Silver Complexes of Azobenzene and Derivatives

Thirty four silver(I) complexes of azobenzene and derivatives have been synthesised, only two of which have been previously published. The azobenzene derivatives used are 2-bromo, 3-bromo, 4-bromo, 3,4’-dibromo, 2,4’-dibromo, 3-nitro, 4-dimethylamino, 4-methoxy, 2,6-dimethyl-4’-chloro, 2,6,2’,6’-tetramethyl and 2,2’-ethyleneazobenzene. 2,2’- and 4,4’-azobispyridine were also used along with diphenyltriazine. Six different silver(I) salts were used to make the complexes; they are tetrafluoroborate, hexafluorophosphate, perchlorate, nitrate, triflate and trifluoroacetate. All of the complexes were analysed using X-ray crystallography. In the complexes with azobenzene the anion was the most crucial factor in determining the resulting structure, as five different molecular topologies were seen with each change of anion. The 2-bromoazobenzene containing complexes continue this trend giving similar topologies to the azobenzene containing complexes. Once we come to the 3-bromo and 4-bromoazobenzene, we get a different molecular topology for the hexafluorophosphate containing complexes when compared to the original azobenzene containing complex, but we see a very similar structure for the perchlorate containing complexes. This would suggest that the coordinating anions give more predictable structures than the non-coordinating anions. The trend continues with both the 3,4’-dibromo and 2,4’-dibromoazobenzene complexes with triflate being structurally similar to the previous triflate containing complexes. The trend is reinforced further with 3-nitro and 4-methoxyazobenzene showing similar structures to the previously discussed complexes. The complex containing 4-dimethylaminoazobenzene can be disregarded, as the ligand has become protonated and therefore is unlike all the previously described results. When we come to the sterically hindered ligands 2,6-dimethyl-4’-chloroazobenzene the first three complexes show the same molecular topology of a silver atom bound to two ligands with a coordinating anion, however once we come to a tridentate coordinating anion triflate a 1-D metallopolymer is observed. This breaks the trend, as the structures are similar regardless of the change in anion. A similar effect is seen in 2,6,2’,6’-tetramethylazobenzene with both structures standing alone as no complexes with a similar molecular topology were observed. This effect is again noted in the complexes containing 2,2’-ethyleneazobenzene. The complexes all form a similar structure regardless of the anion used. As expected the 2,2’- and 4,4’-azobispyridine along with diphenyltriazine do not follow the trend observed earlier with the non-sterically hindered ligands as they can coordinate through additional nitrogen atoms in the aromatic ring or in the case of diphenyltriazine an additional nitrogen atom in the triazine group

Clinical oncologic applications of PET/MRI: a new horizon

Abstract: Positron emission tomography/magnetic resonance imaging (PET/MRI) leverages the high soft-tissue contrast and the functional sequences of MR with the molecular information of PET in one single, hybrid imaging technology. This technology, which was recently introduced into the clinical arena in a few medical centers worldwide, provides information about tumor biology and microenvironment. Studies on indirect PET/MRI (use of positron emission tomography/computed tomography (PET/CT) images software fused with MRI images) have already generated interesting preliminary data to pave the ground for potential applications of PET/MRI. These initial data convey that PET/MRI is promising in neuro-oncology and head & neck cancer applications as well as neoplasms in the abdomen and pelvis. The pediatric and young adult oncology population requiring frequent follow-up studies as well as pregnant woman might benefit from PET/MRI due to its lower ionizing radiation dose. The indication and planning of therapeutic interventions and specifically radiation therapy in individual patients could be and to a certain extent are already facilitated by performing PET/MRI. The objective of this article is to discuss potential clinical oncology indications of PET/MRI

Case Reports1. A Late Presentation of Loeys-Dietz Syndrome: Beware of TGFβ Receptor Mutations in Benign Joint Hypermobility

Background: Thoracic aortic aneurysms (TAA) and dissections are not uncommon causes of sudden death in young adults. Loeys-Dietz syndrome (LDS) is a rare, recently described, autosomal dominant, connective tissue disease characterized by aggressive arterial aneurysms, resulting from mutations in the transforming growth factor beta (TGFβ) receptor genes TGFBR1 and TGFBR2. Mean age at death is 26.1 years, most often due to aortic dissection. We report an unusually late presentation of LDS, diagnosed following elective surgery in a female with a long history of joint hypermobility. Methods: A 51-year-old Caucasian lady complained of chest pain and headache following a dural leak from spinal anaesthesia for an elective ankle arthroscopy. CT scan and echocardiography demonstrated a dilated aortic root and significant aortic regurgitation. MRA demonstrated aortic tortuosity, an infrarenal aortic aneurysm and aneurysms in the left renal and right internal mammary arteries. She underwent aortic root repair and aortic valve replacement. She had a background of long-standing joint pains secondary to hypermobility, easy bruising, unusual fracture susceptibility and mild bronchiectasis. She had one healthy child age 32, after which she suffered a uterine prolapse. Examination revealed mild Marfanoid features. Uvula, skin and ophthalmological examination was normal. Results: Fibrillin-1 testing for Marfan syndrome (MFS) was negative. Detection of a c.1270G > C (p.Gly424Arg) TGFBR2 mutation confirmed the diagnosis of LDS. Losartan was started for vascular protection. Conclusions: LDS is a severe inherited vasculopathy that usually presents in childhood. It is characterized by aortic root dilatation and ascending aneurysms. There is a higher risk of aortic dissection compared with MFS. Clinical features overlap with MFS and Ehlers Danlos syndrome Type IV, but differentiating dysmorphogenic features include ocular hypertelorism, bifid uvula and cleft palate. Echocardiography and MRA or CT scanning from head to pelvis is recommended to establish the extent of vascular involvement. Management involves early surgical intervention, including early valve-sparing aortic root replacement, genetic counselling and close monitoring in pregnancy. Despite being caused by loss of function mutations in either TGFβ receptor, paradoxical activation of TGFβ signalling is seen, suggesting that TGFβ antagonism may confer disease modifying effects similar to those observed in MFS. TGFβ antagonism can be achieved with angiotensin antagonists, such as Losartan, which is able to delay aortic aneurysm development in preclinical models and in patients with MFS. Our case emphasizes the importance of timely recognition of vasculopathy syndromes in patients with hypermobility and the need for early surgical intervention. It also highlights their heterogeneity and the potential for late presentation. Disclosures: The authors have declared no conflicts of interes

Consensus statement on blocking interleukin-6 receptor and interleukin-6 in inflammatory conditions: an update

Background: Targeting interleukin (IL)-6 has become a major therapeutic strategy in the treatment of immune-mediated inflammatory disease. Interference with the IL-6 pathway can be directed at the specific receptor using anti-IL-6Rα antibodies or by directly inhibiting the IL-6 cytokine. This paper is an update of a previous consensus document, based on most recent evidence and expert opinion, that aims to inform on the medical use of interfering with the IL-6 pathway. Methods: A systematic literature research was performed that focused on IL-6-pathway inhibitors in inflammatory diseases. Evidence was put in context by a large group of international experts and patients in a subsequent consensus process. All were involved in formulating the consensus statements, and in the preparation of this document. Results: The consensus process covered relevant aspects of dosing and populations for different indications of IL-6 pathway inhibitors that are approved across the world, including rheumatoid arthritis, polyarticular-course and systemic juvenile idiopathic arthritis, giant cell arteritis, Takayasu arteritis, adult-onset Still’s disease, Castleman’s disease, chimeric antigen receptor-T-cell-induced cytokine release syndrome, neuromyelitis optica spectrum disorder and severe COVID-19. Also addressed were other clinical aspects of the use of IL-6 pathway inhibitors, including pretreatment screening, safety, contraindications and monitoring. Conclusions: The document provides a comprehensive consensus on the use of IL-6 inhibition to treat inflammatory disorders to inform healthcare professionals (including researchers), patients, administrators and payers

Whole-brain dynamical modeling for classification of Parkinson’s disease

Simulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson’s disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

AbstractAge-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55–85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging

Whole-brain dynamical modeling for classification of Parkinson’s disease

Simulated whole-brain connectomes demonstrate an enhanced inter-individual variability depending on data processing and modeling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson’s disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioral data, such as subject classes, which we refer to as behavioral model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine-learning reported in this study also demonstrated that performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where the filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improve prediction performance. Based on our findings, we suggest including the simulation results with empirical data is effective for inter-individual research and its clinical application