Copper (Cu), as an essential metal, plays a crucial role in biochemical reactions and in physiological regulations. Cu in plasma is mostly bound to proteins; about 65-90% of Cu is tightly binds with caeruloplasmin and the rest of Cu is loosely binds with albumin and transcuprein. A small but significant relatively "free" fraction, probably complexed with amino acids, is present at around 5% of the total concentration. We developed and validated a new method for direct measurement of free Cu in serum by ultrafiltration with AMICON®Ultra 100K device and determination with AAS. Also, we checked that there is no trace of albumin in the ultrafiltrates and we demonstrated the ultrafiltration of a known concentration of Cu added in artificial serum without albumin and, on the contrary, the retention of the Cu in artificial serum with albumin. The ultrafiltration procedure and the instrumental determination showed a good repeatability and a very low limit of detection (1μg/L). The method was applied to 30 healthy subjects, the mean value of the total Cu (994.8μg/L) is included in the normal range for healthy people and the values of free Cu (23.6μg/L) corresponding to 2.37% of the Cu total. The determination of free Cu by this simple and cheap method may be useful to measure the most bioavailable Cu fraction possibly implicated in neuro-degenerative and oxidative-stress related diseases.

An emerging clinical need is represented by identifying reliable biomarkers able to discriminate between responders and non-responders among patients showing imaging progression during the administration of immune checkpoints inhibitors for advanced non-small cell lung cancer (NSCLC). In the present study, we analyzed the prognostic power of peripheral-blood systemic inflammation indexes and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in this clinical setting. In 45 patients showing radiological progression (defined as RECIST 1.1 progressive disease) during Nivolumab administration, the following lab and imaging parameters were collected: neutrophil-to-lymphocyte ratio (NLR), derived-NLR (dNLR), lymphocyte-to-monocyte ratio (LMR), platelets-to-lymphocyte ratio (PLR), systemic inflammation index (SII), maximum standardized uptake value, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). MTV and SII independently predicted OS. Their combination in the immune metabolic prognostic index (IMPI) allowed the identification of patients who might benefit from immunotherapy continuation, despite radiological progression. The combination of FDG PET/CT volumetric data with SII also approximates the immune-metabolic response with respect to baseline, providing additional independent prognostic insights. In conclusion, the degree of systemic inflammation, the quantification of the metabolically active tumor burden, and their combination might disclose the radiological progression in NSCLC patients receiving Nivolumab.

Several studies showed high serum copper levels in Alzheimer’s disease (AD). The present study applied a newly developed method to detect serum copper free from proteins (free-Cu). Forty-four patients affected by dementia due to AD, thirty-six patients affected by mild cognitive impairment (MCI) due to AD, and twenty-eight healthy controls underwent clinical, cognitive, and MRI assessment. The new method showed higher free-Cu concentrations in MCI and dementia due to AD compared to controls ( p < 0.0001). No correlation between copper levels, cognitive or MRI measures were found.

Purpose: We hypothesised that applying radiomics to [¹⁸F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. We compared the performance of radiomic features to human visual interpretation. Materials and methods: We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [¹⁸F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard). Using matRadiomics, we extracted features from PET and CT images of each bone uptake and identified the best predictor model for bone metastases using a machine-learning approach to generate a radiomic score. Blinded PET readers with low (n = 2) and high (n = 2) experience rated each bone uptake as either UBU or bone metastasis. The same readers performed a second read three months later, with access to the radiomic score. Results: Of the 178 [¹⁸F]PSMA-1007 bone uptakes, 74 (41.5%) were classified as PCa metastases by the reference standard. A radiomic model combining PET and CT features achieved an accuracy of 84.69%, though it did not surpass expert PET readers in either round. Less-experienced readers had significantly lower diagnostic accuracy at baseline (p < 0.05) but improved with the addition of radiomic scores (p < 0.05 compared to the first round). Conclusion: Radiomics might help to differentiate bone metastases from UBUs. While it did not exceed expert visual assessments, radiomics has the potential to enhance the diagnostic accuracy of less-experienced readers in evaluating [¹⁸F]PSMA-1007 PET/CT bone uptakes.