New Investigations with Lupane Type A-Ring Azepane Triterpenoids for Antimycobacterial Drug Candidate Design

Multivariate analysis and data mining help predict asthma exacerbations

BACKGROUND

Work-related asthma has become a highly prevalent occupational lung disorder.

OBJECTIVE

Our study aims to evaluate occupational exposure as a predictor for asthma exacerbation.

METHOD

We performed a retrospective evaluation of 584 consecutive patients diagnosed and treated for asthma between October 2017 and December 2019 in four clinics from Western Romania. We evaluated the enrolled patients for their asthma control level by employing the Asthma Control Test (ACT < 20 represents uncontrolled asthma), the medical record of asthma exacerbations, occupational exposure, and lung function (i.e. spirometry). Then, we used statistical and data mining methods to explore the most important predictors for asthma exacerbations.

RESULTS

We identified essential predictors by calculating the odds ratios (OR) for the exacerbation in a logistic regression model. The average age was 45.42 ± 11.74 years (19-85 years), and 422 (72.26%) participants were females. 42.97% of participants had exacerbations in the past year, and 31.16% had a history of occupational exposure. In a multivariate model analysis adjusted for age and gender, the most important predictors for exacerbation were uncontrolled asthma (OR 4.79, p < .001), occupational exposure (OR 4.65, p < .001), and lung function impairment (FEV1 < 80%) (OR 1.15, p = .011). The ensemble machine learning experiments on combined patient features harnessed by our data mining approach reveal that the best predictor is professional exposure, followed by ACT.

CONCLUSIONS

Machine learning ensemble methods and statistical analysis concordantly indicate that occupational exposure and ACT < 20 are strong predictors for asthma exacerbation.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Pentacyclic Triterpenoid Phytochemicals with Anticancer Activity: Updated Studies on Mechanisms and Targeted Delivery

Pentacyclic triterpenoids (TTs) represent a unique family of phytochemicals with interesting properties and pharmacological effects, with some representatives, such as betulinic acid (BA) and betulin (B), being mainly investigated as potential anticancer molecules. Considering the recent scientific and preclinical investigations, a review of their anticancer mechanisms, structure-related activity, and efficiency improved by their insertion in nanolipid vehicles for targeted delivery is presented. A systematic literature study about their effects on tumor cells in vitro and in vivo, as free molecules or encapsulated in liposomes or nanolipids, is discussed. A special approach is given to liposome-TTs and nanolipid-TTs complexes to be linked to microbubbles, known as contrast agents in ultrasonography. The production of such supramolecular conjugates to deliver the drugs to target cells via sonoporation represents a new scientific and applicative direction to improve TT efficiency, considering that they have limited availability as lipophilic molecules. Relevant and recent examples of in vitro and in vivo studies, as well as the challenges for the next steps towards the application of these complex delivery systems to tumor cells, are discussed, as are the challenges for the next steps towards the application of targeted delivery to tumor cells, opening new directions for innovative nanotechnological solutions.