Advancing Preoperative Strategies for Thyroidectomy in Graves' Disease: A Narrative Review.

Graves' disease is an autoimmune disorder characterized by thyroid-stimulating antibodies that can potentially lead to thyrotoxicosis, goiter, skin disease, and eye disease. Available treatment options for Graves' disease include management with antithyroid drugs (ATDs), thyroid ablation with radioactive iodine (RAI), and surgical thyroid gland removal. For individuals unable to reach a normal thyroid hormone level, promptly considering a thyroidectomy is essential. Preoperative strategies to achieve a euthyroid state prevent thyroid storms and minimize postoperative complications and are therefore crucial. While variations in professional guidance exist, this review focuses on standard medical interventions as well as compares respective guidelines set forth by the American Thyroid Association, the European Thyroid Association, the American Association of Clinical Endocrinology, and the American Association of Endocrine Surgeons. There is consensus among these organizations underscoring the importance of rendering patients euthyroid prior to surgery and the use of ATDs. Most guidelines recommend screening for vitamin D deficiency as well as endorse thyroidectomy as the preferred treatment option for hyperthyroidism with skilled surgeons. Nevertheless, discrepancies do become apparent in aspects such as potassium iodide (SSKI) course duration and preoperative dexamethasone administration. By understanding these differing approaches, healthcare professionals can more effectively manage Graves' disease prior to surgery, resulting in improved patient outcomes and enhanced surgical success.

Machine learning models can predict subsequent publication of North American Spine Society (NASS) annual general meeting abstracts.

BACKGROUND CONTEXT: Academic meetings serve as an opportunity to present and discuss novel ideas. Previous studies have identified factors predictive of publication without generating predictive models. Machine learning (ML) presents a novel tool capable of generating these models. As such, the objective of this study was to use ML models to predict subsequent publication of abstracts presented at a major surgical conference. STUDY DESIGN/SETTING: Database study. METHODS: All abstracts from the North American Spine Society (NASS) annual general meetings (AGM) from 2013-2015 were reviewed. The following information was extracted: number of authors, institution, location, conference category, subject category, study type, data collection methodology, human subject research, and FDA approval. Abstracts were then searched on the PubMed, Google Scholar, and Scopus databases for publication. ML models were trained to predict whether the abstract would be published or not. Quality of models was determined by using the area under the receiver operator curve (AUC). The top ten most important factors were extracted from the most successful model during testing. RESULTS: A total of 1119 abstracts were presented, with 553 (49%) abstracts published. During training, the model with the highest AUC and accuracy metrics was the partial least squares (AUC of 0.77±0.05, accuracy of 75.5%±4.7%). During testing, the model with the highest AUC and accuracy was the random forest (AUC of 0.69, accuracy of 67%). The top ten features for the random forest model were (descending order): number of authors, year, conference category, subject category, human subjects research, continent, and data collection methodology. CONCLUSIONS: This was the first study attempting to use ML to predict the publication of complete articles after abstract presentation at a major academic conference. Future studies should incorporate deep learning frameworks, cognitive/results-based variables and aim to apply this methodology to larger conferences across other fields of medicine to improve the quality of works presented.

Optimization of chemical conditions for metabolites production by Ganoderma lucidum using response surface methodology and investigation of antimicrobial as well as anticancer activities.

Ganoderma lucidum (G. lucidum) is a medicinal mushroom that is known for its ability to produce compounds with physiological effects on human health. This research was undertaken to amplify the production of bioactive components of G. lucidum under optimal cultivation conditions, obtained in a submerged state and utilized in solid state fermentation, with the purpose of enhancing antimicrobial and anticancer activities. The results indicated that titanium dioxide (TiO2 NPs), magnesium oxide nanoparticles (MgO2 NPs), and B6, along with glucose syrup and CLS syrups, were the most effective for producing GA, while wheat starch and whey protein, along with MgO2 NPs and B6 vitamin, stimulated polysaccharide production using the One Factor at a Time (OFAT) method. After screening, the response surface method (RSM) statistically indicated that the media containing 42.11 g/L wheat starch with 22 g/L whey protein and 50 g/L glucose syrup with 30 g/L CSL were found to be the best conditions for polysaccharide (21.47% of dry weight biomass) and GA (20.35 mg/g dry weight biomass) production, respectively. The moss of the fruit body of G. lucidum produced under optimal GA conditions had the highest diversity in flavonoids and phenolic acids and significant antimicrobial activity against Esherichia coli (E. coli) and Bacillus subtilis (B. subtilis). In addition, the IC50 levels of shell and stem of G. lucidum were 465.3 and 485.7 µg/mL, respectively, while the moss did not reach 50% inhibition. In the end, the statistical approaches utilized in this research to elevate the levels of bioactive components in the fruiting body of G. lucidum produced a promising natural source of antimicrobial and anticancer agents.

The effect of Tai Chi on quality of life in male older people: A randomized controlled clinical trial.

BACKGROUND AND AIM: Physical activity can improve the mental and physical functioning of older people. This study investigated the impact of Tai Chi exercise on the quality of life of older men. METHODS: This randomized controlled clinical trial was conducted on 132 older men in an urban area of Iran. The subjects were randomly assigned into two groups: a Tai Chi intervention group and a control group (n = 66 per group). Quality of life was evaluated using the Leiden-Padua quality of life questionnaire before and after the intervention. Data analysis was performed using analytical statistics via the SPSS software. RESULTS: After eight weeks of Tai Chi exercise, the mean scores of quality of life in different areas demonstrated a statistically significant difference between the two groups (p < 0 0.05). CONCLUSION: This study illustrated that the 8-week Tai Chi intervention had a positive effect on quality of life in older men.

Multi-objective feature selection for warfarin dose prediction.

With increasing the application of decision support systems in various fields, using such systems in different aspects of medical science has been growing. Drug's dose prediction is one of the most important issues which can be improved using decision support systems. In this paper, a new multi-objective feature approach has been proposed to support warfarin dose prediction decision. Warfarin is an anticoagulant normally used in the prevention of the formation of clots. This research was conducted on 553 patients during 2013-2015 who were candidates for using warfarin and their INR was in the target range. Features affecting dose was implemented and evaluated, which were clinical and genetic characteristics extracted, and new methods of feature selection based on multi-objective optimization methods such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) along with the evaluation of artificial neural networks were used. Multi-objective optimization methods have more accuracy and performance compared to the classic methods of feature selection. Furthermore, multi-objective particle swarm optimization algorithm has higher precision than Non-dominated Sorting Genetic Algorithm-II. With a choice of seven features Mean Square Error (MSE), root mean square error (RMSE) and mean absolute error (MAE) were 0.011, 0.1 and 0.109 for MOPSO, respectively.