MS/MS multiple reaction monitoring.
RESULTS: Both sample preparation methods provided similar method performance and acceptable results. The limit of quantification (LOQ) of IND, IND-carboxylic acid, and triazine indanone was 0.001 mg/kg. For diaminotriazine, the LOQ was 0.005 mg/kg. The method accuracy and precision complied with the SANTE/12682/2019 guidelines of analytical quality control.
CONCLUSIONS: The potentiality of the method lies in a high throughput analysis of IND and its metabolites in a single chromatographic run with high selectivity and sensitivity. Considering its fit-for-purpose performance, the method can be implemented in regulatory testing of IND residues in a wide range of palm oil matrices that are consumed and traded worldwide.
HIGHLIGHTS: This work has provided a validated method for simultaneous residue analysis of indaziflam and its metabolites in crude palm oil and its derived matrices with high sensitivity, selectivity, and throughput.
OBJECTIVE: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process.
METHODS: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing.
RESULTS: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety.
CONCLUSIONS: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries.
RESULTS: We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control.
CONCLUSIONS: DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.
METHODS: Eutopic endometrium and serum from patients with endometriosis (n = 8 for tissue and n = 4 for serum) are, respectively, compared to endometrium and serum from females without endometriosis (n = 8 for tissue and n = 4 for serum) using a shotgun quantitative proteomics method. All study participants are at the proliferative phase of their menstrual cycle.
RESULTS: At the tissue and serum level, 1214 and 404 proteins are differentially expressed (DEPs) in eutopic endometrium and serum, respectively, of women with endometriosis versus controls. Gene ontology analysis shows that terms related to immune response/inflammation, cell adhesion/migration, and blood coagulation are significantly enriched in the DEPs of eutopic endometrium, as well as serum. Twenty-one DEPs have the same trend of differential expression in both matrices and can be further examined as potential disease- and tissue-specific serological markers of endometriosis.
CONCLUSIONS: The present integrated proteomic profiling of eutopic endometrium and serum from women with endometriosis identify promising serological markers that can be further validated in larger cohorts for the minimally invasive diagnosis of endometriosis.