Conjugated small molecules which can absorb and emit light in the near infrared (NIR) region have been used for several biomedical applications such as imaging different tissues and tumor cells, and cancer treatment. The NIR region is known as the therapeutic window and ideal region for bioimaging since biomolecules like hemoglobin, oxyhemoglobin and DNA don’t have absorbance in this region and NIR light can penetrate deeper into the tissue with less scattering. These compounds are very bright, and they are used as biomarkers for tissues which helps surgeons to differentiate for diagnosis or treatment. The compounds absorb light from a light source and when they emit light according to the fluorescence phenomenon, emitted light can be detected with a NIR camera. The targeted tissue shines very bright due to the dye present, and the other tissues are seen as dark. We employ this methodology in our lab to target various tissues. In this study, we report the synthesis of new heptamethine cyanine dyes for cartilage targeting. Cartilage is one of the important tissues that gets damaged due to aging, trauma and injury, and detection of the damage is key for treatment. Compounds modified at the meso position with thiochlorine moiety to introduce three positive charges to introduce cartilage targeting ability to compounds in the NIR window. Due to the negatively charged nature of cartilage tissue, the positively charged molecules have shown better targeting ability. The synthesized compounds were characterized by 1H, 13C NMR and high-resolution mass spectroscopy. They have minimal tissue scattering and high brightness.

Antimicrobial resistance (AMR) poses a significant threat to global health, with Methicillin-resistant Staphylococcus aureus (MRSA) contributing substantially to morbidity. Rapid identification of MRSA versus Methicillin-susceptible S. aureus (MSSA) is critical for timely and appropriate antibiotic use. This presentation introduces applications of machine learning, focusing on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), as tools for analyzing Fourier Transform Infrared (FTIR) spectroscopy data to efficiently distinguish MRSA from MSSA. By analyzing the growth patterns and spectral data of SA6538 (MSSA) and SA43300 (MRSA) under antibiotic stress, this work highlights novel approaches to spectral loading curve analysis, revealing biochemical changes over time and across strains. In addition we will demonstrate how LDA applied to PCA-reduced datasets achieves high classification accuracy for resistant strains in less than one hour. This study not only showcases the potential of a rapid diagnostic method for differentiating antibacterially perturbed MRSA and MSSA but also introduces innovative strategies for spectral analysis, offering deeper insights into chemical changes in response to antibiotic exposure. These findings represent a significant step toward understanding AMR and improving clinical outcomes in the fight against its spread.

The entrainment of biological oscillators is a fundamental problem in studying dynamical systems and synchronization phenomena. The Arnold Onion diagram is an essential tool for visualizing entrainment patterns in a two-dimensional parameter space, defined by period (T ) and photoperiod (χ). In this paper, we investigate the entrainment of various types of oscillators within the Novak-Tyson model. While previous studies have documented the presence of Arnold onions featuring a single 1:1 entrainment region, our work introduces the novel emergence of multiple disconnected 1:1 entrainment regions within these diagrams. Based on dynamical systems analysis, our findings suggest that in an unforced system behaving as a damped oscillator, multiple Arnold onions emerge near the Hopf bifurcation (HB) point, providing insights into the intricate mechanisms underlying circadian seasonality.