The destruction of β-cells of the pancreas leads to either insulin shortage or the complete absence of insulin, which in turn causes diabetes Mellitus. For treating diabetes, many trials have been conducted since the 19th century until now. In ancient times, insulin from an animal’s extract was taken to treat human beings. However, this resulted in some serious allergic reactions. Therefore, scientists and researchers have tried their best to find alternative ways for managing diabetes with progressive advancements in biotechnology. However, a lot of research trials have been conducted, and they discovered more progressed strategies and approaches to treat type I and II diabetes with satisfaction. Still, investigators are finding more appropriate ways to treat diabetes accurately. They formulated insulin analogs that mimic the naturally produced human insulin through recombinant DNA technology and devised many methods for appropriate delivery of insulin. This review will address the following questions: What is insulin preparation? How were these devised and what are the impacts (both positive and negative) of such insulin analogs against TIDM (type-I diabetes mellitus) and TIIDM (type-II diabetes mellitus)? This review article will also demonstrate approaches for the delivery of insulin analogs into the human body and some future directions for further improvement of insulin treatment.

Genome-wide association studies (GWAS) are a powerful approach for elucidating the genetic architecture of complex traits. The widespread application of GWAS across diverse crop species has generated vast amounts of GWAS summary statistics, creating a pressing need for effective tools to identify high-confidence quantitative trait loci (QTLs) from these data. Here, we present Candidate QTL Detector (CQD), a tool that enables the rapid and accurate identification of confident QTL regions from GWAS summary statistics. To evaluate the performance of CQD, we reanalyzed 64 phenotypic traits from a previously published maize diversity panel. CQD identified 108 high-confidence QTLs from 4179 significant GWAS signals, and these QTLs harbored several well-characterized genes, including NS2 (a regulator of tassel branch number), DRL1 (a key regulator of plant architecture), and ZmACS6 (an ACC synthase involved in ethylene biosynthesis), demonstrating the reliability of the QTLs detected by CQD. Overall, CQD provides an efficient and flexible framework for extracting robust QTLs from GWAS summary statistics, thereby facilitating the genetic improvement of complex traits and advancing crop functional genomics.

Seed oil content (SOC) is a key quantitative trait in Brassica napus that strongly influences oil yield. However, despite its significance, the genetic basis of natural variation in SOC remains only partially understood. To this purpose, we identified a high-oil, orange-petal mutant (opm1) from an EMS-mutagenized Zhongshuang 11 (ZS11) population. Bulked segregant analysis pinpointed the causal locus for the flower-color phenotype to a single-nucleotide mutation in BnaC09.ZEP, which encodes zeaxanthin epoxidase. However, segregation and linkage analyses showed that the SOC increasement segregates independently of the BnaC09.ZEP mutation, indicating at least one additional causal factor for oil accumulation in opm1. Transcriptomic and enzymatic analyses of opm1 seeds revealed stage-specific reprogramming of lipid metabolism. At early developmental stages, genes involved in fatty acid synthesis and oil body formation were transcriptionally activated, supporting enhanced TAG production. During late maturation, expression of lipid catabolic genes, including GDSL-type esterases, was repressed, accompanied by reduced lipase activity. Despite unchanged oil body morphology, opm1 exhibited reduced seed coat content, collectively contributing to elevated seed oil accumulation. Overall, our findings provide new insights into the regulation of seed oil metabolism and identify a valuable genetic resource for improving oil content in rapeseed.

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production.

Although heterosis plays a crucial role in enhancing crop yield and stress resistance, its underlying genetic mechanism remains not yet fully understood. Previous studies have shown that heterosis tends to increase with greater genetic distance in the absence of reproductive isolation barriers. However, whether variation in parental genome size alone can generate heterosis under near-isogenic backgrounds has not been thoroughly explored. Here, we used a rapeseed double haploid (DH) inducer line to generate progeny from the Pol CMS three-line hybrid Rongyou 18 (RY18). Although the progeny maintained the same ploidy level as the parents, their genome sizes showed notable variation (818.99–1024.88 Mb). To eliminate genetic distance effects, multiple DH progeny carrying restorer genes were crossed as paternal parents with the female parent 0068A of RY18, creating novel F₁ hybrids. Using RY18 as the control, we observed a marked reduction in the genetic distance between the newly induced restorer line and the female parent (0068A). Correlation analysis further revealed a significant negative correlation (r = −0.310 *) between the paternal genome size and heterosis for thousand-seed weight (TSW). Furthermore, the genomic expansion in hybrid offspring relative to the male parent showed that significant correlations were observed between paternal genome size and heterosis over the standard for both TSW (r = 0.300, p < 0.05) and plot yield (r = 0.326, p < 0.05). Resequencing of high-and low-yielding F₁ hybrids identified SNP sites, indicating that under an identical genetic background, heterosis for yield was more pronounced on chromosome A and chromosome C04. The doubled haploid (DH) induction line facilitates the generation of parental lines with distinct genome sizes, potentially providing a potential novel approach for studying heterosis research in Brassica napus.