Optimizing Low-Dose [18F]FDG-PET/CT Scans: Ensuring Quality Amid Radiotracer Availability Challenges - Insights from a Peripheral Tertiary Care Center.

Background: The introduction of positron emission tomography/computed tomography (PET/CT) has significantly advanced medical imaging. In oncology, F-fluorodeoxyglucose (F-FDG) PET/CT is particularly crucial for staging, evaluating treatment response, monitoring follow-up, and planning radiotherapy. However, in resource limiting hospitals, the availability of fluorine-labeled F-FDG limits optimal scan acquisition.

GFR Estimation and Correlation for Oncology Patients by Two Methods, Gates Method and Dual Time Point Plasma Sampling Method.

Background: With the increasing number of oncology cases and a parallel surge in chemotherapeutic drugs for treatment, the treating physicians conducts nephrotoxicity evaluation to provide a personalized dosing strategy. Of the various tests available, glomerular filtration rate (GFR) under gamma camera with help of Gates method has gained importance, being a good index of overall kidney functions. In addition to this, there has been an alternate and old method for GFR estimation: plasma sampling.

Time-efficient HPLC Validation Methodology for the Qualitative Analysis of 68Ga PSMA-11 in Routine Clinical Usage under Isocratic Method.

Background: Prostate-specific membrane antigen (PSMA) has shown to be a promising agent for prostate cancer imaging under PET-CT. With the automation in radiolabeling with 68Ga, using iTG 68Ge/68Ga generator, it has helped introduce various new diagnostic agents and achieve good manufacturing practices (GMP) simultaneously. However, before any radiopharmaceutical is put into clinical usage, it should always be checked for its radiochemical purity and other quality parameters before injecting in the patient.

A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.

Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural network (DNN), which transforms NMR spectra recorded on simple uniformly C-labeled samples to yield high-quality H-C correlation maps of aromatic side chains. Key to the success of the DNN is the design of NMR experiments that produce data with unique features to aid the DNN produce high-resolution spectra.