Androgenetic alopecia is a highly prevalent condition mainly affecting men. This complex trait is related to aging and genetics; however, multiple other factors, for example, lifestyle, are also involved. Despite its prevalence, the underlying biology of androgenetic alopecia remains elusive, and thus advances in its treatment have been hindered. Herein, we review the functional anatomy of hair follicles and the cell signaling events that play a role in follicle cycling. We also discuss the pathology of androgenetic alopecia and the known molecular mechanisms underlying this condition. Additionally, we describe studies comparing the transcriptional differences in hair follicles between balding and non-balding scalp regions. Given the genetic contribution, we also discuss the most significant risk variants found to be associated with androgenetic alopecia. A more comprehensive understanding of this pathology may be generated through using multi-omics approaches.

Discovering new chemical compounds with specific properties can provide advantages for fields that rely on materials for their development, although this task comes at a high cost in terms of complexity and resources. Since the beginning of the data age, deep learning techniques have revolutionized the process of designing molecules by analyzing and learning from representations of molecular data, greatly reducing the resources and time involved. Various deep learning approaches have been developed to date, using a variety of architectures and strategies, in order to explore the extensive and discontinuous chemical space, providing benefits for generating compounds with specific properties. In this study, we present a systematic review that offers a statistical description and comparison of the strategies utilized to generate molecules through deep learning techniques, utilizing the metrics proposed in Molecular Sets (MOSES) or Guacamol. The study included 48 articles retrieved from a query-based search of Scopus and Web of Science and 25 articles retrieved from citation search, yielding a total of 72 retrieved articles, of which 62 correspond to chemical language models approaches to molecule generation and other 10 retrieved articles correspond to molecular graph representations. Transformers, recurrent neural networks (RNNs), generative adversarial networks (GANs), Structured Space State Sequence (S4) models, and variational autoencoders (VAEs) are considered the main deep learning architectures used for molecule generation in the set of retrieved articles. In addition, transfer learning, reinforcement learning, and conditional learning are the most employed techniques for biased model generation and exploration of specific chemical space regions. Finally, this analysis focuses on the central themes of molecular representation, databases, training dataset size, validity-novelty trade-off, and performance of unbiased and biased chemical language models. These themes were selected to conduct a statistical analysis utilizing graphical representation and statistical tests. The resulting analysis reveals the main challenges, advantages, and opportunities in the field of chemical language models over the past four years.

Interferon (IFN) signaling contributes to stemness, cell proliferation, cell death, and cytokine signaling in cancer and immune cells; however, the role of IFN signaling in glioblastoma (GBM) and GBM stem-like cells (GSCs) is unclear. Here, we investigated the role of cancer-cell-intrinsic IFN signaling in tumorigenesis in GBM. We report here that GSCs and GBM tumors exhibited differential cell-intrinsic type I and type II IFN signaling, and high IFN/STAT1 signaling was associated with mesenchymal phenotype and poor survival outcomes. In addition, chronic inhibition of IFN/STAT1 signaling decreased cell proliferation and mesenchymal signatures in GSCs with intrinsically high IFN/STAT1 signaling. IFN-β exposure induced apoptosis in GSCs with intrinsically high IFN/STAT1 signaling, and this effect was abolished by the pharmacological inhibitor ruxolitinib and STAT1 knockdown. We provide evidence for targeting IFN signaling in a specific sub-group of GBM patients. IFN-β may be a promising candidate for adjuvant GBM therapy.

Germline mutations in BRCA1 and BRCA2 (BRCA1/2) genes are present in about 50% of cases of hereditary breast cancer. Proteins encoded by these genes are key players in DNA repair by homologous recombination (HR). Advances in next generation sequencing and gene panels for breast cancer testing have generated a large amount of data on gene variants implicated in hereditary breast cancer, particularly in genes such as PALB2, ATM, CHEK2, RAD51, MSH2, and BARD1. These genes are involved in DNA repair. Most of these variants have been reported for Caucasian, Jewish, and Asian population, with few reports for other communities, like those in Latin American (LA) countries. We reviewed 81 studies from 11 LA countries published between 2000 and 2019 but most of these studies focused on BRCA1/2 genes. In addition to these genes, breast cancer-related variants have been reported for PALB2, ATM, CHEK2, BARD1, MLH1, BRIP1, MSH2, NBN, MSH6, and PMS2 genes. Some of these variants are unique to LA populations. This analysis may contribute to enhance breast cancer variant characterization, and thus to find therapies and implement precision medicine for LA communities.

Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.

The prevalence of breast cancer in young women (YWBC) has increased alarmingly. Significant efforts are being made to elucidate the biological mechanisms concerning the development, prognosis, and pathological response in early-onset breast cancer (BC) patients. Dysfunctional DNA repair proteins are implied in BC predisposition, progression, and therapy response, underscoring the need for further analyses on DNA repair genes. Public databases of large patient datasets such as METABRIC, TCGA, COSMIC, and cancer cell lines allow the identification of variants in DNA repair genes and possible precision drug candidates. This study aimed at identifying variants and drug candidates that may benefit Latin American (LA) YWBC. We analyzed pathogenic variants in 90 genes involved in DNA repair in public BC datasets from METABRIC, TCGA, COSMIC, CCLE, and COSMIC Cell Lines Project. Results showed that reported DNA repair germline variants in the LA dataset are underrepresented in large databases, in contrast to other populations. Additionally, only six gene repair variants in women under 50 years old from the study population were reported in BC cell lines. Therefore, there is a need for new approaches to study DNA repair variants reported in young women from LA.