The growing appearance of antibiotic-resistant strains of microorganisms originated from the widespread use and ubiquitous presence of such drugs is a major concern in the world. The development of methodologies able to detect such substances at low concentration in real water samples is mandatory to overcome this problem. Europium(III) is known to form complexes with tetracycline (TC) with photoluminescent characteristics useful for TC determination. In the present work, we synthesized for the first time carbon nanoparticles (CN) showing delayed photoluminescence using a Europium(III) doping synthesis. The new material (PCNEu) was characterized both morphologically and spectroscopically, showing an analytical photoluminescent signal in presence of TC, arising from the ⁵D₀→⁷F₂ transition of europium, one hundred times higher than that of the europium salt alone in presence of the antibiotic. This enhancement is a consequence of the amplifying effect exerted by nanoparticle structure itself, leading to an efficient synergistic "antenna effect" in the system PCNEu - TC. The analytical signal is affected both by pH and the nature of the buffer used, and it allows the detection of tetracycline in waters with a limit of detection of 2.18 nM and recoveries between 90 and 110%. The analytical performance of the developed methodology enables having lower limits of detection than other luminescent and chemiluminescent reported methodologies.

Fourier Transform Infrared Spectroscopy (FTIR), provides valuable biochemical information for biomedical analysis. It aids in identifying cancerous tissues, diagnosing diseases like acute pancreatitis or Alzheimer's, and has applications in genomics, proteomics, and metabolomics. A combination of FTIR and chemometrics constitute an approach that shows promise in fields like biology, forensics, food quality control, and plant variety identification. The study aims to explore the feasibility of ATR-FTIR spectroscopy for identifying ABO-blood types using spectroscopic tools. We employ various classifying algorithms, including Linear Discriminant Analysis (LDA), Naïve Bayes Classifier (NBC), Principal Component Analysis (PCA), and combinations of these methods, to detect A and B antigens and determine the ABO blood type. The results show that these algorithms predict the blood type to a greater extent than random selection, although they do not match the precision of biochemical blood typing tools. Additionally, our findings suggest a higher sensitivity of the methodology in identifying B antigens compared to A antigens.

Fourier Transform Infrared Spectroscopy (FTIR) provides valuable biochemical information for biomedical analysis. It aids in identifying cancerous tissues, diagnosing diseases like acute pancreatitis or Alzheimer’s, and has applications in genomics, proteomics, and metabolomics. A combination of FTIR and chemometrics constitute an approach that shows promise in fields like biology, forensics, food quality control, and plant variety identification. This study aims to explore the feasibility of ATR-FTIR spectroscopy for identifying ABO-blood types using spectroscopic tools. We employ various classifying algorithms, including Linear Discriminant Analysis (LDA), Naïve Bayes Classifier (NBC), Principal Component Analysis (PCA), and combinations of these methods, to detect A and B antigens and determine the ABO blood type. The results show that these algorithms predict the blood type to a greater extent than random selection, although they do not match the precision of biochemical blood typing tools. Additionally, our findings suggest the higher sensitivity of the methodology in identifying B antigens compared to A antigens.

Early detection of neuroendocrine tumors (NETs) is crucial for early and effective intervention, thus reducing the likelihood of tumor progression and metastasis. These tumors have been thoroughly investigated and associated with an excessive secretion of the biomarker 5-hydroxyindoleacetic acid (5HIAA). Although, HPLC is the most commonly used method for 5HIAA detection, spectroscopic alternatives show interesting advantages such as much shorter analysis times, inexpensiveness, as well as the lack of necessity of environmentally harmful solvents. In this work, we propose a double approach to 5HIAA identification: from one side, we used Artificial Intelligence-assisted design to synthesize pentapetides with high affinity toward 5HIAA. On the other hand, we propose a room temperature phosphorescence methodology (RTP) as a detection technique, to take advantage of its delayed light emission, which effectively suppresses interference from the intrinsic fluorescence present in biological samples. Following optimization, it was determined that the concentrations of Na₂SO₃ and KI are critical parameters for the phosphorescent emission of 5HIAA, as well as maintaining a pH value above the pKa of the biomarker. Additionally, this novel methodology was used to evaluate the interaction of the AI-assisted peptides with 5-HIAA. These studies revealed the formation of 1:1 and 1:2 stoichiometric complexes, with these results being consistent with the computational predictions. The developed AI-assisted peptides, combined with RTP detection, demonstrate promising potential as selective sensing phases for 5HIAA, allowing for sensitive and interference-minimized detection, which may support earlier diagnosis of neuroendocrine tumors.