Novel biomarkers reveal mismatch between tissue and serum thyroid hormone status in amiodarone-induced hyperthyroidism.

CONTEXT: Serum TSH and thyroid hormone (TH) levels are routine markers of thyroid function. However, their diagnostic performance is limited under special conditions, e.g. in amiodarone-induced hyperthyroidism (AIH). Such cases would require the assessment of tissue TH action, which is currently unfeasible. OBJECTIVE: Development of an approach that determines how well serum parameters are reflected in tissue TH action of patients. METHODS: TH-responsive marker genes were identified from human hair follicles (HF) with Next Generation Sequencing, validated by qPCR. A classification model was built with these markers to assess tissue TH action and was deployed on amiodarone treated patients. The impact of amiodarone on tissue TH action was also studied in Thyroid Hormone Action Indicator (THAI) mice. RESULTS: The classification model was validated and shown to predict tissue TH status of subjects with good performance. Serum- and HF-based TH statuses were concordant in hypothyroid and euthyroid amiodarone treated patients. In contrast, amiodarone decreased the coincidence of serum-based and HF-based TH statuses in hyperthyroid patients, indicating that AIH is not unequivocally associated with tissue hyperthyroidism. This was confirmed in the THAI model, where amiodarone prevented tissue hyperthyroidism in THAI mice despite high serum fT4. CONCLUSION: We developed a minimally-invasive approach using HF markers to assess tissue TH economy that could complement routine diagnostics in controversial cases. We observed that a substantial proportion of AIH patients do not develop tissue hyperthyroidism, indicating that amiodarone protects tissues from thyrotoxicosis. Assessing tissue TH action in patients with AIH may be warranted for treatment decisions.

Live-donor kidney transplant outcome prediction (L-TOP) using artificial intelligence.

Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the concurrently used models are of limited discriminative or calibration power and there is a critical need to improve the selection process. We aimed to assess the value of various artificial intelligence (AI) algorithms to improve the risk stratification index. We evaluated pre-transplant variables among 66 914 live-donor kidney transplants (performed between 01/12/2007-01/06/2021) from the United Network of Organ Sharing database, randomized into training (80%) and test (20%) sets. The primary outcome measure was death-censored graft survival. We tested four machine learning models for discrimination (time-dependent concordance index, CTD, and area under the ROC curve) and calibration (integrated Brier score, IBS). We used decision curve analysis to assess the potential clinical utility. Among the models, the deep Cox mixture model showed the best discriminative performance (AUC = 0.70, 0.68, and 0.68 at 5, 10, and 13 years post-transplant, respectively). CTD reached 0.70, 0.67, and 0.66 at 5, 10, and 13 years post-transplant. The IBS score was 0.09, indicating good calibration. In comparison, applying the Living Kidney Donor Profile Index (LKDPI) on the same cohort produced a CTD of 0.56 and an AUC of 0.55-0.58 only. Decision curve analysis showed an additional net benefit compared to the LKDPI, 'Treat all' and 'Treat None' approaches. Our AI-based deep Cox mixture model, termed Live-Donor Kidney Transplant Outcome Prediction outperforms existing prediction models, including the LKDPI, with the potential to improve decisions for optimum live donor selection by ranking potential transplant pairs based on graft survival. This model could be adopted to improve the outcomes of paired exchange programs.

Magnesium Is a Vital Ion in the Body-It Is Time to Consider Its Supplementation on a Routine Basis.

The importance of maintaining proper magnesium intake and total body magnesium content in preserving human health remains underappreciated among medical professionals and laymen. This review aimed to show the importance of hypomagnesemia as a modifiable risk factor for developing disease processes. We searched the PubMed database and Google Scholar using the keywords 'magnesium', 'diabetes', 'cardiovascular disease', 'respiratory disease', 'immune system', 'inflammation', 'autoimmune disease', 'neurology', 'psychiatry', 'cognitive function', 'cancer', and 'vascular calcification'. In multiple contexts of the search terms, all reviews, animal experiments, and human observational data indicated that magnesium deficiency can lead to or contribute to developing many disease states. The conclusions of several in-depth reviews support our working hypothesis that magnesium and its supplementation are often undervalued and underutilized. Although much research has confirmed the importance of proper magnesium supply and tissue levels, simple and inexpensive magnesium supplementation has not yet been sufficiently recognized or promoted.

Deceased-Donor Kidney Transplant Outcome Prediction Using Artificial Intelligence to Aid Decision-Making in Kidney Allocation.

In kidney transplantation, pairing recipients with the highest longevity with low-risk allografts to optimize graft-donor survival is a complex challenge. Current risk prediction models exhibit limited discriminative and calibration capabilities and have not been compared to modern decision-assisting tools. We aimed to develop a highly accurate risk-stratification index using artificial intelligence (AI) techniques. Using data from the UNOS database (156,749 deceased kidney transplants, 2007-2021), we randomly divided transplants into training (80%) and validation (20%) sets. The primary measure was death-censored graft survival. Four machine learning models were assessed for calibration (integrated Brier score [IBS]) and discrimination (time-dependent concordance [CTD] index), compared with existing models. We conducted decision curve analysis and external validation using UK Transplant data. The Deep Cox mixture model showed the best discriminative performance (area under the curve [AUC] = 0.66, 0.67, and 0.68 at 6, 9, and 12 years post-transplant), with CTD at 0.66. Calibration was adequate (IBS = 0.12), while the kidney donor profile index (KDPI) model had lower CTD (0.59) and AUC (0.60). AI-based D-TOP outperformed the KDPI in evaluating transplant pairs based on graft survival, potentially enhancing deceased donor selection. Advanced computing is poised to influence kidney allocation schemes.

Artificial intelligence and machine learning trends in kidney care.

BACKGROUND: The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics. METHODS: The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5425 documents were identified and analyzed. RESULTS: The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology. CONCLUSIONS: The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.

Susac Syndrome and Role of Therapeutic Plasma Exchange: A Case Report.

Susac syndrome is a relatively uncommon autoimmune disease that predominantly affects young females, with the highest incidence between the third and fourth decade of life, presenting classically with encephalopathy, various CNS dysfunctions, visual impairment due to retinal artery occlusion, and hearing loss. Despite treatment options, such as glucocorticoid steroids, intravenous immunoglobulin, methotrexate, azathioprine, mycophenolate mofetil, or rituximab, some patients with Susac syndrome remain refractory to therapy. We present a case report of a 38-year-old female with refractory Susac syndrome who was treated successfully with plasmapheresis.