METHODOLOGY: 88 university students with migraine symptoms are the target participants. 4 of 5 on the Migraine Screen Questionnaire, 5 of 7 on the International Classification of Headache Disorders 3rd edition (ICHD-3), and both genders aged 18-40 years will be included. The participants with a score of more than or equal to 5 on the visual aura rating scale, diagnosed with a secondary headache, pregnancy, medication for neurological and cardiorespiratory conditions, and unwilling to participate will be excluded. Based on the disability questionnaire, the participants will be randomly assigned to either of the three groups. The primary outcome is resting-state electroencephalography (EEG) brain, and the secondary outcomes are sleep quality, quality of life, and migraine pain level. The post-test assessments will be performed at week 6.
RESULT: After the primary EEG analysis using MATLAB, the amplitude, frequency, frequency band ratio, and power spectrum density will be analysed. Mixed design analysis and intention-to-treat analysis will be used to assess the efficacy of aerobic training.
DISCUSSION: Migraines can be unpredictable, sometimes occurring without symptoms. If underdiagnosed or over-looked, it encompasses a serious of long-term effects. Hence with appropriate intervention, the symptoms can be prevented from worsening. But there is an unmet need for evidence-based non-pharmacological approaches to complement pharmacotherapy in migraine prevention. Moreover, an exercise intervention may be more suitable for people with migraine considering their tendency toward inactivity. Although some studies developed exercise programs for untrained patients with migraine, the outcome was primarily in terms of exercise capacity rather than the primary characteristics and secondary brain wave/ sleep quality changes, indicating the need for this study.
SUMMARY: To treat BC, small-molecule inhibitors, phytomedicines, and nanoparticles are conjugated to attenuate BC signaling pathways. Due to their numerous target mechanisms and strong safety records, phytomedicines and nanomedicines have received much attention in studies examining their prospects as anti-BC agents by such unfulfilled demands.
KEY MESSAGES: The processes involved in the affiliation across the progression of tumors and the spread of inflammation are highlighted in this review. Furthermore, we included many drugs now undergoing clinical trials that target cancer-mediated inflammatory pathways, cutting-edge nanotechnology-derived delivery systems, and a variety of phytomedicines that presently address BC.
PURPOSE: The purpose of this in vitro study was to evaluate and compare the accuracy of 3D digital casts generated by 4 photogrammetry software programs (Agisoft Metashape, 3DF Zephyr, Meshroom, and Polycam) and casts from 2 conventional impression materials (alginate and polyvinyl siloxane [PVS]) for the fabrication of nasal maxillofacial prostheses.
MATERIAL AND METHODS: A stone cast of a patient's nose was used as the basis for generating a reference digital 3D cast and another 54 test 3D casts. The reference cast was created by scanning the stone cast using a FARO Optor Lab 3D scanner. The 54 test 3D casts were generated and divided into 6 test groups as follows: Agisoft group: 9 3D casts generated using Agisoft Metashape, a commercial personal computer (PC) software program; 3DF Zephyr group: 9 3D casts generated using 3DF Zephyr, a commercial PC software program; Meshroom group: 9 3D casts generated using Meshroom, a free PC software program; Polycam group: 9 3D casts generated using the Polycam, a commercial Android cloud application; PVS group: 9 3D casts generated indirectly by 3D scanning a gypsum cast made from a polyvinyl siloxane (PVS) impression of the stone nose cast; and Alginate group: 9 3D casts generated indirectly by scanning a master cast made using alginate impressions of the stone nose cast. Deviation measurements of the produced specimens were analyzed using the Geomagic Control X software program, and statistical comparisons were performed employing the Kruskal-Wallis test (α=.05).
RESULTS: The results showed that the 3DF Zephyr group had the smallest deviation measurements (median: 0.057 mm ±0.012) among the 4 photogrammetry software programs, while the alginate impression group had the largest deviations (median: 0.151 mm ±0.094) of the 2 conventional impression materials. Significant differences were observed among the 4 photogrammetry software programs and the 2 conventional impression materials (H=39.41, df=5, P.05).
CONCLUSIONS: Photogrammetry software programs, specifically Agisoft Metashape and 3DF Zephyr, demonstrated better accuracy than conventional impression materials in creating nasal digital casts. Photogrammetry has the potential to improve workflow and reduce patient discomfort during the fabrication of maxillofacial prostheses. Further research is needed to validate these findings in clinical settings.
METHODS: Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts.
RESULTS: The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support.
CONCLUSION: This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.
METHODS: An online questionnaire was developed and disseminated between May and July 2021. The survey was open to all pharmacists and pharmaceutical scientists. The survey consisted of four sections; demographic information, questions about professional organisations, about the International Pharmaceutical Federation (FIP) and its impact on the members. Data were analysed descriptively.
RESULTS: A total of 1033 complete survey responses were received and included in the analysis. Of all respondents, 761 (73.7%) respondents were current members of a professional organisation and 272 (26.3%) were not members of any professional organisation. Overall, findings demonstrated networking, education, training and professional development opportunities as the main interests and anticipated activities, while the lack of clarity or need to join organisation, time, and financial constraints as the main barriers of pharmacy professionals holding membership. The majority of FIP members are satisfied with current FIP activities, and anticipate further networking opportunities, educational resources and grants made available to members.
CONCLUSIONS: Understanding the perceptions and needs, as well as factors that influence engagement of pharmacists and pharmaceutical scientists is the key to enhancing membership. Professional organisations are highly encouraged to strengthen and target activities according to the identified motivators and barriers.
DESIGN/METHODOLOGY/APPROACH: The framework draws on the broader receiver-focussed literature and integrates innovative findings from a series of empirical studies. These studies examined different receiver behaviour within vignettes, retrospective descriptions of real interactions and behaviour in a simulated interaction.
FINDINGS: The authors' findings indicated that speaking up is an intergroup interaction where social identities, context and speaker stance intersect, directly influencing both perceptions of and responses to the message. The authors' studies demonstrated that when spoken up to, health professionals poorly manage their emotions and ineffectively clarify the speaker's concerns. Currently, targeted training for receivers is overwhelmingly absent from speaking-up programmes. The receiver mindset framework provides an evidence-based, healthcare specific, receiver-focussed framework to inform programmes.
ORIGINALITY/VALUE: Grounded in communication accommodation theory (CAT), the resulting framework shifts speaking up training from being only speaker skill focussed, to training that recognises speaking up as a mutual negotiation between the healthcare speaker and receiver. This framework provides healthcare professionals with a novel approach to use in response to speaking up that enhances their ability to listen, understand and engage in point-of-care negotiations to ensure the physical and psychological safety of patients and staff.
METHODS: AI-based chatbots (ie, ChatGPT-3.5, ChatGPT-4, Microsoft Bing AI, and Google Bard) were compared for their abilities to detect clinically relevant DDIs for 255 drug pairs. Descriptive statistics, such as specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), were calculated for each tool.
RESULTS: When a subscription tool was used as a reference, the specificity ranged from a low of 0.372 (ChatGPT-3.5) to a high of 0.769 (Microsoft Bing AI). Also, Microsoft Bing AI had the highest performance with an accuracy score of 0.788, with ChatGPT-3.5 having the lowest accuracy rate of 0.469. There was an overall improvement in performance for all the programs when the reference tool switched to a free DDI source, but still, ChatGPT-3.5 had the lowest specificity (0.392) and accuracy (0.525), and Microsoft Bing AI demonstrated the highest specificity (0.892) and accuracy (0.890). When assessing the consistency of accuracy across two different drug classes, ChatGPT-3.5 and ChatGPT-4 showed the highest variability in accuracy. In addition, ChatGPT-3.5, ChatGPT-4, and Bard exhibited the highest fluctuations in specificity when analyzing two medications belonging to the same drug class.
CONCLUSION: Bing AI had the highest accuracy and specificity, outperforming Google's Bard, ChatGPT-3.5, and ChatGPT-4. The findings highlight the significant potential these AI tools hold in transforming patient care. While the current AI platforms evaluated are not without limitations, their ability to quickly analyze potentially significant interactions with good sensitivity suggests a promising step towards improved patient safety.
METHODS: We report 12-month post-treatment data from a single-blind, active-controlled trial (October 2017-August 2019) where 327 Myanmar refugees in Malaysia were assigned to either six sessions of IAT (n = 164) or cognitive behavioral treatment (CBT) (n = 163). Primary outcomes were posttraumatic stress disorder (PTSD), depression, anxiety, and persistent complex bereavement disorder (PCBD) symptom scores at treatment end and 12-month post-treatment. Secondary outcome was functional impairment.
RESULTS: 282 (86.2%) participants were retained at 12-month follow-up. For both groups, large treatment effects for common mental disorders (CMD) symptoms were maintained at 12-month post-treatment compared to baseline (d = 0.75-1.13). Although participants in IAT had greater symptom reductions and larger effect sizes than CBT participants for all CMDs at treatment end, there were no significant differences between treatment arms at 12-month post-treatment for PTSD [mean difference: -0.9, 95% CI (-2.5 to 0.6), p = 0.25], depression [mean difference: 0.1, 95% CI (-0.6 to 0.7), p = 0.89), anxiety [mean difference: -0.4, 95% CI (-1.4 to 0.6), p = 0.46], and PCBD [mean difference: -0.6, 95% CI (-3.1 to 1.9), p = 0.65]. CBT participants showed greater improvement in functioning than IAT participants at 12-month post-treatment [mean difference: -2.5, 95% CI (-4.7 to -0.3], p = 0.03]. No adverse effects were recorded for either therapy.
CONCLUSIONS: Both IAT and CBT showed sustained treatment gains for CMD symptoms amongst refugees over the 12-month period.