Reflection on Ramadan Fasting Research Related to Sustainable Development Goal 3 (Good Health and Well-Being): A Bibliometric Analysis.

There is a large body of research on Ramadan intermittent fasting (RIF) and health in Muslim communities, that can offer insights to promote the achievement of Sustainable Development Goal 3 (SDG 3), which encompasses good health and well-being. Based on recent bibliometric evidence, we hypothesized that RIF research is highly relevant to SDG 3, particularly Targets 3.1, 3.2, 3.4, and 3.5. Therefore, this bibliometric study quantified RIF literature supporting SDG 3 and associated targets over the past seven decades and explored themes and trends. All types of research articles were extracted from the Scopus database from inception to March 2022. Microsoft Excel, Biblioshiny, and VOSviewer were used to qualitatively and quantitatively examine RIF research trends supporting SDG 3 and associated targets. We identified 1729 relevant articles. The number of publications notably increased since 1986, with a dramatic increase in 2019-2020. RIF research predominantly supported Target 3.4 (reducing risk for non-communicable diseases), with research hotspots being diabetes, diabetes medications, pregnancy, physiology, metabolic diseases, and obesity and metabolism. This target was also the most commonly supported by dedicated authors and institutions publishing on RIF, whereas other SDG 3 targets were negligibly addressed in comparison. Our comprehensive bibliometric analysis of RIF literature showed growing support for SDG 3 through positive contributions to half of the SDG 3 targets, although Target 3.4 received the most attention. We also identified knowledge gaps that may shape further research directions on RIF and promote the achievement of SDG 3 in Muslim communities.

Contemporary Role and Applications of Artificial Intelligence in Dentistry.

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Impacts of severity of Covid-19 infection on the morbidity and mortality of surgical patients.

Introduction: One of the challenges of surgery on patients with active SARS-CoV-2(severe acute respiratory syndrome coronavirus 2) infection is the increased risk of postoperative morbidity and mortality. Aim: This study will describe and compare the postoperative morbidity and mortality in asymptomatic patients or those with mild infection with those with severe COVID-19 infection undergoing elective or and emergency surgery. Materials and methods: This is a retrospective study of 37 COVID19 patients who had the infection 7 days prior to and 30 days after emergency or elective surgery. Patients were divided to two groups. Group1: the asymptomatic or those with mild infection that is diagnosed just before surgery (14 patients). Group 2: those who were admitted to the hospital because of severe COVID-19 and were operated for COVID-19 related complications (23 patients). Morbidity and mortality of both groups was studied. Results: There was no significant difference in gender between the two groups. There were 5 females (2 in group 1, and 3 in group 2) and 32 males (12 in group 1, and 20 in group 2). Mean age for all patients was 49.8years (38 for group 1 and 57 for group2). Median age for all patients was 50 years (37.5 for group 1 and 57 years for group 2). Sepsis developed in 7 patients (1 patient in group 1 and in 6 patients in group 2). Statistically there was no significant difference in occurrence of sepsis between the two groups. There was a significant difference in the intensive care stay between the two groups (higher in group 2). Four deaths were reported in group 1 and fourteen in group 2. Eighteen out of thirty-seven patients died. Conclusion: Severity of COVID-19 infection will prolong the hospitalization and ICU stay in surgical patients with no significant effect on mortality.

The Effectiveness of Artificial Intelligence in Detection of Oral Cancer.

AIM: The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. MATERIALS AND METHODS: From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%. RESULTS: Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%. CONCLUSIONS: There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.

An Evaluation of Er:YAG Laser in an Intact Enamel Treatment on Bonding to the Resin Cement Compared to Four Traditional Techniques.

AIM OF STUDY: Evaluation of the bond strength of the resin cement after enamel conditioning with erbium-doped yttrium aluminum garnet laser compared to other four different techniques. MATERIALS AND METHODS: The study consisted of 50 enamel specimens excluded from the smooth surfaces of newly extracted human 3rd molars. The specimens were randomly divided into five groups in terms of the enamel treatment technique. Each group (n = 10) was conditioned differently by bevel and acid etching group (I), acid etching technique group (II), double acid etching technique group (III), air abrasion followed by acid etching group (IV), and Er:YAG laser followed by acid etching group (V). After enamel treatment, a resin cylinder was installed using split Teflon matrix on each enamel specimen. Then the specimens were exposed to a shear strength device, and shear force was applied. The results were recorded with Newton. The required data were collected and statistically analyzed. A one-way ANOVA test was conducted to compare the five study groups using SPSS version 21 (p < 0.05). RESULTS: All groups showed high shear bond strength values ranging from 19.26 MPa (for group III) to 27.05 MPa (for group V). The enamel treatment with Er:YAG laser followed by acid etching gave the best results with significant differences compared to the other groups. CONCLUSION: Within the limitations of the present study, it can be concluded that the combination between the Er:YAG laser and the acid etching enhances the bond strength of resin cement with the enamel. CLINICAL SIGNIFICANCE: The use of Er:YAG laser followed by acid etching can be a successful technique for enamel conditioning and the results showed their superiority over the other groups.

An Evaluation of GuttaFlow2 in Filling Artificial Internal Resorption Cavities: An in vitro Study.

INTRODUCTION: Obturation of root canal with internal resorption represents a major challenge in Endodontics. In spite of that, usual obturation techniques are often employed without considering the best technique to solve this problem. The goal of this study was to investigate the ability of GuttaFlow2 in filling artificial internal resorption cavities. MATERIALS AND METHODS: The study sample included 36 human upper central incisors that were prepared using Protaper system (F4). Internal resorption cavities were prepared by cutting each tooth at 7 mm from the apex and preparing hemispherical cavities on both the sides and then re-attaching them. The sample was randomly separated into three groups (n = 12 in each group). In the first group, thermal injection technique (Obtura II) was employed and served as the control group. In the second group, injection of cold free-flow obturation technique with a master cone (GF2-C) was employed, whereas in the third group injection of cold free-flow obturation without a master cone (GF2) was followed. The teeth were re-cut at the same level as before and examined under a stereomicroscope. Subsequently, the captured images were transferred to AutoCAD program to measure the percentage of total filling "TF," gutta-percha "G," sealer "S," and voids "V" out of the total surface of the cross sections. RESULTS: All materials showed high filling properties in terms of "total filling," ranging from 99.17% (for Obtura II) to 99.72% (for GF2-C). Regarding gutta-percha percentages of filling, they ranged from 83.15 to 83.93%, whereas those for the sealer ranged from 5.71 to 15.24%. GuttaFlow2 group with a master cone appeared to give the best results despite the insignificant differences among the three groups. CONCLUSION: The GuttaFlow2 with a master cone technique seemed to be a promising filling material and gave results similar to those observed with Obtura II. It is recommended for use to obturate internal resorption cavities in clinical practice due to its good adaptability to root canal walls, ease of handling, and application. CLINICAL SIGNIFICANCE: Internal resorption defects can be successfully filled with GuttaFlow2 material when supplemented with a master cone, and the results are comparable with those obtained with the Obtura II technique.

Building an information security strategy for EHR: guidelines for assessing the current situation.

This paper is looking at electronic health record (EHR) systems and their information security strategy. It focuses on the first step of building an information security strategy which is analysing the current situation of an EHR system. This research is based on different research methods applied to different EHR systems. In this paper we define eight elements that can be used as guidelines for how best to assess the current situation of any EHR system.

Introduction of shared electronic records: multi-site case study using diffusion of innovation theory.

OBJECTIVE: To explore the introduction of a centrally stored, shared electronic patient record (the summary care record (SCR)) in England and draw wider lessons about the implementation of large scale information technology projects in health care. DESIGN: Multi-site, mixed method case study applying utilisation focused evaluation. SETTING: Four early adopter sites for the SCR in England-three in urban areas of relative socioeconomic deprivation and the fourth in a relatively affluent rural area. Data sources and analysis Data included 250 staff interviews, 1500 hours of ethnographic observation, interviews and focus groups with 170 patients and carers, 2500 pages of correspondence and documentary evidence, and incorporation of relevant surveys and statistics produced by others. These were analysed by using a thematic approach drawing on (and extending) a theoretical model of complex change developed in a previous systematic review. Main findings The mixed fortunes of the SCR programme in its first year were largely explained by eight interacting influences. The first was the SCR's material properties (especially technical immaturity and lack of interoperability) and attributes (especially the extent to which potential adopters believed the benefits outweighed the risks). The second was adopters' concerns (especially about workload and the ethicality of sharing "confidential" information on an implied consent model). The third influence was interpersonal influence (for example, opinion leaders, champions, facilitators), and the fourth was organisational antecedents for innovation (for example past experience with information technology projects, leadership and management capacity, effective data capture systems, slack resources). The fifth was organisational readiness for the SCR (for example, innovation-system fit, tension for change, power balances between supporters and opponents, baseline data quality). The sixth was the implementation process (including the nature of the change model and the extent to which new routines associated with the SCR aligned with existing organisational routines). The seventh influence was the nature and quality of links between different parts of the system, and the final one was the wider environment (especially the political context of the programme). CONCLUSION: Shared electronic records are not plug-in technologies. They are complex innovations that must be accepted by individual patients and staff and also embedded in organisational and inter-organisational routines. This process is heavily influenced at the micro-level by the material properties of the technology, individuals' attitudes and concerns, and interpersonal influence; at the meso-level by organisational antecedents, readiness, and operational aspects of implementation; and at the macro-level by institutional and socio-political forces. A case study approach and multi-level theoretical analysis can illuminate how contextual factors shape, enable, and constrain new, technology supported models of patient care.