Lung cancer: risk factors, management, and prognosis

Lung cancer or lung tumor the most common cause of cancer death in men and second most common in women after breast cancer. Highest rates in North America, Europe, and East Asia, with one third of new cases in China, lower rates in Africa and South Asia. Worldwide in 2012 lung cancer resulted in 1.6 million deaths. Risk factors include smoking, exposure to radon gas, asbestos, second-hand smoke, air pollution, and geneticfactors. Pathogenesis is similar to other cancers, by activation of oncogenes or inactivation of tumor suppressor genes. Two main types of lung cancer are small-cell lung carcinoma(SCLC),and non-small-cell lung carcinoma(NSCLC) Clinical manifestation include coughing, coughing blood, weight loss, weakness, fever or clubbing of the fingernails, hypercalcemia, myasthenia syndrome (muscle weakness), and metastases. Metastatic disease includes weight loss, bone pain and neurological symptoms. Diagnosis mainly by chest radiographs and computed tomography (CT) scans. Lung cancers are classified according to histological type, staging uses TNM (tumor, lymph node and metastases) system. Management depends on cancer specific type, by surgery, radiotherapy and chemotherapy. In the U.S 16.8% survive for at least five years, in England overall five year survival less than 10%.Prevention, cessation of smoking, screening for lung cancer for those long smoking history and between 55 and 80 years. Long term intake of vitamin A,vitamin vitamin D, or vitamin E does not reduce risk of lung cancer. Higher intake of vegetables and fruit tend to lower risk. There is no clear association between diet and lung cancer

Sistim gotong royong dalam masyarakat pedesaan Propinsi Daerah Istimewa Aceh

Sistem gotong-royong merupakan suatu hal yang bertambah penting, apalagi kalau diingat bahwa generasi muda Indonesia nanti akan mengetahui bagaimana mereka dari generasi yang lalu mengembangkan dan membina nilai-nilai luhur yang terpancar dari unsur budaya mereka. Buku ini memuat berbagai informasi yang tumbuh dan berkembang dalam masyarakat tempo dulu mengenai sistim gotong royong dalam masyarakat pedesaan Propinsi Daerah Istimewa Aceh

Shallot Peel (Allium cepa L.) Snack Bar as Immunomodulator for Health Improvement in the Digital Era

The digital era has brought many changes, both positive and negative impacts. The challenges of this digital era cover various sectors, including the health sector. One of the effects is the radiation from electromagnetic waves due to the use of cell phones that can interfere with health in the long term, such as a decrease in the immune system. This study aims to determine the secondary metabolite content of shallot peel extract and the formulation of the snack bar as a nutraceutical product with antioxidant properties. It becomes a solution to increase the immune system or immunomodulator. An evidenced by the phytochemical screening test, which gave positive results. In addition, this snack bar also positively contains essential nutrients, such as carbohydrates, protein, and fat. From the hedonic test, the snack bar formulation has the appearance of a snack bar in general, with solid consistency and brownish-yellow color, a characteristic odor of wheat, and a savory taste with a small amount of sweetness. This snack bar formulation has the potential as an immunomodulator against the decline in the immune system. The tastes of the snack bar are acceptable to the public

A hybrid Artistic Model Using Deepy-Dream Model and Multiple Convolutional Neural Networks Architectures

The significant increase in drug abuse cases prompts developers to investigate techniques that mimic the hallucinations imagined by addicts and abusers, in addition to the increasing demand for the use of decorative images resulting from the use of computer technologies. This research uses Deep Dream and Neural Style Transfer technologies to solve this problem. Despite the significance researches on Deep Dream technology, there are several limitations in existing studies, including image quality and evaluation metrics. We have successfully addressed these issues by improving image quality and diversifying the types of generated images. This enhancement allows for more effective use of Deep Dream in simulating hallucinated images. Moreover, the high-quality generated images can be saved for dataset enlargement, like the augmentation process. Our proposed deepy-dream model combines features from five convolutional neural network architectures: VGG16, VGG19, Inception v3, Inception-ResNet-v2, and Xception. Additionally, we generate Deep Dream images by implementing each architecture as a separate Deep Dream model. We have employed autoencoder Deep Dream model as another method. To evaluate the performance of our models, we utilize normalized cross-correlation and structural similarity indexes as metrics. The values obtained for those two quality measures for our proposed deepy-dream model are 0.1863 and 0.0856, respectively, indicating effective performance. When considering the content image, the metrics yield values of 0.8119 and 0.3097, respectively. Whiefor the style image, the corresponding quality measure values are 0.0007 and 0.0073, respectively

Empathy and inclusivity to people from different cultures

The day men came to exist on this planet they became conscious of their differences when compared to others. Things like colour, language, dialect, ethnicity, tribe, religion, religious denomination, country of origin, culture, tradition, etc. can be factors that divide humanity. At times, such division can cause hatred, discrimination, racism and perhaps cause nations to go into war against one another. On the contrary, qualities like feeling empathy and inclusivity can make an individual to be thoughtful and sensitive towards one another. Through this webinar session with the students of COMM 3090 IIUM, we were able to communicate some Islamic ideas to the students and general audience. Ideas from the team were communicated via the Cyberworld. The findings indicated that schools, students, learning institutions, governments, nations around the globe, plus the United Nations should campaign against racism, marginalization of minorities, sectarian violence, foreign occupation, war, etc. in order to create a better world where peace and harmony prevail and people can live in acceptance of one another

Screening for coping style increases the power of gene expression studies

Background: Individuals of many vertebrate species show different stress coping styles and these have a striking influence on how gene expression shifts in response to a variety of challenges. Principal Findings: This is clearly illustrated by a study in which common carp displaying behavioural predictors of different coping styles (characterised by a proactive, adrenaline-based or a reactive, cortisol-based response) were subjected to inflammatory challenge and specific gene transcripts measured in individual brains. Proactive and reactive fish differed in baseline gene expression and also showed diametrically opposite responses to the challenge for 80% of the genes investigated. Significance: Incorporating coping style as an explanatory variable can account for some the unexplained variation that is common in gene expression studies, can uncover important effects that would otherwise have passed unnoticed and greatly enhances the interpretive value of gene expression data

Algorithmic Fairness and Bias in Machine Learning Systems

In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairness-aware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity

PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity

Peripheral Immune Cell Gene Expression Predicts Survival of Patients with Non-Small Cell Lung Cancer

Prediction of cancer recurrence in patients with non-small cell lung cancer (NSCLC) currently relies on the assessment of clinical characteristics including age, tumor stage, and smoking history. A better prediction of early stage cancer patients with poorer survival and late stage patients with better survival is needed to design patient-tailored treatment protocols. We analyzed gene expression in RNA from peripheral blood mononuclear cells (PBMC) of NSCLC patients to identify signatures predictive of overall patient survival. We find that PBMC gene expression patterns from NSCLC patients, like patterns from tumors, have information predictive of patient outcomes. We identify and validate a 26 gene prognostic panel that is independent of clinical stage. Many additional prognostic genes are specific to myeloid cells and are more highly expressed in patients with shorter survival. We also observe that significant numbers of prognostic genes change expression levels in PBMC collected after tumor resection. These post-surgery gene expression profiles may provide a means to re-evaluate prognosis over time. These studies further suggest that patient outcomes are not solely determined by tumor gene expression profiles but can also be influenced by the immune response as reflected in peripheral immune cells

Seasonal variations in carbon, nitrogen and phosphorus concentrations and C:N:P stoichiometry in different organs of a Larix principis-rupprechtii Mayr. plantation in the Qinling Mountains, China

Understanding how concentrations of elements and their stoichiometry change with plant growth and age is critical for predicting plant community responses to environmental change. Weusedlong-term field experiments to explore how the leaf, stem and root carbon (C), nitrogen (N) and phosphorous (P) concentrations and their stoichiometry changed with growth and stand age in a L.principis-rupprechtii Mayr. plantation from 2012–2015 in the Qinling Mountains, China. Our results showed that the C, N and P concentrations and stoichiometric ratios in different tissues of larch stands were affected by stand age, organ type andsampling month and displayed multiple correlations with increased stand age in different growing seasons. Generally, leaf C and N concentrations were greatest in the fast-growing season, but leaf P concentrations were greatest in the early growing season. However, no clear seasonal tendencies in the stem and root C, N and P concentrations were observed with growth. In contrast to N and P, few differences were found in organ-specific C concentrations. Leaf N:P was greatest in the fast-growing season, while C:N and C:P were greatest in the late-growing season. No clear variations were observed in stem and root C:N, C:P andN:Pthroughout the entire growing season, but leaf N:P was less than 14, suggesting that the growth of larch stands was limited by N in our study region. Compared to global plant element concentrations and stoichiometry, the leaves of larch stands had higher C, P, C:NandC:PbutlowerNandN:P,andtherootshadgreater PandC:NbutlowerN,C:Pand N:P. Our study provides baseline information for describing the changes in nutritional elements with plant growth, which will facilitates plantation forest management and restoration, and makes avaluable contribution to the global data pool on leaf nutrition and stoichiometry