Plant lectins: Classical molecules with emerging roles in stress tolerance.

Biotic and abiotic stresses impose adverse effects on plant's development, growth, and production. For the past many years, researchers are trying to understand the stress induced responses in plants and decipher strategies to produce stress tolerant crops. It has been demonstrated that molecular networks encompassing an array of genes and functional proteins play a key role in generating responses to combat different stresses. Newly, there has been a resurgence of interest to explore the role of lectins in modulating various biological responses in plants. Lectins are naturally occurring proteins that form reversible linkages with their respective glycoconjugates. To date, several plant lectins have been recognized and functionally characterized. However, their involvement in stress tolerance is yet to be comprehensively analyzed in greater detail. The availability of biological resources, modern experimental tools, and assay systems has provided a fresh impetus for plant lectin research. Against this backdrop, the present review provides background information on plant lectins and recent knowledge on their crosstalks with other regulatory mechanisms, which play a remarkable role in plant stress amelioration. It also highlights their versatile role and suggests that adding more information to this under-explored area will usher in a new era of crop improvement.

Differential Diagnosis of Hematologic and Solid Tumors Using Targeted Transcriptome and Artificial Intelligence.

Diagnosis and classification of tumors is increasingly dependent on biomarkers. RNA expression profiling using next-generation sequencing provides reliable and reproducible information on the biology of cancer. This study investigated targeted transcriptome and artificial intelligence for differential diagnosis of hematologic and solid tumors. RNA samples from hematologic neoplasms (N = 2606), solid tumors (N = 2038), normal bone marrow (N = 782), and lymph node control (N = 24) were sequenced using next-generation sequencing using a targeted 1408-gene panel. Twenty subtypes of hematologic neoplasms and 24 subtypes of solid tumors were identified. Machine learning was used for diagnosis between two classes. Geometric mean naïve Bayesian classifier was used for differential diagnosis across 45 diagnostic entities with assigned rankings. Machine learning showed high accuracy in distinguishing between two diagnoses, with area under the curve varying between 1 and 0.841. Geometric mean naïve Bayesian algorithm was trained using 3045 samples and tested on 1415 samples, and showed correct first-choice diagnosis in 100%, 88%, 85%, 82%, 88%, 72%, and 72% of acute lymphoblastic leukemia, acute myeloid leukemia, diffuse large B-cell lymphoma, colorectal cancer, lung cancer, chronic lymphocytic leukemia, and follicular lymphoma cases, respectively. The data indicate that targeted transcriptome combined with artificial intelligence are highly useful for diagnosis and classification of various cancers. Mutation profiles and clinical information can improve these algorithms and minimize errors in diagnoses.