Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning.

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

Accelerating photoacoustic microscopy by reconstructing undersampled images using diffusion models.

Photoacoustic Microscopy (PAM) integrates optical and acoustic imaging, offering enhanced penetration depth for detecting optical-absorbing components in tissues. Nonetheless, challenges arise in scanning large areas with high spatial resolution. With speed limitations imposed by laser pulse repetition rates, the potential role of computational methods is highlighted in accelerating PAM imaging. We propose a novel and highly adaptable algorithm named DiffPam that utilizes diffusion models to speed up the photoacoustic imaging process. We leveraged a diffusion model trained exclusively on natural images, comparing its performance with an in-domain trained U-Net model using a dataset focused on PAM images of mice brain microvasculature. Our findings indicate that DiffPam performs similarly to a dedicated U-Net model without needing a large dataset. We demonstrate that scanning can be accelerated fivefold with limited information loss. We achieved a 24.70 % increase in peak signal-to-noise ratio and a 27.54 % increase in structural similarity index compared to the baseline bilinear interpolation method. The study also introduces the efficacy of shortened diffusion processes for reducing computing time without compromising accuracy. DiffPam stands out from existing methods as it does not require supervised training or detailed parameter optimization typically needed for other unsupervised methods. This study underscores the significance of DiffPam as a practical algorithm for reconstructing undersampled PAM images, particularly for researchers with limited artificial intelligence expertise and computational resources.

Machine learning prediction of Gleason grade group upgrade between in-bore biopsy and radical prostatectomy pathology.

This study aimed to enhance the accuracy of Gleason grade group (GG) upgrade prediction in prostate cancer (PCa) patients who underwent MRI-guided in-bore biopsy (MRGB) and radical prostatectomy (RP) through a combined analysis of prebiopsy and MRGB clinical data. A retrospective analysis of 95 patients with prostate cancer diagnosed by MRGB was conducted where all patients had undergone RP. Among the patients, 64.2% had consistent GG results between in-bore biopsies and RP, whereas 28.4% had upgraded and 7.4% had downgraded results. GG1 biopsy results, lower biopsy core count, and fewer positive cores were correlated with upgrades in the entire patient group. In patients with GG > 1 , larger tumor sizes and fewer biopsy cores were associated with upgrades. By integrating MRGB data with prebiopsy clinical data, machine learning (ML) models achieved 85.6% accuracy in predicting upgrades, surpassing the 64.2% baseline from MRGB alone. ML analysis also highlighted the value of the minimum apparent diffusion coefficient ( ADC min ) for GG > 1 patients. Incorporation of MRGB results with tumor size, ADC min value, number of biopsy cores, positive core count, and Gleason grade can be useful to predict GG upgrade at final pathology and guide patient selection for active surveillance.