Colon Cancer

Colorectal cancers (CRCs) are commonly diagnosed malignancy in both men and women. Although it is a common disease, mortality rates decrease with widespread use of screening methods and novel developments in surgery. Physical examination, abdomen and pelvic computerized tomography, and chest imaging are necessary for preoperative staging and surgical planning of a newly diagnosed colon cancer. CRCs usually develop from adenomatous polyps. Although curative treatment of localized colon cancer is surgery, endoscopic polypectomy is sufficient when severe dysplasia or carcinoma in situ is detected on a polyp surface. Total mesorectal excision and neoadjuvant chemoradiotherapy in rectum cancers resulted in significant reductions in morbidity, mortality, and recurrence rates. Recently, complete mesocolic excision and central vascular ligation method has been described in the surgical treatment of colon cancer to achieve similar results. Unfortunately, metastatic colon cancer rate at presentation is approximately 20%. Surgery is a potentially curative option in selected patients with liver and lung metastasis. Pathologic stage of the tumor at presentation is the most important prognostic factor after resection. Therefore, early diagnosis of colon cancer by screening methods and new surgical techniques will lead to better results in survival rates

Binary Feature Mask Optimization for Feature Selection

We investigate feature selection problem for generic machine learning (ML) models. We introduce a novel framework that selects features considering the predictions of the model. Our framework innovates by using a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same ML model during feature selection, unlike other feature selection methods where we need to train the ML model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the ML model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, no study has introduced a training-free framework for a generic ML model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and Multi-Layer Perceptron as our ML models. Additionally, we openly share the implementation code for our methods to encourage the research and the contributions in this area

Post COVID-19 irritable bowel syndrome

Objectives: The long-term consequences of COVID-19 infection on the gastrointestinal tract remain unclear. Here, we aimed to evaluate the prevalence of gastrointestinal symptoms and post-COVID-19 disorders of gut-brain interaction after hospitalisation for SARS-CoV-2 infection. Design: GI-COVID-19 is a prospective, multicentre, controlled study. Patients with and without COVID-19 diagnosis were evaluated on hospital admission and after 1, 6 and 12 months post hospitalisation. Gastrointestinal symptoms, anxiety and depression were assessed using validated questionnaires. Results: The study included 2183 hospitalised patients. The primary analysis included a total of 883 patients (614 patients with COVID-19 and 269 controls) due to the exclusion of patients with pre-existing gastrointestinal symptoms and/or surgery. At enrolment, gastrointestinal symptoms were more frequent among patients with COVID-19 than in the control group (59.3% vs 39.7%, p<0.001). At the 12-month follow-up, constipation and hard stools were significantly more prevalent in controls than in patients with COVID-19 (16% vs 9.6%, p=0.019 and 17.7% vs 10.9%, p=0.011, respectively). Compared with controls, patients with COVID-19 reported higher rates of irritable bowel syndrome (IBS) according to Rome IV criteria: 0.5% versus 3.2%, p=0.045. Factors significantly associated with IBS diagnosis included history of allergies, chronic intake of proton pump inhibitors and presence of dyspnoea. At the 6-month follow-up, the rate of patients with COVID-19 fulfilling the criteria for depression was higher than among controls. Conclusion: Compared with controls, hospitalised patients with COVID-19 had fewer problems of constipation and hard stools at 12 months after acute infection. Patients with COVID-19 had significantly higher rates of IBS than controls. Trial registration number: NCT04691895

The genetic architecture of membranous nephropathy and its potential to improve non-invasive diagnosis

Membranous Nephropathy (MN) is a rare autoimmune cause of kidney failure. Here we report a genome-wide association study (GWAS) for primary MN in 3,782 cases and 9,038 controls of East Asian and European ancestries. We discover two previously unreported loci, NFKB1 (rs230540, OR = 1.25, P = 3.4 × 10-12) and IRF4 (rs9405192, OR = 1.29, P = 1.4 × 10-14), fine-map the PLA2R1 locus (rs17831251, OR = 2.25, P = 4.7 × 10-103) and report ancestry-specific effects of three classical HLA alleles: DRB1*1501 in East Asians (OR = 3.81, P = 2.0 × 10-49), DQA1*0501 in Europeans (OR = 2.88, P = 5.7 × 10-93), and DRB1*0301 in both ethnicities (OR = 3.50, P = 9.2 × 10-23 and OR = 3.39, P = 5.2 × 10-82, respectively). GWAS loci explain 32% of disease risk in East Asians and 25% in Europeans, and correctly re-classify 20-37% of the cases in validation cohorts that are antibody-negative by the serum anti-PLA2R ELISA diagnostic test. Our findings highlight an unusual genetic architecture of MN, with four loci and their interactions accounting for nearly one-third of the disease risk

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Pre-drying of 2-Phase Olive Pomace by Drum Dryer to Improve Processability

WOS: 000557737200001In this study, the minimum quality loss of 2-phase olive pomace and the maximum system's energy efficiency is targeted during pre-drying of olive pomace in a drum dryer. Vapor pressure and valse rotational speed were selected as the independent variables of drum dryer. For each vapor pressure value (1, 2, 3, 3.5 and 4 bar), drying of 2-phase olive pomace was performed at different valse rotational speeds (0.5, 1, 2, 3, 4.5 and 6 rpm). Drum dryer conditions were optimized with desirability function approach by targeting the moisture content range as 30-50%, minimum peroxide value and maximum specific moisture extraction rate (SMER). the optimum drum dryer conditions were determined as 3.27 bar for vapor pressure and 6 rpm for valse rotational speed. Moreover, the effects of vapor pressure and valse rotational speed on the physical and chemical properties of pre-dried 2-phase olive pomace and the system's energy efficiency were examined. [GRAPHICS] .Council of Scientific Research ProjectsEge University [16-MUH-024]The authors acknowledge Ege University, Council of Scientific Research Projects (Project Number: 16-MUH-024) for financial support

Investigation of drying conditions to valorize 2-phase olive pomace in further processing

WOS: 000541983200001Pre-dried 2-phase olive pomace was dried until 8% moisture content of it by tray dryer. the effect of tray dryer's process conditions in terms of drying temperature (70-90 degrees C), air velocity (0.5-1.8 m/s), and sample thickness (0.5-1.5) on the quality of olive pomace and system energy efficiency were investigated. Moreover, tray dryer process parameters were optimized considering of minimum drying time, minimum quality loss of olive pomace, and maximum system energy efficiency. the optimum tray dryer conditions were 85.2 degrees C of drying temperature, 1.72 m/s of air velocity, and 0.5 cm of sample thickness as specified by using the Design Expert packaged software. the effects of drying temperature, air velocity, and sample thickness on the physical and chemical properties of dried 2-phase olive pomace (free fatty acid and specific absorption value [K-232, K-270]) and the system's energy efficiency (in terms of the moisture extraction rate and specific energy consumption) were examined.Council of Scientific Research ProjectsEge University [16-MUH-024]The authors acknowledge Ege University, Council of Scientific Research Projects (Project Number: 16-MUH-024) for financial support

Evaluation of the relationship between health belief of breast cancer screening and health anxiety; A cross-sectional study

Breast cancer (BC) is the most frequent type of cancer among women. Screening and early diagnosis is crucial for reducing the disease burden. However the screening rates for BC is not at desired levels. Health belief and health anxiety are two conditions that affect participation in cancer screening. The aim of this study is to explore the relationship between health beliefs regarding breast cancer screening and health anxiety among women. This cross-sectional study included 301 women between 20 and 69 years of age who were admitted to the family medicine outpatient clinic. The study data was collected using the Health Anxiety Inventory (HAI) and Champions Health Belief Model Scale (CHBMS). The questionnaires were filled with face-to-face interview technique. To explain the relationship between anxiety and the components of the health belief model a multivariate linear regression model was used. High anxiety levels were positively correlated with the seriousness and health motivation components and negatively correlated with the self-efficacy component of the health belief model related to breast cancer (p [Med-Science 2019; 8(2.000): 343-8

Dynamic optimization of long term primary electric distribution network investments based on planning metrics

This paper presents methodologies of dynamic planning algorithms which are developed for optimizing long-term primary electric distribution network investments taking into account some planning metrics. First, an algorithm which calculates a representative primary network model of distribution grids whose primary and secondary networks are intricate is developed. It is aimed to facilitate assessment of primary distribution network investment requirements and thereby defining grid investment candidates effectively by this reduced network representation. Then, a planning algorithm, which considers the representative network model and candidate investments as inputs, is developed based on a mixed integer programming (MIP) technique. Some planning metrics are defined in order to assess optimum investment solutions technically and economically, which are determined by this planning algorithm among the candidate investments along the planning horizon (e.g., 10 year). It is aimed to assess rationality of the investments through these planning metrics. DIgSILENT PowerFactory (PF) software is utilized in technical analysis to assess impacts of candidate grid investments on technical constraints. The algorithms and planning metrics developed in the study are tested satisfactorily on pilot regions of Akdeniz Electric Distribution Company in Turkey