A new review in Current Cell Science brings together the science behind BRCA1, PSA, KRAS, and other biomarkers now used to detect cancer earlier and track how well the treatment is working. Cancer remains one of the leading causes of death worldwide, and part of what makes it so difficult to manage is its sheer diversity — more than 100 distinct types can arise, depending on which tissue or organ is affected. A new review, published ahead-of-print in Current Cell Science, takes stock of one of the most important tools researchers and clinicians now rely on to make sense of this complexity: cancer biomarkers.

New bioinformatics study screens three diabetes-linked genes and builds PCR primers that could support low-cost genetic risk testing, particularly in resource-limited settings. Diabetes is one of the fastest-growing health concerns worldwide, and a large share of that burden falls on developing countries where early screening tools are often too expensive or too complex for routine use. A new study, published ahead of print in Current Science, Engineering and Technology, tackles this problem from the perspective of genetics, i.e., by designing and computationally testing molecular tools that could one day help identify people at higher genetic risk of type 2 diabetes before the disease develops.New bioinformatics study screens three diabetes-linked genes and builds PCR primers that could support low-cost genetic risk testing, particularly in resource-limited settings. Diabetes is one of the fastest-growing health concerns worldwide, and a large share of that burden falls on developing countries where early screening tools are often too expensive or too complex for routine use. A new study, published ahead of print in Current Science, Engineering and Technology, tackles this problem from the perspective of genetics, i.e., by designing and computationally testing molecular tools that could one day help identify people at higher genetic risk of type 2 diabetes before the disease develops.

Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for high throughput maize plant height phenotyping in 270 doubled haploid (DH) lines across multiple stages and environments. Single plant scale estimation outperformed row scale, with R² values of 0.67 vs. 0.56 and RMSE values of 0.12 m vs. 0.17 m, respectively. Using inclusive composite interval mapping, we identified 12 and 20 QTLs for plant height in two DH populations. These results support the feasibility of UAV-based QTL mapping and candidate gene discovery in maize.