Bioinspired Polymerization of Quercetin to Produce a Curcumin-Loaded Nanomedicine with Potent Cytotoxicity and Cancer-Targeting Potential in Vivo

Nanomedicine has had a profound impact on the treatment of many diseases, especially cancer. However, synthesis of multifunctional nanoscale drug carriers often requires multistep coupling and purification reactions, which can pose major scale-up challenges. Here, we leveraged bioinspired oxidation-triggered polymerization of catechols to synthesize nanoparticles (NPs) from the plant polyphenol quercetin (QCT) loaded with a hydrophobic anticancer drug, curcumin, and functionalized with poly(ethylene glycol) (PEG) for steric stabilization in one reaction step. NPs were formed by base-catalyzed oxidative self-polymerization of QCT in the presence of curcumin and thiol-terminated PEG upon mixing in a universal solvent (dimethyl sulfoxide), followed by self-assembly with the gradual addition of water. Dynamic light scattering and X-ray photoelectron spectroscopy were used to confirm NP PEGylation. Drug loading was verified by UV–vis spectroscopy. Curcumin-loaded NPs were efficiently internalized by CT26 murine colon cancer cells as determined by flow cytometry and confocal microscopy. NPs also demonstrated sustained release and potent cytotoxicity in vitro. Moreover, in vivo imaging of CT26 tumor-bearing Balb/c mice following tail vein injection of DiR-labeled QCT NPs showed steady tumor accumulation of the NPs up to 24 h. This was further supported by significant tumor uptake of curcumin-loaded QCT NPs as measured by flow cytometry analysis of tumor homogenates. Our findings present a greener synthetic route for the fabrication of drug-loaded surface-functionalized NPs from poorly water-soluble plant polyphenols such as QCT as promising anticancer delivery systems

Microultrasound characterisation of <i>ex vivo</i> porcine tissue for ultrasound capsule endoscopy

Gastrointestinal (GI) disease development and progression is often characterised by cellular and tissue architectural changes within the mucosa and sub-mucosa layers. Current clinical capsule endoscopy and other approaches are heavily reliant on optical techniques which cannot detect disease progression below the surface layer of the tissue. To enhance the ability of clinicians to detect cellular changes earlier and more confidently, both quantitative and qualitative microultrasound (μUS) techniques are investigated in healthy ex vivo porcine GI tissue. This work is based on the use of single-element, focussed μUS transducers made with micromoulded piezocomposite operating at around 48 MHz. To explore the possibility that μUS can detect Crohn's disease and other inflammatory bowel diseases, ex vivo porcine small bowel tissue samples were cannulised and perfused with phosphate-buffered saline followed by various dilutions of polystyrene microspheres. Comparison with fluorescent imaging showed that the microspheres had infiltrated the microvasculature of the samples and that μUS was able to successfully detect this as a mimic of inflammation. Samples without microspheres were analysed using quantitative ultrasound to assess mechanical properties. Attenuation coefficients of 1.78 ± 0.66 dB/mm and 1.92 ± 0.77 dB/mm were obtained from reference samples which were surgically separated from the muscle layer. Six intact samples were segmented using a software algorithm and the acoustic impedance, Z, for varying tissue thicknesses, and backscattering coefficient, BSC, were calculated using the reference attenuation values and tabulated

Label-set impact on deep learning-based prostate segmentation on MRI

Abstract Background Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance. The automatic segmentation method we used was nnU-Net. Results The selection of training/testing label-set had a significant (p < 0.001) impact on model performance. Furthermore, it was found that model performance was significantly (p < 0.001) higher when the model was trained and tested with the same label-set. Moreover, the results showed that agreement between automatic segmentations was significantly (p < 0.0001) higher than agreement between manual segmentations and that the models were able to outperform the human label-sets used to train them. Conclusions We investigated the impact of label-set selection on the performance of a DL-based prostate segmentation model. We found that the use of different sets of manual prostate gland and zone segmentations has a measurable impact on model performance. Nevertheless, DL-based segmentation appeared to have a greater inter-reader agreement than manual segmentation. More thought should be given to the label-set, with a focus on multicenter manual segmentation and agreement on common procedures. Critical relevance statement Label-set selection significantly impacts the performance of a deep learning-based prostate segmentation model. Models using different label-set showed higher agreement than manual segmentations. Key points • Label-set selection has a significant impact on the performance of automatic segmentation models. • Deep learning-based models demonstrated true learning rather than simply mimicking the label-set. • Automatic segmentation appears to have a greater inter-reader agreement than manual segmentation. Graphical Abstrac

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.6%) or 1.5 T (n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets (e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization (e.g., follow-up, prognosis, interventions, and focal treatment)

