Wheat (Triticum aestivum L.) is a fundamental crop essential for both human and animal consumption. Addressing the challenge of enhancing wheat yield involves sophisticated applications of molecular genetics and genomic techniques. This review synthesizes current research identifying and characterizing pivotal genes that impact traits such as grain size, number, and weight, critical factors influencing overall yield. Key genes including TaSPL17, ABP7, TaGNI, TaCKX6, TaGS5, TaDA1, WAPO1, TaRht1, TaTGW-7A, TaGW2, TaGS5-3A, TaSus2-2A, TaSus2-2B, TaSus1-7A, and TaSus1-7B are examined for their roles in these traits. The review also explores genes responsive to environmental changes, which are increasingly significant under current climate variability. Multi-trait regulatory genes and quantitative trait loci (QTLs) that contribute to these traits are discussed, highlighting their dual influences on grain size and yield. Furthermore, the paper underscores the utility of emerging technologies such as CRISPR/Cas9, Case13, and multi-omics approaches. These innovations are instrumental for future discoveries and are poised to revolutionize wheat breeding by enabling precise genetic enhancements. Facing unprecedented challenges from climate change, the identification and utilization of these candidates is crucial. This review aims to be a comprehensive resource for researchers, providing an integrative understanding of complex traits in wheat and proposing new avenues for research and crop improvement strategies.

In the era of artificial intelligence (AI) and Industry 4.0, pilot-scale platforms for small molecule chemical drugs are undergoing a transformative digital evolution. These platforms serve as a critical link between early-stage laboratory research and full-scale pharmaceutical manufacturing, ensuring process feasibility, scalability, and regulatory compliance. This review offers a comprehensive and forward-looking analysis of the structure, function, and strategic importance of pilot-scale systems within the modern pharmaceutical landscape. Focusing on the integration of AI and intelligent automation, the study highlights innovations such as AI-driven process optimization, predictive maintenance, data integration, digital twin technologies, and continuous manufacturing. These technologies are reshaping conventional production paradigms by enhancing efficiency, improving quality control, and reducing environmental impact. The convergence of computational pharmaceutics and green chemistry is also examined as a key driver of sustainable and intelligent drug development. Moreover, the review addresses the industry’s transition toward Industry 5.0, characterized by human-machine collaboration, data-centric innovation, and an emphasis on sustainability. Persistent challenges such as equipment standardization gaps, data-sharing limitations, and outdated infrastructure are critically discussed. Drawing from industrial case studies, academic research, and best practices, this paper explores both the opportunities and constraints associated with AI-enabled pilot platforms. Ultimately, the review aims to inform future strategies in digital pharmaceutical manufacturing by underscoring the importance of technological innovation, regulatory alignment, and collaborative ecosystems in advancing the development, efficiency, and sustainability of small molecule drug production.

Cadmium is a toxic, carcinogenic element that threatens food safety due to its tendency to be absorbed by plants along with essential nutrients. This study conducted a genome-wide association study (GWAS) using SNP genotyping data from 170 natural maize populations to analyze cadmium content in maize grains across three environments. The MLM_Q+Kinship and MLM_PCA+Kinship models identified 6424 (HN), 991 (JMO), and 1358 (JMT) SNPs linked to cadmium accumulation in the MLM_Q+Kinship model, with 121 SNPs common across all environments. Additionally, the MLM_PCA+Kinship model detected 824 (HN), 950 (JMO), and 910 (JMT) SNPs, with 14 shared loci. In total, 126 reliable SNP loci, representing 14 QTLs, were identified, highlighting 12 superior haplotypes and 2 favorable alleles. A negative correlation between these loci and cadmium content was observed. Within 100 kbp of the QTLs, 45 candidate genes were identified, associated with 11 GO terms and 5 KEGG pathways. Analysis revealed 12 maize lines with at least one stable locus, all of which showed reduced Cd levels. Key hybrids, such as CAU95×CAU65 and CAU95×CAU266, demonstrated the potential for low Cd accumulation. This study provides valuable insights for breeding maize with reduced Cd uptake using stable gene loci discovered through GWAS.

Despite the transition to a post-pandemic phase, SARS-CoV-2 continues to generate genetically diverse variants that challenge existing therapeutic strategies. The 3-chymotrypsin-like protease (3CLpro) is a highly conserved viral enzyme essential for replication, making it an attractive target for variant-agnostic antiviral intervention. Nanobodies offer distinct advantages over conventional antibodies, including small size, high stability, and strong antigen-binding potential, but their rational design remains technically challenging. We developed a scaffold-based, AI-assisted pipeline for nanobody design that integrates large language model-driven generation of complementarity-determining region (CDR) sequences with structure-guided grafting onto a conserved humanized nanobody scaffold. Nanobody structures were predicted using AlphaFold2, and binding performance was evaluated through molecular docking against 3CLpro from multiple SARS-CoV-2 variants. Physicochemical properties and structural stability were assessed using established computational tools. Among the candidates generated, Nb01 consistently showed favorable predicted binding to 3CLpro across major SARS-CoV-2 variants. Compared with benchmark nanobodies (S43, bn03, R14, and 3-2A2-4), Nb01 showed stronger predicted binding performance for Alpha, Gamma, Omicron BA.2, BA.4/5, BF.7, and XBB variants, while maintaining competitive predicted performance across other lineages. Nb01 also exhibited favorable physicochemical properties, including low predicted toxicity, acceptable stability, and a profile consistent with potential biophysical suitability. Nb01 represents a promising computationally identified nanobody candidate targeting the conserved SARS-CoV-2 3CLpro. This scaffold-based, AI-assisted design strategy provides a systematic and generalizable computational pipeline for the development of antiviral nanobody candidates with potential broad variant coverage. Further experimental validation will be required to confirm the binding properties and therapeutic relevance of Nb01.