Early oral antibiotic switch in Staphylococcus aureus bacteraemia: The Staphylococcus aureus Network Adaptive Platform (SNAP) Trial Early Oral Switch Protocol.

BACKGROUND: Staphylococcus aureus bloodstream infection (bacteraemia) is traditionally treated with at least two weeks of IV antibiotics in adults, 3-7 days in children, and often longer for those with complicated disease. The current practice of treating S. aureus bacteraemia (SAB) with prolonged IV antibiotics (rather than oral antibiotics) is based on historical observational research and expert opinion. Prolonged IV antibiotic therapy has significant disadvantages for patients and healthcare systems, and there is growing interest in whether a switch to oral antibiotics following an initial period of IV therapy is a safe alternative for clinically stable patients. PROTOCOL: The early oral switch (EOS) domain of the S. aureus Network Adaptive Platform (SNAP) trial will assess early switch to oral antibiotics compared with continued IV treatment in clinically stable patients with SAB. The primary endpoint is 90-day all-cause mortality. Hospitalised SAB patients are assessed at platform day 7 +/- 2 (uncomplicated SAB) and day 14 +/-2 (complicated SAB) to determine their eligibility for randomisation to EOS (intervention) or continued IV treatment (current standard of care). DISCUSSION: Recruitment is occurring to the EOS domain of the SNAP trial. As of August 2023, 21% of all SNAP participants had been randomised to the EOS domain, a total of 264 participants across 77 centres, with an aim to recruit at least 1000 participants. We describe challenges and facilitators to enrolment in this domain to aid those planning similar trials.

A multi-site, international laboratory study to assess the performance of penicillin susceptibility testing of Staphylococcus aureus.

OBJECTIVES: There is clinical uncertainty over the optimal treatment for penicillin-susceptible Staphylococcus aureus (PSSA) infections. Furthermore, there is concern that phenotypic penicillin susceptibility testing methods are not reliably able to detect some blaZ-positive S. aureus. METHODS: Nine S. aureus isolates, including six genetically diverse strains harbouring blaZ, were sent in triplicate to 34 participating laboratories from Australia (n = 14), New Zealand (n = 6), Canada (n = 12), Singapore (n = 1) and Israel (n = 1). We used blaZ PCR as the gold standard to assess susceptibility testing performance of CLSI (P10 disc) and EUCAST (P1 disc) methods. Very major errors (VMEs), major error (MEs) and categorical agreement were calculated. RESULTS: Twenty-two laboratories reported 593 results according to CLSI methodology (P10 disc). Nineteen laboratories reported 513 results according to the EUCAST (P1 disc) method. For CLSI laboratories, the categorical agreement and calculated VME and ME rates were 85% (508/593), 21% (84/396) and 1.5% (3/198), respectively. For EUCAST laboratories, the categorical agreement and calculated VME and ME rates were 93% (475/513), 11% (84/396) and 1% (3/198), respectively. Seven laboratories reported results for both methods, with VME rates of 24% for CLSI and 12% for EUCAST. CONCLUSIONS: The EUCAST method with a P1 disc resulted in a lower VME rate compared with the CLSI methods with a P10 disc. These results should be considered in the context that among collections of PSSA isolates, as determined by automated MIC testing, less than 10% harbour blaZ. Furthermore, the clinical relevance of phenotypically susceptible, but blaZ-positive S. aureus, remains unclear.

The Staphylococcus aureus Network Adaptive Platform Trial Protocol: New Tools for an Old Foe.

Staphylococcus aureus bloodstream (SAB) infection is a common and severe infectious disease, with a 90-day mortality of 15%-30%. Despite this, <3000 people have been randomized into clinical trials of treatments for SAB infection. The limited evidence base partly results from clinical trials for SAB infections being difficult to complete at scale using traditional clinical trial methods. Here we provide the rationale and framework for an adaptive platform trial applied to SAB infections. We detail the design features of the Staphylococcus aureus Network Adaptive Platform (SNAP) trial that will enable multiple questions to be answered as efficiently as possible. The SNAP trial commenced enrolling patients across multiple countries in 2022 with an estimated target sample size of 7000 participants. This approach may serve as an exemplar to increase efficiency of clinical trials for other infectious disease syndromes.

SynthStrip: skull-stripping for any brain image.

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines - all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.

Nuclear exclusion of SMAD2/3 in granulosa cells is associated with primordial follicle activation in the mouse ovary.

Maintenance and activation of the limited supply of primordial follicles in the ovary are important determinants of reproductive lifespan. Currently, the molecular programme that maintains the primordial phenotype and the early events associated with follicle activation are not well defined. Here, we have systematically analysed these events using microscopy and detailed image analysis. Using the immature mouse ovary as a model, we demonstrate that the onset of granulosa cell (GC) proliferation results in increased packing density on the oocyte surface and consequent GC cuboidalization. These events precede oocyte growth and nuclear translocation of FOXO3a, a transcription factor important in follicle activation. Immunolabelling of the TGFß signalling mediators and transcription factors SMAD2/3 revealed a striking expression pattern specific to GCs of small follicles. SMAD2/3 were expressed in the nuclei of primordial GCs but were mostly excluded in early growing follicles. In activated follicles, GC nuclei lacking SMAD2/3 generally expressed Ki67. These findings suggest that the first phenotypic changes during follicle activation are observed in GCs, and that TGFß signalling is fundamental for regulating GC arrest and the onset of proliferation.

Automatic Geometry-based Estimation of the Locus Coeruleus Region on T1-Weighted Magnetic Resonance Images.

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. We also test the hypothesis that using the phase image as input can improve the robustness of out-of-sample segmentation. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report contributes and provides an evaluation of two computational methods estimating neural structure.

Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images.

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

Multi-Head Graph Convolutional Network for Structural Connectome Classification.

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.

Exploratory Correlation of The Human Structural Connectome with Non-MRI Variables in Alzheimer's Disease.

INTRODUCTION: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. METHODS: We used four diffusion-MRI databases, three related to Alzheimer's disease, to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways. RESULTS: We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. DISCUSSION: Our findings help to elucidate which structural brain networks are affected in Alzheimer's disease and aging and highlight the importance of including indirect connections.

Exploratory correlation of the human structural connectome with non-MRI variables in Alzheimer's disease.

Introduction: Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers. Methods: We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways. Results: We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity. Discussion: Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.

Multi-Head Graph Convolutional Network for Structural Connectome Classification.

We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.

A blueprint for a multi-disease, multi-domain Bayesian adaptive platform trial incorporating adult and paediatric subgroups: the Staphylococcus aureus Network Adaptive Platform trial.

The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registration NCT05137119 . Registered on 30 November 2021.

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex.

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 µm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

A cellular resolution atlas of Broca's area.

Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with a digital stereological approach on the 3D reconstruction at cellular resolution from a custom-made inverted confocal light-sheet fluorescence microscope (LSFM). Mesoscale optical coherence tomography enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system. The outcome is an integrated high-resolution cellular census of Broca's area in a human postmortem specimen, within a whole-brain reference space atlas.

A Randomized Trial of Nafamostat for Covid-19.

BACKGROUND: Nafamostat mesylate is a potent in vitro antiviral agent that inhibits the host transmembrane protease serine 2 enzyme used by severe acute respiratory syndrome coronavirus 2 for cell entry. METHODS: This open-label, pragmatic, randomized clinical trial in Australia, New Zealand, and Nepal included noncritically ill hospitalized patients with coronavirus disease 2019 (Covid-19). Participants were randomly assigned to usual care or usual care plus nafamostat. The primary end point was death (any cause) or receipt of new invasive or noninvasive ventilation or vasopressor support within 28 days after randomization. Analysis was with a Bayesian logistic model in which an adjusted odds ratio <1.0 indicates improved outcomes with nafamostat. Enrollment was closed due to falling numbers of eligible patients. RESULTS: We screened 647 patients in 21 hospitals (15 in Australia, 4 in New Zealand, and 2 in Nepal) and enrolled 160 participants from May 2021 to August 2022. In the intention-to-treat population, the primary end point occurred in 8 (11%) of 73 patients with usual care and 4 (5%) of 82 with nafamostat. The median adjusted odds ratio for the primary end point for nafamostat was 0.40 (95% credible interval, 0.12 to 1.34) with a posterior probability of effectiveness (adjusted odds ratio <1.0) of 93%. For usual care compared with nafamostat, hyperkalemia occurred in 1 (1%) of 67 and 7 (9%) of 78 participants, respectively, and clinically relevant bleeding occurred in 1 (1%) of 73 and 7 (8%) of 82 participants. CONCLUSIONS: Among hospitalized patients with Covid-19, there was a 93% posterior probability that nafamostat reduced the odds of death or organ support. Prespecified stopping criteria were not met, precluding definitive conclusions. Hyperkalemia and bleeding were more common with nafamostat. (Funded by ASCOT and others; ClinicalTrials.gov number, NCT04483960.)

Characterization and significance of adhesion and junction-related proteins in mouse ovarian follicles.

In the ovary, initiation of follicle growth is marked by cuboidalization of flattened granulosa cells (GCs). The regulation and cell biology of this shape change remains poorly understood. We propose that characterization of intercellular junctions and associated proteins is key to identifying as yet unknown regulators of this important transition. As GCs are conventionally described as epithelial cells, this study used mouse ovaries and isolated follicles to investigate epithelial junctional complexes (tight junctions [TJ], adherens junctions [AJ], and desmosomes) and associated molecules, as well as classic epithelial markers, by quantitative PCR and immunofluorescence. These junctions were further characterized using ultrastructural, calcium depletion and biotin tracer studies. Junctions observed by transmission electron microscopy between GCs and between GCs and oocyte were identified as AJs by expression of N-cadherin and nectin 2 and by the lack of TJ and desmosome-associated proteins. Follicles were also permeable to biotin, confirming a lack of functional TJs. Surprisingly, GCs lacked all epithelial markers analyzed, including E-cadherin, cytokeratin 8, and zonula occludens (ZO)-1alpha+. Furthermore, vimentin was expressed by GCs, suggesting a more mesenchymal phenotype. Under calcium-free conditions, small follicles maintained oocyte-GC contact, confirming the importance of calcium-independent nectin at this stage. However, in primary and multilayered follicles, lack of calcium resulted in loss of contact between GCs and oocyte, showing that nectin alone cannot maintain attachment between these two cell types. Lack of classic markers suggests that GCs are not epithelial. Identification of AJs during GC cuboidalization highlights the importance of AJs in regulating initiation of follicle growth.

Gonadotropin-releasing hormone neuron requirements for puberty, ovulation, and fertility.

The absolute requirement for reproduction implies that the hypothalamo-pituitary-gonadal axis, controlling fertility, is an evolutionary robust mechanism. The GnRH neurons of the hypothalamus represent the key cell type within the body dictating fertility. However, the level of functional redundancy within the GnRH neuron population is unknown. As a result of a fortuitous transgene insertion event, GNR23 mice exhibit a marked allele-dependent reduction in GnRH neuron number within their brain. Wild-type mice have approximately 600 GnRH neurons, compared with approximately 200 (34%) and approximately 70 (12%) in GNR23(+/-) and GNR23(-/-) mice, respectively. Using these mice, we examined the minimal GnRH neuron requirements for fertility. Male GNR23(-/-) mice exhibited normal fertility. In contrast, female GNR23(-/-) mice were markedly subfertile, failing to produce normal litters, have estrous cycles, or ovulate. The failure of ovulation resulted from an inability of the few existing GnRH neurons to generate the LH surge. This was not the case, however, for the first cycle at puberty that appeared normal. Together, these observations demonstrate that 12% of the GnRH neuron population is sufficient for pulsatile gonadotropin secretion and puberty onset, whereas between 12 and 34% are required for cyclical control in adult female mice. This indicates that substantial redundancy exists within the GnRH neuronal population and suggests that the great majority of GnRH neurons must be dysfunctional before fertility is affected.

Androgen Stimulates Growth of Mouse Preantral Follicles In Vitro: Interaction With Follicle-Stimulating Hormone and With Growth Factors of the TGFß Superfamily.

Androgens are essential for the normal function of mature antral follicles but also have a role in the early stages of follicle development. Polycystic ovary syndrome (PCOS), the most common cause of anovulatory infertility, is characterized by androgen excess and aberrant follicle development that includes accelerated early follicle growth. We have examined the effects of testosterone and dihydrotestosterone (DHT) on development of isolated mouse preantral follicles in culture with the specific aim of investigating interaction with follicle-stimulating hormone (FSH), the steroidogenic pathway, and growth factors of the TGFß superfamily that are known to have a role in early follicle development. Both testosterone and DHT stimulated follicle growth and augmented FSH-induced growth and increased the incidence of antrum formation among the granulosa cell layers of these preantral follicles after 72 hours in culture. Effects of both androgens were reversed by the androgen receptor antagonist flutamide. FSH receptor expression was increased in response to both testosterone and DHT, as was that of Star, whereas Cyp11a1 was down-regulated. The key androgen-induced changes in the TGFß signaling pathway were down-regulation of Amh, Bmp15, and their receptors. Inhibition of Alk6 (Bmpr1b), a putative partner for Amhr2 and Bmpr2, by dorsomorphin resulted in augmentation of androgen-stimulated growth and modification of androgen-induced gene expression. Our findings point to varied effects of androgen on preantral follicle growth and function, including interaction with FSH-activated growth and steroidogenesis, and, importantly, implicate the intrafollicular TGFß system as a key mediator of androgen action. These findings provide insight into abnormal early follicle development in PCOS.

Onset and Heterogeneity of Responsiveness to FSH in Mouse Preantral Follicles in Culture.

The obligatory role of follicle-stimulating hormone (FSH) in normal development and function of ovarian antral follicles is well recognized, but its function in preantral growth is less clear. The specific objective of this study was to investigate the response, in culture, to FSH of mouse preantral follicles of increasing size, focusing particularly on growth rate and gene expression. Preantral follicles were mechanically isolated from ovaries of C57BL/6 mice, 12 to 16 days postpartum, and single follicles cultured for up to 96 hours in medium alone (n = 511) or with recombinant human FSH 10 ng/mL (n = 546). Data were grouped according to initial follicle diameter in 6 strata ranging from <100 to >140 µm. Follicles of all sizes grew in the absence of FSH (P < 0.01, paired t test). All follicles grew at a faster rate (P < 0.0001) in the presence of 10 ng/mL FSH but larger follicles showed the greatest change in response to FSH. Even the smallest follicles expressed FSH receptor messenger RNA (mRNA). FSH-induced growth was inhibited by KT5720, an inhibitor of protein kinase A (PKA), implicating the PKA pathway in FSH-induced follicle growth. In response to FSH in vitro, FSH receptor mRNA (measured by quantitative polymerase chain reaction) was reduced (P < 0.01), as was Amh (P < 0.01), whereas expression of StAR (P < 0.0001) and the steroidogenic enzymes Cyp11a1 (P < 0.01) and Cyp19 (P < 0.0001) was increased. These results show heterogeneous responses to FSH according to initial follicle size, smaller follicles being less FSH dependent than larger preantral follicles. These findings strongly suggest that FSH has a physiological role in preantral follicle growth and function.

Investigations of TGF-ß signaling in preantral follicles of female mice reveal differential roles for bone morphogenetic protein 15.

Bone morphogenetic protein 15 (BMP15) and growth differentiation factor 9 (GDF9) are 2 closely related TGF-ß ligands implicated as key regulators of follicle development and fertility. Animals harboring mutations of these factors often exhibit a blockage in follicle development beyond the primary stage and therefore little is known about the role of these ligands during subsequent (preantral) stages. Preantral follicles isolated from immature mice were cultured with combinations of BMP15, GDF9, and activin receptor-like kinase (ALK) inhibitors. Individually, GDF9 and BMP15 promoted follicle growth during the first 24 hours, whereas BMP15 subsequently (48-72 h) caused follicle shrinkage and atresia with increased granulosa cell apoptosis. Inhibition of ALK6 prevented the BMP15-induced reduction in follicle size and under basal conditions promoted a rapid increase in granulosa cell proliferation, suggesting BMP15 signals through ALK6, which in turn acts to restrain follicle growth. In the presence of GDF9, BMP15 no longer promoted atresia and in fact follicle growth was increased significantly more than with either ligand alone. This cooperative effect was accompanied by differential expression of Id1-3, Smad6-7, and Has2 and was blocked by the same ALK5 inhibitor used to block GDF9 signaling. Immunostaining for SMAD2/3 and SMAD1/5/8, representing the 2 main branches of TGF-ß signaling, supported the fact that both canonical pathways have the potential to be active in growing follicles, whereas primordial follicles only express SMAD2/3. Overall results highlight differential effects of the 2 main TGF-ß signaling pathways during preantral follicle growth.