Exploring the Genomic Landscape of Hepatobiliary Cancers to Establish a Novel Molecular Classification System.

Taxonomy of hepatobiliary cancer (HBC) categorizes tumors by location or histopathology (tissue of origin, TO). Tumors originating from different TOs can also be grouped by overlapping genomic alterations (GA) into molecular subtypes (MS). The aim of this study was to create novel HBC MSs. Next-generation sequencing (NGS) data from the AACR-GENIE database were used to examine the genomic landscape of HBCs. Machine learning and gene enrichment analysis identified MSs and their oncogenomic pathways. Descriptive statistics were used to compare subtypes and their associations with clinical and molecular variables. Integrative analyses generated three MSs with different oncogenomic pathways independent of TO (n = 324; p < 0.05). HC-1 "hyper-mutated-proliferative state" MS had rapidly dividing cells susceptible to chemotherapy; HC-2 "adaptive stem cell-cellular senescence" MS had epigenomic alterations to evade immune system and treatment-resistant mechanisms; HC-3 "metabolic-stress pathway" MS had metabolic alterations. The discovery of HBC MSs is the initial step in cancer taxonomy evolution and the incorporation of genomic profiling into the TNM system. The goal is the development of a precision oncology machine learning algorithm to guide treatment planning and improve HBC outcomes. Future studies should validate findings of this study, incorporate clinical outcomes, and compare the MS classification to the AJCC 8th staging system.

Microplastics/nanoplastics released from facemasks as contaminants of emerging concern.

Facemasks have become a global medical necessity and are a key preventive measure against COVID-19. Typically, facemasks (FMs) are fabricated from non-renewable polymers, particularly polypropylene (PP) and polyethylene (PE), which release secondary microplastic (MPs) due to the chemical, physical, and biological processes. In light of the widespread usage and improper disposal of single-use facemasks, there is concern about their environmental impact since they contribute to plastic pollution during and after pandemics. The repercussions of this have led to millions of tons of plastic waste being dumped into the environment. Due to lack of awareness and improper disposal, the occurrence of micro/nanoplastics released from facemasks in wastewater treatment plants and landfills poses a concern. Infiltration of wastewater treatment processes by micro/nanoplastics at various levels can be problematic because of their chemical nature and broad but small size. Thus, operational and process stability issues can arise during wastewater treatment processes. In addition, landfilling and illegal waste disposal are being used to dispose of potentially infectious COVID-19 waste, leading to an environmental threat to animal and human health and exacerbating plastic pollution. This paper reviews the fate of facemasks in the environment and the repercussions of improper waste management of facemasks in wastewater treatment plants, landfills, and ultimately the environment.

Hybrid continuous reassessment method with overdose control for safer dose escalation.

Phase 1 oncology studies focus on safety of novel treatments and identifying a dose associated with acceptable toxicity level. Various model-based designs have been proposed for guiding dose escalation and estimating maximum tolerated dose in dose-finding studies. However, these methods are either excessively conservative or imprudent by allowing overly toxic doses. Transparent and easy to implement model-assisted designs have also received increasing attention but require pre-set rules including perceived dose levels. Therefore, we propose a hybrid model-based design that has a high probability to select MTD with a good balance of overdose control by disentangling in two separate models, which is flexible and easy to implement. Extensive simulations show the model to have real promise.

Enriched Random Forest for High Dimensional Genomic Data.

Ensemble methods such as random forest works well on high-dimensional datasets. However, when the number of features is extremely large compared to the number of samples and the percentage of truly informative feature is very small, performance of traditional random forest decline significantly. To this end, we develop a novel approach that enhance the performance of traditional random forest by reducing the contribution of trees whose nodes are populated with less informative features. The proposed method selects eligible subsets at each node by weighted random sampling as opposed to simple random sampling in traditional random forest. We refer to this modified random forest algorithm as "Enriched Random Forest". Using several high-dimensional micro-array datasets, we evaluate the performance of our approach in both regression and classification settings. In addition, we also demonstrate the effectiveness of balanced leave-one-out cross-validation to reduce computational load and decrease sample size while computing feature weights. Overall, the results indicate that enriched random forest improves the prediction accuracy of traditional random forest, especially when relevant features are very few.

Socio-economic impact on COVID-19 cases and deaths and its evolution in New Jersey.

Socio-economic factors could impact how epidemics spread. In this study, we investigated the possible effect of several local socio-economic factors on the case count and time course of confirmed Covid-19 cases and Covid-19-related deaths across the twenty one counties of New Jersey. Socio-economic and geographic factors considered included population, percentage of elders in the population, percentage of low-income households, access to food and health facilities and distance to New York. We found that the counties could be clustered into three groups based on (a) the case totals, (b) the total number of deaths, (c) the time course of the cases and (d) the time course of the deaths. The four groupings were very similar to one another and could all be largely explained by the county population, the percentage of low-income population, and the distance of the county from New York. As for food and health factors, the numbers of local restaurants and pharmacies significantly influenced the total number of cases and deaths as well as the epidemic's evolution. In particular, the number of cases and deaths showed a decrease with the number of McDonald's within the county in contrast to other fast-food or non-fast food restaurants. Overall, our study found that the evolution of the epidemic was influenced by certain socio-economic factors, which could be helpful for the formulation of public health policies.

Inaccurate pretreatment staging can impact survival in early stage esophageal adenocarcinoma.

BACKGROUND: Given the survival advantage of neoadjuvant treatment for locally advanced esophageal cancer, accurate clinical staging is necessary. The aim of this study was to assess the clinical (c) and pathologic (p) staging concordance rates for presumably early stage esophageal adenocarcinoma patients that had upfront esophagectomy (UFE) and evaluate if survival (OS) was negatively affected by inaccurate preoperative staging and subsequent treatment selection. METHODS: An NCDB retrospective review of nonmetastatic esophageal adenocarcinoma patients that had UFE. The rates of concordance between c and p staging system and OS were calculated. RESULTS: Of 2775 patients, most patients presented with cN0 (82.8%) and cT1 tumors (53.6%). The overall concordance between c and p staging was 78.8% for T-classification (moderate agreement; weighted κ = 0.729; P < .001) and 78.8% for N-classification (weak agreement; weighted κ = 0.448; P < .001). Patients that were upstaged due to a lack of concordance between T-classification had decreased 5- and 10-year OS (30% and 16%, P < .001) and those upstaged due to discordant N-classification had decreased 5- and 10-year OS (28% and 23%, P < .001)." CONCLUSIONS: Preoperative staging of esophageal adenocarcinoma has moderate reliability and accuracy for predicting pT and pN classification. Up to 25% of patients have discordant clinical and pathological staging, which impacts OS.

Data science, learning, and applications to biomedical and health sciences.

The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data.