Nucleic acid aptamers, selected through the Systematic Evolution of Ligands by Exponential Enrichment (SELEX), are short nucleic acid sequences that exhibit high affinity and specificity towards diverse targets. Over the past three decades, substantial advancements have been made in both the technology and applications of nucleic acid aptamers. This review provides an in-depth analysis of the historical development and defining characteristics of aptamers, highlighting recent technological innovations in SELEX, including Capillary Electrophoresis SELEX, Microfluidic SELEX, Cell-SELEX, and others. We explore the applications of aptamers in therapeutic and targeted drug delivery, emphasizing their advantages over traditional antibodies such as cost-effectiveness, ease of synthesis, and lower immunogenicity. Key challenges such as stability, specificity, and efficient delivery are discussed, with proposed strategies for improvement including advanced chemical modifications and integration with nanotechnology. By integrating advanced technologies, aptamers hold significant promise for enhancing precision medicine and personalized therapeutic interventions, offering new avenues for the treatment of complex diseases.

A self-developed portable electronic nose and its classification model were designed to detect and differentiate minor mechanical damage to strawberries. The electronic nose utilises four metal oxide sensors and four electrochemical sensors specifically calibrated for strawberry detection. The selected strawberries were subjected to simulated damage using an H2Q-C air bath oscillator at varying speeds and then stored at 4°C to mimic real-life mechanical damage scenarios. Multiple feature extraction methods have been proposed and combined with Principal Component Analysis (PCA) dimensionality reduction for comparative modelling. Following validation with various models such as SVM, KNN, LDA, naive Bayes, and subspace ensemble, the Grid Search-optimised SVM (GS-SVM) method achieved the highest classification accuracy of 0.84 for assessing the degree of strawberry damage. Additionally, the Feature Extraction ensemble classifier achieved the highest classification accuracy (0.89 in determining the time interval of strawberry damage). This experiment demonstrated the feasibility of the self-developed electronic nose for detecting minor mechanical damage in strawberries.

Coryloideae is a subfamily in the family Betulaceae consisting of four extant genera: Carpinus, Corylus, Ostrya, and Ostryopsis. We sequenced the plastomes of six species of Corylus and one species of Ostryopsis for comparative and phylogenetic analyses. The plastomes are 159–160 kb long and possess typical quadripartite cp architecture. The plastomes show moderate divergence and conserved arrangement. Five mutational hotspots were identified by comparing the plastomes of seven species of Coryloideae: trnG-atpA, trnF-ndhJ, accD-psaI, ndhF-ccsA, and ycf1. We assembled the most complete phylogenomic tree for the family Betulaceae using 68 plastomes. Our cp genomic sequence phylogenetic analyses placed Carpinus, Ostrya, and Ostryopsis in a clade together and left Corylus in a separate clade. Within the genus Corylus, these analyses indicate the existence of five subclades reflecting the phylogeographical relationships among the species. The data offer significant genetic information for the identification of species of the Coryloideae, taxonomic and phylogenetic studies, and molecular breeding.

Accurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K‐nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10‐fold cross‐validation technique. All these methods reached an accuracy of 95% or above, with F 1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F 1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef.