Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.

AAPM Truth-based CT (TrueCT) reconstruction grand challenge.

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction.

Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases.

Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions.

Translational nanorobotics breaking through biological membranes.

In the dynamic realm of translational nanorobotics, the endeavor to develop nanorobots carrying therapeutics in rational applications necessitates a profound understanding of the biological landscape of the human body and its complexity. Within this landscape, biological membranes stand as critical barriers to the successful delivery of therapeutic cargo to the target site. Their crossing is not only a challenge for nanorobotics but also a pivotal criterion for the clinical success of therapeutic-carrying nanorobots.

Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation.

Self-diffusion coefficients, *, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean squared displacements (MSDs) of mobile species. MSDs derived from simulations exhibit statistical noise that causes uncertainty in the resulting estimate of *. An optimal scheme for estimating * minimizes this uncertainty, i.

Lower bounds on trees and unicyclic graphs with respect to the misbalance rodeg index.

The Misbalance Rodeg () index stands out among the 148 discrete Adriatic indices demonstrating considerable predictive capabilities in evaluations carried out by the International Academy of Mathematical Chemistry. This index excels particularly in forecasting both the enthalpy and the standard enthalpy of vaporization for octane isomers. Despite its significant chemical applicability, the index has not been extensively explored in the literature.

Correlation Between Dynamic Contrast-Enhanced CT Imaging Signs and Differentiation Grade and Microvascular Invasion of Hepatocellular Carcinoma.

Objective: This study aimed to investigate how dynamic contrast-enhanced CT imaging signs correlate with the differentiation grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to assess their predictive value for MVI when combined with clinical characteristics.

Methods: We conducted a retrospective analysis of clinical data from 232 patients diagnosed with HCC at our hospital between 2021 and 2022. All patients underwent preoperative enhanced CT scans, laboratory tests, and postoperative pathological examinations.

An empirical study of LLaMA3 quantization: from LLMs to MLLMs.

The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.

Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation System.

Assistive technologies for people with visual impairments (PVI) have made significant advancements, particularly with the integration of artificial intelligence (AI) and real-time sensor technologies. However, current solutions often require PVI to switch between multiple apps and tools for tasks like image recognition, navigation, and obstacle detection, which can hinder a seamless and efficient user experience. In this paper, we present NaviGPT, a high-fidelity prototype that integrates LiDAR-based obstacle detection, vibration feedback, and large language model (LLM) responses to provide a comprehensive and real-time navigation aid for PVI.

Real-time evaluation of an automated computer vision system to monitor pain behavior in older adults.

Regular use of standardized observational tools to assess nonverbal pain behaviors results in improved pain care for older adults with severe dementia. While frequent monitoring of pain behaviors in long-term care (LTC) is constrained by resource limitations, computer vision technology has the potential to mitigate these challenges. A computerized algorithm designed to assess pain behavior in older adults with and without dementia was recently developed and validated using video recordings.

Adjoint method-based Fourier neural operator surrogate solver for wavefront shaping in tunable metasurfaces.

We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations.

Virseqimprover: an integrated pipeline for viral contig error correction, extension, and annotation.

Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated from de novo assembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference. To address this issue, we have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality.

A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates.

Predicting Color Development and Texture Changes in Tomatoes Treated With Hot Water and Exposed to High-Temperature Ethylene Using Support Vector Regression.

This study investigated the impacts of hot water treatment (HWT) at 50°C or 25°C for 5 min and high-temperature ethylene (HTE) exposure at varying temperatures (20°C, 30°C, or 35°C) and durations (24, 48, or 72 h) on the postharvest quality and antioxidant properties of mature green tomatoes (MG). Color changes, physicochemical characteristics, antioxidant compounds, and overall antioxidant ability were assessed. HWT increased β-carotene levels and oxygen radical absorbance capacity (ORAC) while preserving color metrics, despite later HTE exposure.

Feasibility of long-range telesurgical robotic radical gastrectomy in a live porcine model.

Background: Telesurgery has been made increasingly possible with the advancements in robotic surgical platforms and network connectivity. However, long-distance transnational complex robotic surgeries such as gastrectomy have yet to be attempted.

Methods: Multiple transnational network connections by Science Innovation Network (SINET), Japan Gigabit Network (JGN), and Arterial Research and Education Network in Asia-Pacific (ARENA-PAC) were established and tested by multiple surgeons in a dry box model.

Radiation tolerance and biodegradation performance of a marine bacterium Acinetobacter sp. Y9 in radioactive composite oil-contaminated wastewater.

The tolerance and degradation characteristics of a marine oil-degrading strain Acinetobacter sp. Y9 were investigated in the presence of diesel oil and simulated radioactive nuclides (Mn, Co, Ni, Sr, Cs) at varying concentrations, as well as exposure to γ-ray radiation (Co-60). The maximum tolerable concentrations for Coand Ni were found to be 5 mg/l and 25 mg/l, respectively, while the tolerable concentrations for Mn, Sr, and Cs exceeded 400 mg/l, 1000 mg/l, and 1000 mg/l, respectively.

Comparative analysis of regression algorithms for drug response prediction using GDSC dataset.

Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.

Mitochondrial dysfunction is a major cause of thromboinflammation and inflammatory cell death in critical illnesses.

Background: Mitochondria generate the adenosine triphosphate (ATP) necessary for eukaryotic cells, serving as their primary energy suppliers, and contribute to host defense by producing reactive oxygen species. In many critical illnesses, including sepsis, major trauma, and heatstroke, the vicious cycle between activated coagulation and inflammation results in tissue hypoxia-induced mitochondrial dysfunction, and impaired mitochondrial function contributes to thromboinflammation and cell death.

Methods: A computer-based online search was performed using the PubMed and Web of Science databases for published articles concerning sepsis, trauma, critical illnesses, cell death, mitochondria, inflammation, coagulopathy, and organ dysfunction.

Prevalence of processed foods in major US grocery stores.

The offering of grocery stores is a strong driver of consumer decisions. While highly processed foods such as packaged products, processed meat and sweetened soft drinks have been increasingly associated with unhealthy diets, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. GroceryDB, a database with over 50,000 food items sold by Walmart, Target and Whole Foods, shows the degree of processing of food items and potential alternatives in the surrounding food environment.

Efficacy and safety of indocyanine green fluorescence navigation versus conventional laparoscopic hepatectomy for hepatocellular carcinoma: a systematic review and meta-analysis.

Background: Indocyanine green (ICG) fluorescence imaging technology is increasingly widely used in laparoscopic hepatectomy. However, previous studies have produced conflicting results regarding whether it is truly superior to traditional laparoscopic hepatectomy. This study investigated the clinical effect of laparoscopic hepatectomy for hepatocellular carcinoma (HCC) using ICG imaging technology.

Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.

Objectives: This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.

Background: Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons.

Could Empathy Be Taught? The Role of Advanced Technologies to Foster Empathy in Medical Students and Healthcare Professionals: A Systematic Review.

The physician-patient relationship relies mostly on doctors' empathetic abilities to understand and manage patients' emotions, enhancing patient satisfaction and treatment adherence. With the advent of digital technologies in education, innovative empathy training methods such as virtual reality, simulation training systems, mobile apps, and wearable devices, have emerged for teaching empathy. However, there is a gap in the literature regarding the efficacy of these technologies in teaching empathy, the most effective types, and the primary beneficiaries -students or advanced healthcare professionals-.

A combinatory approach of non-chain ring and henon map for image encryption application.

Algebraic structures play a vital role in securing important data. These structures are utilized to construct the non-linear components of block ciphers. Since constructing non-linear components through algebraic structures is crucial for the confusion aspects of encryption schemes, relying solely on these structures can result in limited key spaces.

Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees.

CT images of castings made of ductile iron were analyzed in the paper. On these images, objects can be identified that can be considered as graphite precipitates or indicate the presence of a defect in the casting. Research conducted in this area is described, based on experimental data that allows to determine whether the indicated components present in the casting are graphite precipitation.

Basal forebrain innervation of the amygdala: an anatomical and computational exploration.

Theta oscillations of the mammalian amygdala are associated with processing, encoding and retrieval of aversive memories. In the hippocampus, the power of the network theta oscillation is modulated by basal forebrain (BF) GABAergic projections. Here, we combine anatomical and computational approaches to investigate if similar BF projections to the amygdaloid complex provide an analogous modulation of local network activity.

Hierarchical graph-based integration network for propaganda detection in textual news articles on social media.

During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.