Cell Size Uniformity Research Articles

Identification of Important Factors in the Diagnosis of Breast Cancer Cells Using Machine Learning Models and Principal Component Analysis

Breast cancer (BC) is now identified as a disease with a significant impact on morbidity and mortality that is growing and widespread worldwide. This study uses a publicly available clinical dataset of 699 patients from the University of Wisconsin with 9 variables: (1) clump thickness, (2) uniformity of cell size, (3) uniformity of cell shape, (4) marginal adhesion, (5) single epithelial cell size, (6) bare nuclei, (7) bland chromatin, (8) normal nucleoli, and (9) mitoses. This dataset has been used for many studies in the past to pinpoint critical factors in patient diagnosis. Here, we use this data to ensure its unbiasedness and accuracy. We then apply principal component analysis and machine learning models to identify factors in diagnosing a malignant or benign tumor. We investigate and compare the classification accuracy of different machine learning models, including tree, linear discriminant, quadratic discriminant, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble, neural network, and kernel. The best models that can achieve the highest accuracy are medium Gaussian SVM, coarse Gaussian SVM, and cosine KNN, with an accuracy of 96.5%. The principal component analysis method is then performed to identify crucial components and build an accurate model with fewer parameters. The medium Gaussian SVM has the highest cross-validation classification accuracy of 96.98% and requires only three predictors: normal nucleoli, bare nuclei, and cell size uniformity.

EARLY DETECTION OF BREAST CANCER USING THE K-NEAREST NEIGHBOUR (K-NN) ALGORITHM

ABSTRACT- Cancer is one of the Non-Communicable Disease groups whose growth and development are high-speed. One type of cancer is breast cancer (carcinoma mammae). Breast cancer is the leading cause of death for women. The first breast cancer cells can grow into tumors as large as 1 cm, spanning 8-12 years. The prevalence rate of breast cancer in Indonesia is 50 per 100,000 female population. The method used in this study uses the K-Nearest Neighbor (K-NN) algorithm by comparing k values, namely 3, 5, and 7. The dataset used was obtained from the UCI Machine Learning Repository with the Number of datasets after preprocessing, namely 653 data with a class consisting of benign tumors (benign) and malignant tumors (malignant). The variables used in this study take into account the variables of clump thickness, cell size uniformity, cell shape uniformity, marginal adhesion, single epithelial cell size, cell nucleus size, chromatin, normal cell nucleus, and mitosis. The results of the most influential classification for training and testing are using k = 3 with an accuracy of training and testing at a proportion of 70:30 of 83.8074% and 75%; the ratio of 80:20 is 84.6743% and 74.8092%; the percentage of 90:10 is 84.0136% and 84.6154%. Using the value of k = 3, the resulting gap between training and testing is similar.

Processing Effects on the Through-Plane Electrical Conductivities and Tensile Strengths of Microcellular-Injection-Molded Polypropylene Composites with Carbon Fibers.

Polymers reinforced with conducting fibers to achieve electrical conductivity have attracted remarkable attention in several engineering applications, and injection molding provides a cost-effective way for mass production. However, the electrical performance usually varies with the molding conditions. Moreover, high added content of conducting fibers usually results in molding difficulties. In this study, we propose using microcellular (MuCell) injection molding for polypropylene (PP)/carbon fiber (CF, 20, and 30 wt%) composites and hope that the MuCell injection molding process can improve both electrical and mechanical performance as compared with conventional injection molded (CIM) parts under the same CF content. Both molding techniques were also employed with and without gas counter pressure (GCP), and the overall fiber orientation, through-plane electrical conductivity (TPEC), and tensile strength (TS) of the composites were characterized. Based on the various processing technologies, the results can be described in four aspects: (1) Compared with CIM, microcellular foaming significantly influenced the fiber orientation, and the TPECs of the samples with 20 and 30 wt% CF were 18–78 and 5–8 times higher than those of the corresponding samples molded by CIM, respectively; (2) when GCP was employed in the CIM process, the TPEC of the samples with 20 and 30 wt% CF increased by 3 and 2 times, respectively. Similar results were obtained in the case of microcellular injection molding—the TPEC of the 20 and 30 wt% composites increased by 7–74 and 18–32 times, respectively; (3) although microcellular injection molding alone (i.e., without GCP) showed the greatest influence on the randomness of the fiber orientation and the TPEC, the TS of the samples was the lowest due to the uncontrollable foaming cell size and cell size uniformity; (4) in contrast, when GCP was employed in the microcellular foaming process, high TS was obtained, and the TPEC was significantly enhanced. The high foaming quality owing to the GCP implementation improved the randomness of fiber orientation, as well as the electrical and mechanical properties of the composites. Generally speaking, microcellular injection combined with gas counter pressure does provide a promising way to achieve high electrical and mechanical performance for carbon-fiber-added polypropylene composites.

Cytologic diagnosis of neuroendocrine neoplasms including carcinoid tumours- A retrospective study

The classification of neuroendocrine neoplasms has evolved substantially over time but remains a topic of controversy and debate. Cytology has become one of the mainstays of diagnosis for these tumors, and the treatment may be entirely based on the FNA report.: This is a retrospective study which aims to describe the cytological features found in different groups of Neuroendocrine neoplasms. We have also tried to enumerate the not so typical features which we have seen in our cases. Cases of Neuroendocrine neoplasms diagnosed by cytology in the year 2018 were included in this study. The slides of these cases were retrieved, cytological features reviewed, and clinicopathological features evaluated. Histopathological correlation was done wherever possible. In this retrospective study, there were 43 cases which included FNA (n=38), Fluid cytology(n=3), Bronchial washings and Brushings(n=2). FNA sites included lung, cervical lymph nodes, scalp, liver, pancreas, and mesentery with the cytological diagnoses of Small cell carcinoma (n=22) Neuroendocrine tumor (n=7), Large cell Neuroendocrine carcinoma (n=3) and Poorly differentiated carcinoma with neuroendocrine features (n=11). Features that are of help include scanty cytoplasm, fine or coarse granular chromatin, nuclear moulding and streaking, cells adhering to vessels, inconspicuous nucleoli, nuclear debris in small cell carcinomas; larger cell size, a moderate amount of cytoplasm, coarse granular chromatin in large cell neuroendocrine carcinomas; uniformity of cell size, round to plasmacytoid cells with stippled chromatin and rosette formation in carcinoid tumors. The identification of neuroendocrine morphology in cytology specimens is crucial as this would be the initial step towards using the appropriate markers for confirmation, which in turn has got therapeutic and prognostic significance.

Using Gas Counter Pressure and Combined Technologies for Microcellular Injection Molding of Thermoplastic Polyurethane to Achieve High Foaming Qualities and Weight Reduction.

Microcellular injection molding technology (MuCell®) using supercritical fluid (SCF) as a foaming agent offers many advantages, such as material and energy savings, low cycle time, cost-effectiveness, and the dimensional stability of products. MuCell® has attracted great attention for applications in the automotive, packaging, sporting goods, and electrical parts industries. In view of the environmental issues, the shoe industry, particularly for midsole parts, is also seriously considering using physical foaming to replace the chemical foaming process. MuCell® is thus becoming one potential processing candidate. Thermoplastic polyurethane (TPU) is a common material for molding the outsole of shoes because of its outstanding properties such as hardness, abrasion resistance, and elasticity. Although many shoe manufacturers have tried applying Mucell® processes to TPU midsoles, the main problem remaining to be overcome is the non-uniformity of the foaming cell size in the molded midsole. In this study, the MuCell® process combined with gas counter pressure (GCP) technology and dynamic mold temperature control (DMTC) were carried out for TPU molding. The influence of various molding parameters including SCF dosage, injection speed, mold temperature, gas counter pressure, and gas holding time on the foaming cell size and the associated size distribution under a target weight reduction of 60% were investigated in detail. Compared with the conventional MuCell® process, the implementation of GCP technology or DMTC led to significant improvement in foaming cell size reduction and size uniformity. Further improvement could be achieved by the simultaneous combination of GCP with DMT, and the resulting cell density was about fifty times higher. The successful possibility for the microcellular injection molding of TPU shoe midsoles is greatly enhanced.

Effect of blowing agent concentration of ultra‐low density NBR/PVC composite foams on compressive behavior and energy‐absorption characteristics

AbstractUltra‐low density nitrile butadiene rubber (NBR)/polyvinyl chloride (PVC) composite foams that exhibit satisfactory compressive behavior and energy absorption characteristics were prepared by one‐step compression molding. The properties of the NBR/PVC foams, including the density, hardness, cell morphology, compressive behavior, energy absorption characteristics, and shock absorption characteristics, were studied with various concentrations of azodicarbonamide (AC). Further that, the ideality factor and the energy absorption efficiency of the NBR/PVC foams at a low stress for application in compression and energy absorption were determined. The results revealed that the densities of polymeric foams were crucial in determining the uniformity of cell size and energy absorption characteristics of the composite foams. With increasing AC content, the density of the NBR/PVC foams decreased linearly, whereas the uniformity of cell size and the foaming ratio increased; furthermore, the hardness and compression modulus gradually decreased, and the linear elastic region in the compressive stress–strain curves became narrow. When the applied stress was less than 100 kPa, the foams with a high AC content exhibited higher energy absorption compared to foams with a low AC content capacity because of the deformed cell wall and lower elastic modulus; however, the relation between AC contents and energy absorption was complicated above 100 kPa. The optimum springrate ratio was found at the concentration of 25 phr of the AC blowing agent.

Ultra-light poly(lactic acid)/SiO2 aerogel composite foam: A fully biodegradable and full life-cycle sustainable insulation material

In this study, a fully biodegradable ultra-light poly(lactic acid)/silicon dioxide (PLA/SiO2) aerogel nanocomposite with ultra-low thermal conductivity was successfully fabricated. PLA used was a produced from lactic acid, where the lactic acid has been produced from carbohydrates. The rheological properties of PLA were enhanced by diphenylmethane diisocyanate (MDI). The foaming properties, cell density, cell size uniformity, mechanical properties and thermal conductivity and thermal diffusivity of PLA were further improved by SiO2 aerogel, and finally the ultra-low density foamed material was prepared by supercritical CO2. The density of PLA foam can be as low as 0.02 g/cm3 and the thermal conductivity as low as 0.02628 W/m·K. The PLA-based composites can be used in many fields such as thermal insulation, vibration damping and packaging, and can be fully biodegradable and sustainable throughout their life cycle, which meets the global trend of energy saving and emission reduction.

Multifunctional nucleating agents with simultaneous plasticizing, solubilizing, nucleating and their effect on polyvinyl alcohol foams

Currently, preparing microcellular polymer foams by supercritical CO2 (Sc-CO2) is a newly trend and challenge. The main limitation of Sc-CO2 used in microcellular foaming industry is its solubility. In this paper, four kinds of amines (formamide, ethylenediamine, polyamide dendrimer (PAMAM) and polyhedral oligomeric silsesquioxane (POSS)) were introduced into polyvinyl alcohol (PVA) to improve the foaming behaviors. The results of differential scanning calorimetry showed that formamide and POSS could plasticize PVA to a certain extent. Through SEM analysis, formamide and ethylenediamine has a nucleating effect and improves the foaming effect. Furthermore, the content of formamide has a great influence on the foaming behavior of PVA. TG-IR analysis showed that -NH2 can react with CO2 to form -HNCOO- at normal temperature and pressure, which contributed to the diffusion of CO2 and improved the uniformity of cell size. With increasing formamide content, the cell density increased from 3.22 × 109 cm−3 to 1.79 × 1010 cm−3.

Random Forest Classifier for Diagnosis of Breast Cancer in African Women

Introduction: Breast cancer is the highest cause of cancer-related mortality among women globally. It is documented that 15% of all female cancer is breast cancer. Diagnosis and treatment of breast cancer in its earliest stage remains the only way to improve its outcome and reduce mortality, thus early and accurate diagnosis of breast cancer is important. Early detection of breast cancer among women in Sub-Saharan Africa (SSA) is very challenging to say the least as factors such as low knowledge of breast cancer, lack of awareness of early detection treatment, treatment cost, poor perception of breast cancer, socio-cultural factors such as belief, traditions and fears affect health seeking behaviour of African women but there is limited research efforts in computational approach to diagnosis of breast cancer in SSA. Aim: Here, we propose a novel diagnosis model for African women using Random Forest (RF) machine learning technique. Methods: Study data comprised of technical indicators for breast cancer diagnosis, collected from breast cancer patients attending oncology clinic in Lagos State University teaching hospital. A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Nine diagnostic parameters were included. These are clump thickness, marginal adhesion, uniformity of cell size, uniformity of cell shape, single epithelial cell, bare nuclei, bland chromatin, normal nucleoli and mitosis. Principal Component Analysis (PCA) was used for feature selection and RF model was used for classification. Results: The RF model gave an accuracy of 98.23%, sensitivity of 95.24%, and specificity of 100.00% and Area under curve (AUC) of 98%. Conclusion: The proposed Random Forest model has a good potential at classifying breast cancer in African women. Adoption of computational diagnosis approach in SSA can lead to early diagnosis and reduction of mortality rate.

Application of decision tree-based ensemble learning in the classification of breast cancer

As a common screening and diagnostic tool, Fine Needle Aspiration Biopsy (FNAB) of the suspicious breast lumps can be used to distinguish between malignant and benign breast cytology. In this study, we first review published works on the classification of breast cancer where the machine learning and data mining algorithms have been applied by using the Wisconsin Breast Cancer Database (WBCD). This work then introduces useful new tools, based on Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) algorithms to classify breast cancer. The RF and ET strategies use the decision trees as proper classifiers to attain the ultimate classification. The RF and ET approaches include four main stages: input identification, determination of the optimal number of trees, voting analysis, and final decision. The models implemented in this research consider important factors such as uniformity of cell size, bland chromatin, mitoses, and clump thickness as the input parameters. According to the statistical analysis, the proposed methods are able to classify the type of breast cancer accurately. The error analysis results reveal that the designed RF and ET models offer easy-to-use outcomes and the highest diagnostic performance, compared to previous tools/models in the literature for the WBCD classification. The highest and lowest magnitudes of relative importance are attributed to the uniformity of cell size and mitoses among the factors. It is expected that the RF and ET algorithms play an important role in medicine and health systems for screening and diagnosis in the near future.

Predictive Analytics of Selected Datasets using DTREG Data Mining Tool

Predictive analytics is making a significant wave in healthcare Industry. Predictive analytics is an analytics offshoot which helps to make future predictions, resulting in more informed decisions. Data is central to accurate predictions. Several concepts like Data Mining, AI (Artificial Intelligence), Machine Learning and statistics need to work in tandem to ensure precise predictions. The main aim of the research work was to analyse the datasets of selected diseases using the DTREG data mining tool. Two datasets namely Alzheimer’s and Breast Cancer were taken from a public repository and analysed. Various algorithms namely single tree, decision tree, tree boost, support vector machine and neural network were studied. The results obtained were interpreted to understand which algorithm works best in each case. Also, the important predictors in each study were recorded. Interpretation of Alzheimer’s and breast cancer data using DTREG revealed neural network as the best algorithm. The significant predictors for Alzheimer’s were estimated as total intracranial blood volume, clinical dementia rating and age, and for breast cancer were uniformity of cell size, cell shape, benign and malignant and clump thickness. Data mining, artificial Intelligence and machine learning can thus be of very good help in determining the line of treatment to be followed by extracting knowledge from such suitable databases. Keyword : Terms—Algorithms, Alzheimer’s, Breast Cancer, DTREG

Breast Cancer Prediction Using Ensemble Techniques

One of the most dangerous types of cancer affecting women across the world happens to be breast cancer. As per clinical experts, detecting this cancer in its first stage is crucial in saving lives. The variables like sample code number, clump thickness, uniformity of cell size, uniformity of cell shape etc., are some of the important risk factors providing information, allowing identification of the recurrence of breast cancer and cure it in the earliest stages.

Physicochemical and textural quality attributes of gluten-free bread formulated with guar gum

The objective of this study was to assess the combined effect of guar gum (GG) and water content (WC) on the rheological properties of batter, and the physicochemical and textural properties of bread. Batches of gluten-free bread used a base formulation of rice (50%), maize (30%) and quinoa flour (20%), with different levels of GG (2.5, 3.0 or 3.5%) and water (90, 100 or 110%) in a full factorial design. Higher GG doses (p < 0.001) tended to produce batters of lower stickiness, work of adhesion and cohesive strength; yet, of higher firmness, consistency, cohesiveness and viscosity index. These batters yielded loaves of lower (p < 0.001) specific volume and baking loss; and crumbs of lower (p < 0.001) aw, pH, mean cell area, void fraction, mean cell aspect ratio; and higher (p < 0.001) hardness, adhesiveness, springiness, cohesiveness, chewiness, resilience, mean cell density, cell size uniformity and mean cell compactness. The sticker and less consistent batters produced with higher WC rendered larger bread loaves of softer and more cohesive and springy/resilient crumbs with greater mean cell size and void fraction. Gluten-free loaves of good appearance in terms of higher specific volume, lower crumb hardness, higher crumb springiness, and open grain visual texture were obtained in formulations with 110% WC and GG doses between 2.5 and 3.0%.

Preparation of highly interconnected porous poly(ε‐caprolactone)/poly(lactic acid) scaffolds via supercritical foaming

Highly interconnected porous polymer scaffolds were prepared by gas foaming. Poly(ε‐caprolactone)/poly(lactic acid) blends were melt mixed and batch foamed using CO2 as a physical blowing agent. The effects of the foaming temperature, pressure, and CO2 saturation time on the foam morphology were related to the mechanical properties of the scaffolds. The cell size in cross section of scaffolds decreased and became more homogeneous until CO2 saturation time reached 1.5 hours. With an increase in temperature, the cell size increased markedly and the cell size uniformity decreased gradually, whereas the reverse happened when the foaming pressure was increased. Meanwhile, many open cells can also be detected on the surface of scaffolds. High porosity and an open‐cell content of greater than 90% were obtained. The compressive strength of scaffolds was regulated by controlling the foam architecture. The information gathered in this study may provide a theoretical basis for research into porous tissue engineering scaffolds.

Cell size sensing in animal cells coordinates anabolic growth rates and cell cycle progression to maintain cell size uniformity.

Cell size uniformity in healthy tissues suggests that control mechanisms might coordinate cell growth and division. We derived a method to assay whether cellular growth rates depend on cell size, by monitoring how variance in size changes as cells grow. Our data revealed that, twice during the cell cycle, growth rates are selectively increased in small cells and reduced in large cells, ensuring cell size uniformity. This regulation was also observed directly by monitoring nuclear growth in live cells. We also detected cell-size-dependent adjustments of G1 length, which further reduce variability. Combining our assays with chemical/genetic perturbations confirmed that cells employ two strategies, adjusting both cell cycle length and growth rate, to maintain the appropriate size. Additionally, although Rb signaling is not required for these regulatory behaviors, perturbing Cdk4 activity still influences cell size, suggesting that the Cdk4 pathway may play a role in designating the cell's target size.

Combined effect of xanthan gum and water content on physicochemical and textural properties of gluten-free batter and bread

The objective of this study was to evaluate the combined effect of xanthan gum (XG) on physicochemical, rheological and textural properties of gluten-free batter and bread. To prepare gluten-free batter, different levels of XG (1.5, 2.5 and 3.5%) and water (90, 100 and 110%) were added to a base formula of rice (50%), maize (30%) and quinoa flours (20%); and the batters were evaluated in a factorial design. Several properties on both batter (stickiness and back extrusion) and its corresponding bread (loaf specific volume, baking loss, water activity and pH, texture profile, mean cell area, mean cell density, cell size uniformity, void fraction, mean cell compactness and mean cell aspect ratio) were then evaluated. Higher XG doses (p < .001) tended to produce batters of lower stickiness, adhesion and cohesive-strength, yet, of higher firmness, consistency, cohesiveness and viscosity index. After baking, these loaves presented lower specific volume; lower crumb aw, pH, hardness, springiness, mean cell area and void fraction; and higher (p < .001) chewiness, resilience, mean cell density, cell size uniformity and mean cell compactness. The sticker and less consistent batters produced with higher WC rendered larger bread loaves of softer and more springy/resilient crumbs with greater mean cell size and void fraction. Gluten-free loaves of good appearance in terms of higher specific volume, lower crumb hardness, higher crumb springiness, and open grain visual texture were obtained in formulations with 110% WC and XG doses between 1.5 and 2.5%.

Effect of Mold Opening Process on Microporous Structure and Properties of Microcellular Polylactide⁻Polylactide Nanocomposites.

Cell structure is a key factor that determines the final properties of microcellular polylactide (PLA) product. In the mold opening process, adjusting the rate of mold opening can effectively control cell structure. PLA and PLA composites with a void fraction as high as 50% were fabricated using the mold opening technique. The effects of mold opening rate and the addition of nanoclay on the cell structure, mechanical properties, and surface quality of microcellular PLA and PLA composites samples were investigated. The results showed that finer cell structure was received in the microcellular PLA samples and the surface quality was improved effectively when decreasing the rate of mold opening. The effect of mold opening rate on the foaming behavior of microcellular PLA–nanoclay was the same as that of microcellular PLA. The addition of 5 wt % nanoclay significantly improved the foaming properties, such as cell density, cell size, and structural uniformity, which consequently enhanced the mechanical properties of foams and the surface quality.

Size uniformity of animal cells is actively maintained by a p38 MAPK-dependent regulation of G1-length.

Animal cells within a tissue typically display a striking regularity in their size. To date, the molecular mechanisms that control this uniformity are still unknown. We have previously shown that size uniformity in animal cells is promoted, in part, by size-dependent regulation of G1 length. To identify the molecular mechanisms underlying this process, we performed a large-scale small molecule screen and found that the p38 MAPK pathway is involved in coordinating cell size and cell cycle progression. Small cells display higher p38 activity and spend more time in G1 than larger cells. Inhibition of p38 MAPK leads to loss of the compensatory G1 length extension in small cells, resulting in faster proliferation, smaller cell size and increased size heterogeneity. We propose a model wherein the p38 pathway responds to changes in cell size and regulates G1 exit accordingly, to increase cell size uniformity.

Exploration on predicting breast cancer stage with the aid of redesigned ANN incorporated with enhanced social spider optimisation technique

The core intention of this work is to predict the breast cancer stage as benignant or malignant from the given dataset with parameters such as instance clump thickness, uniformity of cell size, uniformity of cell shape, etc. Predicting the cancer stage helps to determine how to contain and eliminate breast cancer. One of the classification methods used is artificial neural network (ANN) which is trained with several training algorithms and the selected algorithm is Levenberg-Marquardt which performs better and gives minimum error value. To obtain the better prediction, the default structure of ANN is redesigned using optimisation techniques. To improve the structural design (hidden layer and neuron), diverse optimisation techniques are used, for example, Cuckoo search, Particle Swarm, Social Spider and Enhanced Social Spider Optimisation (ESSO). Our results show the ESSO is better and evaluate the metrics as Accuracy 97%, Sensitivity 98% and Specificity 95% compared with other techniques.

Exploration on predicting breast cancer stage with the aid of redesigned ANN incorporated with enhanced social spider optimisation technique

The core intention of this work is to predict the breast cancer stage as benignant or malignant from the given dataset with parameters such as instance clump thickness, uniformity of cell size, uniformity of cell shape, etc. Predicting the cancer stage helps to determine how to contain and eliminate breast cancer. One of the classification methods used is artificial neural network (ANN) which is trained with several training algorithms and the selected algorithm is Levenberg-Marquardt which performs better and gives minimum error value. To obtain the better prediction, the default structure of ANN is redesigned using optimisation techniques. To improve the structural design (hidden layer and neuron), diverse optimisation techniques are used, for example, Cuckoo search, Particle Swarm, Social Spider and Enhanced Social Spider Optimisation (ESSO). Our results show the ESSO is better and evaluate the metrics as Accuracy 97%, Sensitivity 98% and Specificity 95% compared with other techniques.