BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.

The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.

An update on global mining land use.

The growing demand for minerals has pushed mining activities into new areas increasingly affecting biodiversity-rich natural biomes. Mapping the land use of the global mining sector is, therefore, a prerequisite for quantifying, understanding and mitigating adverse impacts caused by mineral extraction. This paper updates our previous work mapping mining sites worldwide. Using visual interpretation of Sentinel-2 images for 2019, we inspected more than 34,000 mining locations across the globe. The result is a global-scale dataset containing 44,929 polygon features covering 101,583 km2 of large-scale as well as artisanal and small-scale mining. The increase in coverage is substantial compared to the first version of the dataset, which included 21,060 polygons extending over 57,277 km2. The polygons cover open cuts, tailings dams, waste rock dumps, water ponds, processing plants, and other ground features related to the mining activities. The dataset is available for download from https://doi.org/10.1594/PANGAEA.942325 and visualisation at www.fineprint.global/viewer .

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria.

Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g. Cancer and coronavirus disease 2019, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine and precision medicine. These advances benefit our society and economy, directly impacting people's lives in various areas, such as health care, drug discovery, forensic analysis and food processing. Nevertheless, ML-based approaches to biological data require representative, quantitative and informative features. Many ML algorithms can handle only numerical data, and therefore sequences need to be translated into a numerical feature vector. This process, known as feature extraction, is a fundamental step for developing high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with design and selection of suitable features. Feature engineering, ML algorithm selection and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge. To deal with this problem, we present a new package: BioAutoML. BioAutoML automatically runs an end-to-end ML pipeline, extracting numerical and informative features from biological sequence databases, using the MathFeature package, and automating the feature selection, ML algorithm(s) recommendation and tuning of the selected algorithm(s) hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules: (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyper-parameter tuning modules). We experimentally evaluate BioAutoML in two different scenarios: (i) prediction of the three main classes of noncoding RNAs (ncRNAs) and (ii) prediction of the eight categories of ncRNAs in bacteria, including housekeeping and regulatory types. To assess BioAutoML predictive performance, it is experimentally compared with two other AutoML tools (RECIPE and TPOT). According to the experimental results, BioAutoML can accelerate new studies, reducing the cost of feature engineering processing and either keeping or improving predictive performance. BioAutoML is freely available at https://github.com/Bonidia/BioAutoML.

Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy.

In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.

One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.

Feature extraction approaches for biological sequences: a comparative study of mathematical features.

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability:https://github.com/Bonidia/FeatureExtraction_BiologicalSequences.

The Impact of a 24-h Ultra-Marathon on Circulatory Endotoxin and Cytokine Profile.

The study aimed to determine circulatory endotoxin concentration, cytokine profile, and gastrointestinal symptoms of ultra-endurance runners (UER, n=17) in response to a 24-h continuous ultra-marathon competition (total distance range: 122-208 km) conducted in temperate ambient conditions (0-20 °C) in mountainous terrain. Body mass and body temperature were measured, and venous blood samples were taken before and immediately after competition. Samples were analysed for gram-negative bacterial endotoxin, C-reactive protein, cytokine profile, and plasma osmolality. Gastrointestinal symptoms were also monitored throughout competition. Mean exercise-induced body mass loss was (mean±SD) 1.7±1.8% in UER. Pre- and post-competition plasma osmolality in UER was 286±11 mOsmol·kg(-1) and 286±9 mOsmol·kg(-1), respectively. Pre- to post-competition increases (p<0.05) were observed for endotoxin (37%), C-reactive protein (2832%), IL-6 (3 436%), IL-1ß (332%), TNF-α (35%), IL-10 (511%), and IL-8 (239%) concentrations in UER, with no change in the control group (CON; n=12) observed (p>0.05). Gastrointestinal symptoms were reported by 75% of UER, with no symptoms reported by CON. IL-10 (r=0.535) and IL-8 (r=0.503) were positively correlated with gastrointestinal symptoms. A 24-h continuous ultra-marathon competition in temperate ambient conditions resulted in a circulatory endotoxaemia and pro-inflammatory cytokinaemia, counteracted by a compensatory anti-inflammatory response.

