Harnessing CRISPR interference to resensitize laboratory strains and clinical isolates to last resort antibiotics.

The global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based antimicrobials were proposed to address killing specificity with encouraging results. However, the emergence of target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing the risk of generating new antibiotic-resistance gene (ARG) variants.

Role of gliflozins on hepatocellular carcinoma progression: a systematic synthesis of preclinical and clinical evidence.

Introduction: The risk of HCC is twice as high in diabetic patients compared to non-diabetic ones, suggesting that diabetes advances carcinogenesis in the liver through a variety of mechanisms. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to improve liver outcomes, emerging as promising agents to treat hepatocellular carcinoma (HCC) in patients with type 2 diabetes mellitus (T2DM).

Methods: We searched PubMed and Scopus databases for articles presenting an association between SGLT2is and HCC to explore the putative mechanisms of action underlying the anti-proliferative activity of SGLT2is.

The importance of organic matter in controlling the metal variability and mobility in seagrass sediments.

Metal contamination in seagrass beds has been extensively studied in the past decades. Most of earlier studies have focused on reporting metal concentration in different compartments of seagrass ecosystem, with little attention given to the role of sediment organic matter in controlling the metal mobility and bioavailability. This study investigated metal contamination in seagrass sediments in Hainan Island, China and illustrated how various geochemical factors impact the spatial variability of the metal concentrations.

An integrated view of the pathophysiological crosstalk between adipose tissue, bone and cardiovascular system in men and women.

Background: Obesity, bone-related and cardiovascular diseases (CVD) are among the leading global health concerns. Growing evidence suggests that these conditions share common pathophysiological pathways and disease outcomes. PATHOGENETIC INTERACTIONS OF OBESITY, CVD AND BONE-RELATED DISEASES: Obesity is a well-established risk factor for atherosclerotic CVD (ASCVD), as dysfunctional ectopic adipose tissue may produce endocrine/paracrine hormones modulating metabolic processes and inflammation, predisposing to ASCVD.

Unravelling molecular mechanisms in atherosclerosis using cellular models and omics technologies.

Despite the discovery and prevalent clinical use of potent lipid-lowering therapies, including statins and PCSK9 inhibitors, cardiovascular diseases (CVD) caused by atherosclerosis remain a large unmet clinical need, accounting for frequent deaths worldwide. The pathogenesis of atherosclerosis is a complex process underlying the presence of modifiable and non-modifiable risk factors affecting several cell types including endothelial cells (ECs), monocytes/macrophages, smooth muscle cells (SMCs) and T cells. Heterogeneous composition of the plaque and its morphology could lead to rupture or erosion causing thrombosis, even a sudden death.

Web discussions on cardiovascular diseases: pre-COVID-19 evaluation and impact of the COVID-19 pandemic - Web listening analysis in the Italian population.

Background: Web discussions on health issues are becoming very relevant in the general public. In this context, little information is available regarding cardiovascular diseases, which remain the first cause of morbidity, disability and mortality worldwide. The central objective of the study was to conduct a Web listening analysis on discussions about cardiovascular diseases in Italy, comparing the data relative to the 2-year pre-COVID-19 pandemic period with those collected during the COVID-19 pandemic lockdown (March-July 2020), with quantification of conversations on cardiovascular disease and Web-based discussions and specific evaluation of COVID-19 lockdown impact.

Published questionnaires and clinical observation surveys on traumatic sheep myiasis: optimizing survey design and the integration of these two survey methods.

Questionnaires and clinical observations are significant components of human and veterinary epidemiology surveys, providing a comprehensive prognosis of the occurrence and prevalence of diseases. The information compiled by these two survey methods is equally important for establishing an epidemiological surveillance system for disease outbreak management. This review summarizes 57 previous surveys, including questionnaires and clinical observations on sheep myiasis globally from 1976 to 2023, with an emphasis on their methodologies and areas of findings.

Predicting Tumor Volume Doubling Time and Progression-Free Survival in Untreated Patients from Patient-Derived-Xenograft (PDX) Models: A Translational Model-Based Approach.

Tumor volume doubling time (TVDT) has been shown to be a potential surrogate marker of biological tumor activity. However, its availability in clinics is strongly limited due to ethical and practical reasons, as its assessment requires at least two subsequent tumor volume measurements in untreated patients. Here, a translational modeling framework to predict TVDT distributions in untreated cancer patient populations from tumor growth data in patient-derived xenograft (PDX) mice is proposed.

Comparative Toxicological Analyses of Traditional Matrices and Blow Fly Larvae in Four Cases of Highly Decomposed Human Cadavers.

In forensic investigation, determining the time and cause of death becomes challenging, especially in cases where the remains are found in advanced decomposition, rendering traditional toxicological samples unavailable or unreliable. Entomotoxicology, an emerging methodology within forensic science, leverages insect specimens collected from cadavers as alternative toxicological samples. Several laboratory and field research studies have highlighted the efficacy in detecting various drugs, toxins, and elements absorbed by insects feeding on cadaveric tissues, even at low concentrations.

Model-Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing.

Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize risks in each patient, is essential for drugs with a narrow therapeutic window and severe adverse effects. Adaptive dosing strategies extend the precision dosing concept to time-varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision-making process where clinicians adapt dose administration based on patient response and evolution monitoring.

Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks.

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs).

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

Background: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating.

Variability of soft-bottom macrobenthic invertebrates at different spatial scales: Comparisons between habitats and seasons.

Studies focusing on patterns of spatial variation in marine soft-bottom assemblages suggest that variability is mainly concentrated at small spatial scale (from tens of centimeters to few meters), but there is still a lack of knowledge about the consistency of this spatial pattern across habitats and seasons. To address this issue, we quantified the variability in the structure of macrozoobenthic assemblages and in the abundance of dominant macroinvertebrate species in the Mellah Lagoon (Algeria) at three spatial scales, i.e.

Transferability of Human and Environmental Microbiome on Clothes as a Tool for Forensic Investigations.

Considering the growing importance of microbiome analyses in forensics for identifying individuals, this study explores the transfer of the skin microbiome onto clothing, its persistence on fabrics over time, and its transferability from the environment and between different garments. Furthermore, this project compares three specific QIAGEN microbiome extraction kits to test their extraction efficiency on fabric samples. Additionally, this study aims to check if these extracts contain human DNA, providing a chance to obtain more information from the same evidence for personal identification.

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality.

Morphological changes of larvae and pupae of Lucilia sericata (Diptera: Calliphoridae) reared at two temperatures and on three food types.

Determining the minimum postmortem interval (minPMI) from an entomological perspective relies mainly on development data recorded for various species of flies collected from a crime scene or suspicious death. This study focused on the larval and pupal development of Lucilia sericata (Meigen), with an emphasis on the changes of the external morphology of the puparium and its pupal content throughout the duration of metamorphosis. Colonies of L.

Reinforcement Learning and PK-PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma.

The integration of pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK-PD models into a reinforcement learning (RL) algorithm, Q-learning (QL), to personalize pharmacological treatment.

Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges.

Atherosclerotic disease is a major cause of acute cardiovascular events. A deeper understanding of its underlying mechanisms will allow advancing personalized and patient-centered healthcare. Transcriptomic research has proven to be a powerful tool for unravelling the complex molecular pathways that drive atherosclerosis.

Insulin-like Growth Factor 1, Growth Hormone, and Anti-Müllerian Hormone Receptors Are Differentially Expressed during GnRH Neuron Development.

Gonadotropin-releasing hormone (GnRH) neurons are key neuroendocrine cells in the brain as they control reproduction by regulating hypothalamic-pituitary-gonadal axis function. In this context, anti-Müllerian hormone (AMH), growth hormone (GH), and insulin-like growth factor 1 (IGF1) were shown to improve GnRH neuron migration and function in vitro. Whether AMH, GH, and IGF1 signaling pathways participate in the development and function of GnRH neurons in vivo is, however, currently still unknown.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery.