Predicting Active Surveillance Failure for Patients with Prostate Cancer in the Magnetic Resonance Imaging Era: A Multicentre Transatlantic Cohort Study.

Background And Objective: Magnetic resonance imaging (MRI)-driven active surveillance (AS) is increasingly used for management of prostate cancer (PC). The aim of our study was to determine the oncological safety of contemporary MRI-driven AS and identify patients at higher risk of AS failure.

Methods: This retrospective cohort study included AS patients with MRI-localised PC from three US and UK centres.

Monitoring and timing of delivery in suspected late fetal growth restriction at term.

Background: Infants affected by late fetal growth restriction are at risk of adverse perinatal outcomes, either due to placental insufficiency or the consequences of late iatrogenic prematurity. There is no randomized trial reporting the optimal timing of delivery, and recommending delivery at 37 weeks can cause harm. Antenatal ultrasound parameters can be used to risk-stratify these pregnancies into low-risk cases, which are associated with a lower likelihood of adverse outcomes and are thus suitable for full-term delivery, and high-risk cases, which may benefit from early-term delivery.

Post-COVID-19 Condition Prediction in Hospitalised Cancer Patients: A Machine Learning-Based Approach.

Background: The COVID-19 pandemic has led to widespread long-term complications, known as post-COVID conditions (PCC), particularly affecting vulnerable populations such as cancer patients. This study aims to predict the incidence of PCC in hospitalised cancer patients using the data from a longitudinal cohort study conducted in four major university hospitals in Moscow, Russia.

Methods: Clinical data have been collected during the acute phase and follow-ups at 6 and 12 months post-discharge.

Novel efficient reservoir computing methodologies for regular and irregular time series classification.

Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. It goes without saying that analyzing time series data holds the key to gaining insight into our day-to-day observations. Among the vast spectrum of time series analysis, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection.

Evaluating the Effect of Climate on Viral Respiratory Diseases Among Children Using AI.

: Respiratory viral infections (RVIs) exhibit seasonal patterns influenced by biological, ecological, and climatic factors. Weather variables such as temperature, humidity, and wind impact the transmission of droplet-borne viruses, potentially affecting disease severity. However, the role of climate in predicting complications in pediatric RVIs remains unclear, particularly in the context of climate-change-driven extreme weather events.

Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.

Background: The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers.

Emergent digital bio-computation through spatial diffusion and engineered bacteria.

Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here we present work on the design of bacterial computers using spatial patterning to process information in the form of diffusible morphogen-like signals. We demonstrate, mathematically and experimentally, that single, modular, colonies can perform simple digital logic, and that complex functions can be built by combining multiple colonies, removing the need for further genetic engineering.

Prediction of spontaneous preterm birth in women with previous full dilatation cesarean delivery.

Background: A previous term (≥37 weeks' gestation), full-dilatation cesarean delivery is associated with an increased risk for a subsequent spontaneous preterm birth. The mechanism is unknown. We hypothesized that the cesarean delivery scar characteristics and scar position relative to the internal cervical os may compromise cervical function, thereby leading to shortening of the cervical length and spontaneous preterm birth.

Technology of the photobiostimulation of the brain's drainage system during sleep for improvement of learning and memory in male mice.

In this study on healthy male mice using confocal imaging of dye spreading in the brain and its further accumulation in the peripheral lymphatics, we demonstrate stronger effects of photobiomodulation (PBM) on the brain's drainage system in sleeping vs. awake animals. Using the Pavlovian instrumental transfer probe and the 2-objects-location test, we found that the 10-day course of PBM during sleep vs.

Situation-Based Neuromorphic Memory in Spiking Neuron-Astrocyte Network.

Mammalian brains operate in very special surroundings: to survive they have to react quickly and effectively to the pool of stimuli patterns previously recognized as danger. Many learning tasks often encountered by living organisms involve a specific set-up centered around a relatively small set of patterns presented in a particular environment. For example, at a party, people recognize friends immediately, without deep analysis, just by seeing a fragment of their clothes.

An efficient reduced-lattice model of IPR for probing Ca dynamics.

Numerous cellular processes are regulated by Ca signals, and the endoplasmic reticulum (ER) membrane's inositol triphosphate receptor (IPR) is critical for modulating intracellular Ca dynamics. The IPRs are seen to be clustered in a variety of cell types. The combination of IPRs clustering and IPRs-mediated Ca-induced Ca release results in the hierarchical organization of the Ca signals, which challenges the numerical simulation given the multiple spatial and temporal scales that must be covered.

Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance.

Unlabelled: The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear.

Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance.

Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease.

Serum biomarker-based early detection of pancreatic ductal adenocarcinomas with ensemble learning.

Background: Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough.

Methods: The primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis.

An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation.

The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level.

A mathematical model of in vitro hepatocellular cholesterol and lipoprotein metabolism for hyperlipidemia therapy.

Cardiovascular diseases associated with high cholesterol (hypercholesterolemia) and low-density lipoproteins (LDL) levels are significant contributors to total mortality in developing and developed countries. Mathematical modeling of LDL metabolism is an important step in the development of drugs for hypercholesterolemia. The aim of this work was to develop and to analyze an integrated mathematical model of cholesterol metabolism in liver cells and its interaction with two types of drugs, statins and PCSK9 inhibitors.

Unraveling Aβ-Mediated Multi-Pathway Calcium Dynamics in Astrocytes: Implications for Alzheimer's Disease Treatment From Simulations.

The accumulation of amyloid β peptide (Aβ) in the brain is hypothesized to be the major factor driving Alzheimer's disease (AD) pathogenesis. Mounting evidence suggests that astrocytes are the primary target of Aβ neurotoxicity. Aβ is known to interfere with multiple calcium fluxes, thus disrupting the calcium homeostasis regulation of astrocytes, which are likely to produce calcium oscillations.