Integrative epigenome analysis identifies a Polycomb-targeted differentiation program as a tumor-suppressor event epigenetically inactivated in colorectal cancer.

Aberrant DNA hypermethylation in human cancer has been associated with Polycomb target genes in embryonic stem (ES) cells, but a functional link of the Polycomb-targeted differentiation program to tumorigenesis remains to be established. Here, through epigenome analysis correlating DNA hypermethylation in colon cancer with ES cell pluripotency and differentiation, we identified a set of DNA hypermethylated genes in cancer cells that are Polycomb targets strongly associated with ES cell differentiation, including HAND1, a developmental regulator. Intriguingly, HAND1 is silenced in over 90% of human primary colorectal tumors, and re-expression of HAND1 in colon cancer cells induces terminal differentiation, inhibits proliferation and prevents xenograft tumor formation. Moreover, hypermethylated HAND1 has a minimum enrichment of EZH2-H3K27me3 in cancer cells, but becomes EZH2 bound and bivalent upon the loss of DNA methylation, suggesting a sequential gene silencing event during oncogenesis. These findings established a functional role of Polycomb-targeted differentiation program as a tumor-suppressor event epigenetically inactivated in human cancer.

Cannabinoids and schizophrenia: therapeutic prospects.

Approximately one third of patients diagnosed with schizophrenia do not achieve adequate symptom control with standard antipsychotic drugs (APs). Some of these may prove responsive to clozapine, but non-response to APs remains an important clinical problem and cause of increased health care costs. In a significant proportion of patients, schizophrenia is associated with natural and iatrogenic metabolic abnormalities (obesity, dyslipidaemia, impaired glucose tolerance or type 2 diabetes mellitus), hyperadrenalism and an exaggerated HPA response to stress, and chronic systemic inflammation. The endocannabinoid system (ECS) in the brain plays an important role in maintaining normal mental health. ECS modulates emotion, reward processing, sleep regulation, aversive memory extinction and HPA axis regulation. ECS overactivity contributes to visceral fat accumulation, insulin resistance and impaired energy expenditure. The cannabis plant synthesises a large number of pharmacologically active compounds unique to it known as phytocannabinoids. In contrast to the euphoric and pro-psychotic effects of delta-9-tetrahydrocannabinol (THC), certain non-intoxicating phytocannabinoids have emerged in pre-clinical and clinical models as potential APs. Since the likely mechanism of action does not rely upon dopamine D2 receptor antagonism, synergistic combinations with existing APs are plausible. The anti-inflammatory and immunomodulatory effects of the non-intoxicating phytocannabinoid cannabidiol (CBD) are well established and are summarised below. Preliminary data reviewed in this paper suggest that CBD in combination with a CB1 receptor neutral antagonist could not only augment the effects of standard APs but also target the metabolic, inflammatory and stress-related components of the schizophrenia phenotype.

Therapeutic potential of cannabinoid medicines.

Cannabis was extensively used as a medicine throughout the developed world in the nineteenth century but went into decline early in the twentieth century ahead of its emergence as the most widely used illicit recreational drug later that century. Recent advances in cannabinoid pharmacology alongside the discovery of the endocannabinoid system (ECS) have re-ignited interest in cannabis-based medicines. The ECS has emerged as an important physiological system and plausible target for new medicines. Its receptors and endogenous ligands play a vital modulatory role in diverse functions including immune response, food intake, cognition, emotion, perception, behavioural reinforcement, motor co-ordination, body temperature, wake/sleep cycle, bone formation and resorption, and various aspects of hormonal control. In disease it may act as part of the physiological response or as a component of the underlying pathology. In the forefront of clinical research are the cannabinoids delta-9-tetrahydrocannabinol and cannabidiol, and their contrasting pharmacology will be briefly outlined. The therapeutic potential and possible risks of drugs that inhibit the ECS will also be considered. This paper will then go on to review clinical research exploring the potential of cannabinoid medicines in the following indications: symptomatic relief in multiple sclerosis, chronic neuropathic pain, intractable nausea and vomiting, loss of appetite and weight in the context of cancer or AIDS, psychosis, epilepsy, addiction, and metabolic disorders.

Impact of Miscanthus x giganteus senescence times on fast pyrolysis bio-oil quality.

In this study the impact of senescence and harvest time in Miscanthus on the quality of fast pyrolysis liquid (bio-oil) was investigated. Bio-oil was produced using a 1 kg h(-1) fast pyrolysis reactor to obtain a quantity of bio-oil comparable with existing industrial reactors. Bio-oil stability was measured using viscosity, water content, pH and heating value changes under specific conditions. Plant developmental characteristics were significantly different (P≤0.05) between all harvest points. The stage of crop senescence was correlated with nutrient remobilisation (N, P, K; r2=0.9043, r2=0.9920, r2=0.9977 respectively) and affected bio-oil quality. Harvest time and senescence impacted bio-oil quality and stability. For fast pyrolysis processing of Miscanthus, the harvest time of Miscanthus can be extended to cover a wider harvest window whilst still maintaining bio-oil quality but this may impact mineral depletion in, and long term sustainability of, the crop unless these minerals can be recycled.

Risk factors for asthma in a helminth endemic area in bahia, Brazil.

Protective factors associated with atopy or asthma in rural areas include socioeconomic level, overcrowding, and helminth infection. However, little epidemiological information was originated from schistosomiasis areas. This study aimed to investigate factors associated with asthma in a schistosomiasis endemic area. A questionnaire was used to obtain information on demographics, socioeconomic, and environmental features. The ISAAC questionnaire was used to identify individuals with asthma. Parasitological exam was done in all participants and skin prick test to aeroallergens in all asthmatics. Prevalence of Schistosoma mansoni infection was 57.4% and Ascaris lumbricoides, 30.8%. Asthma was found in 13.1% of the population, and 35.1% of them had a positive SPT. Active and passive smoking was positively associated with asthma, whereas A. lumbricoides was negatively associated. In a schistosomiasis hyperendemic region, current infection with A. lumbricoides is protective against asthma. However, we cannot rule out the involvement of S. mansoni infection in this process.

The effect of antihelminthic treatment on subjects with asthma from an endemic area of schistosomiasis: a randomized, double-blinded, and placebo-controlled trial.

This is a prospective, double-blinded, and placebo-controlled trial evaluating the influence of antihelminthic treatments on asthma severity in individuals living in an endemic area of schistosomiasis. Patients from group 1 received placebo of Albendazole or of Praziquantel and from group 2 received Albendazole and Praziquantel. Asthma severity was assessed by clinical scores and by pulmonary function test. There was no significant difference in the asthma scores from D0 to D1-D7 after Albendazole or Praziquantel and from D0 to D30-90 after Albendazole or Praziquantel in both, group 1 and 2. It was observed, however, a clinical worsening of the overall studied population after 6 months and 12 months of antihelminthic treatments. Additionally, we observed increased frequency of forced expiratory volume in 1 second (FEV1) <80% on 12 and 18 months after treatment. The worsening of asthma severity after repeated antihelminthic treatments is consistent with the hypothesis of the protective role conferred by helminths in atopic diseases.

Factors associated with resistance to Schistosoma mansoni infection in an endemic area of Bahia, Brazil.

Detailed knowledge of factors associated with resistance to Schistosoma mansoni infection in endemic areas might facilitate more effective schistosomiasis control. We conducted a cross-sectional study of persons resistant to schistosomiasis and found no association between socioeconomic status and resistance to infection. Mononuclear cells of resistant subjects produced higher levels of interleukin-5 (IL-5), IL-13 and interferon-γ upon stimulation with soluble egg antigen (SEA) compared with infected persons. When stimulated with Sm21.6 or Sm22.6, levels of IL-10 were higher in cell culture of resistant persons. Levels of IgE against soluble adult worm antigen (SWAP) and against interleukin-4-inducing principle from S. mansoni eggs (IPSE) and levels of IgG4 against SWAP, SEA, and Sm22.6 were lower in the resistant group compared with the susceptible group. Our data suggest that socioeconomic status could not fully explain resistance to S. mansoni infection observed in the studied area. However, a mixture of Th1 and Th2 immune responses and low levels of specific IgG4 against parasite antigens could be mediating resistance to infection.

A flexible quantitative methodology for the analysis of gene-flow between conventionally bred maize populations using microsatellite markers.

Previous studies of gene-flow in agriculture have used a range of physical and biochemical markers, including transgenes. However, physical and biochemical markers are not available for all commercial varieties, and transgenes are difficult to use when trying to estimate gene flow in the field where the use of transgenes is often restricted. Here, we demonstrate the use of simple sequence repeat microsatellite markers (SSRs) to study gene flow in maize. Developing the first quantitative analysis of pooled SSR samples resulted in a high sampling efficiency which minimised the use of resources and greatly enhanced the possibility of hybrid detection. We were able to quantitatively distinguish hybrids in pools of ten samples from non-hybrid parental lines in all of the 24 pair-wise combinations of commercial varieties tested. The technique was used to determine gene flow in field studies, from which a simple model describing gene flow in maize was developed.

Prospective study of outcomes after percutaneous biliary drainage for malignant biliary obstruction.

BACKGROUND: Percutaneous biliary drainage (PBD) is used to relieve malignant bile duct obstruction (MBO) when endoscopic drainage is not feasible. Little is known about the effects of PBD on the quality of life (QoL) in patients with MBO. The aim of this study was to evaluate changes in QoL and pruritus after PBD and to explore the variables that impact these changes. MATERIALS AND METHODS: Eligible patients reported their QoL and pruritus before and after PBD using the Functional Assessment of Cancer Therapy-Hepatobiliary instrument (FACT-HS) and the Visual Analog Scale for Pruritus (VASP). Instruments were completed preprocedure and at 1 and 4 weeks following PBD. RESULTS: A total of 109 (60 male/49 female) patients enrolled; 102 (94%) had unresectable disease. PBD was technically successful (hepatic ducts cannulated at the conclusion of procedure) in all patients. There were 2 procedure-related deaths. All-cause mortality was 10% (N = 11) at 4 weeks and 28% (N = 31) at 8 weeks post-PBD with a median survival of 4.74 months. The mean FACT-HS scores declined significantly (P < .01) over time (101.3, 94.8, 94.7 at baseline, 1 week, 4 weeks, respectively). The VASP scores showed significant improvement at 1 week with continued improvement at 4 weeks (P < .01). CONCLUSIONS: PBD improves pruritus but not QoL in patients with MBO and advanced malignancy. There is high early mortality in this population.

Vitamin D status and parathyroid hormone relationship in adolescents and its association with bone health parameters: analysis of the Northern Ireland Young Heart's Project.

UNLABELLED: In girls, a plateau in parathyroid hormone (PTH) was observed at a 25-hydroxyvitamin D (25(OH)D) concentration of approximately 60 nmol/l. In boys, there was no plateau in PTH concentrations as 25(OH)D concentration increased. A 25(OH)D threshold of 60 nmol/l appears to have implications for bone health outcomes in both girls and boys. INTRODUCTION: Our objective was to investigate if there is a threshold 25(OH)D concentration where a plateau in PTH concentration is evident and to examine the impact of this relationship on bone mineral density (BMD) and bone turnover in a representative sample of adolescents. METHODS: We conducted a cross-sectional analysis among 1,015 Northern Irish adolescents aged 12 and 15 years. Serum 25(OH)D, PTH, osteocalcin, type 1 collagen cross-linked C-telopeptide (CTx), and BMD of the nondominant forearm and heel were measured. Nonlinear regression analysis was used to model the association between 25(OH)D and PTH. RESULTS: In girls, a plateau in PTH was observed at a 25(OH)D concentration of approximately 60 nmol/l (PTH = 47.146 + 370.314 x exp((-0.092 x 25(OH)D))) while no plateau in PTH was observed in boys (PTH = 42.144 + 56.366 x exp((-0.022 x 25(OH)D))). Subjects with 25(OH)D levels <60 nmol/l had significantly higher osteocalcin concentrations (P < 0.05) compared with those who had >or=60 nmol/l, while no significant (P > 0.05) differences were noted for CTx concentrations. In girls only, nondominant forearm BMD but not heel BMD was significantly higher (P = 0.046) in those with 25(OH)D concentrations >or= 60 nmol/l. CONCLUSIONS: Serum 25(OH)D levels above 60 nmol/l in Northern Irish adolescent girls prevent an increase in serum PTH levels and maintaining 25(OH)D >60 nmol/l in both girls and boys may lead to improved bone health outcomes.

Familial multiple coagulation factor deficiencies - chance associations and distinct clinical disorders.

The familial multiple coagulation factor deficiencies (FMCFDs) are a group of rare haemostatic disorders of genetic origin in which there is reduced plasma activity of more than one coagulation factor. FMCFDs may arise from co-incidental inheritance of separate coagulation factor deficiencies or from a single genetic or cytogenetic defect. All the FMCFDs present significant challenges in diagnosis and management yet there is little systematic evidence with which to guide clinical practice. This review summarizes the historical literature that describes the FMCFDs and introduces a refined classification of these disorders. The clinical and laboratory characteristics of the most common FMCFDs are considered in detail.

All-atom force field for the prediction of vapor-liquid equilibria and interfacial properties of HFA134a.

A new all-atom force field capable of accurately predicting the bulk and interfacial properties of 1,1,1,2-tetrafluoroethane (HFA134a) is reported. Parameterization of several force fields with different initial charge configurations from ab initio calculations was performed using the histogram reweighting method and Monte Carlo simulations in the grand canonical ensemble. The 12-6 Lennard-Jones well depth and diameter for the different HFA134a models were determined by fitting the simulation results to pure-component vapor-equilibrium data. Initial screening of the force fields was achieved by comparing the calculated and experimental bulk properties. The surface tension of pure HFA134a served as an additional screening property to help discriminate an optimum model. The proposed model reproduces the experimental saturated liquid and vapor densities, and the vapor pressure for HFA134a within average errors of 0.7%, 4.4%, and 3.1%, respectively. Critical density, temperature, vapor pressure, normal boiling point, and heat of vaporization at 298 K are also in good agreement with experimental data with errors of 0.2%, 0.1%, 6.2%, 0%, 2.2%, respectively. The calculated surface tension is found to be within the experimental range of 7.7-8.1 mN.m(-1). The dipole moment of the different models was found to significantly affect the prediction of the vapor pressure and surface tension. The ability of the HFA134a models in predicting the interfacial tension against water is also discussed. The results presented here are relevant in the development of technologies where the more environmentally friendly HFA134a is utilized as a substitute to the ozone depleting chlorofluorocarbon propellants.

Solvent-solute interactions in hydrofluoroalkane propellants.

Understanding solvation in hydrofluoroalkane (HFA) propellants is of great importance for the development of novel pressurized metered-dose inhaler (pMDI) formulations. HFA-based pMDIs are not only the most widely used inhalation therapy devices for delivering small drug molecules to the respiratory tract, but they also hold promise as vehicles for the delivery of therapeutic biomolecules to and through the lungs. In this work we use binding energy calculations to determine the degree of interaction between HFA propellants and candidate HFA-philes, including a methyl-based tail (isohexane, ISO), and fragments of poly(ethylene oxide) (EO), poly(propylene oxide) (PO), and poly(lactide) (LA). The distinct nature of solvation forces of the two HFA propellants approved by the FDA for use in pMDIs, 1,1,1,2-tetrafluoroethane (HFA134a) and 1,1,1,2,3,3,3-heptafluoropropane (HFA227), is also studied. Binding energy (Ebst) calculations demonstrated that an increase in tail polarity through the addition of oxygen atoms in the fragment backbone provides for sites capable of interacting with the HFA propellant molecules, thus enhancing the stabilization energy of the complexes. The interaction energy between HFA227 and LA (EbHFA227-LA = -24.7 kJ.mol(-1)) is significantly more favorable than that between HFA227 and its hydrocarbon analog (EbHFA227-ISO = -10.0 kJ.mol(-1)). However, it was shown that not only the fragment polarity is of relevance in stabilizing the complexes. The accessibility of the oxygen atoms in the fragments of interest is also relevant. Cluster studies indicate that although both oxygen atoms in the LA fragment are available to form H-bonds with the propellant molecules, the ether oxygen in PO is accessible to only one propellant molecule, thus decreasing significantly the stabilization energy of the cluster. The results shown here serve as a guide for the design of novel HFA-philes for HFA-based pMDIs.