Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that, although preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance, and uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limiting factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bacterial proteins are targeted during the biosynthesis of the cell wall and the energetic metabolism of two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and explains how chemical probes for protein profiling enabled the identification of new targets and biomarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Fucus vesiculosus L. is a common coastal brown seaweed associated with various benefits to human health due to its phenolic content and nutrients and is used as food through different methods of consumption. This study aims to evaluate the influence of the seaweed’s gender and growth stage on different types of biological activities as well as its chemical constitution and elements present. Akin to food preparation, aqueous extracts of the seaweed were prepared at 25 °C (salad) and 100 °C (soup). Biological activities were determined by measuring total phenol content (TPC), antioxidant activity and inhibition of acetylcholinesterase (AChE). Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS/MS) was used for compound identification, and elemental analysis was carried out by using Total Reflection X-ray Fluorescence Spectrometry (TXRF). Older females and males had higher TPC compared to the new ones at 100 °C. Antioxidant activity depended on the extraction temperature but was higher for the youngest male at 100 °C. AChE inhibitory activity was higher for older males at 25 °C, but at 100 °C it was higher for older females. Primary metabolites and various phloroglucinol were the main compounds identified. Additionally, since this seaweed is often harvested in estuarine systems with high anthropogenic impacts, its safety was evaluated through the evaluation of the sample’s metal content. The heavy metals detected are within the limits established by various regulating entities, pointing to a safe food source.

The language of pain is a sub-language used to describe a subjective, private, and painful experience. In the clinical assessment and management of chronic pain, which is not as straightforward as acute pain, verbal communication is key to convey relevant information to health professionals that would otherwise not be accessible, namely, intrinsic qualities of the painful experience and that of the patient. We raise the hypothesis of applying Natural Language Processing techniques to transcribed verbal descriptions of chronic pain, to capture that information in the form of linguistic features that characterize and quantify the experience of pain of each patient. Furthermore, we demonstrate the application of these features for base-pathology prediction, specifically regarding the diagnosis of rheumatoid arthritis and spondyloarthritis. A dataset of verbal descriptions was collected for this work, considering 85 patients. The descriptions were obtained by having each patient freely answer to an interview of seven questions. The dataset was pre-processed, and features were extracted, which were then fed into binary classification machine learning models. We obtained an accuracy of 79%, in a Leave-One-Out cross-validation fashion. Based on an extensive experimental setup, we conclude that the computational analysis of the language of pain can potentially extract useful information to aid health professionals, in this case, focusing on base-pathology prediction. We also conclude on which semantic features provided more useful information for the task (distribution of pain on the body), and which did not.

Reported experiences of chronic pain may convey qualities relevant to the exploration of this private and subjective experience. We propose this exploration by means of the Reddit Reports of Chronic Pain (RRCP) dataset. We define and validate the RRCP for a set of subreddits related to chronic pain, identify the main concerns discussed in each subreddit, model each subreddit according to their main concerns, and compare subreddit models. The RRCP dataset comprises 86,537 submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit was found to have various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Our analysis details each of these concerns and their (dis)similarity relations. Although limited by the intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent, chronic pain experiences.