Trichoscopy for the Hair Transplant Surgeon-Assessing for Mimickers of Androgenetic Alopecia and Preoperative Evaluation of Donor Site Area

Preoperative diagnostic confidence and donor site assessment are important for all hair transplant surgery patients. While the majority of patients seek hair transplantation for male or female pattern hair loss (androgenetic alopecia [AGA]), there are mimickers that must be differentiated from patterned hair loss, as they alter the candidacy of the patient for transplantation. They are termed mimickers as they also can present with patterned hair loss. The use of trichoscopy has become increasingly popular for such use. Patterned hair loss mimickers, which include the underappreciated alopecia areata incognita (AAI) and fibrosing alopecia in patterned distribution (FAPD), can be identified clinically with key trichoscopic findings such as yellow dots and peripilar casts, respectively, that correlate with their histologic diagnosis. Donor hair density and putative hair pathology of the safe donor area can also by assessed via trichoscopy. This article discusses the use of trichoscopy, particularly for diagnosing mimickers of patterned hair loss as well as preoperative donor site assessment

First Use of Tapinarof Monotherapy for Seborrhoeic Dermatitis: A Case Report

Abstract is missing (Short communication

ES-Screen: A Novel Electrostatics-Driven Method for Drug Discovery Virtual Screening

Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein–protein, and protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called ‘ES-Screen’ that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery

Research Techniques Made Simple: Molecular Docking in Dermatology - A Foray into In Silico Drug Discovery

Drug discovery is a complex process with many potential pitfalls. To go to market, a drug must undergo extensive preclinical optimization followed by clinical trials to establish its efficacy and minimize toxicity and adverse events. The process can take 10–15 years and command vast research and development resources costing over $1 billion. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. With the increasing availability of large public datasets (big data) and computational capabilities, data science is quickly becoming a key component of the drug discovery pipeline. One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. Molecular modeling involves the study of the chemical structure of a drug and how it interacts with a potential disease-relevant target, as well as predicting its ADMET properties. The scope of molecular modeling is wide and complex. Here we specifically discuss docking, a tool commonly employed for studying drug-target interactions. Docking allows for the systematic exploration of how a drug interacts at a protein binding site and allows for the rank-ordering of drug libraries for prioritization in subsequent studies. This process can be efficiently used to virtually screen libraries containing over millions of compounds

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced “omics” coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases

Alopecia After Cosmetic Injection Procedures: A Review

BACKGROUND Cosmetic procedures for antiaging carry inherent risks of adverse events. One that has not yet been well characterized is transitory or permanent alopecia. This is attributable to numerous mechanisms including pressure, ischemia, inflammation, and necrosis. Cases of postcosmetic procedure alopecia have been reported after mesotherapy as well as hyaluronic acid filler, deoxycholic acid, and botulinum toxin injections. OBJECTIVE This review serves to describe the currently known causes of postcosmetic procedure alopecia and the mechanisms by which alopecia is attained. Furthermore, this review highlights the risk of unregulated mesotherapy injections for cosmetic enhancement and to bring attention to the increasing number reports of alopecia after these procedures. METHODS A systematic review of the literature from 2000 to 2022 was conducted looking for keywords such as "alopecia," "cosmetic procedures," "mesotherapy," and "hyaluronic acid" in Google Scholar and PubMed. RESULTS Ten articles met the criteria set forth in the authors' literature review. Many of the procedures resulted in partial or complete resolution of alopecia. CONCLUSION Alopecia after cosmetic injection procedures is an underreported adverse effect. More research is needed to further characterize the risk of alopecia after mesotherapy and other injection procedures