Nano-Formulation Approaches to Enhance Transdermal Drug Delivery-An Updated Review of Nanovesicular Carrier "Transethosomes".

Transdermal drug delivery is an attractive and patient-friendly route for administering therapeutic agents. However, the skin's natural barrier, the stratum corneum, restricts the passage of many drugs, limiting their effectiveness. To overcome this challenge, researchers have developed various nanocarriers to enhance drug penetration through the skin. Transethosomes, a novel and promising drug delivery system, have emerged as an innovative solution for improving transdermal drug delivery. Transethosomes are a hybrid of two established nanocarriers: ethosomes and transfersomes. Ethosomes are lipid-based vesicles that can accommodate lipophilic and hydrophilic drugs, while transfersomes are deformable lipid vesicles designed to enhance skin penetration. Transethosomes combine the advantages of both systems, making them ideal candidates for efficient transdermal drug delivery. They are composed of phospholipids, ethanol, and water and exhibit high flexibility, enabling them to squeeze through the tight junctions of the stratum corneum. This abstract reviews the key characteristics of transethosomes, including their composition, preparation methods, mechanisms of action, characterization parameters, and prospects. Moreover, the recent advancements and applications of transethosomes in delivering various therapeutic agents, such as analgesics, anti-inflammatories, hormones, and skincare products, are explored. The enhanced skin penetration capabilities of transethosomes can potentially reduce systemic side effects and improve patient compliance, making them a valuable tool in the field of transdermal drug delivery. In conclusion, transethosomes represent a promising platform for overcoming the challenges of transdermal drug delivery. Their unique properties enable efficient drug permeation through the skin, offering a more controlled and effective means of administering a wide range of pharmaceutical and cosmetic products. This abstract highlights the potential of transethosomes as a valuable addition to the field of transdermal drug delivery and paves the way for further research and development in this area.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

BACKGROUND: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. METHODS: H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. RESULTS: The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. CONCLUSION: Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

Background: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response. Methods: H&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) were separated through a stratified 8-fold cross validation strategy for the first step and leave one out cross validation strategy for the second step. A tile-level histology label prediction pipeline and four machine learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction. Results: The tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD. Conclusion: Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Associations between health insurance status, neighborhood deprivation, and treatment delays in women with breast cancer living in Georgia.

BACKGROUND: Little is known regarding the association between insurance status and treatment delays in women with breast cancer and whether this association varies by neighborhood socioeconomic deprivation status. METHODS: In this cohort study, we used medical record data of women diagnosed with breast cancer between 2004 and 2022 at two Georgia-based healthcare systems. Treatment delay was defined as >90 days to surgery or >120 days to systemic treatment. Insurance coverage was categorized as private, Medicaid, Medicare, other public, or uninsured. Area deprivation index (ADI) was used as a proxy for neighborhood-level socioeconomic status. Associations between delayed treatment and insurance status were analyzed using logistic regression, with an interaction term assessing effect modification by ADI. RESULTS: Of the 14,195 women with breast cancer, 54% were non-Hispanic Black and 52% were privately insured. Compared with privately insured patients, those who were uninsured, Medicaid enrollees, and Medicare enrollees had 79%, 75%, and 27% higher odds of delayed treatment, respectively (odds ratio [OR]: 1.79, 95% confidence interval [CI]: 1.32-2.43; OR: 1.75, 95% CI: 1.43-2.13; OR: 1.27, 95% CI: 1.06-1.51). Among patients living in low-deprivation areas, those who were uninsured, Medicaid enrollees, and Medicare enrollees had 100%, 84%, and 26% higher odds of delayed treatment than privately insured patients (OR: 2.00, 95% CI: 1.44-2.78; OR: 1.84, 95% CI: 1.48-2.30; OR: 1.26, 95% CI: 1.05-1.53). No differences in the odds of delayed treatment by insurance status were observed in patients living in high-deprivation areas. DISCUSSION/CONCLUSION: Insurance status was associated with treatment delays for women living in low-deprivation neighborhoods. However, for women living in neighborhoods with high deprivation, treatment delays were observed regardless of insurance status.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning.

Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Methods: Serial sections from core needle biopsies (n=76) were stained with H&E, and immunohistochemically for the Ki67 and pH3 markers, followed by whole slide image (WSI) generation. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67 + , and pH3 + cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models, and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Results: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67 + , and pH3 + features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Conclusions: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. METHODS: Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. RESULTS: Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. CONCLUSIONS: Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia.

Importance: Increasing evidence suggests that low socioeconomic status and geographic residence in disadvantaged neighborhoods contribute to disparities in breast cancer outcomes. However, little epidemiological research has sought to better understand these disparities within the context of location. Objective: To examine the association between neighborhood deprivation and racial disparities in mortality among Black and White patients with breast cancer in the state of Georgia. Design, Setting, and Participants: This population-based cohort study collected demographic and geographic data from patients diagnosed with breast cancer between January 1, 2004, and February 11, 2020, in 3 large health care systems in Georgia. A total of 19 580 patients with breast cancer were included: 12 976 from Piedmont Healthcare, 2285 from Grady Health System, and 4319 from Emory Healthcare. Data were analyzed from October 2, 2020, to August 11, 2022. Exposures: Area deprivation index (ADI) scores were assigned to each patient based on their residential census block group. The ADI was categorized into quartile groups, and associations between ADI and race and ADI × race interaction were examined. Main Outcomes and Measures: Cox proportional hazards regression models were used to compute hazard ratios (HRs) and 95% CIs associating ADI with overall mortality by race. Kaplan-Meier curves were used to visualize mortality stratified across racial and ADI groups. Results: Of the 19 580 patients included in the analysis (mean [SD] age at diagnosis, 58.8 [13.2] years), 3777 (19.3%) died during the course of the study. Area deprivation index contributed differently to breast cancer outcomes for Black and White women. In multivariable-adjusted models, living in a neighborhood with a greater ADI (more deprivation) was associated with increased mortality for White patients with breast cancer; compared with the ADI quartile of less than 25 (least deprived), increased mortality HRs were found in quartiles of 25 to 49 (1.22 [95% CI, 1.07-1.39]), 50 to 74 (1.32 [95% CI, 1.13-1.53]), and 75 or greater (1.33 [95% CI, 1.07-1.65]). However, an increase in the ADI quartile group was not associated with changes in mortality for Black patients with breast cancer (quartile 25 to 49: HR, 0.81 [95% CI, 0.61-1.07]; quartile 50 to 74: HR, 0.91 [95% CI, 0.70-1.18]; and quartile ≥75: HR, 1.05 [95% CI, 0.70-1.36]). In neighborhoods with an ADI of 75 or greater, no racial disparity was observed in mortality (HR, 1.11 [95% CI, 0.92-1.36]). Conclusions and Relevance: Black women with breast cancer had higher mortality than White women in Georgia, but this disparity was not explained by ADI: among Black patients, low ADI was not associated with lower mortality. This lack of association warrants further investigation to inform community-level approaches that may mitigate the existing disparities in breast cancer outcomes in Georgia.

Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer.

Although polyploid cells were first described nearly two centuries ago, their ability to proliferate has only recently been demonstrated. It also becomes increasingly evident that a subset of tumor cells, polyploid giant cancer cells (PGCCs), play a critical role in the pathophysiology of breast cancer (BC), among other cancer types. In BC, PGCCs can arise in response to therapy-induced stress. Their progeny possess cancer stem cell (CSC) properties and can repopulate the tumor. By modulating the tumor microenvironment (TME), PGCCs promote BC progression, chemoresistance, metastasis, and relapse and ultimately impact the survival of BC patients. Given their pro- tumorigenic roles, PGCCs have been proposed to possess the ability to predict treatment response and patient prognosis in BC. Traditionally, DNA cytometry has been used to detect PGCCs.. The field will further derive benefit from the development of approaches to accurately detect PGCCs and their progeny using robust PGCC biomarkers. In this review, we present the current state of knowledge about the clinical relevance of PGCCs in BC. We also propose to use an artificial intelligence-assisted image analysis pipeline to identify PGCC and map their interactions with other TME components, thereby facilitating the clinical implementation of PGCCs as biomarkers to predict treatment response and survival outcomes in BC patients. Finally, we summarize efforts to therapeutically target PGCCs to prevent chemoresistance and improve clinical outcomes in patients with BC.

Undercutting efforts of precision medicine: roadblocks to minority representation in breast cancer clinical trials.

Precision (or personalized) medicine holds great promise in the treatment of breast cancer. The success of personalized medicine is contingent upon inclusivity and representation for minority groups in clinical trials. In this article, we focus on the roadblocks for the African American demographic, including the barriers to access and enrollment in breast oncology trials, the prevailing classification of race and ethnicity, and the need to refine monolithic categorization by employing genetic ancestry mapping tools for a more accurate determination of race or ethnicity.

Cancer as a prospective sequela of long COVID-19.

As the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to surge worldwide, our knowledge of coronavirus disease 2019 (COVID-19) is rapidly expanding. Although most COVID-19 patients recover within weeks of symptom onset, some experience lingering symptoms that last for months ("long COVID-19"). Early reports of COVID-19 sequelae, including cardiovascular, pulmonary, and neurological conditions, have raised concerns about the long-term effects of COVID-19, especially in hard-hit communities. It is becoming increasingly evident that cancer patients are more susceptible to SARS-CoV-2 infection and are at a higher risk of severe COVID-19 than the general population. Nevertheless, whether long COVID-19 increases the risk of cancer in those with no prior malignancies, remains unclear. Given, the disproportionate impact of the disease on the African American community, yet another unanswered question is whether racial disparities are to be expected in COVID-19 sequelae. Herein, we propose that long COVID-19 may predispose recovered patients to cancer development and accelerate cancer progression. This hypothesis is based on growing evidence of the ability of SARS-CoV-2 to modulate oncogenic pathways, promote chronic low-grade inflammation, and cause tissue damage. Comprehensive studies are urgently required to elucidate the effects of long COVID-19 on cancer susceptibility.

Disentangling the Coronavirus Disease 2019 Health Disparities in African Americans: Biological, Environmental, and Social Factors.

The stark racial disparities related to the coronavirus disease 2019 (COVID-19) pandemic in the United States, wherein minority populations are disproportionately getting infected and succumbing to the disease, is of grave concern. It is critical to understand and address the underlying causes of these disparities that are complex and driven by interacting environmental, social and biological factors. In this article we focus on the African American community and examine how social and environmental determinants of health intersect with biological factors (comorbidities, underlying genetics, host immunity, vitamin D levels, epigenetics) to exacerbate risk for morbidity and mortality.

SARS-CoV-2 Infection in Cancer Patients: Effects on Disease Outcomes and Patient Prognosis.

The severity of coronavirus disease 2019 (COVID-19) symptoms and outcomes vary immensely among patients. Predicting disease progression and managing disease symptoms is even more challenging in cancer patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Cancer therapies, including chemotherapy, radiotherapy, and immunotherapy, often suppress the immune system, rendering cancer patients more susceptible to SARS-CoV-2 infection and the development of severe complications. However, data on the effects of immunosuppression on COVID-19 outcomes in cancer patients remain limited. Further investigations are warranted to better understand the implications of SARS-CoV-2 infection in cancer patients, particularly those that are immunocompromised. In this review, we outline the current knowledge of the effects of SARS-CoV-2 infection in cancer patients.

Quadruple-negative breast cancer: novel implications for a new disease.

Based on the androgen receptor (AR) expression, triple-negative breast cancer (TNBC) can be subdivided into AR-positive TNBC and AR-negative TNBC, also known as quadruple-negative breast cancer (QNBC). QNBC characterization and treatment is fraught with many challenges. In QNBC, there is a greater paucity of prognostic biomarkers and therapeutic targets than AR-positive TNBC. Although the prognostic role of AR in TNBC remains controversial, many studies revealed that a lack of AR expression confers a more aggressive disease course. Literature characterizing QNBC tumor biology and uncovering novel biomarkers for improved management of the disease remains scarce. In this comprehensive review, we summarize the current QNBC landscape and propose avenues for future research, suggesting potential biomarkers and therapeutic strategies that warrant investigation.

Panoptic View of Prognostic Models for Personalized Breast Cancer Management.

The efforts to personalize treatment for patients with breast cancer have led to a focus on the deeper characterization of genotypic and phenotypic heterogeneity among breast cancers. Traditional pathology utilizes microscopy to profile the morphologic features and organizational architecture of tumor tissue for predicting the course of disease, and is the first-line set of guiding tools for customizing treatment decision-making. Currently, clinicians use this information, combined with the disease stage, to predict patient prognosis to some extent. However, tumoral heterogeneity stubbornly persists among patient subgroups delineated by these clinicopathologic characteristics, as currently used methodologies in diagnostic pathology lack the capability to discern deeper genotypic and subtler phenotypic differences among individual patients. Recent advancements in molecular pathology, however, are poised to change this by joining forces with multiple-omics technologies (genomics, transcriptomics, epigenomics, proteomics, and metabolomics) that provide a wealth of data about the precise molecular complement of each patient's tumor. In addition, these technologies inform the drivers of disease aggressiveness, the determinants of therapeutic response, and new treatment targets in the individual patient. The tumor architecture information can be integrated with the knowledge of the detailed mutational, transcriptional, and proteomic phenotypes of cancer cells within individual tumors to derive a new level of biologic insight that enables powerful, data-driven patient stratification and customization of treatment for each patient, at each stage of the disease. This review summarizes the prognostic and predictive insights provided by commercially available gene expression-based tests and other multivariate or clinical -omics-based prognostic/predictive models currently under development, and proposes a more inclusive multiplatform approach to tackling the challenging heterogeneity of breast cancer to individualize its management. "The future is already here-it's just not very evenly distributed."-William Ford Gibson.

Disadvantaged neighborhoods and racial disparity in breast cancer outcomes: the biological link.

Neighborhoods encompass complex environments comprised of unique economic, physical, and social characteristics that have a profound impact on the residing individual's health and, collectively, on the community's wellbeing. Neighborhood disadvantage (ND) is one of several factors that prominently contributes to racial breast cancer (BC) health disparities in American women. African American (AA) women develop more aggressive breast cancer features, such as triple-negative receptor status and more advanced histologic grade and tumor stage, and suffer worse clinical outcomes than European American (EA) women. While the adverse effects of neighborhood disadvantage on health, including increased risk of cancer and decreased longevity, have recently come into focus, the specific molecular mechanisms by which neighborhood disadvantage increases BC risk and worsens BC outcomes (survivorship, recurrence, mortality) are not fully elucidated. This review illuminates the probable biological links between neighborhood disadvantage and predominantly BC risk, with an emphasis on stress reactivity and inflammation, epigenetics and telomere length in response to adverse neighborhood conditions.

'happy on norflurazon' (hon) mutations implicate perturbance of plastid homeostasis with activating stress acclimatization and changing nuclear gene expression in norflurazon-treated seedlings.

Various mutant screens have been undertaken to identify constituents involved in the transmission of signals from the plastid to the nucleus. Many of these screens have been performed using carotenoid-deficient plants grown in the presence of norflurazon (NF), an inhibitor of phytoene desaturase. NF-treated plants are bleached and suppress the expression of nuclear genes encoding chloroplast proteins. Several genomes uncoupled (gun) mutants have been isolated that de-repress the expression of these nuclear genes. In the present study, a genetic screen has been established that circumvents severe photo-oxidative stress in NF-treated plants. Under these modified screening conditions, happy on norflurazon (hon) mutants have been identified that, like gun mutants, de-repress expression of the Lhcb gene, encoding a light-harvesting chlorophyll protein, but, in contrast to wild-type and gun mutants, are green in the presence of NF. hon mutations disturb plastid protein homeostasis, thereby activating plastid signaling and inducing stress acclimatization. Rather than defining constituents of a retrograde signaling pathway specifically associated with the NF-induced suppression of nuclear gene expression, as proposed for gun, hon mutations affect Lhcb expression more indirectly prior to initiation of plastid signaling in NF-treated seedlings. They pre-condition seedlings by inducing stress acclimatization, thereby attenuating the impact of a subsequent NF treatment.