Bacteriophages, Antibiotics and Probiotics: Exploring the Microbial Battlefield of Colorectal Cancer.

Colorectal cancer (CRC), a prevalent malignancy, is a significant global health concern. The intricate interplay of genetic mutations, inflammatory processes, and environmental factors underscores the complexity of CRC's etiology. The human gut harbors a diverse microbial community that plays a key role in maintaining homeostasis and influencing various aspects of host physiology.

Cardiotoxic Effects of HER2-Targeted Therapies: Insights From a Retrospective Study at a Romanian Oncology Center.

Cardiotoxicity remains a significant concern for patients undergoing HER2-targeted therapies for HER2-positive breast cancer. While trastuzumab and pertuzumab have dramatically improved survival outcomes, their impact on cardiovascular health underscores the need for comprehensive risk assessment and preventive strategies.  Methods: This retrospective study evaluates the incidence and risk factors associated with cardiotoxicity in 45 female patients treated with trastuzumab and/or pertuzumab at the Institute of Oncology Bucharest from 2018 to 2022.

Enhancing Prognosis in Advanced Ovarian Cancer: Primary Cytoreductive Surgery and Adjuvant Chemotherapy or Neoadjuvant Chemotherapy and Interval Cytoreduction-A Single-Center Retrospective Observational Study.

Advanced-stage ovarian cancer presents a significant therapeutic challenge, with primary cytoreductive surgery (PCS) followed by chemotherapy and neoadjuvant chemotherapy (NACT) with interval debulking surgery (IDS) as the two main treatment modalities. This study aims to compare the clinical outcomes, surgical complexity, and survival rates between these approaches and to assess the impact of molecular markers such as BRCA and HRD status. This retrospective, single-center observational study included 100 patients diagnosed with stage III-IV high-grade serous ovarian cancer.

Sample-Specific MS/MS Methods in High-Throughput Mass Spectrometry.

The drug discovery process increasingly relies on high-throughput sample analysis to accelerate the identification of viable drug candidates. Recently, chromatographic-free high-throughput mass spectrometry (HT-MS) technologies have emerged, significantly increasing sample readout speed and enabling the analysis of large sample sets. These HT-MS platforms continuously acquire data from various samples into a single data file, presenting challenges in applying distinctive data acquisition methods to specific samples.

Large-Scale Study on AI's Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging.

Rationale And Objectives: Given the high volume of chest radiographs, radiologists frequently encounter heavy workloads. In outpatient imaging, a substantial portion of chest radiographs show no actionable findings. Automatically identifying these cases could improve efficiency by facilitating shorter reading workflows.

Tissue Interleukin-33: A Novel Potential Regulator of Innate Immunity and Biomarker of Disease Severity in Chronic Rhinosinusitis with Nasal Polyps.

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a disease of real interest for researchers due to its heterogenicity and complex pathophysiological mechanisms. Identification of the factors that ensure success after treatment represents one of the main challenges in CRSwNP research. No consensus in this direction has been reached so far.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients.

Contrastive self-supervised learning from 100 million medical images with optional supervision.

Purpose: Building accurate and robust artificial intelligence systems for medical image assessment requires the creation of large sets of annotated training examples. However, constructing such datasets is very costly due to the complex nature of annotation tasks, which often require expert knowledge (e.g.

Modulating the Fatty Acid Profiles of Larvae Fats by Dietary Enrichment with Different Oilseeds: A Sustainable Way for Future Use in Feed and Food.

Edible insects such as the black soldier fly Hermetia illucens L. represent a potential and sustainable source of nutrients for food and feed due to their valuable nutritional composition, which can be modulated through dietary enrichment. The high content of saturated fatty acid (FA) of Hermetia illucens larvae fats can be modulated through dietary enrichment as a result of adding vegetable oils in the rearing substrate.

Particularities of the (L.) (Diptera: Stratiomyidae) Ovipositing Behavior: Practical Applications.

The industrial rearing of Hermetia illucens offers sustainable solutions to the acute challenges of modern society associated with the accumulation of increasing amounts of organic waste, the substantial reduction of natural ocean fish stocks, and the imminent food crisis. Detailed knowledge of the reproductive particularities and reproductive behavior of the species is essential for increasing the efficiency of the breeding technology. This study aimed to identify the affinity shown by females regarding the size of the oviposition slots (1, 2, 3, 4 and 5 mm), the vertical distribution of the ovipositing rate, and the influence of the substrate’s moisture on ovipositing behavior (dry matter/water: 1:0.

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Purpose: To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools.

Materials And Methods: This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; = 25 946) and evaluation ( = 2947) and three "external" centers for calibration ( = 400) and evaluation ( = 16764).

Biochemical and Metabolical Pathways Associated with Microbiota-Derived Butyrate in Colorectal Cancer and Omega-3 Fatty Acids Implications: A Narrative Review.

Knowledge regarding the influence of the microbial community in cancer promotion or protection has expanded even more through the study of bacterial metabolic products and how they can modulate cancer risk, which represents an extremely challenging approach for the relationship between intestinal microbiota and colorectal cancer (CRC). This review discusses research progress on the effect of bacterial dysbiosis from a metabolic point of view, particularly on the biochemical mechanisms of butyrate, one of the main short chain fatty acids (SCFAs) with anti-inflammatory and anti-tumor properties in CRC. Increased daily intake of omega-3 polyunsaturated fatty acids (PUFAs) significantly increases the density of bacteria that are known to produce butyrate.

A Comprehensive Method for the Evaluation of Egg Quality Parameters: Implications and Influence Factors.

The significant momentum received by as a worldwide species is due to its biological traits and large applicability in scientific research, environmental entomoremediation, insect meal production, and for biodiesel yield. The aim of this research is to develop a method for the preparation and precise egg counting of the egg clutch, as well as an accurate technique for evaluating egg biometric parameters. The precise proposed method for egg preparation and counting consists in dispersing the eggs clutch under a stereo microscope and counting the eggs on a photographic capture using the Clickmaster software.

Robust classification from noisy labels: Integrating additional knowledge for chest radiography abnormality assessment.

Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort.

Objectives: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs.

Methods: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

Materials And Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning.

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets.

Quantifying and leveraging predictive uncertainty for medical image assessment.

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance.

No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks.

Detecting malignant pulmonary nodules at an early stage can allow medical interventions which may increase the survival rate of lung cancer patients. Using computer vision techniques to detect nodules can improve the sensitivity and the speed of interpreting chest CT for lung cancer screening. Many studies have used CNNs to detect nodule candidates.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

Materials And Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning.

Lipidomics biomarkers in women with polycystic ovary syndrome (PCOS) using ultra-high performance liquid chromatography-quadrupole time of flight electrospray in a positive ionization mode mass spectrometry.

Polycystic ovary syndrome (PCOS), characterized by oligo-anovulation and androgen excess is considered a high-risk condition for metabolic disorders. Herein, untargeted metabolomics analysis was applied to women with PCOS, aiming to provide deeper insights into lipidomics biomarkers signature of PCOS, for better diagnosis and management. This was a cross-sectional study in which 15 Caucasian women with PCOS and 15 Caucasian healthy, age-matched women were enrolled.

Combo loss: Handling input and output imbalance in multi-organ segmentation.

Simultaneous segmentation of multiple organs from different medical imaging modalities is a crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery, and therapy planning. Thanks to the recent advances in deep learning, several deep neural networks for medical image segmentation have been introduced successfully for this purpose. In this paper, we focus on learning a deep multi-organ segmentation network that labels voxels.

Metabolomics-A Promising Approach to Pituitary Adenomas.

Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA).