Synthesis of Antimicrobial Norlabdane Compounds with Rearranged Cycle B and Molecular Docking Studies.

The synthesis of tetra- and pentanorlabdane compounds with rearranged cycle B based on commercially available (+)-sclareolide is reported. Desired compounds were prepared from intermediate ketones via Baeyer-Villiger oxidation. The structures of synthesized compounds were confirmed by spectral IR, 1D (H, C, and DEPT), and 2D (H-COSY, H,C-HSQC, H,C-HMBC, H,N-HMBC, NOESY) NMR analyses, mass-spectrometry and single crystal X-rays diffraction.

Multimorbidity in the elderly of an educational program in Brazilian capital: A cross-sectional study.

Population aging generated changes in the epidemiological profile and culminated in a high frequency of diseases. The objective of this study was estimating the prevalence of multimorbidity and the associated variables in the elderly of an educational program in the Federal District of Brazil. This is a cross-sectional study with data collection carried out during the period from December 2022 to April 2023 by means of telephone calls.

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology.

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India.

Tramadol Effects on Brain Activity During Cognitive and Emotional Empathy for Pain: A Randomized Controlled Study.

Pain is perceived not only by personal experience but also vicariously. Pain empathy is the ability to share and understand other's intentions and emotions in their painful conditions, which can be divided into cognitive and emotional empathy. It remains unclear how centrally acting analgesics would modulate brain activity related to pain empathy and which component of pain empathy would be altered by analgesics.

Recent advancements to increase success in assisted reproductive technologies in cattle.

Assisted reproductive technologies (ART) are fundamental for cattle breeding and sustainable food production. Together with genomic selection, these technologies contribute to reducing the generation interval and accelerating genetic progress. In this paper, we discuss advancements in technologies used in the fertility evaluation of breeding animals, and the collection, processing, and preservation of the gametes.

Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.

Introduction: Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states.

Using an AI-based avatar for interviewer training at Children's Advocacy Centers: Proof of Concept.

This proof-of- concept study focused on interviewers' behaviors and perceptions when interacting with a dynamic AI child avatar alleging abuse. Professionals ( = 68) took part in a virtual reality (VR) study in which they questioned an avatar presented as a child victim of sexual or physical abuse. Of interest was how interviewers questioned the avatar, how productive the child avatar was in response, and how interviewers perceived the VR interaction.

Characterizing human postprandial metabolic response using multiway data analysis.

Introduction: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time.

Objectives: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.

Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources.

Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads.

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism.

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics.

Analyzing postprandial metabolomics data using multiway models: a simulation study.

Background: Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data.

A dataset for predicting cloud cover over Europe.

Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections difficult. In this paper, we present the European Cloud Cover dataset, which can be used to learn statistical relations between cloud cover and other environmental variables, to potentially improve future climate projections.

Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study.

The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge.

Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Do Commercial Insect Repellents Provide Protection against the Tick (Acari: Ixodidae)?

is a species of public health interest because it is associated with the transmission of the bacteria that causes Brazilian Spotted Fever (BSF). The use of repellents on humans is a prophylactic measure widely used to provide protection against a series of arthropod vectors, including mosquitoes and ticks. However, in Brazil, the effectiveness of commercial repellents against is little known.

Fucoidan-coated cotton dressing functionalized with biomolecules capped silver nanoparticles (LB-Ag NPs-FN-OCG) for rapid healing therapy of infected wounds.

The colonization of pathogenic microbes poses a significant clinical barrier that hinders the physiological wound-healing process. Addressing this challenge, we developed a novel wound dressing using a modified cotton gauze dressing coated with fucoidan and functionalized with silver nanoparticles (LB-Ag NPs-FN-OCG) for the rapid treatment of infected wounds. Firstly, phytochemical-capped LB-Ag NPs were synthesized and characterized using high performance liquid chromatography (HPLC), transmission electron microscopy (TEM), and zeta potential analysis.

Using machine learning model explanations to identify proteins related to severity of meibomian gland dysfunction.

Meibomian gland dysfunction is the most common cause of dry eye disease and leads to significantly reduced quality of life and social burdens. Because meibomian gland dysfunction results in impaired function of the tear film lipid layer, studying the expression of tear proteins might increase the understanding of the etiology of the condition. Machine learning is able to detect patterns in complex data.

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach.

Background: Automated coaches (eCoach) can help people lead a healthy lifestyle (e.g., reduction of sedentary bouts) with continuous health status monitoring and personalized recommendation generation with artificial intelligence (AI).

CELLULAR, A Cell Autophagy Imaging Dataset.

Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of intracellular material, autophagy is also a critical cellular response to stress, such as nutritional depletion. Conversely, the deregulation of autophagy is linked to several diseases, such as cancer, and hence, autophagy constitutes a potential therapeutic target.

Mucoadhesive Mesoporous Silica Particles as Versatile Carriers for Doxorubicin Delivery in Cancer Therapy.

Due to their structural, morphological, and behavioral characteristics (e.g., large volume and adjustable pore size, wide functionalization possibilities, excellent biocompatibility, stability, and controlled biodegradation, the ability to protect cargoes against premature release and unwanted degradation), mesoporous silica particles (MSPs) are emerging as a promising diagnostic and delivery platform with a key role in the development of next-generation theranostics, nanovaccines, and formulations.

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.

Background: e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICTs) to support health and health-related fields. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments.

Enhancing questioning skills through child avatar chatbot training with feedback.

Training child investigative interviewing skills is a specialized task. Those being trained need opportunities to practice their skills in realistic settings and receive immediate feedback. A key step in ensuring the availability of such opportunities is to develop a dynamic, conversational avatar, using artificial intelligence (AI) technology that can provide implicit and explicit feedback to trainees.