Biology Methods and Protocols最新文献

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.].

The global shortage of sonographers has created significant barriers to timely ultrasound diagnostics across medical specialties. Deep learning (DL) algorithms have potential to enhance image acquisition by clinicians without formal sonography training, potentially expanding access to crucial diagnostic imaging in resource-limited settings. This study evaluates whether DL-enabled devices improve acquisition of multi-view limited echocardiograms by healthcare providers without previous cardiac ultrasound training. In this single-center randomized controlled trial (2023-2024), internal medicine residents (N = 38) without prior sonography training received a portable ultrasound device with (N = 19) or without (N = 19) DL capability for a two-week clinical integration period during regular patient care on hospital wards. The DL software provided real-time guidance for probe positioning and image quality assessment across five standard echocardiographic views. The primary outcome was total acquisition time for a comprehensive five-view limited echocardiogram (parasternal long axis, parasternal short axis, apical 4-chamber, subcostal, and inferior vena cava views). Assessments occurred at randomization and after two weeks using a standardized patient. Secondary outcomes included image quality using a validated assessment tool and participant attitudes toward the technology. Baseline scan times and image quality scores were comparable between groups. At two-week follow-up, participants using DL-equipped devices demonstrated significantly faster total scan times (152 s [IQR 115-195] versus 266 s [IQR 206-324]; P < 0.001; Cohen's D = 1.7) and superior image quality with higher modified RACE scores (15 [IQR 10-18] versus 11 [IQR 7-13.5]; P = 0.034; Cohen's D = 0.84). Performance improvements were most pronounced in technically challenging views. Both groups reported similar levels of trust in DL-functionality. Ultrasound devices incorporating deep learning algorithms significantly improve both acquisition speed and image quality of comprehensive echocardiographic examinations by novice users. These findings suggest DL-enhanced ultrasound may help address critical gaps in diagnostic imaging capacity by enabling non-specialists to acquire clinically useful cardiac images.

Histological embedding and staining techniques are essential for examining tissue and cellular morphology. This study compares two embedding methods-JB-4™, a glycol methacrylate-based resin, and conventional paraffin-to determine which method provides superior visualization of liver and long bone tissues under light microscopy. Liver tissues from both embedding protocols were stained using the Periodic Acid-Schiff method and silver impregnation method. JB-4 sections were also stained with acid fuchsin and toluidine blue, while paraffin sections were stained with hematoxylin and eosin staining. Contrary to the common assumption that JB-4 may interferes with certain staining protocols, acid fuchsin and toluidine blue yielded high-contrast, structurally detailed results in JB-4 sections. Both techniques preserved liver morphology. However, JB-4 demonstrated higher resolution and enhanced visualization of intracellular structures. JB4 also preservedglycogen more effectively. Cellular structures including nuclei, nucleoli, bile duct epithelial cells, and Kupffer cells, were observedmore distinctly in JB-4 preparations. Reticular fibers were similarly visualized with both embedding techniques. In contrast, paraffin embedding provided better preserved overall tissue architecture. Whilelong bone specimens, paraffin sections frequently displayed poorly defined structures, while JB-4 offered clearer visualization of chondrocyte lacunae, osteocyte nuclei, lamellar bone, and bone marrow cells. JB-4 and paraffin each offer distinct advantages depending on tissue type and histological objective. JB-4 appears to be compatible with a broader range of stains than was previously reported, which expands its utility in detailed tissue analysis. The selection of an embedding method should align with the morphological characteristics of the target tissue and the specific research goals.

DAP-seq is an in vitro method to analyze the relative binding affinity of transcription factors to DNA. It is a fast and scalable method and its application to plant transcription factors with a binary bound/not bound readout was first published in 2016 by O'Malley and colleagues. Since DAP-seq only requires the transcription factor protein and genomic DNA of a species, it can easily be applied to any species with DNA extraction protocols and available genome sequence resources. We present an optimized DNA Affinity Purification sequencing (DAP-seq) protocol for the relative quantification of protein-DNA interactions and a practical guide for data analysis. The desired transcription factor is expressed in vitro and fused to a tag, such as a HaloTag. Genomic DNA is fragmented and adapters are ligated, added to the purified TF::HaloTag protein, and unspecifically bound DNA is washed away. After the bound DNA is recovered, we add a quantification step which homogenizes library size and improves reproducibility. The expanded downstream bioinformatic analysis identifies transcription factor binding sites in the genome followed by analyses of replicate robustness by comparing three different peak height measures, control characteristics, and relative binding affinity.

Background: Headspace gas chromatography-mass spectrometry (HS GC-MS) traditionally has been applied to analyze samples with a high content of volatile components, such as stool samples. Nevertheless, other types of samples-for example, urine-may also contain volatile compounds and serve as valuable sources of diagnostic information. However, the content of volatile components in urine is considerably lower than in stool samples, necessitating modification of the HS GC/MS method. Such optimization could be particularly valuable for patients with inflammatory bowel disease (IBD), for whom providing a stool sample can sometimes be challenging. The aim of this work was to optimize a method for assessing volatile components in urine samples.

Methods: Urine samples were collected from 10 patients with IBD and 10 healthy controls. Laboratory, endoscopic, and histopathological analyses confirmed the IBD diagnosis. Metabolomic profiling was performed using HS GC/MS (Shimadzu QP2010 Ultra with HS-20 extractor).

Results: Volatile metabolites in urine samples suitable for analysis were acquired through optimized sample preparation procedures, including sampling vapor with a salt mixture, increasing the sample volume, adjusting the temperature regime during preparation, and fine-tuning the delay time prior to mass spectrometer activation. The most comprehensive and high-quality results were obtained using a triple extraction method with cryo-trap technology. As a result of HS GC/MS method optimization, urine metabolome analysis of IBD patients enabled the identification of biomarkers that can be utilized for the clinical detection of IBD. 2-Heptanone and pentadecane were identified as IBD-associated biomarkers.

Conclusions: Optimized preparation protocols enable HS GC/MS method to be effectively applied for the analysis of volatile components in urine samples. The modified HS GC/MS method can be scaled up for large-sample analysis to both detect identified metabolites and explore potential new biomarkers associated with IBD and other pathologies.

The CATH database is a free publicly available online resource that provides annotations about the evolutionary and structural relationships of protein domains. Due to the flux of protein structures coming mainly from the recent breakthrough of AlphaFold and therefore the non-feasibility of manual intervention, the CATH team recently developed an automatic CATH superfamily (SF) classifier called CATHe, which uses a feed-forward neural network (FNN) classifier with protein Language Model (pLM) embeddings as input. Using the same dataset of remote homologues (with a 20% sequence identity threshold), this paper presents CATHe2, which improves on CATHe by switching the old pLM ProtT5 for one of the most recent versions called ProstT5, and by incorporating domain 3D information into the classifier through Structural Alphabet representation, specifically, 3Di sequence embeddings. Finally, CATHe2 implements a new version of the FNN classifier architecture, fine-tuned to perform at the CATH superfamily prediction task. The best CATHe2 model reaches an accuracy of 92.2% ± 0.7% with an F1 score of 82.3% ± 1.3%, which constitutes an improvement of 9.9% on the F1 score and 6.6% on the accuracy, from the previous CATHe version (85.6% ± 0.4% accuracy and 72.4% ± 0.7% F1 score) on its largest dataset (∼1700 superfamilies). This model uses ProstT5 amino acid (AA) sequence and 3Di sequence embeddings as input to the classifier, but a simplified version requiring only AA sequences, already improves CATHe's F1 score by 6.7% ± 1.3% and accuracy by 6.6% ± 0.7% on its largest dataset.

Accurate and timely diagnosis of colorectal cancer (CRC) is essential for effective treatment and better patient outcomes. This study explores the application of deep learning (DL) for automated CRC categories classification using hematoxylin and eosin-stained histopathology (H&E) images. Among the models, ResNet-34 demonstrated a strong balance of performance and complexity, achieving an overall accuracy of 85.04%, with top-2 and top-3 classification accuracies of 96.68% and 99.23%, respectively. ResNet-50 exhibited the highest micro-averaged ROC AUC of 0.9933 and F1-score of 87.51%. Swin Transformer V2 model also showed competitive results, with Swin v2-t-w8 achieving particularly high accuracy in Hyperplasia polyp detection (95.83%) and Adenocarcinoma (93.33%), alongside strong ROC AUCs (0.9926 for Hyperplasia polyp and 0.9864 for Adenocarcinoma), though at the cost of increased computational demands. We further developed a two-stage prediction framework comprising a binary abnormal detection stage followed by a multiclass cancer classifier. This approach substantially improved classification robustness, particularly for underrepresented and morphologically complex classes. Particularly, High-grade dysplasia classification accuracy improved from 53.57% with ResNet-34 to 71.43% in its two-stage extension. These results suggest that moderate-depth architectures can effectively capture the morphological diversity of colorectal cancer stages and provide an interpretable, efficient deep learning-based diagnostic tool to support pathologists.

Conventional methods for measuring glutathione peroxidase (GPx) activity are limited by interference issues, complex protein precipitation steps, and variable reliability, necessitating the development of improved analytical approaches for both research and clinical applications. A modified GPx activity assay has been developed utilizing the Tiron reagent system, which eliminates the need for protein precipitation. The protocol employs a novel termination reagent containing ferrous ion (Fe²⁺) and Tiron (C₆H₄Na₂O₈S₂) to instantly stop enzymatic decomposition of hydrogen peroxide. Following GPx-mediated H₂O₂ consumption, residual hydrogen peroxide undergoes Fenton-type redox reactions with Fe²⁺ ions, generating Fe³⁺ species that coordinate with Tiron through catechol moieties to form a stable ferri-Tiron complex [Fe(C₆H₄Na₂O₈S₂)]³⁺. The assay operates optimally at acidic pH to ensure complex stability and minimize interference from competing reactions. The modified protocol demonstrates superior performance characteristics compared to conventional GPx assays, including elimination of interference effects, enhanced accuracy and precision, and improved reproducibility across diverse sample matrices. The method's spectrophotometric detection system provides reliable quantification with minimal matrix effects, while the simplified workflow reduces technical complexity and analysis time. This interference-free GPx activity assay offers significant advantages for both laboratory research and clinical diagnostics. It achieves this through a combination of analytical precision, operational simplicity, and broad compatibility with standard laboratory practices and equipment. The protocol's robust performance at acidic pH conditions, coupled with its elimination of protein precipitation steps, establishes it as a valuable alternative to existing methodologies for assessing oxidative stress and evaluating antioxidant capacity.

Multiplex PCR-based assays are indispensable platforms for rapid and cost-effective DNA-based multi-target detection. The success of such an assay highly depends on the accurate design of oligonucleotide primers, arguably its most vital component. In this study, the ThermoPlex design tool is introduced, offering an automated design pipeline for target-specific multiplex PCR primers motivated by DNA thermodynamics. From a sequence alignment of all relevant target and non-target sequences, ThermoPlex automatically designs multiplex PCR primer candidates in just a matter of minutes. The software also offers tools for thermodynamic calculations that can either be used apart from the automated primer screening routine or in conjunction with other existing primer design tools, depending on the needs of the user. Evidence presented in this study provides insights into the performance of the software performance through theoretical and experimental analyses, serving to establish the reliability of its framework.