Literature on Dental and Oral Health by King Saud bin Abdulaziz University for Health Science, Saudi Arabia; A Bibliometric Study

ABSTRACT This present paper examines the bibliometric assessment of research performance on dental science literature by researchers affiliated to King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), its teaching hospitals and Research Centre, to determine the statistical scenario and highlight the emerging trends in dental research. It is a retrospective observational study that had been carried out in of College of Dentistry library, KSAU-HS during January 2018. Published research on dentistry in authorship affiliated to KSAU-HS was collected from different online sources; Web of Science, PubMed, Google Scholar, ResearchGate and the archival record of King Abdullah International Research Centre from inception of KSAU-HS to December 2017. Year-wise distribution of articles, subject-wise segregation, collaboration pattern, authorship, and most productive authors have been calculated. The data is analyzed by using Microsoft Excel 2010.The finding of the study exposed that 144 research items published in 84 different journals contributed by 586 authors with an average of 4.06 authors per article. The rising tendency in publication and collaborative research was observed, 68% articles published during 2015-2017. Original research article (72.22%) being favorite design and Public Health Dentistry (30.55%) found to be the most preferred area of research. Dr. Khalid Al Fouzan emerged as most productive author. Authorship and citations pattern had also been calculated. Assessment of dental research revealed that there has been a growing emerging trend in publications. It’s the first bibliometric study on dental research in KSA, highlighted the collaboration style and pointed out the strong and weak areas of research

Management of prostate cancer in older men: recommendations of a working group of the International Society of Geriatric Oncology

Prostate cancer is the most prevalent cancer in men and predominantly affects older men (aged ≥70 years). The median age at diagnosis is 68 years; overall, two-thirds of prostate cancer-related deaths occur in men aged ≥75 years. With the exponential ageing of the population and the increasing life-expectancy in developed countries, the burden of prostate cancer is expected to increase dramatically in the future. To date, no specific guidelines on the management of prostate cancer in older men have been published. The International Society of Geriatric Oncology (SIOG) conducted a systematic bibliographic search based on screening, diagnostic procedures and treatment options for localized and advanced prostate cancer, to develop a proposal for recommendations that should provide the highest standard of care for older men with prostate cancer. The consensus of the SIOG Prostate Cancer Task Force is that older men with prostate cancer should be managed according to their individual health status, which is mainly driven by the severity of associated comorbid conditions, and not according to chronological age. Existing international recommendations (European Association of Urology, National Comprehensive Cancer Network, and American Urological Association) are the backbone for localized and advanced prostate cancer treatment, but need to be adapted to patient health status. Based on a rapid and simple evaluation, patients can be classified into four different groups: 1, ‘Healthy’ patients (controlled comorbidity, fully independent in daily living activities, no malnutrition) should receive the same treatment as younger patients; 2, ‘Vulnerable’ patients (reversible impairment) should receive standard treatment after medical intervention; 3, ‘Frail’ patients (irreversible impairment) should receive adapted treatment; 4, Patients who are ‘too sick’ with ‘terminal illness’ should receive only symptomatic palliative treatment

Distribution of Country of Origin in Studies Used in Cochrane Reviews

Inclusion in systematic reviews is one important component in judging the potential impact of clinical studies upon practice and hence the 'value for money' of spending for clinical research. This study aims to quantify the distribution of countries of origin of clinical studies used in Cochrane Reviews (CRs), and to link these data to the size of a country and to its spending on research. Random sample of publications used for CRs published in Issue 1 2008 and of publications used in CRs in the field of complementary and alternative medicine (CAM). Publications without original data were excluded. Likely countries of origin determined based on abstracts/full texts. CIA World Factbook (population data) and OECD database (economic data) were used. 1,000 random entries out of 140,005 references available in all specialities. In 876 (91.4%) of 959 eligible studies, country of origin was determined. The USA was the leading contributor (36.0% of the studies), followed by UK (13.4%), Canada (5.3%), Australia and Sweden (3.7%). In the CAM sample, country of origin was determined in 458 (93.5%) of 497 assessed studies. Again, the USA was the leading contributor (24.9%), with China also emerging as a significant contributor (24.7%) in this field. For both samples, the contribution of smaller countries (especially Scandinavian countries, Greece, and Ireland) became more noteworthy when considered in relation to population size and research spending. Our results support the leading roles of both the USA and the UK in publishing clinical papers. The emerging role of China can be seen, particularly related to CAM studies. Taking into account size of population and economic power, countries like France, Germany, Italy, and Spain provide small contributions. In contrast, smaller countries like Australia, Denmark, Finland, Ireland, New Zealand, and Sweden also play major roles

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.Comment: Original Title: "Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs" @inproceedings{chen2020doctor, title={Doctor imitator: A graph-based bone age assessment framework using hand radiographs}, author={Chen, Jintai and Yu, Bohan and Lei, Biwen and Feng, Ruiwei and Chen, Danny Z and Wu, Jian}, booktitle={MICCAI}, year={2020}

