Biology Methods and Protocols最新文献

Postoperative atrial fibrillation (PoAF) is a common complication after coronary artery bypass grafting (CABG). Despite its association with increased risk of ischemic stroke, bleeding, acute renal failure and mortality there is still no ideal predictive tool with proper clinical interpretability. A retrospective single-center cohort study enrolled 1305 electronic medical records of patients with elective isolated CABG. PoAF was identified in 280 (21.5%) patients. Prognostic models with continuous variables were developed utilizing multivariate logistic regression (MLR), random forest and eXtreme gradient boosting methods. Predictors were dichotomized via grid search for optimal cut-off points, centroid calculation, and Shapley additive explanation (SHAP). For multilevel categorization, we proposed to use threshold values combination identified during dichotomization, as well as ranking cut-off thresholds by MLR weighting coefficients (multimetric categorization method). Based on multistage selection, nine PoAF predictors were identified and validated. After categorization, prognostic models with continuous and multilevel categorical variables were developed. The best XGB model employing continuous predictors demonstrated an AUC = 0.76. Models in which predictors were derived utilizing the multimetric categorization approach showed comparable predictive performance (AUC = 0.758). The main advantage of models with multilevel predictors categorization was their superior explainability and clinical interpretability in predicting POAF. Multilevel predictors categorization represents a promising tool for improving the explainability of POAF predictive development estimates. Using the developed prognostic models, it was demonstrated that the categorization procedures proposed by the authors ensure both high predictive accuracy and transparency of the generated clinical conclusions.

This article provides protocols that enable researchers to master mesoscopic wide-field optical brain imaging from scratch. The protocols describe surgery for wide-field cranial window creation in mice, as well as the imaging process and setup. The protocols for components of the imaging system selection and assembly, creation of a headplate for fixation, and training mice are also provided. The final section briefly outlines methods for data processing. The described procedure can be used to visualize the dorsal cortex using wide-field optical imaging and laser-speckle contrast imaging methods. The distinguishing features of our protocol include: a wide cranial window (up to 60% of the entire cortex), skull thinning (without craniotomy), a UV-curable transparent coating (gel polish), and the ability to perform measurements in awake, behaving mice. During the surgery, a helicopter-shaped headplate with a lower surface congruent to the skull surface is mounted on the mouse's head. This lightweight headplate allows for secure head fixation during movement eliminating the need for alignment during data analysis. Cranial window remains sufficiently transparent for at least three months. Wide-field optical imaging enables the recording of brain haemodynamics and energy metabolism (FAD concentration dynamics) in wild-type mice. The use of transgenic animals expressing genetically encoded sensors allows for the measurement of ions concentrations (e.g. Ca²⁺-dynamics) and other compounds (e.g. glutamate). This article describes the simultaneous measurement of changes in oxy-, deoxy-, and total haemoglobin concentrations in combination with various intracellular parameters: Δ[FAD], Δ[Ca²⁺], or ΔpH with Δ[Cl^-].

DeepLabCut has transformed behavioral and locomotor research by enabling markerless pose estimation through deep learning. Despite its broad adoption across species and behaviors, quantitative kinematic analyses remained limited by noisy outputs and the computational expertise required for refinement. To address this issue, we introduce refineDLC, a comprehensive post-processing pipeline that streamlines the conversion of noisy DeepLabCut outputs into robust, analytically reliable kinematic data. The pipeline incorporates essential cleaning steps, including inversion of the y-coordinates for intuitive spatial interpretation, removal of zero-value frames, and exclusion of irrelevant body part labels. It further applies dual-stage filtering based on likelihood scores and positional changes, enhancing data accuracy and consistency. Multiple interpolation strategies manage missing values while maintaining data continuity and integrity. We evaluated refineDLC using two datasets: controlled locomotion in cattle and field-recorded trotting horses. Across both contexts, the pipeline substantially improved data quality and interpretability, reducing variability, eliminating false-positive labeling errors, and transforming noisy trajectories into physiologically meaningful kinematic patterns. Outputs were reliable and analysis-ready regardless of recording conditions or species. By simplifying the transformation from raw DeepLabCut outputs to meaningful kinematic insights, refineDLC expands accessibility for researchers, particularly those with limited programming expertise, enabling precise quantitative analyses at scale. Future developments may incorporate adaptive filtering algorithms and real-time quality assessments, further optimizing performance and automation. These enhancements will extend the pipeline's applicability to precision phenotyping, behavioral ecology, animal science, and conservation biology.

The pathophysiology of seizures is complex and could contribute to a range of morbidities including sudden unexpected death of epilepsy (SUDEP). A better understanding of seizure-induced pathophysiology can lead to the development of targeted interventions. Here, we describe the development and characterization of a novel large mammalian model of convulsive seizures in non-anesthetized sheep induced by pentylenetetrazol (PTZ), one of the most widely used proconvulsant drugs in epilepsy research. A dose of intravenous PTZ that reliably induced a reproducible and consistent level of seizure in non-anaesthetized sheep was determined. Convulsive seizures went through a relatively predictable sequence, similar to that seen in other animal models of epilepsy. A species-specific seizure severity scale system, based on the field Racine's scale that is widely used in epilepsy research, was designed to establish a user-friendly scoring system for PTZ-induced seizures in sheep. We demonstrated that convulsive seizures caused substantial increases in mean arterial pressure and heart rate. The translational value of this large animal model can be further enhanced when combined with other translational tools such as quantitative systems physiology and pharmacology, potential biomarker testing and experimental preclinical trials of potential prophylactic treatments. An advanced animal model, such as described in this study, provides a unique opportunity for comprehensive physiological monitoring of neural and systemic pathways activated by interictal and ictal activity and can contribute to the development of preventive therapies for seizures.

Escaped farmed salmon are a major concern for wild Atlantic salmon (Salmo salar) stocks in Norway. Fish scale analysis is a well-established method for distinguishing farmed from wild fish, but the process is labor and time intensive. Deep learning has recently been shown to automate this task with high accuracy, though typically on relatively small and geographically limited datasets. Here we train and validate a new convolutional neural network on nearly 90 000 scale images from two national archives, encompassing heterogeneous imaging protocols, hundreds of rivers, and time series extending back to the 1930s. The model achieved an F1 score of 0.95 on a large, independent test set, with predictions closely matching both genetic reference samples and known farmed-origin scales. By developing and testing this new model on a large and diverse dataset, we demonstrate that deep learning generalizes robustly across ecological and methodological contexts, supporting its use as a validated, large-scale tool for monitoring escaped farmed salmon.

The integration of computational methods with traditional qualitative research has emerged as a transformative paradigm in healthcare research. Computational Grounded Theory (CGT) combines the interpretive depth of grounded theory with computational techniques including machine learning and natural language processing. This systematic review examines CGT application in healthcare research through analysis of eight studies demonstrating the method's utility across diverse contexts. Following systematic search across five databases and PRISMA-aligned screening, eight papers applying CGT in healthcare were analyzed. Studies spanned COVID-19 risk perception, medical AI adoption, mental health interventions, diabetes management, women's health technology, online health communities, and social welfare systems, employing computational techniques including Latent Dirichlet Allocation (LDA), sentiment analysis, word embeddings, and deep learning algorithms. Results demonstrate CGT's capacity for analyzing large-scale textual data (100 000+ documents) while maintaining theoretical depth, with consistent reports of enhanced analytical capacity, latent pattern identification, and novel theoretical insights. However, challenges include technical complexity, interpretation validity, resource requirements, and need for interdisciplinary expertise. CGT represents a promising methodological innovation for healthcare research, particularly for understanding complex phenomena, patient experiences, and technology adoption, though the small sample size (8 of 892 screened articles) reflects its nascent application and limits generalizability. CGT represents a promising methodological innovation for healthcare research, particularly valuable for understanding complex healthcare phenomena, patient experiences, and technology adoption. The small sample size (8 of 892 screened articles) reflects CGT's nascent application in healthcare, limiting generalizability. Future research should focus on standardizing methodological procedures, developing best practices, expanding applications, and addressing accessibility barriers.

Deep learning has become an invaluable tool for bioimage analysis but, while open-source cell annotation software such as Cellpose is widely used, an equivalent tool for three-dimensional (3D) vascular annotation does not exist. With the vascular system being directly impacted by a broad range of diseases, there is significant medical interest in quantitative analysis for vascular imaging. We present a new deep learning model, coupled with a human-in-the-loop training approach, for segmentation of vasculature that is generalizable across tissues, modalities, scales, and pathologies. To create a generalizable model, a 3D convolutional neural network was trained using curated data from modalities including optical imaging, computational tomography, and photoacoustic imaging. Through this varied training set, the model was forced to learn common features of vessels' cross-modality and scale. Following this, the pre-trained 'foundation' model was fine-tuned to different applications with a minimal amount of manually labelled ground truth data. It was found that the foundation model could be specialized to a new datasets using as little as 0.3% of the volume of said dataset for fine-tuning. The fine-tuned model was able to segment 3D vasculature with a high level of accuracy (DICE coefficient between 0.81 and 0.98) across a range of applications. These results show a general model trained on a highly varied data catalogue can be specialized to new applications with minimal human input. This model and training approach enables users to produce accurate segmentations of 3D vascular networks without the need to label large amounts of training data.

HIV is a global public health challenge. The Reverse Transcriptase (RT) enzyme facilitates an important step in HIV replication. Inhibition of this enzyme provides a critical target for HIV treatment. The aim of this study is to employ computational techniques to screen bioactive compounds from different medicinal plants toward identifying potent HIV-1 RT inhibitors better activity than the current ones. We conducted a literature review of HIV-1 RT inhibitors, and eighty-four (84) compounds, while target receptor (1REV) was retrieved from Protein Data Bank. The molecular docking and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) evaluations were performed using the Maestro Schrodinger software user interface. The drug-likeness and pharmacokinetic profile evaluation were carried out using SwissADME and ADMETlab3.0 web servers. Lastly, molecular dynamics simulation study was conducted using the Desmond tool of Schrodinger. The molecular docking study revealed that Rosmarinic acid (-13.265 kcal/mol), Evafirenz/standard drug (-12.175 kcal/mol), Arctigenin (-11.322 kcal/mol), Luteolin (-11.274 kcal/mol), Anolignan A (-11.157 kcal/mol), and Quercetin (-11.129 kcal/mol) can effectively bind with high affinity and low energy values to the HIV-1 RT enzyme. The relative binding free energies of Rosmarinic acid, Evafirenz, Arctigenin, Luteolin, Anolignan A, and Quercetin were -66.85, -66.53, -51.83, -49.77, -58.17, and -49.62 Δg bind, respectively. The ADMET profile of Arctigenin was similar to that of Efavirenz, and better than that of other top compounds. The molecular dynamics simulation study showed better stability of rosmarinic acid with the active site of HIV-1 NNRT than the cocrystalized ligand. Out of the top five compounds identified in this study, Rosmarinic acid, a current inhibitor of HIV-1 RT in vitro, showed the most promising prediction. However, further in vivo studies and human clinical trials are required to provide more concrete information regarding its efficacy as potent HIV-1 RT inhibitors.

The potential of artificial intelligence (AI) to personalize dietary and exercise advice for obesity management is increasingly evident. However, the effectiveness and appropriateness of AI-generated recommendations hinge significantly on input quality and structured guidance. Despite growing interest, there remains a notable gap regarding a robust and validated prompt-generation mechanism designed explicitly for obesity-related lifestyle planning. This study aimed to evaluate and refine the quality of a personalized AI-driven framework (NExGEN-ChatGPT) for dietary and exercise prescriptions in obese adults, employing the Fuzzy Delphi Method (FDM) to capture and integrate expert consensus. A multidisciplinary expert panel, comprising 21 professionals from nutrition, medicine, psychology, fitness, and AI domains, was engaged in this study. Using structured questionnaires, the experts systematically assessed and refined six primary constructs, further detailed into several evaluative elements, resulting in the consensus validation of 111 specific criteria. Findings identified critical consensus-driven standards essential for personalized, safe, and feasible obesity management through AI. Moreover, the study revealed prioritized criteria pivotal for maintaining practical relevance, safety, and high-quality personalized recommendations. Consequently, this validated framework provides a substantial foundation for subsequent real-world application and further research, thereby enhancing the effectiveness, scalability, and individualization of obesity interventions leveraging AI.

[This corrects the article DOI: 10.1093/biomethods/bpaf040.].