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Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic factor for breast cancer (BC) patients and is correlated with improved survival. However, pCR rates are variable to standard NAC, depending on BC subtype. This study investigates quantitative digital histopathology coupled with machine learning (ML) to predict NAC response a priori. Clinicopathologic data and digitized slides of BC core needle biopsies were collected from 149 patients treated with NAC. The nuclei within the tumor regions were segmented on the histology images of biopsy samples using a weighted U-Net model. Five pathomic feature subsets were extracted from segmented digitized samples, including the morphological, intensity-based, texture, graph-based and wavelet features. Seven ML experiments were conducted with different feature sets to develop a prediction model of therapy response using a gradient boosting machine with decision trees. The models were trained and optimized using a five-fold cross validation on the training data and evaluated using an unseen independent test set. The prediction model developed with the best clinical features (tumor size, tumor grade, age, and ER, PR, HER2 status) demonstrated an area under the ROC curve (AUC) of 0.73. Various pathomic feature subsets resulted in models with AUCs in the range of 0.67 and 0.87, with the best results associated with the graph-based and wavelet features. The selected features among all subsets of the pathomic and clinicopathologic features included four wavelet and three graph-based features and no clinical features. The predictive model developed with these features outperformed the other models, with an AUC of 0.90, a sensitivity of 85% and a specificity of 82% on the independent test set. The results demonstrated the potential of quantitative digital histopathology features integrated with ML methods in predicting BC response to NAC. This study is a step forward towards precision oncology for BC patients to potentially guide future therapies.

Regenerative medicine holds significant promise for addressing diseases and irreversible damage that are challenging to treat with conventional methods, making it a prominent research focus in modern medicine. Research on stem cells, a key area within regenerative medicine due to their self-renewal capabilities, is expanding, positioning them as a novel therapeutic option. Stem cells, utilized in various treatments, are categorized based on their differentiation potential and the source tissue. The term ‘stem cell’ encompasses a broad spectrum of cells, which can be derived from embryonic tissues, adult tissues, or generated by reprogramming differentiated cells. These cells, applied across numerous medical disciplines including cardiovascular, neurological, and hematological disorders, as well as wound healing, demonstrate varying therapeutic applications based on their differentiation capacities, each presenting unique advantages and limitations. Nevertheless, the existing literature lacks a comprehensive synthesis examining stem cell therapy and its cellular subtypes across different medical specialties. This review addresses this lacuna by collectively categorizing contemporary stem cell research according to medical specialty and stem cell classification, offering an exhaustive analysis of their respective benefits and constraints, thereby elucidating multifaceted perspectives on the clinical implementation of this therapeutic modality.

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.

Follicular skin disorders, including hidradenitis suppurativa (HS), frequently coexist with systemic autoinflammatory diseases, such as inflammatory bowel disease (IBD) and its subtypes, Crohn’s disease and ulcerative colitis. Previous studies suggest that dysbiosis of the human gut microbiome may serve as a pathogenic link between HS and IBD. However, the role of the microbiome (gut, skin, and blood) in the context of IBD and various follicular disorders remains underexplored. Here, we performed a systematic review to investigate the relationship between follicular skin disorders, IBD, and the microbiome. Of the sixteen included studies, four evaluated the impact of diet on the microbiome in HS patients, highlighting a possible link between gut dysbiosis and yeast-exclusion diets. Ten studies explored bacterial colonization and HS severity with specific gut and skin microbiota, including Enterococcus and Veillonella. Two studies reported on immunological or serological biomarkers in HS patients with autoinflammatory disease, including IBD, and identified common markers including elevated cytokines and T-lymphocytes. Six studies investigated HS and IBD patients concurrently. Our systematic literature review highlights the complex interplay between the human microbiome, IBD, and follicular disorders with a particular focus on HS. The results indicate that dietary modifications hold promise as a therapeutic intervention to mitigate the burden of HS and IBD. Microbiota analyses and the identification of key serological biomarkers are crucial for a deeper understanding of the impact of dysbiosis in these conditions. Future research is needed to more thoroughly delineate the causal versus associative roles of dysbiosis in patients with both follicular disorders and IBD.

Background: Reducing potentially preventable emergency department visits (PPEDs) is important. This study aims to define and describe oncology-related PPEDs at a single institution and use machine learning (ML) to help identify oncology patients at highest risk for PPEDs.Methods: A retrospective cohort study was conducted among five databases. ED visits by oncology patients between April 1st, 2019 – April 1st, 2021 from a single institution were collected. Trends in PPEDs were evaluated using descriptive statistics, logistic regression, and ML modelling.Results: 6,689 oncology patients visited the ED (n=13,415 visits) during the study period. 62.1% were classified as PPEDs. High-risk groups for PPEDs included stage 1-3 breast cancer patients and adjuvant systemic therapy patients. The highest-performing ML model scored an AUC = 0.819.Conclusions: High-risk groups for PPEDs include stage 1-3 breast cancer patients undergoing systemic therapy. Our novel definition of PPEDs at this stage appears reasonable. Future research to validate this work can be impactful.

Background Patients diagnosed with cancer are frequent users of the emergency department (ED). While many visits are unavoidable, a significant portion may be potentially preventable ED visits (PPEDs). Cancer treatments have greatly advanced, whereby patients may present with unique toxicities from targeted therapies and are often living longer with advanced disease. Prior work focused on patients undergoing cytotoxic chemotherapy, and often excluded those on supportive care alone. Other contributors to ED visits in oncology, such as patient-level variables, are less well-established. Finally, prior studies focused on ED diagnoses to describe trends and did not evaluate PPEDs. An updated systematic review was completed to focus on PPEDs, novel cancer therapies, and patient-level variables, including those on supportive care alone. Methods Three online databases were used. Included publications were in English, from 2012-2022, with sample sizes of ≥50, and reported predictors of ED presentation or ED diagnoses in oncology. Results 45 studies were included. Six studies highlighted PPEDs with variable definitions. Common reasons for ED visits included pain (66%) or chemotherapy toxicities (69.1%). PPEDs were most frequent amongst breast cancer patients (13.4%) or patients receiving cytotoxic chemotherapy (20%). Three manuscripts included immunotherapy agents, and only one focused on end-of-life patients. Conclusion This updated systematic review highlights variability in oncology ED visits during the last decade. There is limited work on the concept of PPEDs, patient-level variables and patients on supportive care alone. Overall, pain and chemotherapy toxicities remain key drivers of ED visits in cancer patients. Further work is needed in this realm. Patients diagnosed with cancer are frequent users of the emergency department. This review focuses on potentially preventable emergency department visits, novel cancer therapies, and patient-level variables, such as supportive care.

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Despite therapeutic advancements, malignant melanoma remains a leading cause of skin cancer-related mortality, with incidence continuing to rise globally. Traditional prognostic tools offer important clinical guidance but fail to capture the biological heterogeneity of melanoma or reliably predict responses to emerging therapies. In this review, we summarize recent advances in prognostic and predictive molecular biomarkers reported over the past five years. We discuss immunohistochemical and tissue-based markers, circulating biomarkers, microRNAs, and gene expression profiles that enhance risk stratification and inform surveillance strategies. We also review immune-related markers that may predict response to immune-checkpoint inhibitor therapy. Lastly, we highlight investigational biomarkers—including gene signatures, epigenomic alterations, and microbiome influences—that are shaping the future landscape. Together, these advances reflect a shift toward precision oncology in melanoma, with the integration of biomarker-driven strategies offering the potential to personalize treatment and improve patient outcomes.

In order to further understand the genetic basis for variation in inherent (untrained) exercise capacity, we examined the brains of 32 male rats selectively bred for high or low running capacity (HCR and LCR, respectively). The aim was to characterize the activation patterns of brain regions potentially involved in differences in inherent running capacity between HCR and LCR. Using quantitative in situ hybridization techniques, we measured messenger ribonuclease (mRNA) levels of c-Fos, a marker of neuronal activation, in the brains of HCR and LCR rats after a single bout of acute treadmill running (7.5-15 minutes, 15° slope, 10 m/min) or after treadmill running to exhaustion (15-51 minutes, 15° slope, initial velocity 10 m/min). During verification of trait differences, HCR rats ran six times farther and three times longer prior to exhaustion than LCR rats. Running to exhaustion significantly increased c-Fos mRNA activation of several brain areas in HCR, but LCR failed to show significant elevations of c-Fos mRNA at exhaustion in the majority of areas examined compared to acutely run controls. Results from these studies suggest that there are differences in central c-Fos mRNA expression, and potential brain activation patterns, between HCR and LCR rats during treadmill running to exhaustion and these differences could be involved in the variation in inherent running capacity between lines.