Applications of the metaverse in medicine and healthcare.

The metaverse is a virtual world that is being developed to allow people to interact with each other and with digital objects in a more immersive way. It involves the convergence of three major technological trends: telepresence, the digital twin, and blockchain. Telepresence is the ability of people to "be together" in a virtual way while not being close to each other. The digital twin is a virtual, digital equivalent of a patient, a medical device or even a hospital. Blockchain can be used by patients to keep their personal medical records secure. In medicine and healthcare, the metaverse could be used in several ways: (1) virtual medical consultations; (2) medical education and training; (3) patient education; (4) medical research; (5) drug development; (6) therapy and support; (7) laboratory medicine. The metaverse has the potential to enable more personalized, efficient, and accessible healthcare, improving patient outcomes and reducing healthcare costs. However, the implementation of the metaverse in medicine and healthcare will require careful consideration of ethical and privacy concerns, as well as social, technical and regulatory challenges. Overall, the future of the metaverse in healthcare looks bright, but new metaverse-specific laws should be created to help overcome any potential downsides.

Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform.

Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses.

Background: Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy. Methods: The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248. Results: Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage. Conclusions: Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

Clinical Characterization of Patients Diagnosed with Prostate Cancer and Undergoing Conservative Management: A PIONEER Analysis Based on Big Data.

BACKGROUND: Conservative management is an option for prostate cancer (PCa) patients either with the objective of delaying or even avoiding curative therapy, or to wait until palliative treatment is needed. PIONEER, funded by the European Commission Innovative Medicines Initiative, aims at improving PCa care across Europe through the application of big data analytics. OBJECTIVE: To describe the clinical characteristics and long-term outcomes of PCa patients on conservative management by using an international large network of real-world data. DESIGN, SETTING, AND PARTICIPANTS: From an initial cohort of >100 000 000 adult individuals included in eight databases evaluated during a virtual study-a-thon hosted by PIONEER, we identified newly diagnosed PCa cases (n = 527 311). Among those, we selected patients who did not receive curative or palliative treatment within 6 mo from diagnosis (n = 123 146). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Patient and disease characteristics were reported. The number of patients who experienced the main study outcomes was quantified for each stratum and the overall cohort. Kaplan-Meier analyses were used to estimate the distribution of time to event data. RESULTS AND LIMITATIONS: The most common comorbidities were hypertension (35-73%), obesity (9.2-54%), and type 2 diabetes (11-28%). The rate of PCa-related symptomatic progression ranged between 2.6% and 6.2%. Hospitalization (12-25%) and emergency department visits (10-14%) were common events during the 1st year of follow-up. The probability of being free from both palliative and curative treatments decreased during follow-up. Limitations include a lack of information on patients and disease characteristics and on treatment intent. CONCLUSIONS: Our results allow us to better understand the current landscape of patients with PCa managed with conservative treatment. PIONEER offers a unique opportunity to characterize the baseline features and outcomes of PCa patients managed conservatively using real-world data. PATIENT SUMMARY: Up to 25% of men with prostate cancer (PCa) managed conservatively experienced hospitalization and emergency department visits within the 1st year after diagnosis; 6% experienced PCa-related symptoms. The probability of receiving therapies for PCa decreased according to time elapsed after the diagnosis.

Resource recovery using enriched purple phototrophic bacteria in an outdoor flat plate photobioreactor: Suspended vs. attached growth.

Purple phototrophic bacteria (PPB) can produce single-cell protein from wastewater at high yields. Growing in a biofilm vs suspended can improve product quality and consistency. This study compares suspended and attached growths of enriched PPB cultures in an outdoor flat plate photobioreactor treating poultry-processing wastewater. Attached growth had lower VFA removal efficiencies (95 ± 2.7 vs 84 ± 6.4 %) due to light limitations and low substrate diffusion rates. Nevertheless, similar overall treatment performances and productivities were achieved (16 ± 2.2 and 18 ± 2.4 gCOD·m-2·d-1 for attached and suspended) at loading rates of 1.2-1.5 gCOD·L-1·d-1. Biofilms had higher quality than suspended biomass, with lower ash contents (6.9(0.6)% vs 57(16)%) and higher PPB abundances (0.45-0.67 vs 0.30-0.45). The biofilm (20-50 % of the total biomass) might be used as feed and the suspended fraction as fertiliser, improving the economics of the process. Semi-continuous PPB growth outdoors as biofilm is technically feasible, obtaining a superior product without jeopardising performance.

