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Structural and spectroscopic characterization (SXRD, IR, liq. N2 temperature Raman, UV) of hexaamminecobalt(III) dibromide permanganate, [Co(NH3)6]Br2(MnO4) (compound 1), are described. There is a 3D hydrogen bond network including N–H···O–Mn and N–H···Br interactions, which could serve as potential reaction centres for solid-phase redox reactions between the ammonia ligands and/or bromide ions as reductants and permanganate ions as oxidant agents. The effect of the nature of halogen ions on the structural and spectroscopic properties of [Co(NH3)6]Br2(MnO4) and the analogous chloride compound, [Co(NH3)6]Cl2(MnO4) (compound 2), are discussed in detail.

Background Kidney transplantation (KTx) improves prognosis in children with kidney failure; still, these patients are prone to cardiovascular damage due to multiple risk factors. Our aim was to assess myocardial structure and function in pediatric KTx by conventional and speckle-tracking echocardiography (STE) in association with established cardiovascular risk factors. Methods Forty-two KTx and 39 healthy age- and gender-matched children were evaluated. KTx recipients were further categorized according to the control of hypertension assessed by 24-h ambulatory blood pressure monitoring (ABPM). Subjects underwent pulse wave velocity (PWV) measurement, conventional echocardiography, and 2-dimensional STE. Left and right ventricular (LV, RV) global longitudinal strain (GLS), and LV circumferential strain (GCS) were measured. Glomerular filtration rate (eGFR) was calculated according to the Schwartz formula. Results KTx patients had increased blood pressure and arterial stiffness. LV ejection fraction (EF) was preserved along with elevated LV mass index (LVMi) while LVGLS was significantly lower, whereas LVGCS and RVGLS were increased in KTx. Uncontrolled hypertensives had lower LVGLS compared to those with controlled hypertension. Using multiple forward stepwise regression analysis, 24-h SBP and relative wall thickness (RWT) were independent determinants of LVMi, whereas antihypertensive therapy, eGFR, and HOMA-IR were independent determinants of LVGLS. Conclusions Cardiac morphology and function show distinct changes after KTx. Along with comparable ventricular volumes, LV hypertrophy and subclinical myocardial dysfunction are present. Control of hypertension and kidney graft function are major factors of LV performance. STE may be useful to reveal early myocardial dysfunction in pediatric KTx. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information., Author(s): Adrienn Bárczi [sup.1] , Bálint Károly Lakatos [sup.2] , Mónika Szilágyi [sup.2] , Éva Kis [sup.3] , Orsolya Cseprekál [sup.4] , Alexandra Fábián [sup.2] , Attila Kovács [sup.2] , [...]

Human Galectin-3 (hGal-3) is a protein that selectively binds to β-galactosides and holds diverse roles in both normal and pathological circumstances. Therefore, targeting hGal-3 has become a vibrant area of research in the pharmaceutical chemistry. As a step towards the development of novel hGal-3 inhibitors, we synthesized and investigated derivatives of thiodigalactoside (TDG) modified with different aromatic substituents. Specifically, we describe a high-yielding synthetic route of thiodigalactoside (TDG); an optimized procedure for the synthesis of the novel 3,3′-di-O-(quinoline-2-yl)methyl)-TDG and three other known, symmetric 3,3′-di-O-TDG derivatives ((naphthalene-2yl)methyl, benzyl, (7-methoxy-2H-1-benzopyran-2-on-4-yl)methyl). In the present study, using competition Saturation Transfer Difference (STD) NMR spectroscopy, we determined the dissociation constant (Kd) of the former three TDG derivatives produced to characterize the strength of the interaction with the target protein (hGal-3). Based on the Kd values determined, the (naphthalen-2-yl)methyl, the (quinolin-2-yl)methyl and the benzyl derivatives bind to hGal-3 94, 30 and 24 times more strongly than TDG. Then, we studied the binding modes of the derivatives in silico by molecular docking calculations. Docking poses similar to the canonical binding modes of well-known hGal-3 inhibitors have been found. However, additional binding forces, cation–π interactions between the arginine residues in the binding pocket of the protein and the aromatic groups of the ligands, have been established as significant features. Our results offer a molecular-level understanding of the varying affinities observed among the synthesized thiodigalactoside derivatives, which can be a key aspect in the future development of more effective ligands of hGal-3.

Miscarriages affect 50–70% of all conceptions and 15–20% of clinically recognized pregnancies. Recurrent pregnancy loss (RPL, ≥2 miscarriages) affects 1–5% of recognized pregnancies. Nevertheless, our knowledge about the etiologies and pathophysiology of RPL is incomplete, and thus, reliable diagnostic/preventive tools are not yet available. Here, we aimed to define the diagnostic value of three placental proteins for RPL: human chorionic gonadotropin free beta-subunit (free-β-hCG), pregnancy-associated plasma protein-A (PAPP-A), and placental growth factor (PlGF). Blood samples were collected from women with RPL (n = 14) and controls undergoing elective termination of pregnancy (n = 30) at the time of surgery. Maternal serum protein concentrations were measured by BRAHMS KRYPTOR Analyzer. Daily multiple of median (dMoM) values were calculated for gestational age-specific normalization. To obtain classifiers, logistic regression analysis was performed, and ROC curves were calculated. There were differences in changes of maternal serum protein concentrations with advancing healthy gestation. Between 6 and 13 weeks, women with RPL had lower concentrations and dMoMs of free β-hCG, PAPP-A, and PlGF than controls. PAPP-A dMoM had the best discriminative properties (AUC = 0.880). Between 9 and 13 weeks, discriminative properties of all protein dMoMs were excellent (free β-hCG: AUC = 0.975; PAPP-A: AUC = 0.998; PlGF: AUC = 0.924). In conclusion, free-β-hCG and PAPP-A are valuable biomarkers for RPL, especially between 9 and 13 weeks. Their decreased concentrations indicate the deterioration of placental functions, while lower PlGF levels indicate problems with placental angiogenesis after 9 weeks.

Mutations in activin-like kinase 2 (ALK2), e.g., ALK2-R206H, induce aberrant signaling to SMAD1/5/8, leading to Fibrodysplasia Ossificans Progressiva (FOP). In spite of extensive studies, the underlying mechanism is still unclear. Here, we quantified the homomeric and heteromeric interactions of ACVR2A, ACVR2B, ALK2-WT, and ALK2-R206H by combining IgG-mediated immobilization of one receptor with fluorescence recovery after photobleaching (FRAP) measurements on the lateral diffusion of a co-expressed receptor. ACVR2B formed stable homomeric complexes that were enhanced by Activin A (ActA), while ACVR2A required ActA for homodimerization. ALK2-WT, but not ALK2-R206H, exhibited homomeric complexes unaffected by ActA. ACVR2B formed ActA-enhanced heterocomplexes with ALK2-R206H or ALK2-WT, while ACVR2A interacted mainly with ALK2-WT. The extent of the homomeric complex formation of ACVR2A or ACVR2B was reflected in their ability to induce the oligomerization of ALK2-R206H and ALK2-WT. Thus, ACVR2B, which forms dimers without ligand, induced ActA-independent ALK2-R206H clustering but required ActA for enhancing the oligomerization of the largely dimeric ALK2-WT. In contrast, ACVR2A, which undergoes homodimerization in response to ActA, required ActA to induce ALK2-R206H oligomerization. To investigate whether these interactions are translated into signaling, we studied signaling by the FOP-inducing hyperactive ALK2-R206H mutant, with ALK2-WT signaling as control. The activation of SMAD1/5/8 signaling in cells expressing ALK2-R206H alone or together with ACVR2A or ACVR2B was measured by blotting for pSMAD1/5/8 and by transcriptional activation assays using BRE-Luc reporter. In line with the biophysical studies, ACVR2B activated ALK2-R206H without ligand, while activation by ACVR2A was weaker and required ActA. We propose that the homodimerization of ACVR2B or ACVR2A dictates their ability to recruit ALK2-R206H into higher complexes, enabling the homomeric interactions of ALK2-R206H receptors and, subsequently, their activation.

Hungary’s first national park was created in 1973 in the Hortobágy area to protect Europe’s largest contiguous steppe area and its flora and fauna. The Hortobágy National Park—the Puszta was inscribed on the UNESCO World Heritage List as a cultural landscape in 1999. The park’s outstanding importance is due to the predominantly non-arboreal steppe vegetation, home to a unique bird fauna, and alkaline and chernozem soils with a complex, mosaic-like spatial structure. In addition, the landscape of Hortobágy has a pastoral history stretching back thousands of years. Several hypotheses have been put forward that suggest that the alkaline soils and the habitats that cover them were formed as a result of human activities related to river regulation that began in the second half of the 19th century. However, paleoecological and paleobiological studies over the last 30–40 years have pointed to the natural origin of the alkaline steppes, dating back to the end of the Ice Age. For thousands of years, human activities, in particular, grazing by domestic animals, hardly influenced the natural evolution of the area. The drainage of marshy and flooded areas began in the 19th century, as well as the introduction of more and more intensive agriculture, had a significant impact on the landscape. This paper aims to describe the past natural development of this special alkaline steppe ecosystem, with particular reference to the impacts of past and present human activities, including conservation measures.

Background: Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete training cycles. Traditional techniques rely on subjective assessments to prevent overtraining, which can lead to injury and underperformance. IMUs provide objective, quantitative data on athletes’ physical status during action. AI and machine learning can turn these data into useful insights, enabling data-driven athlete performance management. With IMU-generated multivariate time series data, this paper uses AI to construct a robust model for predicting fatigue and stamina. Materials and Methods: IMUs linked to 19 athletes recorded triaxial acceleration, angular velocity, and magnetic orientation throughout repeated sessions. Standardized training included steady-pace runs and fatigue-inducing techniques. The raw time series data were used to train a supervised ML model based on frequency and time-domain characteristics. The performances of Random Forest, Gradient Boosting Machines, and LSTM networks were compared. A feedback loop adjusted the model in real time based on prediction error and bias estimation. Results: The AI model demonstrated high predictive accuracy for fatigue, showing significant correlations between predicted fatigue levels and observed declines in performance. Stamina predictions enabled individualized training adjustments that were in sync with athletes’ physiological thresholds. Bias correction mechanisms proved effective in minimizing systematic prediction errors. Moreover, real-time adaptations of the model led to enhanced training periodization strategies, reducing the risk of overtraining and improving overall athletic performance. Conclusions: In sports performance analytics, the AI-assisted model using IMU multivariate time series data is effective. Training can be tailored and constantly altered because the model accurately predicts fatigue and stamina. AI models can effectively forecast the beginning of weariness before any physical symptoms appear. This allows for timely interventions to prevent overtraining and potential accidents. The model shows an exceptional ability to customize training programs according to the physiological reactions of each athlete and enhance the overall training effectiveness. In addition, the study demonstrated the model’s efficacy in real-time monitoring performance, improving the decision-making abilities of both coaches and athletes. The approach enables ongoing and thorough data analysis, supporting strategic planning for training and competition, resulting in optimized performance outcomes. These findings highlight the revolutionary capability of AI in sports science, offering a future where data-driven methods greatly enhance athlete training and performance management.