Biology Methods and Protocols最新文献

Leptospirosis poses substantial challenges to global public health. In Malaysia, leptospirosis is endemic, with annual cases peaking during the monsoon season. The microscopic agglutination test (MAT) is the gold-standard serological method for confirmation of leptospirosis. However, it is labor-intensive and time-consuming, as it relies on the subjective interpretation of medical lab technicians. This study describes the development of a semiautomated workflow for Leptospira screening by integrating a TensorFlow and custom-designed Keras-based Deep Convolutional Neural Network (DCNN) with conventional MAT. We used a dataset of 442 positive and 442 negative MAT images, which consisted of a mixture of Leptospira serovars from Malaysia to train the model. The model was subjected to hyperparameter tuning, which modulated the number of convolutional layers, filters, kernel sizes, units in dense layers, activation functions, and learning rate. Verification of our tested model compared to the verified patient MAT results achieved the following metrics: a Precision score of 0.8125, a Recall of 0.9286, and an F1-Score of 0.8667. Combining our model with the current Malaysia Leptospira workflow can significantly speed up, reduce inaccuracies, and improve the management of leptospirosis. Furthermore, the application of this model is practical and adaptable, making it suitable for other labs that observe MAT as their Leptospira diagnosis. To our knowledge, this approach is Malaysia's first hybrid diagnostic approach for Leptospira diagnosis. Scaling up the dataset would enhance the model's accuracy, making it adaptable in other regions where leptospirosis is endemic.

The coronavirus disease 2019 (COVID-19) pandemic has necessitated the development of accurate models to predict disease dynamics and guide public health interventions. This study leverages the COVASIM agent-based model to simulate 1331 scenarios of COVID-19 transmission across various social settings, focusing on the school, community, and work contact layers. We extracted complex network measures from these simulations and applied deep learning algorithms to predict key epidemiological outcomes, such as infected, severe, and critical cases. Our approach achieved an $R^2$ value exceeding 95%, demonstrating the model's robust predictive capability. Additionally, we identified optimal intervention strategies using spline interpolation, revealing the critical roles of community and workplace interventions in minimizing the pandemic's impact. The findings underscore the value of integrating network analytics with deep learning to streamline epidemic modeling, reduce computational costs, and enhance public health decision-making. This research offers a novel framework for effectively managing infectious disease outbreaks through targeted, data-driven interventions.

Point-of-care (POC) diagnostics have revolutionized disease detection by enabling rapid, on-site testing without the need for centralized laboratory infrastructure. This review presents recent advances in aptamer-functionalized nanoparticles (AFNs) as promising tools for enhancing POC diagnostics, particularly in infectious diseases and cancer. Aptamers, with their high specificity, stability, and modifiability, offer significant advantages over antibodies, while nanoparticles contribute multifunctionality, including signal amplification and targeting capabilities. AFNs have demonstrated up to a 2-10 times increase in detection sensitivity and significant reductions in diagnostic timeframes. We discuss various nanoparticle types, conjugation strategies, real-world applications, and highlight innovative developments such as AI-assisted aptamer design, wearable diagnostic devices, and green nanoparticle synthesis. Challenges related to stability, manufacturing scalability, regulatory hurdles, and clinical translation are critically examined. By merging aptamer precision with nanoparticle versatility, AFNs hold transformative potential to deliver rapid, affordable, and decentralized healthcare solutions, especially in resource-limited settings. Future interdisciplinary research and sustainable practices will be pivotal in translating AFN-based diagnostics from promising prototypes to global healthcare standards.

Proteins are essential for life in all organisms: they mediate cell signaling and cell division and provide structure/motility to cells and tissues. All proteins are synthesized on cytoplasmic ribosomes as unfolded precursors that need to find their correct location in the compartmentalized cell. In eukaryotes, ∼30% of the proteome is translocated across or integrated into the endoplasmic reticulum (ER) membrane, a process mostly mediated by the heterotrimeric Sec61 complex that spans the ER membrane. There is significant interest in identifying small-molecule inhibitors of the Sec61 translocon channel that hold great promise as putative anticancer, immunosuppressive, or antiviral drugs. Hence, representative models are needed to study Sec61-dependent protein import into the ER. Microsomal membranes (or microsomes) isolated from dog pancreatic tissue are the primary source of mammalian ER for cell-free in vitro protein translocation research. Here, we demonstrate that for the isolation of microsomal membranes, snap-frozen canine pancreatic tissue can serve as a valuable alternative to freshly isolated organ tissue from euthanized animals. For 17 out of 20 animals, a sufficient yield of microsomes was extracted from defrosted pancreatic tissue. The isolated microsomes contained the essential proteins of the translocation machinery, and proved to be intact as verified by the detection of ER lumenal chaperones. Importantly, 13 out of the 17 microsome samples retained their translocation competence, as reflected by successful in vitro co-translational translocation of wild-type bovine preprolactin. The microsomes supported post-translational modifications of the tested substrates such as signal peptide cleavage and N-linked glycosylation. Furthermore, the tested microsome samples responded well to the translocation inhibitor cyclotriazadisulfonamide in suppressing human CD4 protein translocation into the ER. In conclusion, microsomes isolated from frozen canine pancreatic tissue proved to retain their co-translational translocation functionality that can contribute to our research of Sec61-dependent protein translocation and selective inhibition thereof.

Efficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland. Current fishing practices involve decisions informed by a combination of personal experience, current data on environmental and oceanographic conditions, reports from other captains, and target species within the constraints of the fishing quota. However, the intricate spatiotemporal dynamics of fish behaviour make it difficult to predict fish stock distributions. Despite technological breakthroughs in fishing vessel data collection, much of the decision-making still relies heavily on subjective judgement, highlighting the need for more robust, data-driven predictive methods. This paper presents CATCH, a convolutional long short-term memory neural network model that forecasts fish stock probability densities over time and space in Icelandic waters to support operational planning and adaptive strategy in fisheries. The framework represents the first utilization of large-scale Icelandic fishing fleet data integrating multidimensional inputs, particularly depth, bottom temperature, salinity, dissolved oxygen and catch data, to produce accurate, multivariate forecasts. The model achieves favourable performance with average RMSE, MAE, WD, and SSI of 4.71 × 10^-3, 1.16 × 10^-3, 0.94 × 10^-3, and 0.955, respectively, for cod, while 6.13 × 10^-3, 1.25 × 10^-3, 1.04 × 10^-3, and 0.949, respectively, across other target species (haddock, saithe, golden redfish, and Greenland halibut). Additionally, Syrjala's test yielded nonsignificant P-values (P > .05) in most cases across lags and forecast horizons, indicating that the predicted and observed distributions are statistically indistinguishable. Its promising results suggest deep learning models have the potential to optimize fisheries operations, enhance sustainability, and support data-driven decision-making.

Cancer cell lines have provided invaluable preclinical mechanistic data for cancer health disparities research. Although there are several studies that detail ancestry inference methods using microarray data, there are none that provide investigators with documentation of ancestry inference methods using sequencing data. Here, we describe our computational workflow for inferring genetic ancestry using either whole genome sequencing (WGS) or RNA-sequencing (RNA-seq) data from cancer cell lines. RNA-seq and WGS datasets were generated from four head and neck cancer cell lines with self-identified race/ethnicity (SIRE) as either White or Black. Our workflow included variant calling and genotype imputation via Illumina DRAGEN pipelines, merging genotyping datasets with the 1000 Genomes Project (1KGP), single nucleotide polymorphism (SNP) filtering via PLINK, and ancestry inference with ADMIXTURE. We encountered challenges in workflow development with SNP filtering and clustering of 1KGP superpopulations. Adjusting stringency of filtering parameters to a window size of 100 kb and r ² threshold of 0.8 resulted in 312,821 SNPs remaining for the RNA-seq dataset and 1,569,578 SNPs remaining for the WGS dataset. Clustering with 1KGP improved with a panel of 291 ancestry informative markers. To estimate proportions of genetic ancestry, we used all filtered SNPs. For the WGS dataset, both clustering and genetic ancestry proportions for each cancer cell line showed concurrence with SIRE. In conclusion, our optimized workflow offers investigators a robust approach for transforming cancer cell line sequencing data to infer genetic ancestry and suggests that WGS datasets are superior to RNA-seq datasets in clustering superpopulations and more accurately estimating genetic ancestry.

Drug repurposing presents a promising strategy to accelerate drug discovery by identifying new therapeutic uses for existing compounds, particularly for diseases with limited or no effective treatment options. We introduce DrugPipe, a 'Generative AI-Assisted Virtual Screening Pipeline' developed within the target-centric paradigm of drug repurposing, which aims to discover new indications by identifying compounds that interact with a specific protein target. 'DrugPipe' integrates generative modeling, binding pocket prediction, and similarity-based retrieval from drug databases to enable a scalable and generalizable in silico repurposing workflow. It supports blind virtual screening for any protein target without requiring prior structural or functional annotations, making it especially suited for novel or understudied targets and emerging health threats. By efficiently generating candidate ligands and rapidly retrieving structurally similar approved drugs, 'DrugPipe' accelerates the identification and prioritization of repurposable compounds. In comparative evaluations, it achieves hit rate performance comparable to QVina-W, a widely used blind docking tool, while significantly reducing computational time, highlighting its practical value for large-scale virtual screening and data-scarce repurposing scenarios. The full implementation and evaluation details are available at https://github.com/HySonLab/DrugPipe.

There are approximately 12,000 described species within the class Diplopoda. Only five species, falling within 4 of 16 described orders, have fully sequenced genomes. No whole genomes are available for incredibly diverse families like Xystodesmidae. Furthermore, genetic information attributed to key functions in these species is very limited. There is a growing interest in characterizing genomes of non-model organisms, however, extracting high-quality DNA for organisms with complex morphology can be challenging. Here we describe a detailed methodology for obtaining high-purity DNA from legs, head, and body tissues from wild-caught specimens of the millipede species Cherokia georgiana. Our dissection protocol separates the digestive tract minimizing microbial abundance in the extracted DNA sample. We describe sample homogenization steps that improve total DNA yield. To assess sample quality, concentration, and size we use spectrophotometry, fluorometry, and automated electrophoresis, respectively. We consistently obtain average DNA length upwards of 12-25 kb. We applied Oxford Nanopore Technologies MinION long-read sequencing, an affordable and accessible option with potential for field-based applications. Here we present tissue-specific DNA sequencing metrics, alignment and assembly of mitochondrial DNA consensus sequence, and phylogenetic analysis. While noting the limitations of our nanopore-based sequencing methodology, we provide a framework to process field specimens for PCR-free DNA sequencing data that can be used for gene-specific alignment and analysis.

Protein succinylation is a vital post-translational modification (PTM) that involves the covalent attachment of a succinyl group (-CO-CH2-CH2-CO-) to the lysine residue of a protein molecule. The mechanism underlying the succinylation process plays a critical role in regulating protein structure, stability, and function, contributing to various biological processes, including metabolism, gene expression, and signal transduction. Succinylation has also been associated with numerous diseases, such as cancer, neurodegenerative disorders, and metabolic syndromes. Due to its important roles, the accurate prediction of succinylation sites is essential for a comprehensive understanding of the mechanisms underlying succinylation. Although research on the identification of protein succinylation sites has been increasing, experimental methods remain time-consuming and costly, underscoring the need for efficient computational approaches. In this study, we present KD_MultiSucc, a model for cross-species prediction of succinylation sites using Multi-Teacher Knowledge Distillation and Word Embedding. The proposed method leverages the strengths of both Knowledge Distillation and Word Embedding techniques to reduce computational complexity while maintaining high accuracy in predicting protein succinylation sites across species. Experimental results demonstrate that the proposed predictor outperforms existing predictors, providing a valuable contribution to PTM research and biomedical applications. To assist readers and researchers, the codes and resources related to this work have been made freely accessible on GitHub at https://github.com/nuinvtnu/KD_MultiSucc/.

The skin is the largest organ in the body and is the site for a diverse set of diseases. Yet, given the complexity of the cutaneous tissue, there is a limited availability of data in the literature on skin proteomics. Here, we proposed an adapted and optimized protocol for the extraction of proteins from human skin, using a combination of chemical and mechanical lysis approaches. For this, we used of a lysis buffer containing 2% SDS, 50 mM TEAB, and a 1% protease and phosphatase inhibitor cocktail, in addition to Matrix A beads and a FastPrep-24 5G homogenizer. For the characterization of the samples, the obtained proteins were purified and digested using the SP3 method (Single-pot, solid phase, sample preparation), and analyzed by nano liquid chromatography coupled with tandem mass spectrometry. In this way, we were able to identify around 6000 proteins in the skin samples from healthy individuals and patients with the fungal infection sporotrichosis. Our improved methodology could significantly enrich our understanding of skin biology and provide new perspectives for the discovery of biomarkers and therapeutic targets for cutaneous diseases.