Photon-efficient optical tweezers via wavefront shaping.

Optical tweezers enable noncontact trapping of microscale objects using light. It is not known how tightly it is possible to three-dimensionally (3D) trap microparticles with a given photon budget. Reaching this elusive limit would enable maximally stiff particle trapping for precision measurements on the nanoscale and photon-efficient tweezing of light-sensitive objects. Here, we customize the shape of light fields to suit specific particles, with the aim of optimizing trapping stiffness in 3D. We show, theoretically, that the confinement volume of microspheres held in sculpted optical traps can be reduced by one to two orders of magnitude. Experimentally, we use a wavefront shaping-inspired strategy to passively suppress the Brownian fluctuations of microspheres in every direction concurrently, demonstrating order-of-magnitude reductions in their confinement volumes. Our work paves the way toward the fundamental limits of optical control over the mesoscopic realm.

The Relationship Between the Anaerobic Speed Reserve and Acute Responses to High-Intensity Interval Training in Female Soccer Players.

ABSTRACT: Aspin, GL, Graham, M, Franklin, J, Hicks, KM, and Taylor, JM. The relationship between the anaerobic speed reserve and acute responses to high-intensity interval training in female soccer players. J Strength Cond Res XX(X): 000-000, 2024-The anaerobic speed reserve (ASR) is a popular method of profiling soccer players, often used to individualize training prescription. This study explored the reliability of ASR profiling, and the relationship between the ASR and acute physiological responses to high-intensity interval training (HIIT). Acute physiological responses to different HIIT types were also compared. Thirteen subelite female soccer players aged 20.2 ± 4.6 years completed 6 exercise sessions. In sessions 1-2, players completed a 40-m sprint to assess maximal sprint speed (MSS) and 1600-m time-trial to estimate maximal aerobic speed (MAS), which were used to calculate ASR and assess test-retest reliability. In sessions 3-6, players completed 4 HIIT sessions (repeated-sprint training, sprint interval training, long intervals, and short intervals HIIT). Intensities for long and short intervals HIIT were individualized according to MAS. Ratings of perceived exertion (RPE), heart rate (HR), and postsession blood lactates were recorded throughout. Relationships between the ASR and acute responses to HIIT, and between HIIT session comparisons in outcome measures were assessed. Anaerobic speed reserve (coefficient of variation ± 95% confidence limits; 3.1 ± 1.5%), MAS (1.8 ± 1.3%), and MSS (0.8 ± 0.6%) indicated acceptable reliability. Moderate correlations between ASR and RPE (r = 0.33), postsession blood lactate (r = 0.34), and HR (r = 0.37) were observed during long intervals HIIT. A strong correlation was observed between ASR and RPE during SIT (r = 0.50). Sprint interval training elicited higher RPE's and postsession blood lactate's than other HIIT sessions. Anaerobic speed reserve has good reliability and may influence acute physiological responses to HIIT in female soccer players.

Lung ultrasound is associated with distinct clinical phenotypes in COVID-19 ARDS: A retrospective observational study.

BACKGROUND: ARDS is a heterogeneous syndrome with distinct clinical phenotypes. Here we investigate whether the presence or absence of large pulmonary ultrasonographic consolidations can categorize COVID-19 ARDS patients requiring mechanical ventilation into distinct clinical phenotypes. METHODS: This is a retrospective study performed in a tertiary-level intensive care unit in Israel between April and September 2020. Data collected included lung ultrasound (LUS) findings, respiratory parameters, and treatment interventions. The primary outcome was a composite of three ARDS interventions: prone positioning, high PEEP, or a high dose of inhaled nitric oxide. RESULTS: A total of 128 LUS scans were conducted among 23 patients. The mean age was 65 and about two-thirds were males. 81 scans identified large consolidation and were classified as "C-type", and 47 scans showed multiple B-lines with no or small consolidation and were classified as "B-type". The presence of a "C-type" study had 2.5 times increased chance of receiving the composite primary outcome of advanced ARDS interventions despite similar SOFA scores, Pao2/FiO2 ratio, and markers of disease severity (OR = 2.49, %95CI 1.40-4.44). CONCLUSION: The presence of a "C-type" profile with LUS consolidation potentially represents a distinct COVID-19 ARDS subphenotype that is more likely to require aggressive ARDS interventions. Further studies are required to validate this phenotype in a larger cohort and determine causality, diagnostic, and treatment responses.

Inhibition of soluble epoxide hydrolase by natural isothiocyanates.

GOAL: The long-term goal of our research is to develop safe and effective soluble epoxide hydrolase (sEH) inhibitors. The objective of this study is to evaluate the potency and selectivity of six natural isothiocyanates (ITCs) as sEH inhibitors. METHODS: Molecular docking was used to model likely interactions between the ligands and receptors. The sEH inhibitory activity was tested using a validated fluorescence-based assay and PHOME as a substrate. To evaluate their selectivity as sEH inhibitors, the inhibitory potential of the ITCs was determined on microsomal epoxide hydrolase (mEH) and cytochrome P450 (CYP) enzymes in human liver microsomes. Probe substrates such as styrene oxide (mEH substrate) and established substrates for CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4 were used in this study. The metabolites of these substrates were analyzed using validated LC-MS/MS and HPLC-UV assays. RESULTS: Molecular Docking revealed significant differences in binding site preference among the ITCs in silico and pointed to important interactions between the ligands and the catalytic residues of the sEH enzyme. In vitro, the ITCs showed varying degrees of sEH inhibition, but sulforaphane (SFN) and phenyl isothiocyanate (PITC) were the most potent inhibitors with IC50 values of 3.65 and 7.5 µM, respectively. mEH was not significantly inhibited by any of the ITCs. Erucin and iberin were the only ITCs that did not inhibit the activity of any of the tested CYP enzymes. CONCLUSION: Our results demonstrate that natural ITCs have the potential to offer safe, selective, and potent sEH inhibition.

Corrigendum to "An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD" [Kidney International Reports Volume 9, Issue 2, February 2024, Pages 249-256].

[This corrects the article DOI: 10.1016/j.ekir.2023.10.029.].

Physical and Cognitive Impairment in Acute Respiratory Failure.

Recent research has brought renewed attention to the multifaceted physical and cognitive dysfunction that accompanies acute respiratory failure (ARF). This state-of-the-art review provides an overview of the evidence landscape encompassing ARF-associated neuromuscular and neurocognitive impairments. Risk factors, mechanisms, assessment tools, rehabilitation strategies, approaches to ventilator liberation, and interventions to minimize post-intensive care syndrome are emphasized. The complex interrelationship between physical disability, cognitive dysfunction, and long-term patient-centered outcomes is explored. This review highlights the need for comprehensive, multidisciplinary approaches to mitigate morbidity and accelerate recovery.

An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD.

Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV). Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed. Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan. Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.

The Effects of Exercise-Based Injury Prevention Programmes on Injury Risk in Adult Recreational Athletes: A Systematic Review and Meta-Analysis.

BACKGROUND: Injuries are common in adult recreational athletes. Exercise-based injury prevention programmes offer the potential to reduce the risk of injury and have been a popular research topic. Yet, syntheses and meta-analyses on the effects of exercise-based injury prevention programmes for adult recreational athletes are lacking. OBJECTIVES: We aimed to synthesise and quantify the pooled intervention effects of exercise-based injury prevention programmes delivered to adults who participate in recreation sports. METHODS: Studies were eligible for inclusion if they included adult recreational athletes (aged > 16 years), an exercise-based intervention and used a randomised controlled trial design. Exclusion criteria were studies without a control group, studies using a non-randomised design and studies including participants who were undertaking activity mandatory for their occupation. Eleven literature databases were searched from earliest record, up to 9 June, 2022. The Physiotherapy Evidence Database (PEDro) scale was used to assess the risk of bias in all included studies. Reported risk statistics were synthesised in a random-effects meta-analysis to quantify pooled treatment effects and associated 95% confidence intervals and prediction intervals. RESULTS: Sixteen studies met the criteria. Risk statistics were reported as risk ratios [RRs] (n = 12) or hazard ratios [HRs] (n = 4). Pooled estimates of RRs and HRs were 0.94 (95% confidence interval 0.80-1.09) and 0.65 (95% confidence interval 0.39-1.08), respectively. Prediction intervals were 0.80-1.09 and 0.16-2.70 for RR and HR, respectively. Heterogeneity was very low for RR studies, but high for HR studies (tau = 0.29, I2 = 81%). There was evidence of small study effects for RR studies, evidenced by funnel plot asymmetry and Egger's test for small study bias: - 0.99 (CI - 2.08 to 0.10, p = 0.07). CONCLUSIONS: Pooled point estimates were suggestive of a reduced risk of injury in intervention groups. Nevertheless, these risk estimates were insufficiently precise, too heterogeneous and potentially compromised by small study effects to arrive at any robust conclusion. More large-scale studies are required to clarify whether exercise-based injury prevention programmes are effective in adult recreational athletes. CLINICAL TRIAL REGISTRATION: The protocol for this review was prospectively registered in the PROSPERO database (CRD42021232697).

Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population.

OBJECTIVES: Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs. DESIGN: This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs. PARTICIPANTS: 5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer). RESULTS: Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases. CONCLUSIONS: The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.

A TRIM21-based bioPROTAC highlights the therapeutic benefit of HuR degradation.

Human antigen R (HuR) is a ubiquitously expressed RNA-binding protein, which functions as an RNA regulator. Overexpression of HuR correlates with high grade tumours and poor patient prognosis, implicating it as an attractive therapeutic target. However, an effective small molecule antagonist to HuR for clinical use remains elusive. Here, a single domain antibody (VHH) that binds HuR with low nanomolar affinity was identified and shown to inhibit HuR binding to RNA. This VHH was used to engineer a TRIM21-based biological PROTAC (bioPROTAC) that could degrade endogenous HuR. Significantly, HuR degradation reverses the tumour-promoting properties of cancer cells in vivo by altering the HuR-regulated proteome, highlighting the benefit of HuR degradation and paving the way for the development of HuR-degrading therapeutics. These observations have broader implications for degrading intractable therapeutic targets, with bioPROTACs presenting a unique opportunity to explore targeted-protein degradation through a modular approach.

Fast algorithm for 3D volume reconstruction from light field microscopy datasets.

Light field microscopy can capture 3D volume datasets in a snapshot, making it a valuable tool for high-speed 3D imaging of dynamic biological events. However, subsequent computational reconstruction of the raw data into a human-interpretable 3D+time image is very time-consuming, limiting the technique's utility as a routine imaging tool. Here we derive improved equations for 3D volume reconstruction from light field microscopy datasets, leading to dramatic speedups. We characterize our open-source Python implementation of these algorithms and demonstrate real-world reconstruction speedups of more than an order of magnitude compared with established approaches. The scale of this performance improvement opens up new possibilities for studying large timelapse datasets in light field microscopy.

Brain PET and SPECT imaging and quantification: a survey of the current status in the UK.

OBJECTIVES: With disease-modifying therapies in development for neurological disorders, quantitative brain imaging techniques become increasingly relevant for objective early diagnosis and assessment of response to treatment. The aim of this study was to evaluate the use of Brain SPECT and PET scans in the UK and explore drivers and barriers to using quantitative analysis through an online survey. METHODS: A web-based survey with 27 questions was used to capture a snapshot of brain imaging in the UK. The survey included multiple-choice questions assessing the availability and use of quantification for DaTscan, Perfusion SPECT, FDG PET and Amyloid PET. The survey results were reviewed and interpreted by a panel of imaging experts. RESULTS: Forty-six unique responses were collected and analysed, with 84% of responses from brain imaging sites. Within these sites, 88% perform DaTscan, 50% Perfusion SPECT, 48% FDG PET, and 33% Amyloid PET, while a few sites use other PET tracers. Quantitative Brain analysis is used in 86% of sites performing DaTscans, 40% for Perfusion SPECT, 63% for FDG PET and 42% for Amyloid PET. Commercial tools are used more frequently than in-house software. CONCLUSION: The survey showed variations across the UK, with high availability of DaTscan imaging and quantification and lower availability of other SPECT and PET scans. The main drivers for quantification were improved reporting confidence and diagnostic accuracy, while the main barriers were a perception of a need for an appropriate database of healthy controls and a lack of training, time, and software availability.

Using Confocal Microscopy and Pigment Analyses to Detect Adverse Insecticide Effects in non-target Freshwater Diatom species - a proof-of-concept Using Nitzschia palea.

The persistence of insecticides in aquatic environments is a cause of concern and to date hardly any studies have focused on the effects that DDT and deltamethrin have on non-target freshwater diatom communities. The application of diatoms in ecotoxicological studies is well acknowledged and therefore this study used laboratory bioassays to determine the effects that DDT and deltamethrin have on a monoculture of a diatom indicator species, Nitzschia palea. The insecticides affected the morphology of chloroplasts at all exposure concentrations. These effects were a maximum reduction in chlorophyll concentrations (4.8% and 2.3%), cell viability (51% and 42%), and increases in cell deformities (3.6% and 1.6%) following exposure to DDT and deltamethrin respectively. Based on the results we propose that methods, such as confocal microscopy, chlorophyll-α analysis and cell deformities are useful tools in assessing the effects of insecticides on diatoms.

Point-of-Care Ultrasonography in a Pulmonary Hypertension Clinic: A Randomized Pilot Study.

Pulmonary arterial hypertension (PAH) is a rare condition with the potential to progress to right heart failure. Point-of-Care Ultrasonography (POCUS), used and interpreted in real time at the bedside to further the cardiopulmonary assessment, has the potential to improve the longitudinal care of PAH patients in the ambulatory setting. Patients from PAH clinics at two academic medical centers were randomized to either a POCUS assessment cohort or non-POCUS standard care (ClinicalTrials.gov identifier NCT05332847). The POCUS group received blinded heart, lung, and vascular ultrasound assessments. Thirty-six patients were randomized to the study and followed over time. Mean age was 65 in both groups and majority female (76.5% and 88.9% females in POCUS and control, respectively). Median time for POCUS assessment was 11 min (range 8-16). There were significantly more changes in management in the POCUS group than control (73% vs. 27%, p-value < 0.001). Multivariate analysis revealed that management changes were more likely to occur with a POCUS assessment, with an odds ratio (OR) of 12 when POCUS was added to physical exam vs. OR of 4.6 compared to physical examination alone (p < 0.001). POCUS in the PAH clinic is feasible and, when combined with physical examination, increases the number of findings and results in changes in management without significantly prolonging visit encounters. POCUS may help support clinical evaluation and decision making in ambulatory PAH clinics.

Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views.

AIMS: Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views. METHODS AND RESULTS: A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations (n = 1700, with 1400/100/200 cases for training/validating/testing). An independent test dataset from another institution was also evaluated, using cardiac magnetic resonance (CMR) 2CH and 4CH segmentations as input and 3D CCTA segmentations as the ground truth (n = 20). For the 200 test cases generated from CCTA, the network achieved a mean Dice score value of 93.7%, showing excellent 3D shape reconstruction from two views compared with the 3D segmentation Dice of 97.4%. The network also showed significantly lower mean absolute error values of 3.5 mL/4.9 cm2 for LA volume/surface area respectively compared to the area-length method errors of 13.0 mL/34.1 cm2 respectively (P < 0.05 for both). For the independent CMR test set, the network achieved accurate 3D shape estimation (mean Dice score value of 87.4%), and a mean absolute error values of 6.0 mL/5.7 cm2 for left atrial volume/surface area respectively, significantly less than the area-length method errors of 14.2 mL/19.3 cm2 respectively (P < 0.05 for both). CONCLUSIONS: Compared to the bi-plane area-length method, the network showed higher accuracy and robustness for both volume and surface area.

Characterization of the Native Disulfide Isomers of the Novel χ-Conotoxin PnID: Implications for Further Increasing Conotoxin Diversity.

χ-Conotoxins are known for their ability to selectively inhibit norepinephrine transporters, an ability that makes them potential leads for treating various neurological disorders, including neuropathic pain. PnID, a peptide isolated from the venom of Conus pennaceus, shares high sequence homology with previously characterized χ-conotoxins. Whereas previously reported χ-conotoxins seem to only have a single native disulfide bonding pattern, PnID has three native isomers due to the formation of different disulfide bond patterns during its maturation in the venom duct. In this study, the disulfide connectivity and three-dimensional structure of these disulfide isomers were explored using regioselective synthesis, chromatographic coelution, and solution-state nuclear magnetic resonance spectroscopy. Of the native isomers, only the isomer with a ribbon disulfide configuration showed pharmacological activity similar to other χ-conotoxins. This isomer inhibited the rat norepinephrine transporter (IC50 = 10 ± 2 µM) and has the most structural similarity to previously characterized χ-conotoxins. In contrast, the globular isoform of PnID showed more than ten times less activity against this transporter and the beaded isoform did not display any measurable biological activity. This study is the first report of the pharmacological and structural characterization of an χ-conotoxin from a species other than Conus marmoreus and is the first report of the existence of natively-formed conotoxin isomers.

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.

Introduction: Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA. Methods: A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort. Results: Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases. Conclusion: Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.

Snapshot volumetric imaging with engineered point-spread functions.

