Mycobacterium tuberculosis (Mtb). Despite being central to ongoing TB transmission, no routine diagnostic assay exists to measure Mtb in bioaerosols. Furthermore, published studies of Mtb in bioaerosol samples have been limited to individuals with sputum-positive pulmonary TB. Notably, TB diagnosis is based on clinical symptoms and sputum laboratory findings. This is despite the fact that approximately half of all patients commencing TB treatment are sputum-negative, resulting in a high proportion of presumptive treatments. Here, we propose to use a sensitive air sampling protocol to investigate the prevalence of Mtb-containing bioaerosols in both sputum-positive and sputum-negative TB suspects, at the same time evaluating the potential to identify unrecognized transmitters of TB.Tuberculosis (TB) is transmitted in bioaerosols containing Mtb in bioaerosols produced by individuals attending a TB clinic in South Africa. Sampling will be performed on eligible individuals presenting with symptoms indicative of TB and repeated at 14 days if initially positive. Participants will be prospectively classified into three distinct groups based on National TB Control Program (NTBCP) criteria: Group A, TB notification with sputum-based laboratory confirmation; Group B, TB notification with empiric diagnosis; and Group C, individuals not notified. Group C individuals with detectable Mtb bioaerosol will be monitored until resolution of clinical and laboratory status. Collection of bioaerosol specimens will be via two consecutive sampling modalities: (1) direct sampling following a specific respiratory manoeuvre; and (2) indirect sampling during passive respiratory activity. Bioaerosol specimens will be analyzed for viable Mtb using DMN-trehalose staining and live-cell fluorescence microscopy. Mtb genomes and mycobacterial and host lipids will be detected using droplet digital PCR and mass spectrometry analyses, respectively. The primary objective is to determine the prevalence of Mtb bioaerosols in all TB clinic attendees and in each of the groups. Secondary objectives are to investigate differences in prevalence of Mtb bioaerosol by HIV status and current isoniazid preventive therapy (IPT) use; we will also determine the impact of anti-TB chemotherapy on Mtb-containing bioaerosol production.Our parallel-group design will identify viable Respiratory bioaerosol has a potential role in non-invasive TB diagnosis, infectivity measurement and treatment monitoring.NCT04241809. Date of Registration: 27/1/2020.ClinicalTrials.gov:  Mycobacterium tuberculosis (Mtb), is the foremost infectious cause of death in adults globally [Mtb bioaerosol expulsion and inhalation by susceptible individuals. Interventions to interrupt this cycle are critical to curb the TB epidemic, however knowledge of the physical process of Mtb transmission is limited. This is primarily due to the paucity of investigations detecting, quantifying and characterizing Mtb bioaerosols. As others have highlighted [Mtb bioaerosols remain viable in the environment. Furthermore, while sputum Mtb positivity and presence of lung cavitation predict infectiousness, prediction is partial  thus limiting accurate risk stratification for contact screening. To address these knowledge gaps, tools to facilitate direct detection of viable aerosolized Mtb bacilli in exhaled breath are urgently required.Tuberculosis (TB), which is caused by the obligate human pathogen, globally . Persisthlighted , it remaMtb bioaerosol sampling, it is essential to maximize detection yield. Three key elements to consider are: (1) patient bioaerosol production; (2) an efficient bioaerosol collection apparatus; and (3) the Mtb detection modality. Previous bioaerosol studies have used a spectrum of sampling approaches. These include subjects coughing into an apparatus for 10 min with culture-based detection [Mtb bioaerosol in a sampled individual may only indicate Mtb numbers below the detection threshold of the sampling system, rather than non-production. Consequently, optimizing all elements of the sampling process is essential to identify Mtb bioaerosol in paucibacillary individuals. Critical factors include the site in the respiratory tract of bioaerosol generation [Mtb bacilli concentrations greater than in sputum [To improve the utility of tus; and  the Mtb etection , and weaetection , 6. Thouneration , bioaeron sputum , standarMtb bacilli based on DMN-trehalose probe incorporation patterns and differential bacterial cellular morphology [This study will assess an optimized bioaerosol sampling system based on the previously described Respiratory Aerosol Sampling Chamber (RASC) . Patientrphology .Mtb bacilli in exhaled bioaerosol from individuals attending a TB clinic in Cape Town, South Africa. The specific objectives are divided into primary, secondary and exploratory.The aim of this study is to identify viable Mtb-containing bioaerosol in sputum-positive and sputum-negative pulmonary TB cases (Groups A & B).To compare the prevalence proportions of 2.Mtb organisms in bioaerosols per litre of air sampled and per nanolitre of bioaerosol particulate volume collected in both sputum-positive and sputum-negative pulmonary TB cases (Groups A & B).To quantify the numbers of viable 3.Mtb-containing bioaerosols in TB suspects not diagnosed with clinical TB (Group C).To determine the prevalence of 4.Mtb in subjects receiving or not receiving IPT prophylaxis.To determine the prevalence and numbers of viable 5.Mtb in subjects with and without HIV co-infection.To determine the prevalence and numbers of viable 6.Mtb in subjects before and after TB therapy.To determine the prevalence and numbers of viable 7.Mtb in bioaerosols collected by direct and indirect sampling.To determine the numbers of viable 8.Mtb genomes (ddPCR) to viable Mtb organisms (DMN-tre positivity) in Mtb-containing bioaerosols.To determine the ratio of 9.Mtb and host lipids are present in Mtb-containing bioaerosols.To identify which Mtb. Group C individuals will be re-evaluated clinically, with induced sputa and repeated bioaerosol sampling at monthly intervals until two consecutive samples are negative or treatment is commenced.This study will utilize a pragmatic, parallel group design Fig. . All parConsecutive patients with respiratory and/or constitutional symptoms suggestive of pulmonary TB who self-present to either of two local TB clinics will be offered recruitment into the study. The expected timeline to recruit the target number of patients is 1–2 years.age over 13 years suspected TBwritten, informed consent The eligibility criteria are:2 concentration of sampled air will be measured. Ozone sterilization and empty RASC sampling will be carried out between every participant to provide negative controls.Bioaerosol specimens will be collected by two sampling methods. In addition, exhaled breath will be simultaneously sampled for aerodynamic particle counting and the COA direct sampling protocol was developed by modifying the RASC . BrieflySampling of patient-derived respiratory bioaerosol during natural respiratory activity will also be performed in the same chamber without the use of the elliptical cone. This method is similar to a previous study  but has Corynebacteriaceae (including Mycobacterium) by the antigen 85 protein complex [The pellet from the centrifuged bioaerosol sample is stained using the solvatochromic trehalose probe, DMN-trehalose, which is incorporated as trehalose monomycolate into the cell walls of metabolically active  complex . After o complex . The dev complex .Mtb bacilli is extracted using an in-house lysis buffer with subsequent pelleting of DNA (by centrifugation at 9000 x g for 10 min), discarding of supernatant, and resuspension in 20 μl of Tris-EDTA buffer . Given that ddPCR is relatively robust against inhibitors, no further DNA purification is required. The primer/probe combinations and reaction conditions for Mtb specific ddPCR have been described previously [Mtb H37Rv genomic DNA are included as positive internal controls for ddPCR, and nuclease-free water is included as a negative control. Data generated from the ddPCR reaction are analyzed via the Umbrella pipeline [DNA from eviously . Serial pipeline  using onThe supernatant from the centrifuged bioaerosol sample is lyophilized followed by lipid extraction by the Folch method. Samples are introduced into an LTQ Orbitrap Mass Spectrometer (Thermo Fisher Scientific) via electrospray ionization by direct infusion. Positive and negative ion calibration solutions are used to optimize ion source conditions and mass calibration. For data acquisition, resolving power of 60,000 at 200 m/z is used for both positive and negative ions in the mass range 200–2000 m/z.p < 0.05).Historical clinic data suggest individuals suspected of TB divide into groups A, B, and C in the ratio 3:5:10. Based on this ratio, a sample size of 250 TB clinic attendees will give a 90% power to detect a 20% bioaerosol yield in Group B. Assuming 100% bioaerosol yield in Group A, this gives an absolute difference of 12.5% between sputum and bioaerosol yields in notified TB cases  and microbiology data will be entered into a secure RedCap Database. To maintain confidentiality, participant identifying information will be removed. All analyses will be based on the groups to which patients are originally assigned. Analyses of the primary and secondary aims will be by comparison of paired proportions using McNemar’s test or comparison of independent proportions using a chi-square test. Mixed effect models can be used to adjust comparisons for any potential confounding variables.Ethical approval has been obtained from the University of Cape Town Faculty of Health Sciences Human Research Ethics Committee (HREC/REF: 529/2019). Written informed consent including for publication of medical details will be obtained from the participants, with parental consent and participant assent for those under 18 years of age.Mtb organisms produced from the respiratory tract regardless of potential role in transmission. In contrast, the indirect capture method samples an aged bioaerosol product from the close breathing zone of the patient. The smaller size distribution of bioaerosol following drop-out of the larger diameters and desiccation to hygroscopic equilibrium has the capacity to penetrate the lungs of susceptible contacts. It follows, therefore, that this bioaerosol has the greatest propensity for disease transmission. Furthermore, enumeration of viable Mtb bacilli derived from this method may more closely reflect host infectivity, as similar investigations have related high numbers of Mtb colony forming units in cough aerosols to increased infection rates in household contacts [Mtb bioaerosol production.The implications of bioaerosol positivity in people with suspected pulmonary TB differ depending on the subgroup sampled. Prevalence in patients with sputum-confirmed TB (Group A) establishes the sensitivity of bioaerosol sampling comparable with previous studies –5. This contacts . Using tMtb bacilli in sputum, or do not produce sputum, or do not expectorate adequately. Poor quality sputum samples are a particular problem in children and in HIV-infected individuals, leading to suboptimal diagnostic sensitivity. The finding of culture or Xpert MTB/RIF positivity in bronchoalveolar lavage from sputum-negative or sputum-scarce patients [Mtb is frequently present in the peripheral airways. The bronchiole fluid film burst method of bioaerosol generation [To our knowledge, bioaerosol sampling in sputum-negative pulmonary TB cases has not been performed previously, despite evidence of transmission from this group –18. We apatients –23 suggeneration  rapidly Mtb in bioaerosols from patients investigated but not treated for TB (Group C) indicates either a missed diagnosis or Mtb transmitters in the absence of discernible TB disease. Observations from multiple studies in different high-burden settings indicate that only 1–30% of new Mtb infections can be linked with known TB cases [Mtb transmitters who are not on treatment. As an exploratory aim, individuals identified with bioaerosol positivity will be re-sampled at weekly intervals  to characterize the natural history of sub-clinical Mtb shedding.Identifying TB cases –27. ThisMtb detection across groups of TB clinic attendees would support the future development of bioaerosol sampling methods to inform TB control strategies. Widespread application may potentially reduce diagnostic uncertainty and enable improved case-finding with both individual and public health benefit.This study aims to demonstrate the utility of bioaerosol sampling for TB diagnosis and therapeutic monitoring. High rates of successful bioaerosol The outcomes of the analysis in addition to the unprocessed data will be submitted for publication in open-source peer-reviewed journals and presented at international conferences.


Drosophila revealed a spatially accurate representation of visual cues in the central complex. This raised the question how, on a circuit level, the topographic features are implemented, as the majority of visual neurons enter the central brain converge in optic glomeruli. We discovered a spatial segregation of topographic versus nontopographic projections of distinct classes of medullo-tubercular (MeTu) neurons into a specific visual glomerulus, the anterior optic tubercle (AOTU). These parallel channels synapse onto different tubercular-bulbar (TuBu) neurons, which in turn relay visual information onto specific central complex ring neurons in the bulb neuropil. Hence, our results provide the circuit basis for spatially accurate representation of visual information and highlight the AOTU's role as a prominent relay station for spatial information from the retina to the central brain.One hallmark of the visual system is a strict retinotopic organization from the periphery toward the central brain, where functional imaging in   •Drosophila visual circuit conveys input from the periphery to the central brainA •Several synaptic pathways form parallel channels using the anterior optic tubercle•Some pathways maintain topographic relationships across several synaptic steps•Different target neurons in the central brain are identified Cellular Neuroscience; Molecular Neuroscience ; Optical Imaging ; Sensory Neuroscience Drosophila melanogaster, has provided considerable insights into how information is processed beyond photoreceptor cells  on the receiving end, has been identified as distinct classes of Ring neurons (R neurons), which form a stack of several ring-shaped layers in osophila . Afferenosophila . These cosophila . The traosophila . Functioosophila . Secondlosophila . In contosophila . The AOTosophila , receiviosophila , spatialosophila . Howeverosophila . Hence, Here, we show that stereotyped topographic maps are built by distinct MeTu neuron subtypes within the SU of the AOTU, which is spatially separated from LC representation in the LU. Interestingly, the overlapping dendritic fields of different MeTu subtypes in the medulla diverge into multiple parallel visual channels that are subsequently maintained via parallel synaptic pathways from the AOTU to the bulb neuropil. Within the bulb, topographic channels connect with distinct receptive fields of CX ring neurons, whereas nontopographic channels have different R-neuron targets. Based on these data we propose a model in which specific domains of the AOTU form a central relay station for both topographic and nontopographic visual information, organized in multiple parallel channels, ideally suited for conveying distinct visual features to the central brain.Optic glomeruli and olfactory glomeruli are prominent neuropil structures located in different regions of the adult brain, with olfactory glomeruli concentrated within the antennal lobes of the deutocerebrum, whereas optic glomeruli form the AOTU, the posterior ventrolateral protocerebrum (PVLP), and the posterior lateral protocerebrum (PLP) A. To detInputs from LC neurons to optic glomeruli in the PLP/PVLP region are restricted to the ventrolateral brain region  Figure D. In conTo characterize afferent arborizations within optic glomeruli, we first generated single cell clones see G and 1H.a versus SU-lp, a and SU-cp subdomains was found  co-labeled with GFP  might exist, the transsynaptic tracer “transTango”  was expr did not E and 3F.with GFP G–3H. Finwith GFP I. In sumeceptors J.Figure Next, we proceeded to a more systematic characterization of how AOTU subdomains correlate with MeTu neuron identity at a single cell level. Clonal analysis revealed a stereotypical, subtype-specific pattern of MeTu innervation, where any given MeTu axon terminates in only one of the five SU subdomains A–4C. ForDendritic fields of single MeTu-neurons always covered multiple medulla columns, yet the specific field size of individual MeTu-neuron clones varied considerably: at the anterior and posterior medulla border, neurons can be found that stretch across a major part of the dorsal medulla, either covering a large dendritic area in both axes I or spreIf the AOTU served as a relay station of spatial information from the optic lobes to central integration centers of the brain, one would expect a matching pattern of connections between MeTu subtypes and corresponding AOTU output neurons along the d-v axis, at least for the lateral and central SU domains. We and other groups identified a large set of expression lines for AOTU projection neurons targeting the bulb region (TuBu neurons) . An overs), whereas axons of TuBu-m neurons targeted into the inferior bulb (BUi) , we could dissect one fully matching pair of TuBu → R neuron circuit, as well as another pair with a partial overlap. Thus, yet another synaptic level is added to the parallel visual pathways described here, as distinct AOTU efferents remain separated and contact different EB rings (Efferent neurons from the bulb region have been shown to target specific ring layers within the EB (R neurons) . To char neurons G but not neurons . In the with R4d A–7A″ butwith R4d B–7C‴. Fowith R4d E and 7E′with R4d G–7I. In EB rings J.Figure Drosophila is currently being investigated using molecular genetic tools in combination with cell-type-specific driver lines , thereby building several pathways arranged in parallel (a and SU-cp) and posterior halves (SU-lp and SU-cp). Nevertheless, it should be noted that the total number of subdomains remains the same in both nomenclatures, with the major difference being the posterior-lateral subdomain (‘lp’), which has been attributed to the central domain (SU-cp) in our study, as part of the Connectin-positive central neuropil. Based on the connectome reconstruction of the hemibrain dataset domains. MeTu-l and -c classes have a similar neuronal morphology with dendrite arborization restricted to a single medulla layer (M6) and spatially narrow axon termination areas in four separate AOTU subdomains , both these classes target to areas within the superior bulb (BUs). More broadly expressed driver lines revealed exclusive TuBu neuron innervation of the BUs, indicating that additional TuBu classes target to this bulb area (data not shown). Thus, we expect at least four different classes of TuBu neurons to exclusively innervate the BUs , each of them connecting to a different set of output neurons, indicating an even more complex organization of the bulb, in particular the BUs. Thirdly, TuBu classes project onto dendritic areas of R neuron classes  within the bulb, and specific connections are formed between TuBu neurons and R neuron classes. Although we could identify three R neuron classes within the BUs, there probably exists a much higher diversity of connections within this small area of the bulb, reaching beyond the scope of this study. For instance, the postsynaptic partners of one subset of TuBu-ca neurons as well as neurons contacted by R2 and R5 dendrites remain to be identified. Additional postsynaptic partners other than R neurons are contacted by TuBu neurons, like contralaterally projecting neurons described in the locust  and efferent (R neurons) channels has been intensively studied in recent years , there se locust  and the e locust , which cp and SU-ca efferents mapping onto separate ring neurons (R4d versus R2). Hence, we could define at least two distinct topographic MeTu channels into the central brain. Although functional differences between the BUi and BUs have been described  separated from other TuBu neurons further suggests a different role for this pathway. i, supporting a model in which the SU-m pathway is involved in suppressing ipsilateral stimuli with the expense of reduced spatial resolution.A morphologically distinct class of MeTu cells is formed by MeTu-m cells. One distinguishing feature in respect to other MeTu cell types is that many cells arborize broadly in their respective AOTU domain. We found axon terminals of single MeTu-m neurons invariably spread across the a-p axis of their SU-domain, whereas in the d-v axis they either covered their domain completely or partially—the former case being reminiscent of the afferent organization of LC neurons from the lobula within optic glomeruli in the PVLP regions, whereas the latter case is similarly described for lobula neurons innervating the AOTU's large unit (LU) , where tTaken together, topographic and nontopographic afferents generate an interesting assembly of adjacent domains within the AOTU, from exclusively topographic medulla input in SU-l and SU-c domains, nontopographic medullar  input in SU-m, and another large area of nontopographic input exclusively from the lobula in the LU . Thus, wThis principle visual pathway involving the AOTU as a central relay station between medullar/lobular inputs and the central brain is widely shared among different insect taxa, where homologous structures can be found, e.g. orthopterans , hymenopDrosophila is among the smallest species for which the AOTU has been characterized and is believed to be a behavioral generalist, even more sophisticated architectures of the SU-homologue could exist in other insect taxa. On the anatomical and functional level, optic glomeruli share many features with the synaptic neuropil within the antennal lobe, which led to the postulation that the glomerular organization in the protocerebrum (optic glomeruli) and the deutocerebrum (olfactory glomeruli) are in fact homologous structures  to justify the establishment of further pathways, and we might have missed these cell types in our single cell labeling experiment, as this method involves random events where scarcer neurons can easily remain unnoticed. thomashummel@univie.ac.at).Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Thomas Hummel (The ort-mCD8::GFP construct is available on request without restriction.This study did not generate datasets or analyze codes.All methods can be found in the accompanying


Nutritional and metabolic disturbances are observed in patients critically ill with Coronavirus disease 19 (COVID-19) patients. The aim of this review is to describe these disturbances during the progression of the disease, from the pre-intubation phase through the ventilated condition to the post extubation phase. The analysis of new data describing the prevalence of malnutrition, the modifications in energy expenditure and body composition are guiding medical nutritional therapy to prevent patients from experiencing severe energy deficit and muscle loss. Rehabilitation may be extremely prolonged and therefore, nutrition is mandatory to decrease this recondition period. This review also comments on the European Society of Parenteral and Enteral Nutrition (ESPEN) nutritional statements. From the beginning of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, pathophysiology of the disease is better understoodithe ,2. GastrIn a recent study conducted on 523 patients with COVID-19 Once admitted to the ICU, patients with respiratory distress not requiring invasive ventilation are generally treated with high-flow nasal cannula (HFNC) oxygen therapy or non-invasive ventilation (NIV), are suffering respiratory distress and while breathing spontaneously, are treated with oxygen supplementation up to HFNC oxygen therapy or NIV. As a result, oral intake decreases significantly and enteral/parenteral nutrition is prescribed - often times in a non-optimal fashion but not optimally Recently, a longitudinal observational study described EE in 22 ventilated patients with COVID-19 measured by indirect calorimetry Patients who received more oxygen therapy had a significant decrease in oral intake. Of the 63 (23.5%) patients who received medical nutrition therapy, 16 patients received oral nutritional supplements, 34 patients received enteral nutrition, and 13 patients received parenteral nutrition. The energy and protein targets were achieved according to the ESPEN guidelines for nutrition in the ICU There is a strong association between the circulating levels of 25-hydroxyvitamin D and COVID -19 positivity rates Selenium deficiency is associated with increased COVID-19 mortality From the time the recent ESPEN guidelines on nutritional therapy were released In the first wave, patients with COVID-19 with early signs of respiratory distress were promptly treated with mechanical ventilation, based on the fear of disseminatoin of the virus if non-invasive therapy was used. During the second wave, oxygen flow nasal cannula (FNC), HFNC or NIV using a mask or a helmet were prescribed for a longer period of time to avoid mechanical ventilation.Statement Consideration number 7The If there are limitations for the enteral route, it could be advised to prescribe peripheral/central parenteral nutrition in the population that does not reach energy-protein target by oral or enteral nutrition. This approach has been confirmed by the French and Chinese positions papers ,23. The The exact timing to initiate mechanical ventilation is still unknown Recommendation Statement Consideration 8: Enteral nutrition(EN) should be started through a nasogastric tube; post-pyloric feeding should be performed in patients with gastric intolerance after prokinetic treatment or in patients at high-risk for aspiration; and the prone position per se does not represent a limitation or contraindication for EN. Patient EE should be determined to evaluate energy needs by using indirect calorimetry when available. The use of indirect calorimetry was discussed by scientific societies because of the fear of virus spread vs.10.3%, P<0.001).Energy administration should be guided by the recent ESPEN ICU Nutrition guidelines Protein requirements: The ESPEN statements recommend to deliver 1.3 g/kg protein equivalents per day progressively. This statement is endorsed by Thibault et al. Recommendation Statement Consideration 9: In ICU patients who do not tolerate full dose EN during the first week in the ICU, initiating parenteral nutrition (PN) should be weighed on a case-by-case basis. PN should not be started until all strategies to maximize EN tolerance have been attempted.Patients with uncontrolled life-threatening hypoxemia, hypercapnia or acidosis, and uncontrolled shock should not be fed enterally or parenterally until the shock is controlled with fluids and vasopressors or inotropes Prone position has become a frequent and valid technique to improve oxygenation After prolonged intubation, swallowing problems and dysphagia occur and limit oral nutrient intake. Two hundred and eight patients were assessed for dysphagia, (102 post ICU patients of which 82 were tracheostomized) Recommendation Consideration Statement 10: In ICU patients with dysphagia, texture-adapted food can be considered after extubation. If swallowing is proven unsafe, EN should be administered. In cases with a very high aspiration risk, postpyloric EN or, if not possible, temporary PN during swallowing training with removed nasoenteral tube can be performed.According to Curci et al. At each step of the progression of the disease, ICU patients with COVID-19 should be carefully evaluated in terms of malnutrition risk and medical nutritional support. The tremendous challenges posed by the continuous clinical changes requires adequate answers at each stage. A delay will induce more energy and protein deficit, longer length of stay, and rehabilitation for the survivors. Metabolic control and nutritional support are a cornerstone in the management of critically ill patients with COVID-19.The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources  and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework,  These emerging techniques have also demonstrated the potential for identifying novel disease pathways and previously unrecognized morphologies.Large-scale architectural motifs and repetitive patterns of functional tissue sub-units  form the basis of histopathology. While normal tissue is relatively homogenous, cancer contains disordered structures / phenotypes that reflect driving genetic alterations. As neoplastic transformation progresses, the extent of infiltration and destruction of normal tissue is used to grade and stage cancers. Practitioners of histopathology are thus highly sensitive to disruptions in normal structure. A wide variety of computational methods have been developed to augment traditional histological inspection2. In comparison to prior modeling techniques that use handcrafted features, deep learning applies parameterized filters and pooling mechanisms via convolutional neural networks (CNN) to capture and integrate lower level image features into successively higher levels of complexity3. These approaches have been used to automatically stage liver fibrosis4, identify morphological features correspondent with somatic alterations5, assess urine slides for bladder cancer6, and circumvent costly chemical staining procedures8, amongst many others9. Many research groups are developing high-throughput clinical pipelines to take advantage of these healthcare technologies. Validating and scaling these technologies is essential for successful deployment10.Deep learning has been particularly successful in digital pathology11. Alternatively, a ‘bag of images’ approach can be taken, in which patch representations are aggregated using autoregressive or attention-based mechanisms to generate a whole slide representation, ignoring non-tissue regions14. These integrative approaches may be highly stochastic and insufficiently reproducible / reliable to be properly integrated into the clinic or with other omics-based modalities. These methods may additionally undervalue the higher order context between a patch and its immediate neighbors which may be vitally important to the targeted prediction.As a result of the gigapixel resolution of Whole Slide Images (WSI), which contain a diverse range of tissue and morphological features, researchers typically must partition the WSI into smaller sub-images. These sub-images are then evaluated separately via the deep learning model for classification or segmentation tasks, from which their results may be aggregated for slide-level inferences. Aggregation via a CNN incorporates excessive whitespace and places unnecessary dependence on the orientation and positioning of the tissue section11, GNN relax the convolutional operator to aggregate information across an unfixed number of neighbors to update the patch-level embedding15.Graphs are mathematical constructs that model pairwise relationships between entities. Accordingly, graphs are well suited to model dyadic relationships between single patches (nodes) in a WSI as defined by their spatial distance/correlation (edges). Graph Neural Networks (GNN) have been developed to encapsulate information from adjacent tissue regions/cells in order to inform the representation of the current patch of interest. GNN naturally capture the intermingling of various tissue sub-compartments while remaining permutationally invariant (the ordering/rotation of patches on slides does not impact prediction). While square-grid convolutions over WSI sub-images propagate information within a fixed neighborhood of patches and require consistent ordering of patches16. However, this approach underappreciates the diagnostic/prognostic information conveyed by tissue macro-architectural structures. Constructing cell-centered graphs are limited by cell detection accuracy (a surprisingly difficult problem) and more importantly, incorporating all cells in a graph model is subject to complexity constraints. Despite these potential limitations, there remain numerous techniques to study WSI using GNN at various scales17. Here, we seek methods to explain graph convolution results post-hoc to elucidate mechanisms by which tissue regions interact.Prior GNN research on WSIs center graph nodes on cells under the assumption that cell-cell interactions are the most salient points of information18. TDA is well-suited for summarizing Whole Slide Graphs  to identify and relate key tissue architectures, regions of interest, and their intermingling. However, the sheer quantity, complexity, and dimensionality of histology data makes interpretation challenging. A recently developed TDA-tool, Mapper18, alleviates this issue by providing a succinct summary of high-dimensional data to elucidate obscured relationships. Mapper projects the data to a lower dimensionality, packs the data into overlapping sets, which are then clustered to form a simplified, easily interpretable graph. Unlike pooling approaches that are built into the deep learning model and must be pre-specified, Mapper is generalizable and can be configured to study WSI information at multiple resolutions after fitting a GNN model. These models have the capacity to provide higher order descriptors of information flow for any GNN model, greatly simplifying analysis. Abstractions can then be analyzed to learn new disease biology through interrogation of patch-level embeddings. While TDA methods have previously been applied to high dimensional omics data21 and histopathology images23, to our knowledge, there have been no applications of TDA methods to GNN models fit on histological data, where these methods may be of great benefit.Topological Data Analysis (TDA) quantifies the underlying shape and structure of data by collapsing persistent topological structures24. The Colon is divided into distinct layers including epithelium, lamina propria, submucosa, muscularis propria, pericolic fat, and serosa . Tumor staging comprises tissue and nodal stages with higher numerals indicating a greater depth of invasion and greater number of lymph nodes (LN) involved by the tumor, respectively.Colorectal Cancer (CRC) is a common cancer with approximately 150,000 new cases annually in the United States and an estimated 63% 5-year survival rate. CRC most commonly arises from dysplastic adenomatous polyps with somatic alterations in the APC pathway or the mismatch repair (MMR) pathwayWSI-GTFE)We present a Whole Slide Image GNN Topological Feature Extraction workflow (2.2.1.PathFlowAI10 was utilized to extract and preprocess the slides into NPY format. A board-certified pathologist provided coarse segmentation maps in the following categories: 1) epithelium, 2) submucosa, 3) muscularis propria, 4) fat, 5) serosa, 6) debris, 7) inflammation, 8) lymph node, and 9) cancer. We extracted 2.69 million 256×256 pixel patches correspondent to these slides.We selected Colon (n=172) and accompanying Lymph Node (n=84) resection slides from 36 patients at Dartmouth Hitchcock Medical Center. Briefly, samples were grossed, embedded in paraffin blocks, sliced into five-micron sections and scanned using the Leica Aperio-AT2 scanner at 20×, stored in SVS image format, from which our in-house pipeline 2.2.WSI-GTFE framework  –EEstimating the degree of information flow and intermingling between the regions Optionally using measures of information flow as additional markers for clinical or molecular associations The ramework , provide2.2.1.f: X →Z, 25. Features may also be acquired using unsupervised approaches such as variational autoencoders (VAE)26 or self-supervised techniques such as contrastive predictive coding (CPC)14 or SimCLR27. Finally, patch-level features may be learned after pretraining on histology targets of interest, such as classified objects or ROI. We utilized both an ImageNet-pretrained CNN as well as a CNN we pretrained for tissue sub-compartment classification task, generating two separate sets of patch-level embeddings for comparison.A WSI  2.2.2.G = . The set of nodes/patches or vertices V are related to each other via edgelist E. Alternatively, the edgelist may be represented by a sparse adjacency matrix A, of which binary indicators Aij depict a relationship between node i and node j. Node/patch-level embeddings or features are represented by attribute matrix X. A WSI may be encoded a graph by storing patch level embeddings (i for select patch) in the attribute matrix X (m patches by n embedding dimensions) and recording spatial adjacency (via a k or radius nearest neighbors) of all patch coordinates as A. GNN utilize message passing operations to update the embeddings of nodes by their neighbors via the following convolution operation28:Graphs are represented via following expression i, in neighborhood N, are themselves updated via some parameterized functional ϕ, which is scattered in parallel across GPUs, and then aggregated to update the embedding of patch i via the parameterized operation γ. Information from neighboring are passed as such. Multiple applications of these convolutions expand the neighborhood from which information is propagated. Additional pooling mechanisms, AGG, such as DiffPool or MinCutPool30, serve to aggregate the patch level representations into cluster or slide-level representations:The embeddings of the neighbors of patch 31 and spectral clustering objectives, and 3) graph-level supervised, eg. WSI-GTFE method is agnostic of WSG creation approach. These graphs also may be built using cell / nucleus detection methods, though such methods are beyond the scope of this work.There exist multiple modeling objectives for updating these embeddings, which include: 1) node-level classification, where 2.2.3.32 methods randomly set predictors at a particular neural network layer to 0 with a certain probability, while DropEdge33 randomly prunes edges of a graph, which in this case corresponds to the adjacency matrix of the WSI. While both of these techniques have been utilized to improve the generalization of graph neural networks through perturbations to the input data and intermediate outputs, applications of these techniques during prediction may be used to make multiple posterior draws of a patch-level categorial distribution for class label assignment. Both the variance of the predictive posterior distribution after numerous posterior draws and the entropy in the class labels after averaging the results for a sample after application of SoftMax layer may be used as estimates of uncertainty in prediction34. Nodes that exhibit high uncertainty may be pruned and the remaining class labels may be propagated to the unlabeled patches.Graph convolutions aim to contextualize patches with their neighbors and as such are able to smooth the map of predictions across a slide. However, small deformities in an otherwise homogenous decision map, , may be a source of signal noise. To further smooth the classification map of patches across a slide, assigned patch-level labels may be refined using label propagation techniques. Dropout2.2.4.35 to identify important subgraphs for classification can be performed. However, these may be difficult to interpret because they attempt to summarize complex interactions between high-dimensional data at the scale of thousands of patches per WSI. The complexity of such visualizations makes them difficult to understand and highlights the need for a simplified visualization.Once a GNN model has been fit, post-hoc model explanation techniques such as visualization of the attention weights or the use of GNNExplainer36 . Once the data is projected, it is separated into overlapping sets , the number of which determines the resolution of the data summary. In each set, a Clustering algorithm  is applied to the datapoints. The output of applying Mapper to this structure is a graph, where a node represents a cluster of WSI patches and an edge represent the degree of shared patches between the clusters37. This Mapper graph summarizes higher-order architectural relationships between patches and their shared histological information. In our framework, we refer to the nodes (collection of patches) as ROI, and the topological connectivity between the ROIs as their functional connectedness or “intermingling”. For instance, if a tumor ROI was connected to an ROI of the submucosa, we would say that the tumor has invaded (intermingled with) the submucosa. The degree of intermingling is quantified by the amount of overlap as defined by covering U and weighted by the incidence of cancer in each ROI. The expressiveness of this summary graph may be modified by selection of different Filter, Cover, and Cluster parameters which allows the user to interrogate ROIs in the WSI at different scales (a degree of flexibility beyond that of currently existing GNN pooling operations). We implemented Mapper using the Deep Graph Mapper implementation; however, python-based Kepler-Mapper and giotto-tda also present software solutions that may be readily employed39.Because similarity-based distances between patch-level GNN embeddings reflect higher-order connectivity and perceptually similar histological information, topological methods (such as Mapper) can compress this data to its essential structures while revealing the most salient aspects. For a given WSI, Mapper operates on the resulting point cloud of the patch-level GNN embeddings to first project the points to a lower dimensional space via techniques such as PCA, UMAP or NCVis2.2.5.Cover operation, between two ROI , the final pairwise associations between the regions are given by: TIS with cancer staging greater than 2 for the Colon samples, and positive Lymph Node status for Lymph Nodes.Once ROIs have been extracted using Mapper, measures of functional relatedness between the regions may be correlated with slide-level clinical outcomes. In our simple use case, we developed a Tumor Invasion Score (TIS) that measures the degree of overlap between the tumor and an adjacent tissue region. To construct this score, we first decompose each ROI into a vector encoding the frequency of each predicted tissue sub-compartment, 2.3.40 and DropEdge layers, followed by one layer of DropOut and finally a linear layer with SoftMax activation for node level prediction  through evaluation of the F1-score. We saved the model parameters correspondent to the training epoch with the highest validation F1-score and extracted graph-node level embeddings and predictions on the validation and test sets of slides for each cross-validation fold. We refined patch-level predictions for all validation and test slides for the models with ImageNet-pretrained image features. To evaluate node-level tissue sub-compartment classification, we then calculated AUROC and F1-Score fit statistics across test slides. Finally, we applied Mapper to extract ROIs and TIS for all test slides  and lymph node  classification networks with 10-fold cross validation (partitioning 10 separate training (82%), validation (8%) and test (10%) sets), evaluated using the area under the receiver operating curve (AUC/AUROC) and a weighted F1-Score. We extracted features from the penultimate layer of a ResNet50 neural network for about 2.7 million images per fold (26.9 million embedding extractions across 10-folds), using the pretrained network and separately from an ImageNet-pretrained model. After extraction of image features, we constructed graph datasets through calculation of the spatial adjacency (k-nearest neighbors) between the patches and storing node level embeddings into the attribute matrix. We created and trained a GNN that featured four graph attention layers, interspersed with ReLU activation functionsediction . Models t slides , of whic3.3.1.An acceptable patch-level classification performance indicates room to further interrogate the slides for functional relationships between patches. In 3.2.TIS score derived for the higher stage indicates higher intermingling of tumor with regions of fat and is confirmed by pathologist annotations. We extracted an average of 32 ROIs from each WSI (range 5 – 155 ROI). Inspections of ROIs indicated some with clusters of tissue finely localized to histological tissue layer, and a few ROI that went undetected by the initial pathologist inspection (e.g. pockets of inflammation and tumor seeding in the fat).TIS scores correlated very well with Tumor staging. Ten-fold CV AUC was 0.91 for advanced Colon cancer staging and 0.92 for positive Lymph Nodes  as to their relation to tumor staging using linear mixed effects models . As expected, fat interaction was highly associated with progression to a stage 3 or higher  at multiple scales of inference through propagation of patch-wise information. However, when employing GNNs, the route of propagation often becomes obfuscated by the sheer quantity of patches being studied. This, in turn may make it difficult for researchers, clinicians or biologists to accept or understand these graph neural network technologies and their predictions. However, the compartmentalized and repetitive nature of tissue means that histology images can be greatly simplified via grouping of spatially adjacent subimages with perceptually similar and complementary input features. We have introduced methods from TDA to capture and reduce these motifs. In colon histology, we distilled information across WSI to better quantitate how the intermingling of different tissue sub-compartments inform disease stage. These results warrants investigation of other spatially driven processes, such as identifying ROIs correspondent to spatial transcriptomicsFilter function, Cover and Cluster parameters and algorithms to generate a topological map. While these features offer flexibility to study the WSI at multiple resolutions, which includes expanding the large range of ROI extracted, full exploration of the parameter space to identify an ideal range of parameters for Mapper graphs for the slide in study are beyond the scope of this work.Mapper has proven to be a useful TDA tool for elucidating high-level topology of the WSI. However, Mapper is highly dependent on the We also assessed the impact of domain-specific pre-training of a CNN on the resulting GNN predictions. Our preliminary results showed negligible impact on GNN accuracy. Integration of signal from the surrounding tissue context via GNNs may therefore be sufficient to overcome domain differences between histology images and real-world images (ImageNet). Further experimentation is needed on more nuanced examples to test this hypothesis.TIS scores we account for this using mixed effects modeling, extracting samples from different patients would have been preferred to reduce cluster-level effects . Due to the number of free parameters, we did not perform robust hyperparameter scans over the GNN.There are a few limitations to our study. We assumed that GNNs are able to adequately capture patch-level information and their surrounding tissue architecture. The accuracy of our model was constrained by relatively coarse physician annotations that tended to ignore small structures like veins in the fat region of the lymph nodes, or small pockets of inflammation bounded by other tissue compartments, thereby reducing the accuracy of the model. However, inspection of regions with high uncertainty and label propagation allowed for correction of some of these issues. We also acknowledge the possibility of bias in given cross-validation folds. While we stratified the slides by whether they were representative of high or low stage, slides may contain different macroarchitectural features, and may, for instance, be completely devoid of serosa (which is only present in certain regions of the abdomen), which made it difficult to predict its presence. The colon WSI sections were analyzed from 36 patients (representing 256 slides). We acknowledge that there were repeated measurements taken across different slides from the same patient, the results of these sections may be correlated. While in our final inference on the 18. Simplicial complexes represent series of points, lines, triangles and higher-dimensional tetrahedra. Persistence diagrams discover topological features in the form of simplicial complexes that persist over wide changes in proximity between points. These approaches can be readily applied to GNN embeddings to establish “barcodes” of various ROIs contained within the slide43, which may be used to supplement existing efforts to hash WSI to further assess the composition of other slides by the presence of characteristic topologies44. In addition to utilizing persistence based TDA methods, we aim to apply the aforementioned methods to GNN embeddings after applying graph pooling layers to identify topology and ROIs which may be related to molecular targets of interest, dense omics profiles and unlabeled clusters of tissue.In the future, we intend to utilize extracted GNN features contained within our ROIs to better identify the core topological structures that form a pathologist’s understanding of a slide5.WSI-GTFE, presents a framework from which to flexibly summarize the key insights acquired from fitting any GNN model to histological data. We hope that topological methods continue to see usage and integration with their deep learning graph counterparts for WSI level histological analyses given the benefits they provide in terms of model interpretability, quantitation of tissue compartment interaction, and potential for new biological discovery and disease prognostication.As multimodal deep learning approaches become increasingly important, GNNs are emerging as an attractive modeling tool for WSI representation where proper integration and association with slide-level outcomes is required. Conveniently, these approaches learn to identify key information pathways which may be simplified and visualized using TDA tools such as Mapper. Our method,


During the coronary bifurcation intervention procedure, imaging including intravascular ultrasound and optical coherence tomography is essential to provide precise anatomy of the lesion and morphological information. This consensus document between the Korean Bifurcation Club and the Japanese Bifurcation Club summarizes practical guidelines and current evidences on lesion assessment, device selection, procedural guidance, and the optimization of bifurcation intervention by the imaging. Percutaneous coronary intervention (PCI) for bifurcation lesions remains challenging because of a relatively low success rate, high incidence of procedural complications and inferior clinical outcomes compared to those in non-bifurcation lesions; even in the new-generation drug-eluting stent (DES) era . In bifuThere are several mechanisms responsible for treatment failure in bifurcation lesions. Stent under-expansion due to insufficient preparation of severely calcified or hard fibrous plaques in lesions is one of the most leading causes for in-stent restenosis (ISR). Edge dissection after oversized stenting or aggressive post-dilatation also increases the risk of ISR. Coronary angiography is not able to clearly elucidate these contributing factors for stent failure and frequently shows ambiguity during bifurcation stenting . In addiIn the current guidelines, IVUS-guided PCI is recommended for complex lesions including LM, bifurcation, long lesions, and chronic total occlusions due to particular challenges in angiographic evaluation and procedural complexity . The ‘ImOn the other hand, the usefulness of IVUS is emphasized in LM PCI, especially when complex PCI is necessary. Extensive evidence supports the use of IVUS-guided LM PCI in non-randomized studies. Park et al. demonstrated that IVUS-guided PCI tended to induce lower mortality rates without demonstrating a difference in myocardial infarction or target lesion revascularization (TLR) . De La TA few studies that used greyscale or virtual histology IVUS demonstrated that plaque accumulation and vulnerability were more frequently shown in the proximal MV than in the distal MV in a bifurcation lesion , 14, sugp < 0.0001) .–31.29–31= 0.077) . Another= 0.077) . Calcifi= 0.077) . General2 in PCI using first-generation DES   .An additional two-stent strategy is necessary in 3–47% of cases after provisional stenting –51. SB dp = 0.0004), which was beneficial for both true and non-true bifurcation lesions. They emphasized that accurate assessment of the bifurcated vessel in terms of vessel size, plaque and intima by IVUS lead to optimal KBI treatment without increasing MV events [The KBI is crucial in the two-stent strategy. However, in true bifurcation lesions treated with provisional single stenting, the impact of KBI remains controversial according to previous studies –56. AlthV events . In addiV events . This coDiameter stenosis at the SB ostium and smaller carina angles is associated with SB compromise after MV stenting –63. FuruIn cases of LM distal bifurcation, careful IVUS imaging usually showed continuous plaques from the LM into the proximal left anterior descending artery (LAD) was seen in 90% and from the LM into the LCx in 66%, with the disease from the LM into both the LAD and LCx in 62% . Of thesThe pre-procedural OCT findings are also helpful to predict SB compromise after cross-over stenting as shown in Table The assessment of stent configuration over SB orifice and GW recrossing position with 3D OCT imaging before KBI provides important information to achieve optimal bifurcation stenting Fig. a. Appropi.IVUS-guided bifurcation PCI.ii.OCT-guided bifurcation PCIAs shown in Figs. As shown in Figs. The accurate morphological assessment of MV and SB using an intra-luminal imaging device before and after the procedure are important for optimization of the device during complex bifurcation PCI procedures. Imaging guidance can provide adequate stent expansion, less malapposition, appropriate stent landing, and treatment of dissection; which lead to improvement of clinical outcome. Moreover, the assessment of the recrossing position and stent link location with 3D- OCT imaging has the potential to improve the clinical outcome due to optimal SB treatment.


Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy. A crucial source of information in this context is the proteome and, notably, the activation status of dynamic cell signalling pathways through post-translational protein modifications. Indeed, genomic mutations are not always associated with activated signalling pathways and, conversely, pathway activation can occur in the absence of mutations, as exemplified by PIK3CA3 and human epidermal growth factor receptor 2  signalling4 in breast cancer.In the era of personalised medicine and targeted cancer therapies, identifying those patients that will benefit from existing and new therapies is paramount. Genetics is already being used to assist clinical decision-making in specific cases7, among which reverse-phase protein array (RPPA) is a technology of choice for its unequalled sample throughput. The RPPA technology uses panels of monospecific affinity reagents  to quantify, with high precision and sensitivity, the abundance of specific proteins and their post-translationally modified forms in biological specimens9. Protein samples derived from cells or tissues are immobilised on a solid substrate, deposited as small spots on multiple arrays, and each array is probed with a single, epitope-specific antibody. This enables simultaneous quantification of multiple proteins and post-translational modifications in hundreds of samples, a multiplex capability not available in any other current proteomic technology. The capacity to analyse large sample numbers enables analysis of multiple sample conditions, such as drug treatments, dose responses and time courses, resulting in data series that can support systems biology and drug discovery pipelines11. The high sensitivity (in the picomole–femtomole range) and good reproducibility of RPPA technology16 have motivated its application to a wide range of sample types, including cell lines, preclinical (e.g. xenograft) models and patient-derived material. Indeed, the microscale printing of very small amounts of samples is of particular benefit for analysis of limited clinical or preclinical material, and RPPA has become a powerful addition to the biomedical analytical toolbox for the investigation of disease mechanisms, diagnostics and prognostics, notably in cancer26.Several technologies exist for protein biomarker discovery and validation27. Moreover, no standard tools exist to quantify, normalise and quality control RPPA data. Several RPPA data processing methods have been developed40, but these are often designed for specific technical set-ups, requiring, for example, particular array layouts or raw data formats. To our knowledge, no extensive cross-platform validation has been reported for RPPA technology. There is, therefore, a need to assess and understand the impact of variation between RPPA data derived from different platforms across international research centres.The RPPA workflow is composed of several distinct steps, which can each be adapted to the needs of the laboratory or the study. RPPAs thus offer a highly flexible, modular proteomic technology, enabling numerous possible technical set-ups and protocols. As most laboratories using RPPA technology have developed a customised set-up, there are essentially as many workflows as there are RPPA platforms. Differences between platforms are diverse and can include the type of printer used to create the arrays, sample spotting conditions, slide substrate chemistry, primary and secondary antibodies used for immunostaining and slide scanner optics42, there is a growing need for the evaluation of the reproducibility of RPPA platform outputs. Herein, we use collaborative RPPA-based proteomics, employing distinct RPPA workflows at multiple research sites in different countries, to characterise a range of human breast cancer cell lines and their biochemical responses to two clinically relevant cancer drugs. We combine data derived from different RPPA platforms to assess inter-platform variation and use integrative analysis to identify platform-independent protein markers of response to drug treatment. Such cross-platform validation may thus have utility in multi-centre evaluation of robust protein markers of disease and therapeutic response.As RPPA workflows are being developed for use in clinical settings, for which robust assays are paramountTo assess the reproducibility of RPPA technology across multiple laboratories, we developed an international multi-platform approach that integrated RPPA data derived from three research sites across Europe . We selected for this study six breast cancer cell lines, encompassing different breast cancer molecular subtypes and presenting distinct drug sensitivities  for the Paris, Heidelberg and Edinburgh platforms, respectively. Normalised RPPA data derived from each platform were then clustered antibody-wise to identify similarities between validated antibodies  and analysed the integrated dataset using unsupervised learning. For dataset integration, antigens targeted by each antibody were classified, capturing antibody recognition of related protein isoforms or family members where applicable . Each data point was linked to the RPPA platform from which it was derived to enable downstream data analysis. We reduced the dimensionality of the integrated multi-platform RPPA dataset using principal component analysis. This unsupervised analysis of the six breast cancer cell lines revealed separation in feature space for several of the cell types, indicating cell type-specific expression of the proteins and phosphoproteins measured Fig. . MoreoveTo interrogate the similarity between antigen expression profiles for distinct antibodies used on different RPPA platforms, we devised a data-driven representation of antibody similarity, which we termed a clustered antibody antigen map. Antibodies used at each RPPA platform  clustered with expression of the same antigen determined by at least one other RPPA platform Fig. c,d. In a43; selumetinib is a selective ATP-independent allosteric inhibitor of mitogen-activated protein kinase kinase 44. To capture the signalling dynamics of the proteins under investigation, cells were treated with either drug or with DMSO (vehicle control) for 20 min and 24 h , we extended the integrative analysis to include multi-platform RPPA data of the six breast cancer cell lines treated with two clinically relevant drugs, lapatinib  and selumetinib . Lapatinib is a reversible ATP-mimetic tyrosine kinase inhibitor of epidermal growth factor receptor  and Her2rs = 0.70) , as expected  were generally well correlated , with replicate samples clustering together, indicating robust data-driven grouping of the dataset Fig. . ClusterWith RPPA technology poised for adoption into routine clinical laboratory assays, there is a need to assess reproducibility and variation between RPPA data derived from different platforms. Herein, we employed a collaborative RPPA-based proteomics approach to evaluate the consistency of results obtained from the same samples using different RPPA workflows. We combined the RPPA data derived from multiple research sites to assess inter-platform variation and found that different RPPA platforms using distinct set-ups yield remarkably consistent results, including when several different antibodies are used to recognise the same antigen (protein or phosphoprotein) of interest. Indeed, antigens detected by different antibodies used at different RPPA platforms were, in general, almost as well correlated as those used at the same RPPA platform. These observations strongly suggest that the different instrumental set-ups and analytical workflows used at these representative RPPA platforms do not preclude the generation of comparable and reproducible data when using high-quality, validated antibodies.Using different workflows, the layout of samples spotted on arrays differed among the three RPPA platforms, which prevented application of the same automated data normalisation method. Each research site therefore applied their own in-house data normalisation procedure tailored for the respective RPPA platform. All platforms normalised signal intensities for total amounts of printed protein, but the way they accounted for sample dilutions differed. The Paris platform applied non-parametric curve fitting to the serial dilutions of each sample, the Heidelberg platform fitted curves for control samples only and reported experimental samples to these curves and the Edinburgh platform computed a linear fit of the serially diluted samples. Despite these different approaches, we show that normalised data improves the distinction between concordant data (derived from antibodies recognising the same antigen class) and non-concordant data , as compared to raw data. Future efforts to develop normalisation pipelines that are compatible with multiple array layouts may further improve data reproducibility among platforms and expand opportunities for cross-platform validation of RPPA technology. Such cross-platform validation may have utility in the appraisal of robust markers of disease and therapeutic response and their application as prognostic or predictive biomarkers.7. Proteomic and phosphoproteomic datasets are well placed to complement genetics-based biomarker strategies by providing additional information on activation states of dynamic pathway signalling. However, the application of proteomic technologies presents a number of challenges, including consistent high-quality sample preparation, sensitive detection of low-abundance proteins and post-translational modifications, high sample throughput at reasonable cost and rapid turnaround time necessary for clinical application or drug discovery and development pipelines. The development of RPPA workflows attempts to address many of these challenges, yet the reproducibility of RPPA data generation and analysis across distinct RPPA platforms and research centres has not been extensively evaluated. We used an integrative RPPA approach to characterise a range of human breast cancer cell lines and their biochemical responses to two clinically relevant cancer drugs. The cell lines were chosen to represent different molecular subtypes of breast cancer, each of which having a different prognosis and treatment response50. The sensitivity of these cell lines towards lapatinib and selumetinib and the expected changes in major signalling pathways are known53 and thus served as benchmarks in this study. We demonstrated that RPPA technology can identify expected protein markers for response to treatment and resistance in a robust and platform-independent manner. Although we analysed cell lines herein to enable benchmarking, RPPA is routinely applied to clinical samples, including small biopsies, on the platforms used in this study. In such cases, the amount of lysis buffer is adapted to the amount and the cellularity of the tissue in order to obtain sufficiently concentrated samples. We therefore expect that RPPA can robustly assess response to treatment and resistance independently of the analysed samples. Understanding how resistance can be predicted and prevented is a major therapeutic challenge, and the use of proteomic approaches such as RPPA will, by defining the functional state of cells and tissues, enable the validation and assessment of resistance mechanisms in clinical samples54.There is an outstanding need for the development of robust markers of response and resistance to targeted cancer therapy55. The development of robust biomarkers is thus becoming an essential component of new clinical trial designs to guide patient selection, optimise dosing schedules and minimise ineffective or over-treatment. For many complex diseases, biomarkers at the genetic, proteomic, metabolomic and phenotypic levels are required to characterise individual patient disease and response to therapy sufficiently. Integration of multiple biomarker modalities with emerging computational and statistical approaches represents the future direction of personalised medicine strategies. However, the successful implementation of personalised medicine is dependent upon the validation and reproducibility of biomarker tests performed across distinct research centres and national boundaries.High attrition rates in clinical drug development present significant challenges to drug developers, with only one in eight oncology drugs that enter clinical development in phase 1 achieving US Food and Drug Administration approval, and a 1-in-15 success rate when these candidate drugs are under evaluation in secondary oncology indicationsOur data show that RPPA analyses of drug-treated breast cancer cells using distinct antibodies and different RPPA platforms can identify robust profiles of protein markers reporting signalling pathway responses to pharmacological inhibition. This suggests that the consistency of RPPA-based assays will enable the validation and assessment of treatment response and resistance mechanisms in clinical samples across international laboratories. These data provide, to our knowledge, the first extensive cross-platform validation of RPPA technology, which paves the way for further investigation and improvement of technology robustness.g, 10 min, room temperature). Clarified lysates were aliquoted and snap-frozen in liquid nitrogen prior to shipment to the various research sites. Protein concentration was determined using a reducing agent-compatible BCA kit (Pierce).MCF7, MDA-MB-231, MDA-MB-468, MDA-MB-453, HCC1954 and SKBR3 breast cancer cells were purchased from American Type Culture Collection and grown according to supplied instructions  were prepared as 10 mM stock solutions in DMSO. Cells were treated with 1 µM lapatinib, 1 µM selumetinib or DMSO in growth medium for 20 min or 24 h. Control cells were treated with DMSO in growth medium for 20 min.56. We confirmed that > 99% of data points derived from undiluted samples at the Heidelberg platform were within the dynamic range of the representative dilution series 58 and compiled using Illustrator (Adobe). Principal component analyses were performed using Python (version 3.7.4) or Perseus (version 1.5.2.6)59.Binary, agglomerative hierarchical cluster analyses of centred normalised abundances for proteins and phosphoproteins were performed using Cluster 3.0 For data-driven representation of antibody similarity across multiple RPPA platforms, we devised the clustered antibody antigen map. For all antibodies used in the integrative analysis, centred normalised abundances for proteins and phosphoproteins were clustered on the basis of Spearman rank correlation coefficient-based distance, Euclidean distance or Kendall tau coefficient-based distance using Cluster 3.0, computing distances with an average-linkage matrix. Clustered antibody (column) node memberships were stored for antibody mapping, and clustered antibodies were expanded in a second dimension according to antibody antigen classification. Antibodies targeting the same antigen class were indexed according to unique antibody identifier, enumerating from 1. A matrix of integer-indexed antibody identifier elements was generated according to clustered antibodies (columns) and corresponding antibody antigen classes (rows) .No statistical methods were used to pre-determine sample size. Spearman rank correlation coefficients were calculated for every pair-wise combination of antibody identifiers. Kernel density estimates were computed using R (version 3.4.1)Supplementary InformationSupplementary InformationSupplementary InformationSupplementary InformationSupplementary InformationSupplementary InformationSupplementary Information


Intracranial necrotizing granulomatous space-occupying lesions are sparsely reported in literature. Variability in presenting symptomatology and radiographic features makes diagnostic work-up difficult.This report presents the case of a 77-year-old female with sinusitis and fatigue who underwent an MRI revealing a posterior fossa lesion compressing the fourth ventricle. Subsequent contrast CT of the chest, abdomen, and pelvis was negative for primary malignancy. Histopathologic examination of the lesion following biopsy showed it to be a necrotizing granuloma in an antineutrophil cytoplasmic antibody (ANCA) negative patient. The most likely diagnosis was determined to be spontaneous necrotizing granuloma, a rare entity with only one previous report noted.Spontaneous necrotizing granuloma of the CNS is a rare entity that represents an important differential consideration in the work-up of space occupying lesions of the CNS. Granulomas are compact collections of inflammatory cells classically resulting from persistence of a non-degradable product or cell mediated hypersensitivity , 2. WhilIntracranial granulomatous space occupying lesions are sparsely reported in the literature and are predominantly caused by infection, retained surgical or foreign objects, or granulomatous disorders. As these lesions are not primarily found in a specific intracranial fossa, the presenting symptoms vary greatly as the inflammation and mass lesion can cause local and distant irritation leading to focal and/or global neurological deficits depending on the location. Moreover, the radiographical features of these lesions have not been well defined and range from diffuse edema to contrast enhancing mass lesions resembling tumors . Here, wA 77-year-old female with a history of chronic kidney disease and type II diabetes mellitus underwent workup for ongoing sinusitis, fatigue, malaise, and 20-pound weight loss. Patient quit smoking 30 years ago and denies any drug or alcohol use. She reports no significant family history including history of malignancy. MRI of the brain performed at an outside hospital to evaluate the extent of sinusitis revealed a posterior fossa lesion with surrounding edema causing compression on the fourth ventricle. The patient was transferred to our institution for neurosurgical evaluation. On presentation the patient was found to have mild cerebellar signs but an otherwise non-focal exam. Patient was surprised to learn of the cerebellar findings considering her lack of significant symptoms. MRI of the brain, including 3DT1, and T2 Flair showed an irregular enhancing lesion along the inferior & posterior surface of the right cerebellar hemisphere suggestive of an infiltrative malignancy , and visual disturbances  –11. It iAutoimmune granulomatous disorders with known head and neck manifestations include Behcet disease, Churg- Strauss syndrome, and granulomatous polyangiitis  . All thrOther possible causes of intracranial granulomas include sarcoidosis, foreign bodies, IgG4-related hypertrophic pachymeningitis, and infectious etiologies. Neurosarcoidosis is present in 5% of patients with systemic sarcoid . There hIn light of the exclusion of other possibilities described above, a spontaneous necrotizing granulomatous mass is the best explanation in this case. There has only been one case reported of a spontaneous intracranial granuloma that mimicked a brain tumor . The patThis is the second reported case of likely spontaneous necrotizing intracranial granuloma, although ANCA negative GPA cannot be completely excluded. Though rare, spontaneous necrotizing granuloma is an important consideration in the assessment of intracranial mass lesions when other more common etiologies have been excluded.


TACTs are developed to counter the increasing problem of Plasmodium falciparum resistance to artemisinins and their partner drugs in artemisinin combination therapies (ACTs).We thank Chengchao Xu and colleagues5 The alternative of prolonging the standard 3-day ACT course might improve treatment efficacy but for several ACTs this would require a shift to a second ACT halfway through the treatment course to avoid partner-drug accumulation and toxicity. This more complex treatment regimen would likely compromise treatment adherence. Unfortunately, improved artemisinin derivatives and other new antimalarial compounds are not expected within the next 5 years.Xu and colleagues suggest that rotating ACTs with different partner drugs, adjusting the time course of artemisinin treatments, or exploring improved artemisinin derivatives would be better strategies to counter these resistance problems. Drug rotation is what has been happening already, albeit reactively, but it is operationally challenging. Experience from several countries in southeast Asia suggests that changing first-line antimalarial therapy often takes several years to implement, even when treatment failure rates have risen. Meanwhile, artemisinin resistance facilitates the emergence and selection of partner-drug resistance, jeopardising the small number of available ACT partner drugs. Combining the potent, but short-acting, artemisinin component with two slower, but longer-acting, matching partner drugs in TACTs provides mutual protection against resistance.We agree that reducing adverse effects and increasing cost-effectiveness are essential in the development of TACTs. The expected longer therapeutic lifespan of TACTs compared with ACTs will also be a crucial element of this cost−benefit analysis.6 Ideally, a triple combination would include only drugs that are individually curative, and without existing resistance. However, the current reality is a choice between a small number of available antimalarials. Artemether−lumefantrine−amodiaquine was studied because of the well matched pharmacokinetic profiles of the partner drugs and the in-vitro counteracting resistance mechanisms.7 In addition, the combination has shown excellent safety and efficacy in areas of highly resistant falciparum malaria in the Greater Mekong subregion, in which the number of cases is falling but elimination has not yet been achieved.5 Artemether−lumefantrine−amodiaquine is now being further evaluated in a large randomised trial in Africa and a fixed-dose combination is in development.Rasmussen and Ringwald state that well matched (triple) combinations might be the future of malaria treatment. Delaying antimalarial drug resistance with TACTs has become an increasingly relevant consideration with the emergence of artemisinin resistance in Africa.


Vaccination has been well recognised as a critically important tool in preventing infectious disease, yet incomplete immunisation coverage remains a major obstacle to achieving disease control and eradication. As medical products for global access, vaccines need to be safe, effective and inexpensive. In line with these goals, continuous improvements of vaccine delivery strategies are necessary to achieve the full potential of immunisation. Novel technologies related to vaccine delivery and route of administration, use of advanced adjuvants and controlled antigen release (single-dose immunisation) approaches are expected to contribute to improved coverage and patient compliance. This review discusses the application of micro- and nano-technologies in the alternative routes of vaccine administration , oral vaccine delivery as well as vaccine encapsulation with the aim of controlled antigen release for single-dose vaccination. Estimated global immunisation coverage varies per country and per vaccine, with some vaccines such as pneumococcal and rotavirus ranging between 35–47%, falling significantly short of the WHO’s recommended coverage of >90%. Although many different factors affect these figures, vaccination compliance is one of the major obstacles to broader immunisation coverage globally. Pain that is felt during parenteral vaccine administration is one of the most frequently reported causes of vaccine hesitancy. Success of the oral polio vaccine in bringing the disease close to eradication (notwithstanding recent issues related to the vaccine itself ), and thMucosal immunity is the first and foremost line of defence against pathogens that enter the body through the oral mucosa and the nasal cavity, such as influenza and viruses causing meningitis, measles or whooping cough. Estimates of the surface area of the mucus membranes lining the lungs vary between 50–75 square meters, representing the largest epithelial surface exposed to the outside environment. However, most licensed vaccines are administered by intramuscular injection, which preferentially induces systemic immune responses . ProtectThe airway mucosa is the site of substantial immunological activity where constant immune monitoring recruits highly professional innate and adaptive immune cells, to protect the host from microbial and environmental insults. Nasopharynx-associated lymphoid tissue (NALT) is an organised mucosal immune system that consists of lymphoid tissue, B cells, T cells and antigen presenting cells (APCs), covered by an epithelial layer containing microfold (M) cells . M cellsBordetella bronchiseptica vaccine, the intranasal vaccine conferred stronger clinical immunity ..Vibrio cE. coli overexpressing CFA/I, and the orally active adjuvant α-GalCer, was evaluated under various temperature and humidity conditions. Longet and colleagues determined that SmPill® minispheres maintained both the antigenicity of CFA/I and the immunostimulatory activity of the α-GalCer adjuvant after the storage of SmPill® minispheres under room temperature and extreme storage conditions for several months [® minisphere approach to enhance the immunogenicity of orally delivered antigens [Finally, a key concern in vaccine formulation is stability. Indeed, the development of thermostable vaccine formulations may improve vaccine coverage, especially in low-income countries. The stability of this integrated oral delivery system containing formalin-killed whole-cell l months . Collectantigens  and mainantigens . This exSkin or (trans)cutaneous vaccination is a route of immunisation mediated by topical, intradermal (ID) or intraepidermal delivery . In line® electroporation device  has shown good safety and efficacy [Electroporation is an electro-permeabilisation method, being intensively explored in different fields: DNA manipulation in vitro, drug delivery and gene therapy. It is based on the transitory structural perturbation of lipid membranes (such as the cell membrane) through the application of high-voltage electrical impulses. Typically, electroporation involves the short-term exposure (few µs to ms) to high-voltage pulses (50–1500 V) with up to 1 s intervals. It is hypothesised that a structural rearrangement in the lipid bilayer occurs, consequently forming transient pores and facilitating molecular transport for both small molecules and biologics . In the efficacy  and is cefficacy .Among the different thermal microporation technologies, fractional infrared laser ablation stands out as particularly suitable for bypassing the skin barrier properties. It enables the disruption of the stratum corneum in a highly controlled and adjustable fashion, simultaneously providing an intrinsic adjuvanting effect. Consequently, this technology has been intensively investigated for the pain-free prophylactic and therapeutic treatment of type I allergies and tumours ,98,99. DMicroneedle arrays represent another interesting approach for intradermal vaccine delivery. Although these devices comprise needles, their length (10–2000 μm) offers relatively pain-free application . Among tIn the search for an improved needle-free vaccination strategy, the application of dry vaccine powders using ballistic (powder) injectors seems to be a promising approach for delivering antigens to the skin, owing to improved vaccine stability and cold-chain independent logistics ,104. As Jet injection is another needle-free approach that delivers liquid vaccine formulations in a 2–500 μL range using a highly pressured propulsion system connected to a needle-free syringe or nozzle ,107. The+ T cell-biased immune response (due to a large number of perifollicular APCs) that could be beneficial for the development of vaccines against intracellular pathogens, viruses and cancers [The aforementioned physical devices, although efficient in antigen delivery into the skin, may lead to some skin barrier damage, making them less suitable for mass vaccination under critical hygienic conditions ,109. As  cancers . HoweverSince the early 1990s, there has been extensive research on controlled release delivery systems for vaccine applications ,114. PriThe biodegradable polymer of choice is the FDA-approved poly lactic-co-glycolic acid (PLGA), which can be formulated into nano- and microparticles using a range of methods, the most common being water-in-oil-in-water (W/O/W) double emulsion solvent evaporation. An advantage of using polymer particles for vaccine delivery is that they can be adapted in size and/or structure to resemble a pathogen or to achieve the targeted delivery to promote humoral and CMI responses ,118. ParThe main method to achieve controlled antigen release has been the encapsulation into a polymer matrix by the W/O/W method. To this day, it remains the classic method that has been used to produce a wide array of particles with varying characteristics . In geneThe potential of encapsulated vaccines was initially demonstrated using ovalbumin (OVA). Superior IgG responses were detected in mice receiving subcutaneous immunisation of OVA encapsulated in PLGA particles (10 µm) compared to OVA combined with Freund’s adjuvant . Liu et Vibrio cholerae [Vibrio cholerae-loaded PLA/PEG microparticles (4–5 µm), inducing high antibody titres as well as protection against a lethal challenge after oral administration in CD-1 outbred mice [Alternative polymer materials have also been explored. Microparticle formulation strategies based on poly  (PLA) have been investigated in encapsulating various antigens including rotavirus, tetanus toxoid and cholerae . PLA parcholerae ,132. Thered mice .The W/O/W method development includes combining PLGA with polysaccharides, or using polysaccharides alone, to formulate antigen delivery systems. A combination of PLGA and sodium alginate investigated for the intradermal delivery of encapsulated malaria peptides demonstrated that particles of 1 µm induced a balanced Th1/Th2 response in BALB/c mice. In combination with immunostimulatory peptides (Arg–Gly–Asp), a strong CMI response was measured compared to the antigen alone or encapsulated in PLGA only . CationiThe co-delivery of the encapsulated antigen and an adjuvant to enhance antigen presentation has been one of the focal points in the last decade, as characterisation methods have become increasingly advanced. Microparticle formulations containing aluminium salts and TLR9 agonist CpG oligonucleotides have shown that combining the antigen and adjuvant into a particulate delivery system can result in robust immune responses for single-dose vaccines compared to multi-immunisation schedules ,132,137.Multi-platform systems such as the single-dose pandemic influenza vaccine of recombinant outer membrane vesicles (rOMVs) encapsulated in PLGA demonstrated equivalent IgG titres to a prime and boost regimen, and protected mice against a challenge with a lethal dose of H1N1, six months post the initial vaccination. These microparticles (10–20 µm), manufactured using W/O/W, had a prolonged antigen release over 30 days . This woThe classic W/O/W emulsification method has a number of shortcomings that have impeded the successful commercial development of an encapsulated antigen in polymer microparticles for single-dose vaccines. Formulation issues and the lack of particle uniformity lead to uneven antigen release profiles, including an initial burst (release of antigen upon injection) . ConsideTo encapsulate antigens, stability is critical and the encapsulation method needs to be optimised for each vaccine. Antigen stability may be impaired due to mechanical and chemical stress induced during emulsification steps. Novel encapsulation technologies that use milder processing methods are likely to mitigate the mechanical stress and reduce solvent-surface interactions; one such approach is coaxial electrospraying . FurtherThe very adaptable process parameters of the W/O/W method have allowed the optimisation of encapsulation processes to reach suitable encapsulation efficiencies. Nonetheless, the (double) emulsification step means that the encapsulation process occurs at random. The spontaneous emulsification (SE) solvent diffusion technique has been suggested as an improved method for controlled protein encapsulation. With SE, a more homogenous distribution of the encapsulated antigen is achieved, thus reducing the initial burst effect . The SE It has been demonstrated that the W/O/W method can be adapted to produce particles of desired size, however, it is quite difficult to achieve an extremely narrow size distribution (monodispersity). The size variation is largely due to the emulsification step, during which the particles have different droplet sizes, followed by variable precipitation rates during the solidification phase. The uniformity of particle size and distribution is also an important factor regarding the reproducibility and feasibility to scale up. Sieving the particles after emulsification can result in more uniform particle preparations. This is referred to as extrusion and achieved with Shirasu porous glass (SPG) beads ,124,145.There is an increasing interest in developing core-shell particles with hydrophilic cores to maintain the microenvironment and therefore the conformation and biological integrity of the antigen . They caMicrofluidics involves the manipulation of fluid flows at a microscale, giving the fluids a laminar behaviour, and is used in specific applications such as droplet microfluidics. Thus, microfluidics can produce highly monodispersed droplets in a controlled and repeatable manner, a feature utilised in drug delivery, and in particular for vaccine encapsulation.In microfluidics, droplets are generated by intersection designs, where two immiscible or partially miscible phases are put into contact, and subsequently produce droplets by the combined actions of shear stress, viscous forces, and interfacial tension. This process can be conducted in capillaries assembled coaxially in “co-flow” type intersections or in microfluidic chips, often made with polydimethylsiloxane (PDMS)—a silicon polymer poured onto a mould and hardened to form microfluidic chips—or with glass, where the “T-junction” or “flow-focusing” designs are embedded. Multiple emulsions are formed with the different fluids flowing through successive intersections, generating successive layers of the multiple emulsion see . These tAs mentioned previously, the droplets generated by microfluidics exhibit an unmatched uniformity. The usual coefficient of variation (CV) of the droplet diameter is below 5% ,154. ThiMicrofluidics also demonstrate high encapsulation efficiencies (EE) as the emulsification process is not random but happens in a user-controlled way. Thus, Pessi et al. reported a 84% EE for BSA in polycaprolactone microcapsules produced with capillary microfluidic double emulsion . MoreoveDroplet microfluidic methods exhibit a user-friendly modularity, where the size and frequency generation can be optically monitored and changed by modifying the flow rates of the injected fluids in the microfluidic setup, as experimental law has been reported between the flow rates ratio and the size of the droplets produced . This reDespite all the benefits of microfluidics outlined here, no direct use of this technology in the production of encapsulated vaccines for delayed delivery has been reported yet. Difficulties in setting up adequate microfluidics platform and processes could be the main reason. Indeed, microfluidics applications require both an accurate fluid flow controller, such as pressure pumps and connected reservoirs, or syringe pumps, and an imaging system, composed of a microscope, or a similar set of lenses and light, and a high-speed camera. These components are not frequently found in biology laboratories, and their setup and use also involves an engineering skill set.Moreover, the microfluidic element itself necessitates manufacturing that has not been upscaled for mass production yet by the industry. Thus, capillary microfluidics systems are homemade, with many challenges to face in the precise assembly, while microfluidic chips are in most cases produced by soft lithography, involving clean room facilities and expertise.The inability of microfluidics systems to achieve the yields of production needed by the drug manufacturing industry is often mentioned as one of the major drawbacks of microfluidics approaches. Indeed, although microfluidic droplet generation rate can reach several kilohertz, the total volume encapsulated is limited due to the inherent microscale aspect of the technology. However, parallelisation approaches, where multiple droplet microfluidic processes are performed simultaneously with the adequate designs, have been published and demonstrated more than acceptable throughputs ,157,158.Finally, the cost of vaccination must be kept in consideration, in particular when developing sophisticated technologies for vaccine formulation and delivery. The mobilisation of global funds for vaccine development, led by the WHO and dedicated organisations such as the Gavi Alliance, along with the scalability of methods and increasing the vaccine production capacity in the developing countries which significantly reduces the cost per dose , can helFrom the above considerations, it is evident that non-invasive vaccination systems are in development as well as are advanced delivery systems that may prove to be an essential part of promising multi-platform vaccine strategies. However, it has yet to be shown whether the presented technologies may provide reliable and cost-effective approaches to vaccination, yielding more efficient vaccines and improved patient compliance to immunisation programmes in both the developed and the developing countries.