Cytotoxic activity of liposomal Thymus capitatus essential oil on HT-29 human colorectal cancer cell line.

Multidrug resistance, severe side effects, and high cancer treatment costs are still well-known issues and remain an open challenge. These factors reduce the therapy's efficiency and safety, seriously affecting human health. Developing therapeutic approaches based on plant extracts, especially based on essential oils with cytotoxic and antioxidant properties, could be of efficacious strategies. This work incorporated Thymus capitatus essential oil (TEO) in liposomes. Thymus capitatus is a plant native to the northern region of Albania and found specifically in the Mediterranean region. TEO has several biological activities and cytotoxic properties. Due to its volatility, poor solubility, and chemical instability, however, its applicability is restricted. Incorporation into liposomes enables its effective use because the exposure time to the active compounds can be extended, increasing its efficacy against colorectal cancer cell lines, as highlighted in in vitro studies. TEO demonstrated detectable cytotoxic action against HT-29 colorectal cancer cells, and this action could be enhanced by applying various formulations of TEO-loaded liposomes to this cell line. Among the tested nanosystems, TEO-Phospholipon 90H liposomes showed more significant cytotoxic effects than TEO-Lipoid S100 liposomes and TEO-Phospholipon 85G liposomes. TEO-Phospholipon 90 H liposomes also maintained its physicochemical stability for six months at 25 °C. This research suggests that TEO, particularly when encapsulated in TEO-Phospholipon 90 H liposomes, may offer a promising therapeutic approach. However, these findings are based on in vitro studies and further in vivo research is needed to validate the efficacy and safety of this approach in clinical settings.

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images.

Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established progress in artificial intelligence, through the deep learning paradigm, may provide a key tool for an effective support to medical decision for pancreatic cancer. In this work, we follow this trend, by proposing a novel AI-based IPMN classifier that leverages the recent success of transformer networks in generalizing across a wide variety of tasks, including vision ones. We specifically show that our transformer-based model exploits pre-training better than standard convolutional neural networks, thus supporting the sought architectural universalism of transformers in vision, including the medical image domain and it allows for a better interpretation of the obtained results.

Nanoencapsulation of Origanum vulgare essential oil into liposomes with anticancer potential.

Origanum vulgare L. essential oil possesses a wide spectrum of biological activities. Nanoencapsulation of O. vulgare essential oil into liposomes seems to be a promising strategy to maintain and improve these biological properties. This research was carried out to develop a suitable liposomal formulation for the effective encapsulation of O. vulgare essential oil in order to improve the antioxidant and cytotoxic activities. The characterization of liposomal nanocarriers was conducted in terms of size, zeta potential, and encapsulation efficiency. An MTT assay was used to assess the cytotoxic activity of the prepared and characterized O. vulgare essential oil liposomes in MCF-7 cancer cell lines. Antioxidant activity was determined by assessing DPPH scavenging activity. O. vulgare essential oil exerted cytotoxic activity with an IC50 of 50 µg/ml. The essential oil of O. vulgare was effectively encapsulated in liposomes, with no significant change observed among the formulations. The antioxidant activity was significantly enhanced after encapsulating the essential oil in liposomes. Origanum vulgare essential-oil-loaded Phospholipon 90H liposomes demonstrated considerably increased cytotoxic activity against MCF-7 cells, whereas Lipoid S100 liposomes showed no significant differences from the non-encapsulated essential oil. Phospholipon 85G liposomes had the least cytotoxic impact. As a result, liposomes containing O. vulgare essential oil may be promising nanocarriers for the development of anticancer agents.

The impact of COVID-19 on African American communities in the United States.

IMPORTANCE: The novel Coronavirus Disease 2019 (COVID-19), declared a pandemic in March 2020, may present with disproportionately higher rates in underrepresented racial/ethnic minority populations in the United States, including African American communities who have traditionally been over-represented in negative health outcomes. STUDY OBJECTIVE: To understand the impact of the density of African American communities (defined as the percentage of African Americans in a county) on COVID-19 prevalence and death rate within the three most populous counties in each U.S. state and territory (n=152). Design: An ecological study using linear regression was employed for the study. SETTING: The top three most populous counties of each U.S. state and territory were included in analyses for a final sample size of n=152 counties. PARTICIPANTS: Confirmed COVID-19 cases and deaths that were accumulated between January 22, 2020 and April 12, 2020 in each of the three most populous counties in each U.S. state and territory were included. MAIN OUTCOME MEASURES: Linear regression was used to determine the association between African American density and COVID-19 prevalence (defined as the percentage of cases for the county population), and death rate (defined as number of deaths per 100,000 population). The models were adjusted for median age and poverty. RESULTS: There was a direct association between African American density and COVID-19 prevalence; COVID-19 prevalence increased 5% for every 1% increase in county AA density (p<.01). There was also an association between county AA density and COVID-19 deaths, such; the death rate increased 2 per 100,000 for every percentage increase in county AA density (p=.02). CONCLUSION: These study findings indicate that communities with a high African American density have been disproportionately burdened with COVID-19. Further study is needed to indicate if this burden is related to environmental factors or individual factors such as types of employment or comorbidities that members of these community have.

Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However, a key barrier in the required training of CNNs is obtaining large-scale and precisely annotated imaging data. We sought to address the lack of annotated data with eye tracking technology. As a proof of principle, our hypothesis was that segmentation masks generated with the help of eye tracking (ET) would be very similar to those rendered by hand annotation (HA). Additionally, our goal was to show that a CNN trained on ET masks would be equivalent to one trained on HA masks, the latter being the current standard approach. Step 1: Screen captures of 19 publicly available radiologic images of assorted structures within various modalities were analyzed. ET and HA masks for all regions of interest (ROIs) were generated from these image datasets. Step 2: Utilizing a similar approach, ET and HA masks for 356 publicly available T1-weighted postcontrast meningioma images were generated. Three hundred six of these image + mask pairs were used to train a CNN with U-net-based architecture. The remaining 50 images were used as the independent test set. Step 1: ET and HA masks for the nonneurological images had an average Dice similarity coefficient (DSC) of 0.86 between each other. Step 2: Meningioma ET and HA masks had an average DSC of 0.85 between each other. After separate training using both approaches, the ET approach performed virtually identically to HA on the test set of 50 images. The former had an area under the curve (AUC) of 0.88, while the latter had AUC of 0.87. ET and HA predictions had trimmed mean DSCs compared to the original HA maps of 0.73 and 0.74, respectively. These trimmed DSCs between ET and HA were found to be statistically equivalent with a p value of 0.015. We have demonstrated that ET can create segmentation masks suitable for deep learning semantic segmentation. Future work will integrate ET to produce masks in a faster, more natural manner that distracts less from typical radiology clinical workflow.