The biological world involves intracellular and intercellular interactions that occur at high speed, at multiple scales and in three dimensions. Acquiring 3D images, however, typically requires a compromise in either spatial or temporal resolution compared to 2D imaging. Conventional 2D fluorescence imaging provides high spatial resolution but requires plane-by-plane imaging of volumes. Conversely, snapshot methods such as light-field microscopy allow video-rate imaging, but at the cost of spatial resolution. Here we introduce 3D engineered point-spread function microscopy (3D-EPM), enabling snapshot imaging of real-world 3D extended biological structures while retaining the native resolution of the microscope in space and time. Our new computational recovery strategy is the key to volumetrically reconstructing arbitrary 3D structures from the information encapsulated in 2D raw EPM images. We validate our technique on both point-like and extended samples, and demonstrate its power by imaging the intracellular motion of chloroplasts undergoing cyclosis in a sample of Egeria densa. Our technique represents a generalised computational methodology for 3D image recovery which is readily adapted to a diverse range of existing microscopy platforms and engineered point-spread functions. We therefore expect it to find broad applicability in the study of rapid biological dynamics in 3D.

Biologics-based degraders - an expanding toolkit for targeted-protein degradation.

Targeted protein degradation (TPD) is a broadly useful proteome editing tool for biological research and therapeutic development. TPD offers several advantages over functional inhibition alone, including the ability to target previously undruggable proteins and the substantial and sustained knockout of protein activity. A variety of small molecule approaches hijack endogenous protein degradation machinery, but are limited to proteins with a cytosolic domain and suitable binding pocket. Recently, biologics-based methods have expanded the TPD toolbox by allowing access to extracellular and surface-exposed proteins and increasing target specificity. Here, we summarize recent advances in the use of biologics to deplete proteins through either the ubiquitin-proteasome system or the lysosomal degradation pathway, and discuss routes to their effective delivery as potential therapeutic interventions.

Molecular Dynamics Study of the Aggregation Behavior of N,N,N',N'-Tetraoctyl Diglycolamide.

Liquid-liquid extraction is a commonly used technique to separate metals and is a process that has particular relevance to the nuclear industry. There has been a drive to use environmentally friendly ligands composed only of carbon, hydrogen, nitrogen, and oxygen. One example is the i-SANEX process that has been developed to separate minor actinides from spent nuclear fuel. The underlying science of such processes, is, however, both complex and intriguing. Recent research indicates that the liquid phases involved are frequently structured fluids with a hierarchical organization of aggregates. Effective flow-sheet modeling of such processes is likely to benefit from the knowledge of the fundamental properties of these phases. As a stepping stone toward this, we have performed molecular dynamics simulations on a metal free i-SANEX system composed of the ligand N,N,N',N'-tetraoctyl diglycolamide (TODGA), diluent hydrogenated tetrapropylene (TPH), and polar species water and nitric acid. We have also studied the effects of adding n-octanol and swapping TPH for n-dodecane. It would seem sensible to understand this simpler system before introducing metal complexes. Such an understanding would ideally arise from studying the system's properties over a wide range of compositions. The large number of components, however, precludes a comprehensive scan of compositions, so we have chosen to study a fixed concentration of TODGA while varying the concentrations of water and nitric acid over a substantial range. Reverse aggregates are observed, with polar species in the interior in contact with the polar portions of the TODGA molecules and the organic diluent on the exterior in contact with the TODGA alkyl chains. These aggregates are irregular in shape and grow in size as the amount of water and nitric acid increases. At a sufficiently high polar content, a single extended cluster forms corresponding to the third phase formation. No well-defined bonding motifs were observed between the polar species and TODGA. The cluster size distribution fits an isodesmic model, where the Gibbs energy change of adding a TODGA molecule to a cluster ranges between 4.5 and 7.0 kJ mol-1, depending on the system composition. The addition of n-octanol was found to reduce the degree of aggregation, with n-octanol acting as a co-surfactant. Exchanging the diluent TPH for n-dodecane also decreased the aggregation. We present evidence that this is due to the greater penetration of n-dodecane into the reverse aggregates. It is known, however, that the propensity for the third phase formation is greater with n-dodecane as the diluent than is the case with TPH, but we argue that these two results are not contradictory. This research casts light on the driving forces for aggregation, informs process engineers as to what species are present, and indicates that flow-sheet liquid-liquid extraction modeling might benefit by incorporating an isodesmic aggregation approach.