NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.

Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer (NSSC), a hybrid AI framework that integrates neurosymbolic methods with named entity recognition (NER) and entity linking (EL) to transform unstructured clinical notes into structured terms using medical vocabularies, with the Unified Medical Language System (UMLS) as a case study. NSSC was evaluated on a dataset of clinical notes from breast cancer patients, demonstrating significant improvements in the accuracy of both entity recognition and linking compared to state-of-the-art models.

Transformers for extracting breast cancer information from Spanish clinical narratives.

The wide adoption of electronic health records (EHRs) offers immense potential as a source of support for clinical research. However, previous studies focused on extracting only a limited set of medical concepts to support information extraction in the cancer domain for the Spanish language. Building on the success of deep learning for processing natural language texts, this paper proposes a transformer-based approach to extract named entities from breast cancer clinical notes written in Spanish and compares several language models.

[Experience in Obstructive Hyperthrophic Myocardiopathy Surgery at a National Reference Center].

Objectives: To identify the clinical characteristics, analyze the results and show the efficacy of surgical treatment of hypertrophic obstructive cardiomyopathy (HOC), in a national reference institute.

Methods: A descriptive, retrospective, case series of patients with the diagnosis of HOC operated at the National Cardiovascular Institute, was performed between December 2016 and January 2019. We analyzed the postoperative evolution of symptomatology, functional class (FC), left ventricular outflow tract gradient (LVOTG) and mitral regurgitation (MR).

First Star-Forming Structures in Fuzzy Cosmic Filaments.

In hierarchical models of structure formation, the first galaxies form in low-mass dark matter potential wells, probing the behavior of dark matter on kiloparsec scales. Even though these objects are below the detection threshold of current telescopes, future missions will open an observational window into this emergent world. In this Letter, we investigate how the first galaxies are assembled in a "fuzzy" dark matter (FDM) cosmology where dark matter is an ultralight ∼10^{-22}  eV boson and the primordial stars are expected to form along dense dark matter filaments.

SIDM on FIRE: hydrodynamical self-interacting dark matter simulations of low-mass dwarf galaxies.

We compare a suite of four simulated dwarf galaxies formed in 10 M haloes of collisionless cold dark matter (CDM) with galaxies simulated in the same haloes with an identical galaxy formation model but a non-zero cross-section for DM self-interactions. These cosmological zoom-in simulations are part of the Feedback In Realistic Environments (fire) project and utilize the fire-2 model for hydrodynamics and galaxy formation physics. We find the stellar masses of the galaxies formed in self-interacting dark matter (SIDM) with = 1 cm g are very similar to those in CDM (spanning ≈ 10 M) and all runs lie on a similar stellar mass-size relation.

Effects of sustained daily latanoprost application on anterior chamber anatomy and physiology in mice.

Latanoprost is a common glaucoma medication. Here, we study longitudinal effects of sustained latanoprost treatment on intraocular pressure (IOP) in C57BL/6J mice, as well as two potential side-effects, changes in iris pigmentation and central corneal thickness (CCT). Male C57BL/6J mice were treated daily for 16 weeks with latanoprost.

Galaxy formation with BECDM - I. Turbulence and relaxation of idealized haloes.

We present a theoretical analysis of some unexplored aspects of relaxed Bose-Einstein condensate dark matter (BECDM) haloes. This type of ultralight bosonic scalar field dark matter is a viable alternative to the standard cold dark matter (CDM) paradigm, as it makes the same large-scale predictions as CDM and potentially overcomes CDM's small-scale problems via a galaxy-scale de Broglie wavelength. We simulate BECDM halo formation through mergers, evolved under the Schrödinger-Poisson equations.

Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes.

The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes.

Structural, kinetic, and docking studies of artificial imine reductases based on biotin-streptavidin technology: an induced lock-and-key hypothesis.

An artificial imine reductase results upon incorporation of a biotinylated Cp*Ir moiety (Cp* = C5Me5(-)) within homotetrameric streptavidin (Sav) (referred to as Cp*Ir(Biot-p-L)Cl] ⊂ Sav). Mutation of S112 reveals a marked effect of the Ir/streptavidin ratio on both the saturation kinetics as well as the enantioselectivity for the production of salsolidine. For [Cp*Ir(Biot-p-L)Cl] ⊂ S112A Sav, both the reaction rate and the selectivity (up to 96% ee (R)-salsolidine, kcat 14-4 min(-1) vs [Ir], KM 65-370 mM) decrease upon fully saturating all biotin binding sites (the ee varying between 96% ee and 45% ee R).

Three-dimensional spatial distribution of synapses in the neocortex: a dual-beam electron microscopy study.

In the cerebral cortex, most synapses are found in the neuropil, but relatively little is known about their 3-dimensional organization. Using an automated dual-beam electron microscope that combines focused ion beam milling and scanning electron microscopy, we have been able to obtain 10 three-dimensional samples with an average volume of 180 µm(3) from the neuropil of layer III of the young rat somatosensory cortex (hindlimb representation). We have used specific software tools to fully reconstruct 1695 synaptic junctions present in these samples and to accurately quantify the number of synapses per unit volume.

Age-based comparison of human dendritic spine structure using complete three-dimensional reconstructions.

Dendritic spines of pyramidal neurons are targets of most excitatory synapses in the cerebral cortex. Recent evidence suggests that the morphology of the dendritic spine could determine its synaptic strength and learning rules. However, unfortunately, there are scant data available regarding the detailed morphology of these structures for the human cerebral cortex.

Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods.

A review of estimation of distribution algorithms in bioinformatics.

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms.

Machine learning in bioinformatics.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

Bayesian network multi-classifiers for protein secondary structure prediction.

Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet.