Systemic Inflammatory Response Following Preoperative Chemoradiotherapy Can Affect Oncologic Outcomes in MSI-H/dMMR Rectal Cancer.

Systemic inflammatory response (SIR) is a crucial determinant of disease progression and survival in patients with colorectal cancer. This study investigated the prognostic relevance of changes in the platelet count on survival and the predictive value of changes in the platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) on the pathological tumor response to preoperative chemoradiotherapy (CRT) in patients with microsatellite instability-high (MSI-H) rectal cancer. From 2011 to 2022, data of 46 consecutive patients with MSI-H rectal cancer who were treated with preoperative CRT followed by curative surgery at Kyungpook National University Chilgok Hospital (Daegu, South Korea) were retrospectively analyzed.

A machine learning based framework for IoT devices identification using web traffic.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks.

Robotic and laparoscopic right lobe living donation compared to the open approach: A multicenter study on 1194 donor hepatectomies.

Due to the success of minimally invasive liver surgery, laparoscopic and robotic minimally invasive donor hepatectomies (MIDH) are increasingly performed worldwide. We conducted a retrospective, multicentre, propensity score-matched analysis on right lobe MIDH by comparing the robotic, laparoscopic, and open approaches to assess the feasibility, safety, and early outcomes of MIDHs. From January 2016 until December 2020, 1194 donors underwent a right donor hepatectomy performed with a robotic (n = 92), laparoscopic (n = 306), and open approach (n = 796) at 6 high-volume centers.

Breast Cancer Detection and Prevention Using Machine Learning.

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women.

Abrogation of greater graft failure risk of female-to-male liver transplantation with donors older than 40 years or graft macrosteatosis greater than 5.

Greater graft-failure-risk of female-to-male liver transplantation (LT) is thought to be due to acute decrease in hepatic-estrogen-signaling. Our previous research found evidence that female hepatic-estrogen-signaling decreases after 40 years or with macrosteatosis. Thus, we hypothesized that inferiority of female-to-male LT changes according to donor-age and macrosteatosis.

LI-RADS category is associated with treatment outcomes of small single HCC: surgical resection vs. radiofrequency ablation.

Objectives: To assess whether the Liver Imaging Reporting and Data System (LI-RADS) category is associated with the treatment outcomes of small single hepatocellular carcinoma (HCC) after surgical resection (SR) and radiofrequency ablation (RFA).

Methods: This retrospective study included 357 patients who underwent SR (n = 209) or RFA (n = 148) for a single HCC of ≤ 3 cm between 2014 and 2016. LI-RADS categories were assigned.

Implications of war on the food, beverage, and tobacco industry in South Korea.

The Food, Beverage & Tobacco (F&B) industry is an essential sector in the competitive economy. Procurement of production factors mainly depends on sales forecasting and the supply chain of raw materials. However, the conflict between Russia and Ukraine has jeopardized the global supply chain.

Development of convolutional neural network model for diagnosing osteochondral lesions of the talus using anteroposterior ankle radiographs.

Deep learning is an advanced machine learning technique that is used in several medical fields to diagnose diseases and predict therapeutic outcomes. In this study, using anteroposterior ankle radiographs, we developed a convolutional neural network (CNN) model to diagnose osteochondral lesions of the talus (OLTs) using ankle radiographs as input data. We evaluated whether a CNN model trained on anteroposterior ankle radiographs could help diagnose the presence of OLT.

Mutations as a Prognostic Factor in Patients With Residual Rectal Cancer After Neoadjuvant Chemoradiotherapy.

Background/aim: PIK3CA mediates various cellular processes, such as transformation, tumor initiation and proliferation, and resistance to apoptosis. This study was conducted to identify the clinical significance and prognostic effect of PIK3CA mutations in patients with residual rectal cancer who underwent surgery after neoadjuvant chemoradiotherapy (NACRT).

Patients And Methods: Formalin-fixed and paraffin-embedded surgical specimens were collected from 128 patients between January 2006 and December 2011 and analyzed using real-time polymerase chain reaction for hotspot mutations in exons 9 and 20 of the PIK3CA gene.

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.

Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts.

Assessment of Therapeutic Responses Using a Deep Neural Network Based on F-FDG PET and Blood Inflammatory Markers in Pyogenic Vertebral Osteomyelitis.

This study investigated the usefulness of deep neural network (DNN) models based on F-fluorodeoxyglucose positron emission tomography (FDG-PET) and blood inflammatory markers to assess the therapeutic response in pyogenic vertebral osteomyelitis (PVO). This was a retrospective study with prospectively collected data. Seventy-four patients diagnosed with PVO underwent clinical assessment for therapeutic responses based on clinical features during antibiotic therapy.

Development of convolutional neural network model for diagnosing tear of anterior cruciate ligament using only one knee magnetic resonance image.

Deep learning is an advanced machine learning approach used in diverse areas such as image analysis, bioinformatics, and natural language processing. In the current study, using only one knee magnetic resonance image of each patient, we attempted to develop a convolutional neural network (CNN) to diagnose anterior cruciate ligament (ACL) tear. We retrospectively recruited 164 patients who had knee injury and underwent knee magnetic resonance imaging evaluation.

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier.

Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data.

The role of demographic and academic features in a student performance prediction.

Educational Data Mining is widely used for predicting student's performance. It's a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location.

Prediction of Metachronous Peritoneal Metastases After Radical Surgery for Colon Cancer: A Scoring System Obtained from an International Multicenter Cohort.

Background: Since novel strategies for prevention and treatment of metachronous peritoneal metastases (mPM) are under study, it appears crucial to identify their risk factors. Our aim is to establish the incidence of mPM after surgery for colon cancer (CC) and to build a statistical model to predict the risk of recurrence.

Patients And Methods: Retrospective analysis of consecutive pT3-4 CC operated at five referral centers (2014-2018).

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data.

COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people's perceptions regarding death risk.

Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data.

Background: Deep learning techniques can outperform traditional machine learning techniques and learn from unstructured and perceptual data, such as images and languages. We evaluated whether a convolutional neural network (CNN) model using whole axial brain T2-weighted magnetic resonance (MR) images as input data can help predict motor outcomes of the upper and lower limbs at the chronic stage in stroke patients.

Methods: We collected MR images taken at the early stage of stroke in 1,233 consecutive stroke patients.

Development of convolutional neural network model for diagnosing meniscus tear using magnetic resonance image.

Background: Deep learning (DL) is an advanced machine learning approach used in diverse areas, such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is advantageous for image recognition and classification. In this study, we aimed to develop a CNN to detect meniscal tears and classify tear types using coronal and sagittal magnetic resonance (MR) images of each patient.

PlantMWpIDB: a database for the molecular weight and isoelectric points of the plant proteomes.

The molecular weight and isoelectric point of the proteins are very important parameters that control their subcellular localization and subsequent function. Although the genome sequence data of the plant kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the molecular weight and isoelectric point of the plant proteins and reported them in this database.

Classification of movie reviews using term frequency-inverse document frequency and optimized machine learning algorithms.

The Internet Movie Database (IMDb), being one of the popular online databases for movies and personalities, provides a wide range of movie reviews from millions of users. This provides a diverse and large dataset to analyze users' sentiments about various personalities and movies. Despite being helpful to provide the critique of movies, the reviews on IMDb cannot be read as a whole and requires automated tools to provide insights on the sentiments in such reviews.