Australian Dentist's Knowledge and Perceptions of Factors Affecting Radiographic Interpretation.

BACKGROUND: Errors of interpretation of radigraphic images, also known as interpretive errors, are a critical concern as they can have profound implications for clinical decision making. Different types of interpretive errors, including errors of omission and misdiagnosis, have been described in the literature. These errors can lead to unnecessary or harmful treat/or prolonged patient care. Understanding the nature and contributing factors of interpretive errors is important in developing solutions to minimise interpretive errors. By exploring the knowledge and perceptions of dental practitioners, this study aimed to shed light on the current understanding of interpretive errors in dentistry. METHODS: An anonymised online questionnaire was sent to dental practitioners in New South Wales (NSW) between September 2020 and March 2022. A total of 80 valid responses were received and analysed. Descriptive statistics and bivariate analysis were used to analyse the data. RESULTS: The study found that participants commonly reported interpretive errors as occurring 'occasionally', with errors of omission being the most frequently encountered type. Participants identified several factors that most likely contribute to interpretive errors, including reading a poor-quality image, lack of clinical experience and knowledge, and excessive workload. Additionally, general practitioners and specialists held different views regarding factors affecting interpretive errors. CONCLUSION: The survey results indicate that dental practitioners are aware of the common factors associated with interpretive errors. Errors of omission were identified as the most common type of error to occur in clinical practice. The findings suggest that interpretive errors result from a mental overload caused by factors associated with image quality, clinician-related, and image interpretation. Managing and identifying solutions to mitigate these factors are crucial for ensuring accurate and timely radiographic diagnoses. The findings of this study can serve as a foundation for future research and the development of targeted interventions to enhance the accuracy of radiographic interpretations in dentistry.

Water fluoridation in Australia: A systematic review.

Water fluoridation is considered a safe and effective public health strategy to improve oral health. This review aimed to systematically summarize the available evidence of water fluoridation in Australia, focusing on the history, health impacts, cost effectiveness, challenges, and limitations. A systematic search was conducted on the Ovid Medline, Web of Science, Scopus, ProQuest Central, Cinahl, and Informit databases to identify literature on water fluoridation in Australia. A grey literature search and backward snowballing were used to capture additional literature. Primary studies, reviews, letters, and opinion papers were included in the quantitative analysis and summarized based on the year of publication and geographical location. The data were extracted from primary studies and summarized under three subheadings: history, community health impacts and the limitations and challenges. Water fluoridation in Australia was first implemented in 1953 in Tasmania. Most states and territories in Australia embraced water fluoridation by 1977 and currently, 89% of the Australian population has access to fluoridated drinking water. Studies report that water fluoridation has reduced dental caries by 26-44% in children, teenagers, and adults, benefiting everyone regardless of age, income, or access to dental care. It has been recognized as a cost-effective intervention to prevent dental caries, especially in rural and low-income areas. Water fluoridation as a public health measure has faced challenges, including political and public opposition, implementation and maintenance costs, access and equity, communication and education, and ethical concerns. Variations in research activities on water fluoridation across Australian states and territories over the last seven decades can be due to several factors, including the time of implementation, funding, and support. Ongoing monitoring and research to review and update optimal fluoride levels in drinking water in Australia is warranted to ensure sustainable benefits on oral health while preventing any adverse impacts.

Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome.

Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry.

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

From De-collectivization to Re-collectivization: New Transformation Trend in Agriculture Production in Taicang City of the Eastern China from the Perspective of 'Governmentality'.

The continuous development of agricultural technologies and produces trade and updated state reforms strongly shape the dominant organizing styles of local agriculture production. Since the end of the 1970s, rural China has witnessed a drastic agriculture transformation featured by de-collectivization, and recently in Taicang, a developed city in the eastern China, a kind of new cooperative farms have replaced the smallholders by pooling households' contracted farmland, signaling a new shift tendency towards agriculture recollectivization that is distinctive compared to other countries. Given the state governance's leadership in promoting this latest transformation, drawing on the theoretical concept of 'governmentality' that is powerful in understanding how to govern the society to meet certain objectives, this paper examines the accurate processes of agriculture re-collectivization in Taicang City based on the methods of semi-structured interview and participant observation. Our research shows that the China's tax-sharing and relevant reforms in the 1990s framed a new governance structure in line with the new mode of 'governmentality' closely linked to 'advanced liberalism', creating the responsibility shift from central to local governments and a set of evaluation technologies, and guiding various actors to engage in the agriculture re-collectivization practice in Taicang. Importantly, the considerable subsidies and the impressive extension services in terms of farming personnel training and agricultural techniques diffusion underpin the normal running of cooperative farms and contribute to the local implementation of national strategy of agriculture modernization. We argue that the combination of distinctive institutional arrangement of rural land owned by village collectives in China and the burgeoning local economy allows the developed areas to re-collectivizing the agriculture production for ensuring grain self-sufficiency rather than capturing more share in global agriculture products trade, and that the cooperative farms in our case have been deeply integrated into the 'modern procurement system' and become more sensitive to market fluctuation, requiring rural extension service to transform towards farms' capacity building for maintaining the long-term development momentum.

Mapping the forage nitrogen, phosphorus, and potassium contents of alpine grasslands by integrating Sentinel-2 and Tiangong-2 data.

Nitrogen (N), phosphorus (P), and potassium (K) contents are crucial quality indicators for forage in alpine natural grasslands and are closely related to plant growth and reproduction. One of the greatest challenges for the sustainable utilization of grassland resources and the development of high-quality animal husbandry is to efficiently and accurately obtain information about the distribution and dynamic changes in N, P, and K contents in alpine grasslands. A new generation of multispectral sensors, the Sentinel-2 multispectral instrument (MSI) and Tiangong-2 moderate-resolution wide-wavelength imager (MWI), is equipped with several spectral bands suitable for specific applications, showing great potential for mapping forage nutrients at the regional scale. This study aims to achieve high-accuracy spatial mapping of the N, P, and K contents in alpine grasslands at the regional scale on the eastern Qinghai-Tibet Plateau. The Sentinel-2 MSI and Tiangong-2 MWI data, coupled with multiple feature selection algorithms and machine learning models, are applied to develop forage N, P, and K estimation models from data collected at 92 sample sites ranging from the vigorous growth stage to the senescent stage. The results show that the spectral bands of both the Sentinel-2 MSI and Tiangong-2 MWI have an excellent performance in estimating the forage N, P, and K contents (the R2 values are 0.68-0.76, 0.54-0.73, and 0.74-0.82 for forage N, P, and K estimations, respectively). Moreover, the model integrating the spectral bands of these two sensors explains 78%, 74%, and 84% of the variations in the forage N, P, and K contents, respectively. These results indicate that the estimation ability of forage nutrients can be further improved by integrating Tiangong-2 MWI and Sentinel-2 MSI data. In conclusion, integration of the spectral bands of multiple sensors is a promising approach to map the forage N, P, and K contents in alpine grasslands with high accuracy at the regional scale. This study offers valuable information for growth monitoring and real-time determination of forage quality in alpine grasslands.

Employability in health professional education: a scoping review.

BACKGROUND: The concept of employability can help educators understand the variability in the career outcome of graduates. Within the health professional education (HPE) literature, various conceptions of employability are used and implied. This review considers how the concept 'employability' is depicted and characterised in HPE literature. METHODS: A scoping review was conducted. The authors searched Medline, Web of Science and Scopus databases for English language literature relevant to employability in HPE. Arksey and O'Malley's review protocol and the criteria defined in the preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews Checklist (PRISMA-ScR) were used with methodological guidance provided by Levac et al. and Peters et al. with the exception of formal stakeholder consultation. RESULTS: The search resulted in 158 articles, of which 34 articles were included in this review. Charting the included articles revealed that within the set of articles, there is much diversity in study design, geographical setting and health profession. Three conceptions of employability were identified: acquiring a professional job, sustaining employment and thriving in the workforce. CONCLUSION: Conceptions of employability in HPE are largely focused on listing skills and capabilities for entry into employment and sustaining a career. To address gaps in research, structural contributions to employability and institutional strategies to promote conditions for thriving in disruption should be explored.

Factors affecting interpretation of dental radiographs.

OBJECTIVES: To identify the factors influencing errors in the interpretation of dental radiographs. METHODS: A protocol was registered on Prospero. All studies published until May 2022 were included in this review. The search of the electronic databases spanned Ovid Medline, PubMed, EMBASE, Web of Science and Scopus. The quality of the studies was assessed using the MMAT tool. Due to the heterogeneity of the included studies, a meta-analysis was not conducted. RESULTS: The search yielded 858 articles, of which eight papers met the inclusion and exclusion criteria and were included in the systematic review. These studies assessed the factors influencing the accuracy of the interpretation of dental radiographs. Six factors were identified as being significant that affected the occurrence of interpretation errors. These include clinical experience, clinical knowledge, and technical ability, case complexity, time pressure, location and duration of dental education and training and cognitive load. CONCLUSIONS: The occurrence of interpretation errors has not been widely investigated in dentistry. The factors identified in this review are interlinked. Further studies are needed to better understand the extent of the occurrence of interpretive errors and their impact on the practice of dentistry.

DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Mohammad-Rahimi H, Reza Motamedian S, Hossein Rohban M, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F, Deep learning for caries detection: A systematic review, J Dent, 2022,122, 104115. ISSN 0300-5712 https://doi.org/10.1016/j.jdent.2022.104115. SOURCE OF FUNDING: Information not available TYPE OF STUDY/DESIGN: Systematic review.

Comparative Evaluation of Proteome Discoverer and FragPipe for the TMT-Based Proteome Quantification.

Isobaric labeling-based proteomics is widely applied in deep proteome quantification. Among the platforms for isobaric labeled proteomic data analysis, the commercial software Proteome Discoverer (PD) is widely used, incorporating the search engine CHIMERYS, while FragPipe (FP) is relatively new, free for noncommercial purposes, and integrates the engine MSFragger. Here, we compared PD and FP over three public proteomic data sets labeled using 6plex, 10plex, and 16plex tandem mass tags. Our results showed the protein abundances generated by the two software are highly correlated. PD quantified more proteins (10.02%, 15.44%, 8.19%) than FP with comparable NA ratios (0.00% vs. 0.00%, 0.85% vs. 0.38%, and 11.74% vs. 10.52%) in the three data sets. Using the 16plex data set, PD and FP outputs showed high consistency in quantifying technical replicates, batch effects, and functional enrichment in differentially expressed proteins. However, FP saved 93.93%, 96.65%, and 96.41% of processing time compared to PD for analyzing the three data sets, respectively. In conclusion, while PD is a well-maintained commercial software integrating various additional functions and can quantify more proteins, FP is freely available and achieves similar output with a shorter computational time. Our results will guide users in choosing the most suitable quantification software for their needs.

Perceptions and attitudes of dental practitioners towards impacts of Covid 19 pandemic on clinical dentistry: a cross-sectional study.

BACKGROUND: The COVID-19 pandemic challenged all healthcare providers including dental practitioners. This cross-sectional study aimed to investigate the dental practitioners' perceptions and attitudes towards the impacts of COVID-19 on their professional practice, career decision and patient care. METHODS: Data was collected from dental practitioners registered in New South Wales (NSW), Australia using an online survey. RESULTS AND CONCLUSION: Responses received from 206 dental practitioners revealed their perceptions and attitudes towards COVID-19 infection risk, clinical guidelines, and measures adopted to deliver patient care. Majority of participants perceived the risk of infection in dentistry was higher compared with other health professionals. Most dental practices have followed guidelines received from professional associations and adopted multiple measures such as providing hand sanitizer, social distancing, and risk screen, to ensure safe delivery of oral health care. Over 80% of dental practitioners raised concerns on patients' accessibility to dental care during the pandemic. Despite tele-dentistry was introduced, almost half of the participants did not recognize tele-dentistry as an effective alternative. Moreover, negative impacts of COVID-19 pandemic on dental practitioner's professional career have been reported, including lower practice safety, reduction in working hours and income. Noteworthy, one quarter of participants even considered changing their practice environment, moving sectors or even leaving their career in dentistry. However, majority of the dental practitioners are willing to stay in their current practice environment and continue their career in dentistry. Our observations demonstrate the systematic disruption to dental practice faced in Australia due to the COVID-19 pandemic. Providing dental practitioners with timely educational training and support is important to minimise negative impacts of the challenges and to optimise dental care.

LIMITED EVIDENCE SUGGESTS THAT END STAGE KIDNEY DISEASE AND HEMODIALYSIS MAY IMPACT SALIVARY FLOW RATE AND COMPOSITION.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Rodrigues RP, Vidigal MT, Vieira WA, Nascimento GG, Sabino-Silva R, Blumenberg C, Siqueira MF, Siqueira WL, Paranhos LR. Salivary changes in chronic kidney disease and in patients undergoing hemodialysis: a systematic review and meta-analysis. Journal of Nephrology. 2022 Mar 2:1-29. SOURCE OF FUNDING: The work has been partially funded by CAPES-Finance Code 001. The authors have also acknowledged The Brazilian National Council for Scientific and Technological Development (CNPq) and Minas Gerais State Agency for Research and Development, Brazil (FAPEMIG) for the support. TYPE OF STUDY/DESIGN: Systematic review with meta-analysis of data.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Proteotypic Differences of Follicular-Patterned Thyroid Neoplasms.

The diagnosis of follicular-patterned thyroid tumors such as follicular thyroid adenoma (FA), follicular thyroid carcinoma (FTC), and follicular variant of papillary thyroid carcinoma (FvPTC) remains challenging. This study aimed to explore the molecular differences among these three thyroid tumors by proteomic analysis. A pressure cycling technology (PCT)-data-independent acquisition (DIA) mass spectrometry workflow was employed to investigate protein alterations in 52 formalin-fixed paraffin-embedded (FFPE) specimens: 18 FA, 15 FTC, and 19 FvPTC specimens. Immunohistochemical (IHC) analysis of 101 FA, 67 FTC, and 65 FvPTC specimens and parallel reaction monitoring (PRM) analysis of 20 FA, 20 FTC, and 20 FvPTC specimens were performed to validate protein biomarkers. A total of 4107 proteins were quantified from 52 specimens. Pairwise comparisons identified 287 differentially regulated proteins between FTC and FA, and 303 between FvPTC and FA and 88 proteins were co-dysregulated in the two comparisons. However, only 23 discriminatory proteins between FTC and FvPTC were detected. Additionally, the quantitative results for ANXA1 expression based on IHC staining and PRM-MS quantification were consistent with the proteomic results, showing that ANXA1 can be used to distinguish FvPTC from FA and FTC. The differentially regulated proteins found in this study can differentiate FA from FvPTC. In addition, ANXA1 is a promising biomarker for differentiating FvPTC from the other thyroid tumors.

Fabrication of Cellulose-Graphite Foam via Ion Cross-linking and Ambient-Drying.

Conventional plastic foams are usually produced by fossil-fuel-derived polymers, which are difficult to degrade in nature. As an alternative, cellulose is a promising biodegradable polymer that can be used to fabricate greener foams, yet such a process typically relies on methods (e.g., freeze-drying and supercritical-drying) that are hardly scalable and time-consuming. Here, we develop a fast and scalable approach to prepare cellulose-graphite foams via rapidly cross-linking the cellulose fibrils in metal ions-containing solution followed by ambient drying. The prepared foams exhibit low density, high compressive strength, and excellent water stability. Moreover, the cross-linking of the cellulose fibrils can be triggered by various metal ions, indicating good universality. We further use density functional theory to reveal the cross-linking effect of different ions, which shows good agreement with our experimental observation. Our approach presents a sustainable route toward low-cost, environmentally friendly, and scalable foam production for a range of applications.

Programmable heating and quenching for efficient thermochemical synthesis.

Conventional thermochemical syntheses by continuous heating under near-equilibrium conditions face critical challenges in improving the synthesis rate, selectivity, catalyst stability and energy efficiency, owing to the lack of temporal control over the reaction temperature and time, and thus the reaction pathways1-3. As an alternative, we present a non-equilibrium, continuous synthesis technique that uses pulsed heating and quenching (for example, 0.02 s on, 1.08 s off) using a programmable electric current to rapidly switch the reaction between high (for example, up to 2,400 K) and low temperatures. The rapid quenching ensures high selectivity and good catalyst stability, as well as lowers the average temperature to reduce the energy cost. Using CH4 pyrolysis as a model reaction, our programmable heating and quenching technique leads to high selectivity to value-added C2 products (>75% versus <35% by the conventional non-catalytic method and versus <60% by most conventional methods using optimized catalysts). Our technique can be extended to a range of thermochemical reactions, such as NH3 synthesis, for which we achieve a stable and high synthesis rate of about 6,000 µmol gFe-1 h-1 at ambient pressure for >100 h using a non-optimized catalyst. This study establishes a new model towards highly efficient non-equilibrium thermochemical synthesis.

Stratification of follicular thyroid tumours using data-independent acquisition proteomics and a comprehensive thyroid tissue spectral library.

Thyroid nodules occur in about 60% of the population. A major challenge in thyroid nodule diagnosis is to distinguish between follicular adenoma (FA) and carcinoma (FTC). Here, we present a comprehensive thyroid spectral library covering five types of thyroid tissues. This library includes 121 960 peptides and 9941 protein groups. This spectral library can be used to quantify up to 7863 proteins from thyroid tissues, and can also be used to develop parallel reaction monitoring (PRM) assays for targeted protein quantification. Next, to stratify follicular thyroid tumours, we compared the proteomes of 24 FA and 22 FTC samples, and identified 204 differentially expressed proteins (DEPs). Our data suggest altered ferroptosis pathways in malignant follicular carcinoma. In all, 31 selected proteins effectively distinguished follicular tumours. Of those DEPs, nine proteins were further verified by PRM in an independent cohort of 18 FA and 19 FTC. Together, we present a comprehensive spectral library for DIA and targeted proteomics analysis of thyroid tissue specimens, and identified nine proteins that could potentially distinguish FA and FTC.

HOST-DERIVED SALIVARY BIOMARKERS DEMONSTRATE POTENTIALS FOR THE DIAGNOSIS OF PERIODONTAL DISEASES.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Sukriti KC. Wang XZ, Gallagher JE. Diagnostic sensitivity and specificity of host-derived salivary biomarkers in periodontal disease amongst adults: Systematic review. J Clin Periodontol 2020;47(3):289-308. doi:10.1111/jcpe.13218. SOURCE OF FUNDING: Information not available. TYPE OF STUDY/DESIGN: Systematic review.

Rapid Synthesis of High-Entropy Oxide Microparticles.

High-entropy nanoparticles have received notable attention due to their tunable properties and broad material space. However, these nanoparticles are not suitable for certain applications (e.g., battery electrodes), where their microparticle (submicron to micron) counterparts are more preferred. Conventional methods used for synthesizing high-entropy nanoparticles often involve various ultrafast shock processes. To increase the size thereby achieving high-entropy microparticles, longer reaction time (e.g., heating duration) is usually used, which may also lead to undesired particle overgrowth or even densified microstructures. In this work, an approach based on Joule heating for synthesizing high-entropy oxide (HEO) microparticles with uniform elemental distribution is reported. In particular, two key synthesis conditions are identified to achieve high-quality HEO microparticles: 1) the precursors need to be loosely packed to avoid densification; 2) the heating time needs to be accurately controlled to tens of seconds instead of using milliseconds (thermal shock) that leads to nanoparticles or longer heating duration that forms bulk structures. The utility of the synthesized HEO microparticles for a range of applications, including high-performance Li-ion battery anode and water oxidation catalyst. This study opens up a new door toward synthesizing high-entropy microparticles with high quality and broad material space.