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition

Objectives To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. Materials and methods Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization. Results The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones. Conclusion An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation

Do cerebral microbleeds increase the risk of dementia? A systematic review and meta-analysis

Background: Dementia is a neurological disorder that commonly affects the elderly. Cerebral microbleeds (CMBs) are small, tiny lesions of the cerebral blood vessels and have been suggested as a possible risk factor for dementia. However, data about the association between CMBs and dementia risk are inconsistent and inconclusive. Therefore, we conducted this systematic review and meta-analysis to investigate the association between CMBs and dementia and highlight the possible explanations. Methods: We followed the standard PRISMA statement and the Cochrane Handbook guidelines to conduct this study. First, we searched medical electronic databases for relevant articles. Then, we screened the retrieved articles for eligibility, extracted the relevant data, and appraised the methodological quality using the Newcastle-Ottawa Scale. Finally, the extracted data were pooled as risk ratios (RR) and hazard ratios (HR) in the random-effects meta-analysis model using the Review Manager software. Results: We included nine studies with 14,221 participants and follow-up periods > 18 months. Overall, CMBs significantly increased the risk of developing dementia (RR 1.84, 95% CI [1.27–2.65]). This association was significant in the subgroups of studies on high-risk populations (RR 2.00, 95% CI [1.41–2.83], n = 1657 participants) and those in the general population (RR 2.30, 95% CI [1.25–4.26], n = 12,087 participants) but not in the memory clinic patients. Further, CMBs increased the risk of progressing to incident dementia over time (HR 2, 95% CI [1.54–2.61]). Conclusion: Individuals with CMBs have twice the risk of developing dementia or progressing to MCI than those without CMBs. The detection of CMBs will help identify the population at higher risk of developing dementia. Physicians should educate individuals with CMBs and their families on the possibility of progressing to dementia or MCI. Regular cognitive assessments, cognitive training, lifestyle modifications, and controlling other dementia risk factors are recommended for individuals with CMBs to decrease the risk of cognitive decline and dementia development

High Resolution Microultrasound (μUS) Investigation of the Gastrointestinal (GI) Tract

High resolution, microultrasound (μUS) scanning of the gastrointestinal (GI) tract has potential as an important transmural imaging modality to aid in diagnosis. Operating at higher frequencies than conventional clinical ultrasound instruments, μUS is capable of providing scanned images of the GI tract with higher resolution. To investigate the use of μUS for this application, a phantom which is cost effective, within ethical guidelines and, most importantly, similar in histology to the human GI tract is necessary. Therefore, a phantom utilizing porcine small bowel tissue has been developed for custom assembled μUS scanning systems. Two such systems, a stepping scanner and a continuous sweep scanner were utilized to repeatedly scan regions of prepared samples of porcine small bowel tissue. The porcine small bowel tissue phantom was perfused with degassed phosphate buffer saline (dPBS) solution through a cannula inserted in its mesenteric vessel to simulate in vivo conditions and achieve better μUS mucosal characterization. The μUS system scans a transducer across the tissue phantom to acquire RF echo data, which is then processed using MATLAB. A B-scan reconstruction produces 2D images with relative echo strength mapped to a color map of the user's choice. The phantom developed also allows for modifications such as the insertion of fiducial markers to detect tissue change over time and simultaneous perfusion and scanning, providing a platform for more detailed research and investigation into μUS scanning of the GI tract

A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI

Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations

Design and Synthesis of Thionated Levofloxacin: Insights into a New Generation of Quinolones with Potential Therapeutic and Analytical Applications

Levofloxacin is a widely used fluoroquinolone in several infectious diseases. The structure–activity relationship of levofloxacin has been studied. However, the effect of changing the carbonyl into thiocarbonyl of levofloxacin has not been investigated up to the date of this report. In this work, levofloxacin structure was slightly modified by making a thionated form (compound 3), which was investigated for its antibacterial activity, biocompatibility, and cytotoxicity, as well as spectroscopic properties. The antibacterial susceptibility testing against five different bacteria showed promising minimum inhibitory concentrations (MICs), particularly against B. spizizenii and E. coli, with an MIC value of 1.9 µM against both bacteria, and 7.8 µM against P. mirabilis. The molecular docking experiment showed similar binding interactions of both levofloxacin and compound 3 with the active site residues of topoisomerase IV. The biocompatibility and cytotoxicity results revealed that compound 3 was more biocompatible with normal cells and more cytotoxic against cancer cells, compared to levofloxacin. Interestingly, compound 3 also showed an excitation profile with a distinctive absorption peak at λmax 404 nm. Overall, our results suggest that the thionation of quinolones may provide a successful approach toward a new generation with enhanced pharmacokinetic and safety profiles and overall activity as potential antibacterial agents