Identification of a novel plasmid lineage associated with the dissemination of metallo-β-lactamase genes among pseudomonads

Acquisition of metallo-\u3b2-lactamases (MBLs) represents one of most relevant resistance mechanisms to all \u3b2-lactams, including carbapenems, ceftolozane and available \u3b2-lactamase inhibitors, in Pseudomonas spp. VIM-type enzymes are the most common acquired MBLs in Pseudomonas aeruginosa and, to a lesser extent, in other Pseudomonas species. Little is known about the acquisition dynamics of these determinants, that are usually carried on integrons embedded into chromosomal mobile genetic elements. To date, few MBL-encoding plasmids have been described in Pseudomonas spp., and their diversity and role in the dissemination of these MBLs remains largely unknown. Here we report on the genetic features of the VIM-1encoding plasmid pMOS94 from P. mosselii AM/94, the earliest known VIM-1-producing strain, and of related elements involved in dissemination of MBL. Results of plasmid DNA sequencing showed that pMOS94 had a modular organization, consisting of backbone modules associated with replication, transfer and antibiotic resistance. Plasmid pMOS94, although not typable according to the PBRT scheme, was classifiable either in MOBF11 or MPFT plasmid families. The resistance region included the class I integron In70, carrying blaVIM-1, in turn embedded in a defective Tn402-like transposon. Comparison with pMOS94-like elements led to the identification of a defined plasmid lineage circulating in different Pseudomonas spp. of clinical and environmental origin and spreading different MBL-encoding genes, including blaIMP-63, blaBIM, and blaVIM-type determinants. Genetic analysis revealed that this plasmid lineage likely shared a common ancestor and had evolved through the acquisition and recombination of different mobile elements, including the MBL-encoding transposons. Our findings provide new insights about the genetic diversity of MBL-encoding plasmids circulating among Pseudomonas spp., potentially useful for molecular epidemiology purposes, and revealed the existence and persistence of a successful plasmid lineage over a wide spatio-temporal interval, spanning over five different countries among two continents and over 20-years

Coaching Competencies Deconstructed

The purpose of this capstone is to explore four qualities considered essential to professional coaching: authenticity, coaching presence, empathy, and openness. Through research in psychology and coaching literature, as well as interviews with experienced coach practitioners, this study first deconstructs each quality, and then creates a reconceptualization of each to enhance their use and understanding by novice and experienced professionals alike. As practitioners who are focused on human development, professional coaches are committed to developing ongoing mastery. One way to cultivate coaching competence is through Mindfulness Meditation. The attitudinal foundations of Mindfulness Meditation are highly relevant to coaching. Mindfulness Meditation, in particular, facilitates integration of several coaching qualities, and ultimately leads us to maximum resourcefulness and creativity for our clients

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Functional And Structural Consequences Of Nine Cyp21a2 Mutations Ranging From Very Mild To Severe Effects

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)We present the functional and structural effects of seven novel (p.Leu12Met, p.Arg16Cys, p.Ser101Asn, p.Ser202Gly, p.Pro267Leu, p.Gln389 Ala391del, and p.Thr450Met) and two previously reported but not studied (p.Ser113Phe and p.Thr450Pro) CYP21A2 mutations. Functional analyses were complemented with in silico prediction of mutation pathogenicity based on the recently crystallized human CYP21A2 structure. Mutated proteins were transiently expressed in COS-1 cells and enzyme activities towards 17-hydroxyprogesterone and progesterone were determined. Residual enzyme activities between 43% and 97% were obtained for p.Arg16Cys, p.Ser101Asn, p.Ser202Gly, p.Pro267Leu, and p.Thr450Met, similar to the activities of the well-known nonclassic mutations p.Pro453Ser and p.Pro482Ser, whereas the p.Leu12Met variant showed an activity of 100%. Conversely, the novel p.Ser113Phe, p. Gln389 Ala391del, and p.Thr450Promutations drastically reduced the enzyme function below 4%. The K-m values for all novel variants were in the same order of magnitude as for the wild-type protein except for p.The450Met. The maximum velocity was decreased for all mutants except for p.Leu12Met. We conclude that p. Leu12Met is a normal variant; the mutations p.Arg16Cys, p.Ser101Asn, p.Ser202Gly, p.Pro267Leu, and p.Thr450Met could be associated with very mild nonclassic CAH, and the mutations p.Ser113Phe, p.Gln389 Ala391del, and p.Thr450Pro are associated with classic CAH. The obtained residual activities indicated a good genotype-phenotype correlation.Sao Paulo Research Foundation (FAPESP) [2011/51808-2, 2014/09844-0, 2012/16815-0]IFCAH/ESPEMarianne and Marcus Wallenberg FoundationStockholm County Council (ALF-SLL)Stiftelsen Frimurare BarnhusetStiftelsen SamaritenJerringfondenStiftelsen Wera EkstromSallskapet BarnavardFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP