Search results for: lung cancer detection

Authors: Siva Chander Chabattula, Piyush Kumar Gupta, Debashis Chakraborty, Rama Shanker Verma

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Green nanoparticles have gotten a lot of attention because of their potential applications in tissue regeneration, bioimaging, wound healing, and cancer therapy. The physical and chemical methods to synthesize metal oxide nanoparticles have an environmental impact, necessitating the development of an environmentally friendly green strategy for nanoparticle synthesis. In this study, we used Annona muricata plant leaf extract to synthesize Zinc Oxide nanoparticles (Am-ZnO NPs), which were evaluated using UV/Visible spectroscopy, FTIR spectroscopy, X-Ray Diffraction, DLS, and Zeta potential. Nanoparticles had an optical absorbance of 355 nm and a net negative surface charge of ~ - 2.59 mV. Transmission Electron Microscope characterizes the Shape and size of the nanoparticles. The obtained Am-ZnO NPs are biocompatible and hemocompatible in nature. These nanoparticles caused an anti-cancer therapeutic effect in MIA PaCa2 and MOLT4 cancer cells by inducing oxidative stress, and a change in mitochondrial membrane potential leads to programmed cell death. Further, we observed a reduction in the size of lung cancer spheroids (act as tumor micro-environment) with doxorubicin as a positive control.

Keywords: Biomaterials, nanoparticle, anticancer activity, ZnO nanoparticles

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Authors: Ahmad Jawad Fardin

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Over 60% of patients with breast cancer in Afghanistan present late with advanced stage III and IV, a major cause for the poor survival rate. The objectives of this study were to identify the contributing factors for the diagnosis and treatment delay and its outcome. This cross-sectional study was conducted on 318 patients with histologically confirmed breast cancer in the oncology department of Jamhoriat hospital, which is the first and only national cancer center in Afghanistan; data were collected from medical records and interviews conducted with women diagnosed with breast cancer, linear regression and logistic regression were used for analysis. Patient delay was defined as the time from first recognition of symptoms until first medical consultation and doctor form first consultation with a health care provider until histological confirmation of breast cancer. The mean age of patients was 49.2+_ 11.5years. The average time for the final diagnosis of breast cancer was 8.5 months; most patients had ductal carcinoma 260.7 (82%). Factors associated with delay were low education level 76% poor socioeconomic and cultural conditions 81% lack of cancer center 73% lack of screening 19%. The stage distribution was as follows stage IV 4 22% stage III 44.4% stage II 29.3% stage I 4.3%. Complex associated factors were identified to delayed the diagnosis of breast cancer and increased adverse outcomes consequently. Raising awareness and education in women, the establishment of cancer centers and providing accessible diagnosis service and screening, training of general practitioners; required to promote early detection, diagnosis and treatment.

Keywords: delayed diagnosis and poor outcome, breast cancer in Afghanistan, poor outcome of delayed breast cancer treatment, breast cancer delayed diagnosis and treatment in Afghanistan

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Authors: Eddy K. Mukadi

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This work is a documentary and descriptive study devoted to the epidemiological, clinical, histopathological and therapeutic profile of breast cancer deals with the department of gynecology and obstetrics of the university clinics of Kinshasa during the period from 1 January 2014 to 31 December 2014. We have identified 56 cases of breast cancer. These cancers accounted for 45.2% of gynecological mammary cancers. The youngest in our series was 18 years old while the oldest was 74 years old; And the mean age of these patients was 43.4 years and mostly multiparous (35.7%). Brides (60.7%) and bachelors (26.8%) were the most affected by breast cancer. The reasons for consultation were dominated by nodules in the breast (48.2%) followed by pain (35.7%) and nipple discharge (14.3%). In 89.2% of the cases, it was the advanced clinical stage (stage 3 and 4) and the infiltrating ductal carcinoma was the most frequent histological type (75%) The malignant tumor was mainly in the left breast (55.3%), and chemotherapy with hormone therapy and patey was the most convenient treatment (42.8%), while patey mastectomy was performed in 12.5% of patients. Because of the high incidence of breast cancer identified in our study, some preventive measures must be taken into account to address this public health problem, including breast autopalpation once a month, Early detection system development of a national breast cancer policy and the implementation of a national breast cancer control program.

Keywords: breast cancer, histopathological profile, epidemiological profile, Kinshasa

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Authors: Fatima Abu Baker Hamad

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Anaemia is a public health problem that affects population in both rich and poor countries. Although the primary cause is iron deficiency, it is seldom present in isolation. More frequently it coexists with a number of other causes, such as malaria, parasitic infection, nutritional deficiencies and hemoglobin apathies. That was the people in Sudan suffered from it .Anaemia has a high prevalence in patients with cancer. The aim of this study was to find the incidence of anaemia in new cases of Sudanese female breast patients attending the National Cancer Institute (NCI), Gezira University, Sudan. The study was performed on 250 female breast cancer patients, the age range was (20-70) years and the mean age was 45.99±0.82. The hemoglobin level was measured by SYSMEX-KX2lM.As result 144(58.8) of patients presented with anaemia, between moderate to severe. Forty four (17.6%) of the patients were found to be under weight, 31 of them were anaemic. While 105(42%) of the patients were overweight and obese, 52 of them were anaemic. The incidence of anaemia in newly diagnosed Sudanese female breast cancer patients presented at NCI is association presentation with advance disease stage. Also it is related to age, state of nutrition and social economic factors. Early cancer detection which leads to effective treatment and reduced complication of diseases included anaemia is recommended.

Keywords: anaemia, breast cancer, stages of disease, malaria

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Authors: Parvin Samadi Pakchin, Reza Saber, Hossein Ghanbari, Yadollah Omidi

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Ovarian cancer is one of the leading cause of mortality among the gynecological malignancies, and it remains the one of the most prevalent cancer in females worldwide. Tumor markers are biochemical molecules in blood or tissues which can indicates cancers occurrence in the human body. So, the sensitive and specific detection of cancer markers typically recruited for diagnosing and evaluating cancers. Recently extensive research efforts are underway to achieve a simple, inexpensive and accurate device for detection of cancer biomarkers. Compared with conventional immunoassay techniques, electrochemical immunosensors are of great interest, because they are specific, simple, inexpensive, easy to handling and miniaturization. Moreover, in the past decade nanotechnology has played a crucial role in the development of biosensors. In this study, a signal-off electrochemical immunosensor for the detection of CA125 antigen has been developed using chitosan-gold nanoparticles (CS-AuNP) and multi-wall carbon nanotubes (MWCNT) composites. Toluidine blue (TB) is used as redox probe which is immobilized on the electrode surface. CS-AuNP is synthesized by a simple one step method that HAuCl4 is reduced by NH2 groups of chitosan. The CS-AuNP-MWCNT modified electrode has shown excellent electrochemical performance compared with bare Au electrode. MWCNTs and AuNPs increased electrochemical conductivity and accelerate electrons transfer between solution and electrode surface while excessive amine groups on chitosan lead to the effective loading of the biological material (CA125 antibody) and TB on the electrode surface. The electrochemical, immobilization and sensing properties CS-AuNP-MWCNT-TB modified electrodes are characterized by cyclic voltammetry, electrochemical impedance spectroscopy, differential pulse voltammetry and square wave voltammetry with Fe(CN)63−/4−as an electrochemical redox indicator.

Keywords: signal-off electrochemical biosensor, CA125, ovarian cancer, chitosan-gold nanoparticles

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Authors: Ying Li, Rui Hu, Shizhen Chen, Xin Zhou, Yunhuang Yang

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Prostate cancer is one of the leading causes of cancer-related death among men. Prostate-specific antigen (PSA), a specific product of prostatic epithelial cells, is an important indicator of prostate cancer. Though PSA is not a specific serum biomarker for the screening of prostate cancer, it is recognized as an indicator for prostate cancer recurrence and response to therapy for patient’s post-prostatectomy. Since radical prostatectomy eliminates the source of PSA production, serum PSA levels fall below 50 pg/mL, and may be below the detection limit of clinical immunoassays (current clinical immunoassay lower limit of detection is around 10 pg/mL). Many clinical studies have shown that intervention at low PSA levels was able to improve patient outcomes significantly. Therefore, ultra-sensitive and precise assays that can accurately quantify extremely low levels of PSA (below 1-10 pg/mL) will facilitate the assessment of patients for the possibility of early adjuvant or salvage treatment. Currently, the commercially available ultra-sensitive ELISA kit (not used clinically) can only reach a detection limit of 3-10 pg/mL. Other platforms developed by different research groups could achieve a detection limit as low as 0.33 pg/mL, but they relied on sophisticated instruments to get the final readout. Herein we report a microfluidic platform for point-of-care (POC) detection of PSA with a detection limit of 0.5 pg/mL and without the assistance of any equipment. This platform is based on a previously reported volumetric-bar-chart chip (V-Chip), which applies platinum nanoparticles (PtNPs) as the ELISA probe to convert the biomarker concentration to the volume of oxygen gas that further pushes the red ink to form a visualized bar-chart. The length of each bar is used to quantify the biomarker concentration of each sample. We devised a long reading channel V-Chip (LV-Chip) in this work to achieve a wide detection window. In addition, LV-Chip employed a unique enzyme-free ELISA probe that enriched PtNPs significantly and owned 500-fold enhanced catalytic ability over that of previous V-Chip, resulting in a significantly improved detection limit. LV-Chip is able to complete a PSA assay for five samples in 20 min. The device was applied to detect PSA in 50 patient serum samples, and the on-chip results demonstrated good correlation with conventional immunoassay. In addition, the PSA levels in finger-prick whole blood samples from healthy volunteers were successfully measured on the device. This completely stand-alone LV-Chip platform enables convenient POC testing for patient follow-up in the physician’s office and is also useful in resource-constrained settings.

Keywords: point-of-care detection, microfluidics, PSA, ultra-sensitive

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Authors: Firas Gerges, Frank Y. Shih

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Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: Zakaria Baka, Halima Alem

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Cancer is a major worldwide health problem that has caused around ten million deaths in 2020. In addition, efforts to develop new anti-cancer drugs still face a high failure rate. This is partly due to the lack of preclinical models that recapitulate in-vivo drug responses. Indeed conventional cell culture approach (known as 2D cell culture) is far from reproducing the complex, dynamic and three-dimensional environment of tumors. To set up more in-vivo-like cancer models, 3D bioprinting seems to be a promising technology due to its ability to achieve 3D scaffolds containing different cell types with controlled distribution and precise architecture. Moreover, the introduction of microfluidic technology makes it possible to simulate in-vivo dynamic conditions through the so-called “cancer-on-chip” platforms. Whereas several cancer types have been modeled through the cancer-on-chip approach, such as lung cancer and breast cancer, only a few works describing ovarian cancer models have been described. The aim of this work is to combine 3D bioprinting and microfluidic technics with setting up a 3D dynamic model of ovarian cancer. In the first phase, alginate-gelatin hydrogel containing SKOV3 cells was used to achieve tumor-like structures through an extrusion-based bioprinter. The desired form of the tumor-like mass was first designed on 3D CAD software. The hydrogel composition was then optimized for ensuring good and reproducible printability. Cell viability in the bioprinted structures was assessed using Live/Dead assay and WST1 assay. In the second phase, these bioprinted structures will be included in a microfluidic device that allows simultaneous testing of different drug concentrations. This microfluidic dispositive was first designed through computational fluid dynamics (CFD) simulations for fixing its precise dimensions. It was then be manufactured through a molding method based on a 3D printed template. To confirm the results of CFD simulations, doxorubicin (DOX) solutions were perfused through the dispositive and DOX concentration in each culture chamber was determined. Once completely characterized, this model will be used to assess the efficacy of anti-cancer nanoparticles developed in the Jean Lamour institute.

Keywords: 3D bioprinting, ovarian cancer, cancer-on-chip models, microfluidic techniques

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Authors: Tolulope Allo, Mofoluwake P. Ajayi, Adenike E. Idowu, Emmanuel O. Amoo, Fadeke Esther Olu-Owolabi

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Breast cancer which was more prevalent in Europe and America in the past is gradually being mirrored across the world today with greater economic burden on low and middle income countries (LMCs). Breast cancer is the most common cancer among women globally and current studies have shown that a woman dies with the diagnosis of breast cancer every thirteen minutes. The economic cost of breast cancer is overwhelming particularly for developing economies. While it causes billion of dollar in losses of national income, it pushes millions of people below poverty line. This study examined the economic burden of breast cancer on Nigerian women, its impacts on their standard of living and its effects on Nigeria’s socio economic development. The study adopts a qualitative research approach using the in-depth interview technique to elicit valuable information from respondents with cancer experience from the Southern part of Nigeria. Respondents constituted women in their reproductive age (15-49 years) that have experienced and survived cancer and also those that are currently receiving treatment. Excerpts from the interviews revealed that the cost of treatment is one of the major factors contributing to the late presentation of breast cancer incidences among women as many of them could not afford to pay for their own treatment. The study also revealed that many women prefer to explore other options such as herbal treatments and spiritual consultations which is less expensive and affordable. The study therefore concludes that breast cancer diagnosis and treatment should be subsidized by the government in order to facilitate easy access and affordability thereby promoting early detection and reducing the economic burden of treatment on women.

Keywords: breast cancer, development, economic burden, women

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Authors: Abdul Matin, Muhammad Ajmal, Uzma Yunus, Noaman-ul Haq, Hafiz M. Shohaib, Ambreen G. Muazzam

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Carbon nanoparticles (CNPs) have unique properties that are useful for the diagnosis and treatment of cancer due to their precise properties like small size (ideal for delivery within the body) stability in solvent and tunable surface chemistry for targeted delivery. Here, highly fluorescent, monodispersed and water-soluble CNPs were synthesized directly from a suitable carbohydrate source (glucose and sucrose) by one-step acid assisted ultrasonic treatment at 35 KHz for 4 hours. This method is green, simple, rapid and economical and can be used for large scale production and applications. The average particle sizes of CNPs are less than 10nm and they emit bright and colorful green-blue fluorescence under the irradiation of UV-light at 365nm. The CNPs were characterized by scanning electron microscopy, fluorescent spectrophotometry, Fourier transform infrared spectrophotometry, ultraviolet-visible spectrophotometry and TGA analysis. Fluorescent CNPs were used as fluorescent probe and nano-carriers for anticancer drug. Functionalized CNPs (with ethylene diamine) were attached with anticancer drug-Methotrexate. In vitro bioactivity and biocompatibility of CNPs-drug conjugates was evaluated by LDH assay and Sulforhodamine B assay using human lung carcinoma cell lines (H157). Our results reveled that CNPs showed biocompatibility and CNPs-anticancer drug conjugates have shown potent cytotoxic effects and high antitumor activities in lung cancer cell lines. CNPs are proved to be excellent substitute for conventional drug delivery cargo systems and anticancer therapeutics in vitro. Our future studies will be more focused on using the same nanoparticles in vivo model system.

Keywords: carbon nanoparticles, carbon nanoparticles-methotrexate conjugates, human lung carcinoma cell lines, lactate dehydrogenase, methotrexate

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Authors: Janani Selvaraj, M. Prashanthi Devi, P. B. Harathi

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Epidemiologic studies conducted over several decades have provided evidence to suggest that long-term exposure to elevated ambient levels of particulate air pollution is associated with increased mortality. Air quality risk management is significant in developing countries and it highlights the need to understand the role of ecologic covariates in the association between air pollution and mortality. Several new methods show promise in exploring the geographical distribution of disease and the identification of high risk areas using epidemiological maps. However, the addition of the temporal attribute would further give us an in depth idea of the disease burden with respect to forecasting measures. In recent years, new methods developed in the reanalysis were useful for exploring the spatial structure of the data and the impact of spatial autocorrelation on estimates of risk associated with exposure to air pollution. Based on this, our present study aims to explore the spatial and temporal distribution of the lung cancer cases in the Coimbatore district of Tamil Nadu in relation to air pollution risk areas. A spatio temporal moving average method was computed using the CrimeStat software and visualized in ArcGIS 10.1 to document the spatio temporal movement of the disease in the study region. The random walk analysis performed showed the progress of the peak cancer incidences in the intersection regions of the Coimbatore North and South taluks that include major commercial and residential regions like Gandhipuram, Peelamedu, Ganapathy, etc. Our study shows evidence that daily exposure to high air pollutant concentration zones may lead to the risk of lung cancer. The observations from the present study will be useful in delineating high risk zones of environmental exposure that contribute to the increase of cancer among daily commuters. Through our study we suggest that spatially resolved exposure models in relevant time frames will produce higher risks zones rather than solely on statistical theory about the impact of measurement error and the empirical findings.

Keywords: air pollution, cancer, spatio-temporal analysis, India

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Charles Lee

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The Principal Orthogonal Decomposition (POD) technique has been used as a model reduction tool for many applications in engineering and science. In principle, one begins with an ensemble of data, called snapshots, collected from an experiment or laboratory results. The beauty of the POD technique is that when applied, the entire data set can be represented by the smallest number of orthogonal basis elements. It is the such capability that allows us to reduce the complexity and dimensions of many physical applications. Mathematical formulations and numerical schemes for the POD method will be discussed along with applications in NASA’s Deep Space Large Antenna Arrays, Satellite Image Reconstruction, Cancer Detection with DNA Microarray Data, Maximizing Stock Return, and Medical Imaging.

Keywords: reduced-order methods, principal component analysis, cancer detection, image reconstruction, stock portfolios

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

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Authors: G. Wojcik, J. Gindlhuber, A. Syarif, K. Hoetzenecker, P. Bohm, P. Vesely, V. Biasin, G. Kwapiszewska

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Pulmonary fibrosis is a rare disease where uncontrolled wound healing processes damage the lung structure. Intensive changes within the extracellular matrix (ECM) and its interaction with fibroblasts have a major role in pulmonary fibrosis development. Among others, collagen is one of the main components of the ECM, and it is important for lung structure. In IPF, constant production of collagen by fibroblast, through TGFβ1-SMAD2/3 pathways, leads to an uncontrolled deposition of matrix and hence lung remodeling. Abnormal changes in lipid production, alterations in fatty acids (FAs) metabolism, enhanced oxidative stress, and lipid peroxidation in fibrotic lung and fibrotic fibroblasts have been reported; however, the interplay between the collagen and lipids is not yet established. One of the FAs influx regulators is Angiopoietin-like 4 (ANGPTL4), which inhibits lipoprotein lipase work, decreasing the availability of FAs. We hypothesized that altered lipid composition or availability could have the capability to influence the phenotype of different fibroblast populations in the lung and hence influence lung fibrosis. To prove our hypothesis, we aim to investigate lipids and their influence on human, animal, and in vitro levels. In the bleomycin model, treatment with the well-known metabolic drugs Rosiglitazone or Metformin significantly lower collagen production. Similar results were noticed in ANGPTL4 KO animals, where the KO of ANGPTL4 leads to an increase of FAs availability and lower collagen deposition after the bleomycin challenge. Currently, we study the treatment of different FAs on human lung para fibroblasts (hPF) isolated from donors. To understand the lipid composition, we are collecting human lung tissue from donors and pulmonary fibrosis patients for Liquid chromatography-mass spectrometry. In conclusion, our results suggest the lipid influence on collagen deposition during lung fibrosis, but further research needs to be conducted to understand the matter of this relationship.

Keywords: collagen, fibroblasts, lipidomics, lung, pulmonary fibrosis

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Authors: Nadia H. Mohammed, Anmar Al-Taie, Fadia H. Al-Sultany

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Although breast cancer prevalence continues to increase, mortality has been decreasing as a result of early detection and improvement in adjuvant systemic therapy. Nevertheless, this disease required further efforts to understand and identify the associated potential risk factors that could play a role in the prevalence of this malignancy among Iraqi women. The objective of this study was to assess the perception of certain predictive risk factors on the prevalence of breast cancer types among a sample of Iraqi women diagnosed with breast cancer. This was a retrospective observational study carried out at National Cancer Research Center in College of Medicine, Baghdad University from November 2017 to January 2018. Data of 100 patients with breast cancer whose biopsies examined in the National Cancer Research Center were included in this study. Data were collected to structure a detailed assessment regarding the patients’ demographic, medical and cancer records. The majority of study participants (94%) suffered from ductal breast cancer with mean age 49.57 years. Among those women, 48.9% were obese with body mass index (BMI) 35 kg/m². 68.1% of them had positive family history of breast cancer and 66% had low parity. 40.4% had stage II ductal breast cancer followed by 25.5% with stage III. It was found that 59.6% and 68.1% had positive oestrogen receptor sensitivity and positive human epidermal growth factor (HER2/neu) receptor sensitivity respectively. In regard to the impact of prediction of certain variables on the incidence of ductal breast cancer, positive family history of breast cancer (P < 0.0001), low parity (P< 0.0001), stage I and II breast cancer (P = 0.02) and positive HER2/neu status (P < 0.0001) were significant predictive factors among the study participants. The results from this study provide relevant evidence for a significant positive and potential association between certain risk factors and the prevalence of breast cancer among Iraqi women.

Keywords: Ductal Breast Cancer, Hormone Sensitivity, Iraq, Risk Factors

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Authors: Salim Cerig, Fatime Geyikoglu, Pınar Akpulat, Suat Colak, Hasan Turkez, Murat Bakir, Mirkhalil Hosseinigouzdagani, Kubra Koc

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This study was designed to evaluate whether carvacrol (CAR) could provide protection against lung injury by acute pancreatitis development. The rats were randomized into groups to receive (I) no therapy; (II) 50 μg/kg cerulein at 1h intervals by four intraperitoneal injections (i.p.); (III) 50, 100 and 200 mg/kg CAR by one i.p.; and (IV) cerulein+CAR after 2h of cerulein injection. 12h later, serum samples were obtained to assess pancreatic function the lipase and amylase values. The animals were euthanized and lung samples were excised. The specimens were stained with hematoxylin-eosin (H&E), periodic acid–Schif (PAS), Mallory's trichrome and amyloid. Additionally, oxidative DNA damage was determined by measuring as increases in 8-hydroxy-deoxyguanosine (8-OH-dG) adducts. The results showed that the serum activity of lipase and amylase in AP rats were significantly reduced after the therapy (p<0.05). We also found that the 100 mg/kg dose of CAR significantly decreased 8-OH-dG levels. Moreover, the severe pathological findings in the lung such as necrosis, inflammation, congestion, fibrosis, and thickened alveolar septum were attenuated in the AP+CAR groups when compared with AP group. Finally, the magnitude of the protective effect on lung is certain, and CAR is an effective therapy for lung injury caused by AP.

Keywords: antioxidant activity, acute pancreatitis, carvacrol, experimental, lung injury, oxidative DNA damage

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Authors: Roohallah Fallah-Moshkani, Mohammad Ali Mohsenpour, Reza Ghiasvand, Hossein Khosravi-Boroujeni, Seyed Mehdi Ahmadi, Paula Brauer, Amin Salehi-Abargouei

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Purpose: Several investigations have proposed the protective association between dietary approaches to stop hypertension (DASH) style diet and risk of cancers; however, they have led to inconsistent results. The present study aimed to systematically review the prospective cohort studies conducted in this regard and, if possible, to quantify the overall effect of using meta-analysis. Methods: PubMed, EMBASE, Scopus, and Google Scholar were searched for cohort studies published up to December 2017. Relative risks (RRs) which were reported for fully adjusted models and their confidence intervals were extracted for meta-analysis. Random effects model was incorporated to combine the RRs. Results: Sixteen studies were eligible to be included in the systematic review from which 8 reports were conducted on the effect of DASH on the risk of mortality from all cancer types, four on the risk of colorectal cancer, and three on the risk of colon and rectal cancer. Four studies examined the association with other cancers (breast, hepatic, endometrial, and lung cancer). Meta-analysis showed that high concordance with DASH significantly decreases the risk of all cancer types (RR=0.83, 95% confidence interval (95%CI):0.80-0.85); furthermore participants who highly adhered to the DASH had lower risk of developing colorectal (RR=0.79, 95%CI: 0.75-0.83), colon (RR=0.81, 95%CI: 0.74-0.87) and rectal (RR=0.79, 95%CI: 0.63-0.98) cancer compared to those with the lowest adherence. Conclusions: DASH-style diet should be suggested as a healthy approach to protect from cancer in the community. Prospective studies exploring the effect on other cancer types and from regions other than the United States are highly recommended.

Keywords: cancer, DASH-style diet, dietary patterns, meta-analysis, systematic review

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Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

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In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

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Authors: Arab M., Ait Abdallah M., Zeraoulia N., Boumaza H., Aoutia M., Griene L., Ait Abdelkader B.,

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breast and ovarian cancer are respectively the first and fourth leading causes of cancer among women in Algeria. A family story of cancer in the most important risk factor, and in most cases of families with breast and /or ovarian cancer, the pattern of cancer family can be attributed to mutation in BRCA1/2genes. objectibes: the aim of our study in to investigate the spectrum of BRCA1/2 germiline mutation in familial breast and /or ovarian cancer and to determine the prevalence and the nature of BRCA1/2mutation in Algeria methods: we deremined the prevalence of BRCA1/2 mutation within a cohort of 161 probands selected according the eisinger score double stranded sanger sequencing of all coding exons of BRCA1/2including flanking intronic region were performed results: we identified a total of 23 distinct deleterious mutations (class5) 12 differents mutations in BRCA1(52%) and 11 in BRCA2(48%). 78% (18/23) were protein truncating and 22%(5/23) missens mutations.3 novel deleterious mutations have been identified, which have not been described in public mutation database. one new mutation were found in two unrelated patients. the overall mutation detection rate in our study is 28,5%(46/161).more over, an UVS c7783 located in BRCA2 is found in two unrelated probands and segregate in the 02 families/ conclusion: our results sugget of large spectrum of BRCA1/2 mutation in Algerian breast/ovarian cancer family. The nature and prevalence of BRCA1/2mutation in algerian families are ongoing in a larger study, 80 probands are to day under investigation. This study which may therefore identify the genetic particularity of Algerian breast /ovarian cancer.

Keywords: BRCA1/2 mutations, hereditary breast cancer, algerian women, prvalence

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Authors: S. Subramaniam, J. S. A. Rao, P. Ramdas, K. R. Selvaduray, N. M. Han, M. K. Kutty, A. K. Radhakrishnan

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Immune system is a complex system where the immune cells have the capability to respond against a wide range of immune challenges including cancer progression. However, in the event of cancer development, tumour cells trigger immunosuppressive environment via activation of myeloid-derived suppressor cells and T regulatory (Treg) cells. The Treg cells are subset of CD4+ T lymphocytes, known to have crucial roles in regulating immune homeostasis and promoting the establishment and maintenance of peripheral tolerance. Dysregulation of these mechanisms could lead to cancer progression and immune suppression. Recently, there are many studies reporting on the effects of natural bioactive compounds on immune responses against cancer. It was known that tocotrienol-rich-fraction consisting 70% tocotrienols and 30% α-tocopherol is able to exhibit immunomodulatory as well as anti-cancer properties. Hence, this study was designed to evaluate the effects of gamma-tocotrienol (G-T3) supplementation on T-reg cells in a syngeneic mouse model of breast cancer. In this study, female BALB/c mice were divided into two groups and fed with either soy oil (vehicle) or gamma-tocotrienol (G-T3) for two weeks followed by inoculation with tumour cells. All the mice continued to receive the same supplementation until day 49. The results showed a significant reduction in tumour volume and weight in G-T3 fed mice compared to vehicle-fed mice. Lung and liver histology showed reduced evidence of metastasis in tumour-bearing G-T3 fed mice. Besides that, flow cytometry analysis revealed T-helper cell population was increased, and T-regulatory cell population was suppressed following G-T3 supplementation. Moreover, immunohistochemistry analysis showed that there was a marked decrease in the expression of FOXP3 in the G-T3 fed tumour bearing mice. In conclusion, the G-T3 supplementation showed good prognosis towards breast cancer by enhancing the immune response in tumour-bearing mice. Therefore, gamma-T3 can be used as immunotherapy agent for the treatment of breast cancer.

Keywords: breast cancer, gamma tocotrienol, immune suppression, supplement

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Authors: Diriba Dereje

Abstract:

Background: Cardiac and renal disease are the most prevalent chronic non-communicable diseases (CNCD) affecting the community in a significant manner. The best and recommended method in halting CNCD is by working on prevention as early as possible. This is only possible if early surrogate markers are identified. As part of the stated solution, this study will identify an association between microalbuminuria (an early surrogate marker of renal and cardiac disease) and lung function test among adult in the community. Objective: The main aim of this study was to assess an association between microalbuminuria (an early surrogate marker of renal and cardiac disease) and lung function test among adult in the community. Methodology: Community based cross sectional study was conducted among 384 adult in Jimma town. A systematic sampling technique was used in selecting participants to the study. In searching for the possible association, binary and multivariate logistic regression and t-test was conducted. Finally, the association between microalbuminuria and lung function test was well stated in the form of figures and written description. Result and Conclusion: A significant association was found between microalbuminuria and different lung function test parameters.

Keywords: microalbuminuria, lung function, association, test

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Authors: Vishnu Varthan Vaithiyalingamjagannathan, M. N. Sathishkumar, K. S. Lakhsmi, D. Satheeshkumar, Srividyaammayappanrajam

Abstract:

Background:Naringenin, aglycone flavonoid possess certain activities like anti-oxidant, anti-estrogenic, anti-diabetic, cardioprotective, anti-obesity,anti-inflammatory, hepatoprotective and also have anti-cancer characteristics like carcinogenic inactivation, cell cycle arrest, anti-proliferation, apoptosis, anti-angiogenesis and enhances anti-oxidant activity. Methodology:The inhibitory effect of Naringenin in lung tumor progression estimated with adenocarcinoma (A549) cell lines (in vitro) and C57BL/6 mice injected with 5 X 106A549 cell lines (in vivo) in a tri-dose manner (Naringenin 100mg/kg,150mg/kg, and 200mg/kg) compared with standard chemotherapy drug cisplatin (7mg/kg). Results:The results of the present study revealed a dose-dependent activity in Naringenin and combination with cisplatin at a higher dose which showed decreased tumor progression in mice. In vitro studies carried out for estimation of cell survival and Nitric Oxide (NO) level, shows dose dependent action of Naringenin with IC50 value of 42µg/ml. In vivo studies were carried out in C57BL/6 mice. Naringenin satisfied the condition of an anti-cancer molecule with its characteristics in fragmentation assay, Zymography assay, anti-oxidant, and myeloperoxidase studies, than cisplatin which failed in anti-oxidant and myeloperoxidase effect. Both in vitro and in vivo establishes dose dependent decrease in NO levels. But whereas, Naringenin showed adverse results in Matrix Metalloproteinase (MMP) enzymatic levels with increase in dose levels. Conclusion:From the present study, Naringenin could suppress the lung tumor progression when given individually and also in combinatorial with standard chemotherapy drug.

Keywords: naringenin, in vitro, cell line, anticancer

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Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Betzabet García-Mendoza, Rocío Abascal-Mena

Abstract:

Breast and cervical cancer are among the leading causes of death of women in Mexico. The mortality rate for these diseases is alarming, even though there have been many campaigns for making people self-aware of the importance of conducting gynecological studies for a timely prevention and detection, these have not been enough. This paper presents a mobile application for organizing and controlling gynecological studies in order to help and boost women to take care of their bodies and health. The process of analyzing and designing the mobile application is presented, along with all the steps carried out by following a user-centered design methodology.

Keywords: breast cancer, cervical cancer, gynecological mobile application, paper prototyping, storyboard, women health

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Authors: Ramin Mehdiabadi

Abstract:

Background: Breast cancer remains the most prevalent cancer diagnosis and the leading cause of cancer death among women globally, representing 11.7% of new cases and 6.9% of deaths. While the incidence and mortality of major cancers are declining in developed regions like the United States and Western Europe, underdeveloped and developing countries exhibit an increasing trend, attributed to lifestyle factors such as smoking, physical inactivity, and high-calorie diets. Objective: This study explores the intricate relationship between the mammalian transcription factor forkhead box (FoxM1) and the microRNA miR-216b-5p in various subtypes of breast cancer, aiming to deepen the understanding of their roles in tumorigenesis, metastasis, and drug resistance. Methods: Breast cancer subtypes were categorized based on key biomarkers: estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2. These include luminal A, luminal B, HER2 enriched, triple-negative, and normal-like subtypes. We focused on analyzing the expression levels of FoxM1 and miR-216b-5p, given the known role of FoxM1 in cell proliferation and its implications in cancer pathologies such as lung, gastric, and breast cancers. Concurrently, miR-216b-5p's function as a tumor suppressor was evaluated to ascertain its regulatory effects on FoxM1. Results: Preliminary data indicate a nuanced interplay between FoxM1 and miR-216b-5p, suggesting a potential inverse relationship that varies across breast cancer subtypes. This relationship underscores the dual role of these biomarkers in modulating cancer progression and response to treatments. Conclusion: The findings advocate for the potential of miR-216b-5p to serve as a prognostic biomarker and a therapeutic target, particularly in subtypes where FoxM1 is prominently expressed. Understanding these molecular interactions provides crucial insights into the personalized treatment strategies and could lead to more effective therapeutic interventions in breast cancer management. Implications: The study highlights the importance of molecular profiling in breast cancer treatment and emphasizes the need for targeted therapeutic approaches in managing diverse cancer subtypes, particularly in varying global contexts where lifestyle factors significantly impact cancer dynamics.

Keywords: breast cancer, gene expression, FoxM1, microRNA

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Authors: Jaiden X. Schraut

Abstract:

Medical Imaging Segmentation of Chest X-rays is used for the purpose of identification and differentiation of lung cancer, pneumonia, COVID-19, and similar respiratory diseases. Widespread application of computer-supported perception methods into the diagnostic pipeline has been demonstrated to increase prognostic accuracy and aid doctors in efficiently treating patients. Modern models attempt the task of segmentation and classification separately and improve diagnostic efficiency; however, to further enhance this process, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. The proposed model achieves a final Jaccard Index of .9634 for image segmentation and a final accuracy of .9600 for classification on the COVID-19 radiography database.

Keywords: chest X-ray, deep learning, image segmentation, image classification

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Authors: Rahul Agarwal, Ashutosh Singh

Abstract:

Cervical cancer is one of the women related cancers which starts from the pre-cancerous cells and a fraction of women with pre-cancers of the cervix will develop cervical cancer. Cervical pre-cancers if treated in pre-invasive stage can prevent almost all true cervical squamous cell carcinoma. The present study investigates the genes and pathways that are involved in the progression of cervical cancer and are responsible in transition from pre-invasive stage to other advanced invasive stages. The study used GDS3292 microarray data to identify the stage specific genes in cervical cancer and further to generate the network of the significant genes. The microarray data GDS3292 consists of the expression profiling of 10 normal cervices, 7 HSILs and 21 SCCs samples. The study identifies 70 upregulated and 37 downregulated genes in HSIL stage while 95 upregulated and 60 downregulated genes in SCC stages. Biological process including cell communication, signal transduction are highly enriched in both HSIL and SCC stages of cervical cancer. Further, the ppi interaction of genes involved in HSIL and SCC stages helps in identifying the interacting partners. This work may lead to the identification of potential diagnostic biomarker which can be utilized for early stage detection.

Keywords: cervical cancer, HSIL, microarray, SCC

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Authors: Laila Seada, Hanan Oreiby, Fawaz Al Rashid, Ashraf Negm

Abstract:

Background and Objective: In most areas of the world, the incidence of thyroid cancer has been increasing over the last decade, mostly due to a combination of early detection of the neoplasm resulting from sensitive procedures and increased population exposure to radiation and unrecognized carcinogens. Methods: Cases of thyroid cancer have been retrieved from the cancer registry at King Khalid Hospital during the period from August 2012 to April 2016. Age, gender and histopathologic types have been recorded. Results: Thyroid carcinoma ranked as the second most common malignancy in females (25%) after breast cancer (31%). It constituted 20.8% of all newly diagnosed cancer cases. As for males, it ranked the 4^th type of malignancy after gastrointestinal cancer, lymphomas and soft tissue sarcomas. Mean age for females and males was 38.7 +/- 13.2 and 60.25 +/- 11.5 years, respectively, and the difference between the two groups was statistically significant (p value = 0.0001). Fifty-five (82%) were papillary carcinomas including 10 follicular variant of papillary (FVPC), and eight papillary micro carcinomas (PMC) and two tall cell/oncocytic variants. Follicular carcinomas constituted two (3.1%), while two (3.1%) were anaplastic, and two (3.1%) were medullary. Conclusion: Thyroid cancer incidence in Hail is ranking as the 2^nd most common female malignancy similar to other regions in the Kingdom. However, this high incidence contrasts with much lower rates worldwide.

Keywords: thyroid, hail, papillary, microcarcinoma

Procedia PDF Downloads 282

Authors: Esther Friedlander, Philip Friedlander

Abstract:

The use of immunotherapy that inhibits programmed death-1 (PD-1) or its ligand PD-L1 confers survival benefits in patients with non-small cell lung cancer (NSCLC). However, approximately 45% of patients experience primary treatment resistance, necessitating the development of strategies to improve efficacy. While the renin-angiotensin system (RAS) has systemic hemodynamic effects, tissue-specific regulation exists along with modulation of immune activity in part through regulation of myeloid cell activity, leading to the hypothesis that RAS inhibition may improve anti-PD-1/L-1 efficacy. A retrospective analysis was conducted that included 173 advanced solid tumor cancer patients treated at Valley Hospital, a community Hospital in New Jersey, USA, who were treated with a PD-1/L-1 inhibitor in a defined time period showing a statistically significant relationship between RAS pathway inhibition (RASi through concomitant treatment with an ACE inhibitor or angiotensin receptor blocker) and positive efficacy to the immunotherapy that was independent of age, gender and cancer type. Subset analysis revealed strong numerical benefit for efficacy in both patients with squamous and nonsquamous NSCLC as determined by documented clinician assessment of efficacy and by duration of therapy. A PUBMED literature search was now conducted to identify studies assessing the effect of RAS pathway inhibition on anti-PD-1/L1 efficacy in advanced solid tumor patients and compare these findings to those seen in the Valley Hospital retrospective study with a focus on NSCLC specifically. A total of 11 articles were identified assessing the effects of RAS pathway inhibition on the efficacy of checkpoint inhibitor immunotherapy in advanced cancer patients. Of the 11 studies, 10 assessed the effect on survival of RASi in the context of treatment with anti-PD-1/PD-L1, while one assessed the effect on CTLA-4 inhibition. Eight of the studies included patients with NSCLC, while the remaining 2 were specific to genitourinary malignancies. Of the 8 studies, two were specific to NSCLC patients, with the remaining 6 studies including a range of cancer types, of which NSCLC was one. Of these 6 studies, only 2 reported specific survival data for the NSCLC subpopulation. Patient characteristics, multivariate analysis data and efficacy data seen in the 2 NSLCLC specific studies and in the 2 basket studies, which provided data on the NSCLC subpopulation, were compared to that seen in the Valley Hospital retrospective study supporting a broader effect of RASi on anti-PD-1/L1 efficacy in advanced NSLCLC with the majority of studies showing statistically significant benefit or strong statistical trends but with one study demonstrating worsened outcomes. This comparison of studies extends published findings to the community hospital setting and supports prospective assessment through randomized clinical trials of efficacy in NSCLC patients with pharmacodynamic components to determine the effect on immune cell activity in tumors and on the composition of the tumor microenvironment.

Keywords: immunotherapy, cancer, angiotensin, efficacy, PD-1, lung cancer, NSCLC

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