Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning

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.

Hazards Associated with the Combined Application of Fungicides and Poultry Litter in Agricultural Areas

In recent decades, the poultry farming industry has assumed a pivotal role in meeting the global demand for affordable animal proteins. While poultry farming makes a substantial contribution to food security and nutrition, it also presents environmental and public health challenges. The use of poultry litter as fertilizer for agricultural soils raises concerns about the transfer of pathogens and drug-resistant microorganisms from poultry farms to crop production areas. On the other hand, according to the Food and Agriculture Organization of the United Nations (FAO), fungicides represent the second most used chemical group in agricultural practices. In this context, agricultural soils receive the application of both poultry litter as a fertilizer and fungicides used in agricultural production. This practice can result in fungal contamination of the soil and the development of antifungal resistance. This article explores the necessity of monitoring antifungal resistance, particularly in food production areas with co-application of poultry litter and fungicides. It also highlights the role of fungi in ecosystems, decomposition, and mutualistic plant associations. We call for interdisciplinary research to comprehensively understand fungal resistance to fungicides in the environment. This approach seeks to promote sustainability in the realms of human health, agriculture, and the environment, aligning seamlessly with the One Health concept.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

Apparent Absence of Selective Pressure on Pneumocystis jirovecii Organisms in Patients with Prior Methotrexate Exposure

Pneumocystis jirovecii infections occur in patients treated with methotrexate (MTX) because of immunosuppressive effects of this highly potent dihydrofolate reductase (DHFR) inhibitor. Conversely, MTX may act as an anti-P. jirovecii drug and consequently may exert a selective pressure on this fungus. In this context, we compared the sequences of the dhfr gene of P. jirovecii isolates obtained from two groups of patients with P. jirovecii infections. The first group, with systemic diseases or malignancies, had prior exposure to MTX (21 patients), whereas the second group (22 patients), the control group, did not. Three single nucleotide polymorphisms (SNPs) were observed at positions 278, 312, and 381. The first one was located in the intronic region and the two others were synonymous. Based on these SNPs, three P. jirovecii dhfr alleles, named A, B, and C, were specified. Allele A was the most frequent, as it was observed in 18 patients (85.7%) and in 16 patients (72.7%) of the first and second groups, respectively. No significant difference in P. jirovecii dhfr gene diversity in the two patient groups was observed. In conclusion, these original results suggest that MTX does not exert an overt selective pressure on P. jirovecii organisms.

Effectiveness of front line and emerging fungal disease prevention and control interventions and opportunities to address appropriate eco-sustainable solutions

Fungal pathogens contribute to significant disease burden globally; however, the fact that fungi are eukaryotes has greatly complicated their role in fungal-mediated infections and alleviation. Antifungal drugs are often toxic to host cells and there is increasing evidence of adaptive resistance in animals and humans. Existing fungal diagnostic and treatment regimens have limitations that has contributed to the alarming high mortality rates and prolonged morbidity seen in immunocompromised cohorts caused by opportunistic invasive infections as evidenced during HIV and COVID-19 pandemics. There is a need to develop real-time monitoring and diagnostic methods for fungal pathogens and to create a greater awareness as to the contribution of fungal pathogens in disease causation. Greater information is required on the appropriate selection and dose of antifungal drugs including factors governing resistance where there is commensurate need to discover more appropriate and effective solutions. Popular azole fungal drugs are widely detected in surface water and sediment due to incomplete removal in wastewater treatment plants where they are resistant to microbial degradation and may cause toxic effects on aquatic organisms such as algae and fish. UV has limited effectiveness in destruction of anti-fungal drugs where there is increased interest in the combination approaches such as novel use of pulsed-plasma gas-discharge technologies for environmental waste management. There is growing interest in developing alternative and complementary green eco-biocides and disinfection innovation. Fungi present challenges for cleaning, disinfection and sterilization of reusable medical devices such as endoscopes where they (example, Aspergillus and Candida species) can be protected when harboured in build-up biofilm from lethal processing. Information on the efficacy of established disinfection and sterilization technologies to address fungal pathogens including bottleneck areas that present high risk to patients is lacking. There is a need to address risk mitigation and modelling to inform efficacy of appropriate intervention technologies that must consider all contributing factors where there is potential to adopt digital technologies to enable real-time analysis of big data, such as use of artificial intelligence and machine learning. International consensus on standardised protocols for developing and reporting on appropriate alternative eco-solutions must be reached, particularly in order to address fungi with increasing drug resistance where research and innovation can be enabled using a One Health approach.

General Equation to Express Changes in the Physicochemical Properties of Organic Homologues

Changes in various physicochemical properties (P _(n)) of organic compounds with the number of carbon atoms (n) can be roughly divided into linear and nonlinear changes. To date, there has been no general equation to express nonlinear changes in the properties of organic homologues. This study proposes a general equation expressing nonlinear changes in the physicochemical properties of organic homologues, including boiling point, viscosity, ionization potential, and vapor pressure, named the "NPOH equation", as follows: P _(n) = P ₍₁₎ α ^n - 1 e ^{∑_i=2 n(β/(i - 1))} where α and β are adjustable parameters, and P ₍₁₎ represents the property of the starting compound (pseudo-value at n = 1) of each homologue. The results show that various nonlinear changes in the properties of homologues can be expressed by the NPOH equation. Linear and nonlinear changes in the properties of homologues can all be correlated with n and the "sum of carbon number effects", ∑_i=2 ⁿ(1/i - 1). Using these two parameters, a quantitative correlation equation can be established between any two properties of each homologue, providing convenient mutual estimation of the properties of a homologue series. The NPOH equation can also be used in property correlation for structures with functionality located elsewhere along a linear alkyl chain as well as for branched organic compounds. This work can provide new perspectives for studying quantitative structure-property relationships.