bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advances in the diagnosis of colorectal cancer: the application of molecular biomarkers and imaging techniques: a literature review
Authors: Kwan Alicia Su Huey , Uwishema Olivier , Mshaymesh Sarah , Choudhary Karan , Salem Fatma K. , Sengar Aman Singh , Patel Raj Pravin , Kazan Zeinab , Wellington Jack 
Published: December 2024

Background:
Following neoplasms of the lung and breast, colorectal cancer (CRC) is the third most frequent malignancy globally. Screening for CRC at the age of 50 years is strongly encouraged for prompt earlier diagnosis owing to prognoses being greatly correlated with time of detection and cancer staging.

Aim:
This review aimed to elucidate the most recent advancements in the detection of CRC, with an emphasis on the latest innovations in diagnostic molecular biomarkers in conjunction with radiological imaging alongside stool-based tests for CRC screening.

Methods:
A comprehensive review of the literature was performed, focusing on specific terms in different electronic databases, including that of PubMed/MEDLINE. Keywords pertaining to “colorectal cancer,” “diagnosis,” “screening,” “imaging,” and “biomarkers,” among others, were employed in the search strategy. Articles screened and evaluated were deemed relevant to the study aim and were presented in the medium of the English language.

Results:
There have been several innovations in the diagnostics and identification of CRC. These generally comprise molecular biomarkers, currently being studied for suitability in disease detection. Examples of these include genetic, epigenetic, and protein biomarkers. Concurrently, recent developments in CRC diagnostics highlight the advancements made in radiological imaging that offer precise insights on tumor biology in addition to morphological information. Combining these with statistical methodologies will increase the sensitivity and specificity of CRC diagnostics. However, putting these strategies into reality is hampered by several issues.

Conclusion:
Progress in diagnostic technology alongside the identification of a few prognostic predictive molecular biomarkers suggested great promise for prompt detection and management of CRC. This clearly necessitates further efforts to learn more in this specific sector.
Data analytics in pharmacy practice – current review and future directions and recommendations
Authors: Khalil Viviane 
Published: December 2024

Purpose of review
Efficient application of data management is becoming vital in demonstrating value-based healthcare in health organizations more broadly as well as improving pharmacy practice capabilities.

Recent findings
The uptake of data analytics in pharmacy settings has been variable. Limited guidance and recommendations have been published on the use of data analytics in current practice and future recommendations in different pharmacy settings. Data analytics in pharmacy settings to date has been limited mostly to its use for optimization of health information for clinical decision making, standardization of processes, formulary management and tracking and measuring of performance indicators and costs.

Summary
Developing a data driven culture in pharmacy settings is important in advancing pharmacy practice and leveraging of the plethora of data available in healthcare. Possible expansion of applications of data analytics in pharmacy practice could focus on creating a data driven culture, establishing partnerships with digitally advanced departments and leveraging of their practice and expertise as well as investing in upskilling staff in digital health literacy.
Translation, cultural adaptation to Brazilian Portuguese, and validation of the Prosthetic Limb Users Survey of Mobility
Authors: Secco Milena Zavatini , Balbi Larissa Lavoura , Pinheiro Beatriz Bregantin , Pereira Maira Stéfanie de Castro , Barros Ana Regina Bavaresco , Fonseca Marisa de Cássia Registro 
Published: December 2024

Background:
Several outcome measures can be performed to assess and quantify mobility after a lower limb amputation. The Prosthetic Limb Users Survey of Mobility (PLUS-M) is a self-report instrument developed to assess mobility in prosthesis users. It has already been translated into different languages but not into Brazilian Portuguese.

Objectives:
To perform the translation and cultural adaptation into Brazilian Portuguese of the PLUS-M and to assess its validity and reliability.

Study design:
Observational cross-sectional study.

Methods:
Translation and cultural adaptation were performed by following internationally recognized standards. To assess construct validity, the T-scores of PLUS-M/12-Item Short Form—Brazilian Portuguese (BR-12) item short form were correlated to the 2-Minute Walk Test and the scores from the Medical Outcomes Study 36-Short Form Health Survey domains by the Spearman correlation coefficient (ρ). A second administration of the PLUS-M/BR-12 was done by phone call to assess test-retest reliability.

Results:
Some cultural adaptations were made to improve understanding. The PLUS-M/BR-12 T-scores of 61 participants showed a strong correlation between the scores of the 2-Minute Walk Test (ρ = 0.69) and the Physical Function domain of the 36-Short Form Health Survey (ρ = 0.83), and an excellent test-retest reliability (intraclass correlation coefficient = 0.90 [IC 95% = 0.833–0.943]; F(49,49) = 19,037; P < 0.001).

Conclusions:
The PLUS-M/BR-12 demonstrated evidence of validity and reliability in assessing mobility in Brazilian adults with lower limb amputations who use a prosthetic device, and it is suitable for use in clinical care.
Undernutrition in Cleft Palate Surgery: Effects on Postoperative Fistula Formation in a Tertiary Children’s Hospital in Southern Africa
Authors: Adams Saleigh , Mahieu Amber , Verreijdt Pien M , Isaacs Samuel , Breugem Corstiaan C. 
Published: December 2024

Background:
Cleft palate surgery can be complicated by fistula development. It is well known that undernutrition in general leads to more postoperative complications. However, little is known about postoperative fistula development after cleft palate surgery in low- and middle-income countries (LMIC’s).

Objective:
The aim of this study was to investigate the influence of peri-operative undernutrition on the risk of postoperative fistula formation in South African children operated on for cleft palate (CP).

Design and Setting:
Three hundred thirty-seven cleft palate patients undergoing initial cleft palate repair at Red Cross War Memorial Children’s Hospital were retrospectively reviewed.

Interventions:
Undernutrition was measured as World Health Organisation (WHO) Weight-For-Age (WFA) Z-scores, postoperative fistula was noted when the patient received revision surgery for fistula after the initial repair.

Main Outcome Measure:
Longitudinal Generalized Estimation Equations (GEE) analysis was used to examine the influence of the undernutrition Z-Score on post-operative fistula formation.

Results:
Our cohort had a mean Z-score of −0.873 (SD=1.6043). Among patients undergoing initial cleft palate repair, 21.9% were perioperatively underweight for age (Z-Score < −2), and 8.8% were severely underweight (Z-Score < −3). The cohort had a total symptomatic fistula rate of 19.9%. Decreasing perioperative WFA Z-Score (worsening undernutrition), predicts a significant increase in the odds of postoperative fistula (odds ratio = 1.233, P = 0.018).

Conclusions:
Worsening perioperative undernutrition in initial cleft palate surgery is associated with a significant postoperative increase in fistula formation.
Hepato-Pancreato-Biliary Surgery
Authors: Tsilimigras Diamantis I. , Chatzipanagiotou Odysseas , Woldesenbet Selamawit , Ruff Samantha , Cloyd Jordan M. , Pawlik Timothy M. 
Published: December 2024

Objective:
To characterize the association between type of fellowship training and patient outcomes following hepatopancreatic (HP) surgery across different surgeon career stages using a national representative cohort of Medicare beneficiaries.

Background:
The current state of training in HPB surgery in North America is defined through three main pathways: the Complex General Surgical Oncology (CGSO), the Americas HPB Association fellowship, and the American Society of Transplant surgeons fellowship. Each pathway offers a unique perspective on HPB surgery with different number of training years, yet outcomes of graduates performing HP surgery relative to type of fellowship training have not been defined.

Methods:
Medicare claims were used to identify patients who underwent HP surgery for cancer between 2016-2021. The association of textbook outcomes (TO), defined as no postoperative complications, no prolonged LOS, no 90-day mortality and no 90-day readmission, relative to different fellowship pathways was examined.

Results:
Overall, 15,411 cancer operations (pancreatectomy: 11,003, 71.4%; hepatectomy: 4,408, 28.6%) were performed by 1,030 HPB surgeons. A total of 9,390 patients (60.9%) were operated on by a CGSO/SONC graduate, 2,315 patients (15.0%) by an HPB fellowship graduate and 3,706 (24.1%) by a transplant fellowship graduate. Patients who were operated on by an HPB fellowship graduate more frequently had a higher Charlson score (>5: 44.8% vs. 38.1%) and more frequently were operated on an urgent basis (7.7% vs. 6.1%) compared with individuals treated by CGSO/SONC graduates. After adjusting for patient, procedural, hospital, and surgeon related factors, the likelihood of TO following an HP operation by an HPB fellowship graduate was 47.7% versus 45.2% among CGSO (% difference 2.7%) and 42.8% among transplant fellowship graduates (% difference 4.9%, P=0.01). While the probability of TO was higher among surgeons having completed a dedicated HPB fellowship during early (1-7th year of independent practice, P=0.032) and middle (8-14th year, P<0.001) career stages, the probability of TO did not differ based on fellowship type among late career surgeons (>15th year, P=0.257)

Conclusions:
Achievement of TO following an HP procedure was higher among surgeons who had completed a dedicated HPB fellowship, especially during early and middle career stages. Further efforts should be made to enhance HP surgery exposure and training for CGSO fellows interested in a career in HPB surgery.
Emergence of Sparse Coding, Balance and Decorrelation from a Biologically-Grounded Spiking Neural Network Model of Learning in the Primary Visual Cortex
Authors: Ruslim Marko A. , Spencer Martin J. , Hogendoorn Hinze , Meffin Hamish , Lian Yanbo , Burkitt Anthony N. 
Published: December 2024

1AbstractMany computational studies attempt to address the question of information representation in biological neural networks using an explicit optimization based on an objective function. These approaches begin with principles of information representation that are expected to be found in the network and from which learning rules can be derived.This study approaches the question from the opposite direction; beginning with a model built upon the experimentally observed properties of neural responses, homeostasis, and synaptic plasticity. The known properties of information representation are then expected to emerge from this substrate.A spiking neural model of the primary visual cortex (V1) was investigated. Populations of both inhibitory and excitatory leaky integrate-and-fire neurons with recurrent connections were provided with spiking input from simulated ON and OFF neurons of the lateral geniculate nucleus. This network was provided with natural image stimuli as input. All synapses underwent learning using spike-timing-dependent plasticity learning rules. A homeostatic rule adjusted the weights and thresholds of each neuron based on target homeostatic spiking rates and mean synaptic input values.These experimentally grounded rules resulted in a number of the expected properties of information representation. The network showed a temporally sparse spikeresponseto inputs and this was associated with a sparsecodewith Gabor-like receptive fields. The network was balanced at both slow and fast time scales; increased excitatory input was balanced by increased inhibition. This balance was associated with decorrelated firing that was observed as population sparseness. This population sparseness was both the cause and result of the decorrelation of receptive fields. These observed emergent properties (balance, temporal sparseness, population sparseness, and decorrelation) indicate that the network is implementing expected principles of information processing: efficient coding, information maximization (’infomax’), and a lateral or single-layer form of predictive coding.These emergent features of the network were shown to be robust to randomized jitter of the values of key simulation parameters.
Advances and applications of the closest-tree algorithm and Hadamard conjugation in phylogenetic inference
Authors: González Ernesto Álvarez , Balám-Narváez Ricardo , Angulo Diego F. , Duchen Pablo 
Published: December 2024

AbstractIn phylogenetic inference Hadamard methods and the closest-tree algorithm have been a promising alternative to likelihood-based methods. However, applications to actual biological problems have been limited so far. In the early nineties, Hendy and Penny (1993) developed the two-state closest-tree algorithm for estimating the optimal branch lengths of a phylogenetic tree, whose parameters correspond to the Cavender’s molecular evolution model (CFN). Steel et al. (1992) then developed the four-state version of this method, whose parameters correspond to the Kimura 3ST’s molecular evolution model (K3ST). In both cases, formulas for solving the optimization problems were provided. Here, we do not only contribute with proofs for these formulas, but we also adapt this methodology to the orchid genusLophiarella, whose phylogenetic relationships remain unclear. With this biological application, we show the efficacy of the closest-tree algorithm coupled with Hadamard conjugation, phylogenetic invariants and edge-parameter inequalities (in Fourier coordinates) in jointly inferring the tree topology and the molecular evolution model that best explains the data. Finally, we reconcile this phylogeny with biogeographical and morphological aspects within this genus.
Parallelization of particle-based reaction-diffusion simulations using MPI
Authors: Guo Sikao , Korolija Nenad , Milfeld Kent , Jhaveri Adip , Sang Mankun , Ying Yue Moon , Johnson Margaret E 
Published: December 2024

ABSTRACTParticle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.
The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus
Authors: Nikhil K.L. , Singhal Bharat , Granados-Fuentes Daniel , Li Jr-Shin , Kiss István Z. , Herzog Erik D. 
Published: December 2024

AbstractCircadian rhythms in mammals arise from the spatiotemporal synchronization of ∼20,000 neuronal clocks in the Suprachiasmatic Nucleus (SCN). While anatomical, molecular, and genetic approaches have revealed diverse cell types and signaling mechanisms, the network wiring that enables SCN cells to communicate and synchronize remains unclear. To overcome the challenges of revealing functional connectivity from fixed tissue, we developed MITE (Mutual Information & Transfer Entropy), an information theory approach that infers directed cell-cell connections with high fidelity. By analyzing 3447 hours of continuously recorded clock gene expression from 9011 cells in 17 mice, we found that the functional connectome of SCN was highly conserved bilaterally and across mice, sparse, and organized into a dorsomedial and a ventrolateral module. While most connections were local, we discovered long-range connections from ventral cells to cells in both the ventral and dorsal SCN. Based on their functional connectivity, SCN cells can be characterized as circadian signal generators, broadcasters, sinks, or bridges. For example, a subset of VIP neurons acts as hubs that generate circadian signals critical to synchronize daily rhythms across the SCN neural network. Simulations of the experimentally inferred SCN networks recapitulated the stereotypical dorsal-to-ventral wave of daily PER2 expression and ability to spontaneously synchronize, revealing that SCN emergent dynamics are sculpted by cell-cell connectivity. We conclude that MITE provides a powerful method to infer functional connectomes, and that the conserved architecture of cell-cell connections mediates circadian synchrony across space and time in the mammalian SCN.HighlightsWe developed MITE, an information theory method, to accurately infer directed functional connectivity among circadian cells.SCN cell types with conserved connectivity patterns spatially organize into two regions and function as generators, broadcasters, sinks, or bridges of circadian information.One-third of VIP neurons serve as hubs that drive circadian synchrony across the SCN.Key connectivity features mediate the generation and maintenance of intercellular synchrony and daily waves of clock gene expression across the SCN.
Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs
Authors: Provasi Davide , Filizola Marta 
Published: December 2024

ABSTRACTG protein-coupled receptors (GPCRs) remain a focal point of research due to their critical roles in cell signaling and their prominence as drug targets. However, directly linking drug efficacy to receptor-mediated activation of specific intracellular transducers and the resulting physiological outcomes remains challenging. It is unclear whether the enhanced therapeutic window of certain drugs — defined as the dose range that provides effective therapy with minimal side effects — stems from their low intrinsic efficacy across all signaling pathways or ligand bias, wherein specific transducer subtypes are preferentially activated in a given cellular system compared to a reference ligand. Accurately predicting safer compounds, whether through low intrinsic efficacy or ligand bias, would greatly advance drug development. While AI models hold promise for such predictions, the development of deep learning models capable of reliably forecasting GPCR ligands with defined bioactivities remains challenging, largely due to the limited availability of high-quality data. To address this, we pre-trained a model on receptor sequences and ligand datasets across all class A GPCRs, and then refined it to predict low-efficacy compounds or biased agonists for individual class A GPCRs. This was achieved using transfer learning and a neural network incorporating natural language processing of target sequences and receptor mutation effects on signaling. These two fine-tuned models—one for low-efficacy agonists and one for biased agonists—are available on demand for each class A GPCR and enable virtual screening of large chemical libraries, thereby facilitating the discovery of compounds with potentially improved safety profiles.
Kolmogorov-Arnold Networks for Genomic Tasks
Authors: Cherednichenko Oleksandr , Poptsova Maria 
Published: December 2024

AbstractKolmogorov-Arnold Networks (KANs) emerged as a promising alternative for multilayer perceptrons in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures in the domains of computer vision and natural language processing. Integrating KANs into deep learning models for genomic tasks has not been explored. Here, we tested linear KANs (LKANs) and convolutional KANs (CKANs) as replacement for MLP in baseline deep learning architectures for classification and generation of genomic sequences. We used three genomic benchmark datasets: Genomic Benchmarks, Genome Understanding Evaluation, and Flipon Benchmark. We demonstrated that LKANs outperformed both baseline and CK-ANs on almost all datasets. CKANs can achieve comparable results but struggle with scaling over large number of parameters. Ablation analysis demonstrated that the number of KAN layers correlates with the model performance. Overall, linear KANs show promising results in improving the performance of deep learning models with relatively small number of parameters. Unleashing KAN potential in different SOTA deep learning architectures currently used in genomics requires further research.
Cluster-Specific Urban Contexts Associated with High Levels of Sleep Impairment and Daytime Sleepiness: Findings from the Urbasan Collaborative Study
Authors: Voruz Philippe , Ruas Marco Vieira , Fellay Noé , Romano Noemi , Mussini Michelangelo , Saubade Mathieu , Faivre Vincent , Gremeaux Vincent , Jeanneret Ophélia , Tonnerre Quentin , Beetschen Patrick , Domon-Aubort Marie-Noëlle , Spini Dario , Kayser Bengt , Rappo Daniel , Joost Stéphane 
Published: December 2024

AbstractIntroductionImpaired sleep is a global health concern. However, the environmental factors contributing to sleep impairment in urban settings are still not well understood.MethodologyThis study involved 179 participants from a Swiss municipality (Yverdon-les-Bains), where sleep quality and diurnal sleepiness were measured using validated questionnaires, alongside environmental and geo-referenced data.ResultsThe findings revealed a high prevalence of sleep disorders across diverse demographic groups (respectively 15.6% for diurnal sleepiness and 91.1% for significantly altered sleep quality). Additionally, sleep disorders were associated with both environmental and socio-demographic factors. Geospatial analysis identified clusters of sleep disturbances in specific neighborhoods, with distinct associations to specific sub-scores (factors) of the sleep evaluation.ConclusionAssessing sleep in urban environments is crucial, as it is linked to elevated levels of sleepiness. Environmental and socio-demographic variables play significant roles in these disturbances. The incorporation of geospatial analyses allows for a more precise identification of patterns within the city, offering opportunities for tailored interventions to address the different patterns of sleep disorders.