Using (1,3)-β-D-glucan concentrations in serum to monitor the response of azole therapy in patients with eumycetoma caused by Madurella mycetomatis

Introduction: (1,3)-β-D-glucan is a panfungal biomarker secreted by many fungi, including Madurella mycetomatis, the main causative agent of eumycetoma. Previously we demonstrated that (1,3)-β-D-glucan was present in serum of patients with eumycetoma. However, the use of (1,3)-β-D-glucan to monitor treatment responses in patients with eumycetoma has not been evaluated. Materials and Methods: In this study, we measured (1,3)-β-D-glucan concentrations in serum with the WAKO (1,3)-β-D-glucan assay in 104 patients with eumycetoma treated with either 400 mg itraconazole daily, or 200 mg or 300 mg fosravuconazole weekly. Serial serum (1,3)-β-D-glucan concentrations were measured at seven different timepoints. Any correlation between initial and final (1,3)-β-D-glucan concentrations and clinical outcome was evaluated. Results: The concentration of (1,3)-β-D-glucan was obtained in a total of 654 serum samples. Before treatment, the average (1,3)-β-D-glucan concentration was 22.86 pg/mL. During the first 6 months of treatment, this concentration remained stable. (1,3)-β-D-glucan concentrations significantly dropped after surgery to 8.56 pg/mL. After treatment was stopped, there was clinical evidence of recurrence in 18 patients. Seven of these 18 patients had a (1,3)-β-D-glucan concentration above the 5.5 pg/mL cut-off value for positivity, while in the remaining 11 patients, (1,3)-β-D-glucan concentrations were below the cut-off value. This resulted in a sensitivity of 38.9% and specificity of 75.0%. A correlation between lesion size and (1,3)-β-D-glucan concentration was noted. Conclusion: Although in general (1,3)-β-D-glucan concentrations can be measured in the serum of patients with eumycetoma during treatment, a sharp decrease in β-glucan concentration was only noted after surgery and not during or after antimicrobial treatment. (1,3)-β-D-glucan concentrations were not predictive for recurrence and seem to have no value in determining treatment response to azoles in patients with eumycetoma.</p

Evaluating the Effect of Resection on the Sealing Ability of MTA and CEM Cement

Introduction: In cases of limited access to the surgical site, an alternative approach is to obturate the canal prior to surgery. Endodontic surgery is subsequently performed by root-end resection without retro-cavity preparation. This in vitro study was designed to compare the sealing ability of resected roots filled with either mineral trioxide aggregate (MTA) or calcium enriched mixture (CEM) cement. Materials and Methods: Seventy maxillary anterior teeth were selected. Following canal preparation, the teeth were randomly divided into four experimental (n=15) and two control (n=5) groups. In group 1, CEM cement was placed into the apical 6-mm of the canal. The remainder of the canal was filled with gutta-percha/AH26 and 3-mm root-ends were resected. In group 2: the teeth were treated as described above except that MTA was used instead of CEM cement. Group 3: The canals were obturated with gutta-percha/AH26. After root-end resection, retro cavities were prepared and filled with CEM cement. Group 4: The teeth were treated as described for group 3 except that MTA was used instead of CEM cement. The root apices of teeth were then placed in India ink, and maximum dye penetration was measured with a stereomicroscope. Nested ANOVA and Independent samples t-test were used to evaluate the statistical significance. Results: The mean dye leakage values for groups 1 to 4 were 402.6, 526.4, 141.0, and 177.4, respectively. Overall, the retrofilled groups (3 and 4) had less microleakage compared to the resected materials; in the CEM cement groups this was statistically significant (P<0.05), i.e. root-end resection had no significant influence on the sealing ability of MTA, but significantly increased the microleakage of CEM cement (P=0.017). Overall, CEM cement showed less microleakage compared to MTA, however the difference was not significant. Conclusion: Within the limitations of this dye leakage study, we can conclude that if limited access prohibits retrofill placement, MTA or CEM cement can be used to fill the canal prior to root-end resection; as they have similar sealing ability. However, further laboratory and clinical studies are required to evaluate this alternative method

AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research

Introduction: As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected. Results: The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility. Conclusion: The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety

Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care: A Scoping Review of Current Clinical Implementations

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians’ perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes

Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes

Epidemiological cut-off values for itraconazole and ravuconazole for Madurella mycetomatis, the most common causative agent of mycetoma

Background Eumycetoma is a neglected tropical disease. It is a chronic inflammatory subcutaneous infection characterised by painless swellings which produce grains. It is currently treated with a combination of itraconazole and surgery. In an ongoing clinical study, the efficacy of fosravuconazole, the prodrug of ravuconazole, is being investigated. For both itraconazole and ravuconazole, no clinical breakpoints or epidemiological cut-off values (ECV) to guide treatment are currently available. Objective To determine tentative ECVs for itraconazole and ravuconazole in Madurella mycetomatis, the main causative agent of eumycetoma. Materials and Methods Minimal inhibitory concentrations (MICs) for itraconazole and ravuconazole were determined in 131 genetically diverse clinical M. mycetomatis isolates with the modified CLSI M38 broth microdilution method. The MIC distributions were established and used to determine ECVs with the ECOFFinder software. CYP51A sequences were sequenced to determine whether mutations occurred in this azole target gene, and comparisons were made between the different CYP51A variants and the MIC distributions. Results The MICs ranged from 0.008 to 1 mg/L for itraconazole and from 0.002 to 0.125 mg/L for ravuconazole. The M. mycetomatis ECV for itraconazole was 1 mg/L and for ravuconazole 0.064 mg/L. In the wild-type population, two CYP51A variants were found for M. mycetomatis, which differed in one amino acid at position 499 (S499G). The MIC distributions for itraconazole and ravuconazole were similar between the two variants. No mutations linked to decreased susceptibility were found. Conclusion The proposed M. mycetomatis ECV for itraconazole is 1 mg/L and for ravuconazole 0.064 mg/L

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions