University of Mazandaran

Abstract: Amiodarone, a widely prescribed antiarrhythmic drug, is associated with significant thyroid dysfunction, including both hypothyroidism and hyperthyroidism. This systematic review and meta analysis aim to synthesize the available evidence on the prevalence, types, and management of amiodarone induced thyroid dysfunction (AITD) in patients on long term therapy. We conducted a systematic review and meta analysis of studies that examined thyroid dysfunction in patients receiving long term amiodarone therapy. A comprehensive search of PubMed, Google Scholar, and the Cochrane Library was performed, identifying studies published between 1990 and 2024. Eligible studies included prospective or retrospective cohort studies and randomized controlled trials (RCTs) reporting on the incidence of hypothyroidism, hyperthyroidism, and management strategies in this population. Twenty three studies, with a combined population of 12,000 patients, were included in the final analysis. The pooled incidence of hypothyroidism was 14.3% (95% confidence interval [CI]: 12.1%–16.9%, P < 0.001) and hyperthyroidism was 6.7% (95%CI: 5.1%–8.4%, P < 0.001). Geographical variation in incidence was noted, with higher rates of hyperthyroidism in iodine deficient regions. Amiodarone induced hypothyroidism was generally well managed with levothyroxine without discontinuing amiodarone. Amiodarone induced hyperthyroidism required either antithyroid drugs (for Type 1) or corticosteroids (for Type 2). Thyroid dysfunction, especially hypothyroidism, is common in patients receiving long term amiodarone therapy. Regular thyroid function monitoring is essential to manage this condition effectively. Further studies are needed to improve the management of amiodarone induced hyperthyroidism and optimize long term outcomes for these patients.

This study presents a comprehensive host–guest investigation of the p-sulfonatothiacalix[4]arene macrocycle and its application as a dopant in the fabrication of a self-doped polyaniline adsorbent for Zn(II) removal. Proton nuclear magnetic resonance (1H NMR) titration of p-sulfonatothiacalix[4]arene with Zn(II) ions revealed a strong binding affinity, quantified by an association constant (Ka) exceeding 104 M–1, indicative of robust encapsulation at the macrocyclic cavity. Due to the limited solubility of the polymer, solid-state NMR spectroscopy was employed to elucidate the complexation behavior of Zn(II) within the self-doped polyaniline, revealing marked heterogeneity and localized variations in charge density while preserving the intrinsic conductivity characteristics of the polymer matrix. Complementary density functional theory (DFT) calculations identified the sulfonate (−SO3–) rim of the thiacalix as the primary binding site for Zn(II), whereas in the self-doped polyaniline, the strongest adsorption resulted from cooperative interactions between the sulfonate groups and the polyaniline backbone. Quantum Theory of Atoms in Molecules (QTAIM) analysis further characterized the interactions, showing predominantly ionic binding with minor covalent contributions in thiacalix–Zn complexes and ionic/dative bonding with weak covalent character in the polymer-Zn system. The integrated NMR and computational insights provide a detailed understanding of the adsorption mechanisms and chelation sites, highlighting the efficacy of p-sulfonatothiacalix[4]arene as a functional dopant for advanced conductive polymer adsorbents targeting heavy metal ions.

ABSTRACT Aim European grasslands rank among the most species‐rich ecosystems at small spatial scales, yet their biodiversity and functioning face significant threats from climate change and land‐use intensification. Functional traits more effectively explain ecosystem functions (EFs) than species identity or diversity. This study examines how future climate and land cover changes will shape grassland functional composition, addressing gaps in trait‐environment relationships and large‐scale functional predictions. Location Europe. Time Period 1971–2000 and 2081–2100. Major Taxa Studied 4406 distinct grassland plant species. Methods We used Boosted Regression Trees to model trait‐environment relationships based on vegetation plot data from sPlotOpen, GrassPlot, and the Nordic‐Baltic Grassland Vegetation Database (NBGVD). We mapped the 17 trait community‐weighted means (CWMs) and three functional richness (FRic) metrics under historical conditions and two future climate scenarios to assess temporal and spatial changes in grassland functional composition. Results The trait–environment relationships are highly trait‐dependent: structural and size‐related traits such as plant height, leaf area and seed number were consistently well‐predicted, whereas other traits were less well predicted. Mean annual temperature emerged as the strongest predictor of grassland functional composition. Climate and land cover change were predicted to drive significant spatial shifts in trait CWMs and FRic. Specifically, leaf area was predicted to decline in the Baltic Sea region and Pannonian Basin, while plant height was expected to increase across Europe. Seed number was predicted to rise at higher latitudes and in mountainous regions. Moreover, FRic was expected to decrease in temperate grasslands but increase at high latitudes and mountainous regions. Main Conclusions Our findings reveal distinct spatial patterns in functional shifts, reflecting plant adaptation to future environmental conditions. The increase in FRic at high latitudes and mountainous regions also signals ecosystem transitions that may pose additional threats to further complicate grassland conservation efforts.