Expanding mechanistic models to represent purple phototrophic bacteria enriched cultures growing outdoors.

The economic feasibility of purple phototrophic bacteria (PPB) for resource recovery relies on using enriched-mixed cultures and sunlight. This work presents an extended Photo-Anaerobic Model (ePAnM), considering: (i) the diverse metabolic capabilities of PPB, (ii) microbial clades interacting with PPB, and (iii) varying environmental conditions. Key kinetic and stoichiometric parameters were either determined experimentally (with dedicated tests), calculated, or gathered from literature. The model was calibrated and validated using different datasets from an outdoors demonstration-scale reactor, as well as results from aerobic and anaerobic batch tests. The ePAnM was able to predict the concentrations of key compounds/components (e.g., COD, volatile fatty acids, and nutrients), as well as microbial communities (with anaerobic systems dominated by fermenters and PPB). The results underlined the importance of considering other microbial clades and varying environmental conditions. The model predicted a minimum hydraulic retention time of 0.5 d-1. A maximum width of 10 cm in flat plate reactors should not be exceeded. Simulations showed the potential of a combined day-anaerobic/night-aerobic operational strategy to allow efficient continuous operation.

From big data to better patient outcomes.

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

Light attenuation in enriched purple phototrophic bacteria cultures: Implications for modelling and reactor design.

Light attenuation in enriched purple phototrophic bacteria (PPB) cultures has not been studied, and its understanding is critical for proper process modelling and reactor design, especially for scaled systems. This work evaluated the effect of different biomass concentrations, reactor configurations, wastewater matrices, and growth conditions, on the attenuation extent of near infra-red (NIR) and ultraviolet-visible (UV-VIS) light spectra. The results show that increased biomass concentrations lead to higher light attenuation, and that PPB absorb both VIS and NIR wavelengths, with both fractions of the spectrum being equally absorbed at biomass concentrations above 1,000 g COD·m-3. A flat plate configuration showed less attenuation compared with cylindrical reactors illuminated from the top, representative for open ponds. Neither a complex wastewater matrix nor the presence of polyhydroxyalkanoates (under nutrient limited conditions) affected light attenuation significantly. The pigment concentration (both bacteriochlorophyll and carotenoids) however, had a strong effect, with significant attenuation in the presence of pigments. Attenuation predictions using the Lambert-Beer law (excluding scattering) and the Schuster model (including scattering) indicated that light scattering had a minimal effect. A proposed mathematical model, based on the Lambert-Beer law and a Monod function for light requirements, allowed effective prediction of the kinetics of photoheterotrophic growth. This resulted in a half saturation coefficient of 4.6 W·m-2. Finally, the results showed that in dense outdoor PPB cultures (≥1,000 g COD·m-3), effective light penetration is only 5 cm, which biases design away from horizontal lagoons, and towards non-incident multi-panel systems such as flat plate reactors.

Method development for PPB culture screening, pigment analysis with UPLC-UV-HRMS vs. spectrophotometric methods, and spectral decomposition-based analysis.

PPB carotenoids are usually measured through spectrophotometric analysis, measuring total carotenoids (TCs) which has low accuracy and cannot identify individual carotenoids or isomers. Here, we developed an ultra-performance liquid chromatography method with ultraviolet and high-resolution mass spectrometry detection (UPLC-UV-HRMS) to quantify neurosporene, lycopene, and bacteriochlorophyll a contents in PPB cultures. The method exhibited satisfactory recoveries for individual pigments (between 82.1% and 99.5%) and was applied to a range of mixed PPB cultures. The use of a C30 column also enabled the detection of three different isomers of lycopene. In addition, a method for anaerobic photoheterotrophic PPB cultivation to acquire live-cell spectrophotometric information was developed and tested by modifying a standard microbial culture microplate system. A rapid, and relatively low effort principal component analysis (PCA) based decomposition of the whole-cell spectra for pigment analysis in the microplates was also developed. Analysing whole-cell spectra via PCA allowed more accurate prediction of individual pigments compared to absorption methods, and can be done non-destructively, during live-cell growth, but requires calibration for new media and microbial matrices.

Creating value from purple phototrophic bacteria via single-cell protein production.

Mixed culture purple phototrophic bacteria (PPB) is a rapidly emerging technology for resource recovery from wastewaters. PPB biomass can be used as single-cell protein, with a high protein content complemented by value-added components (e.g. pigments and polyhydroxyalkanoates), merging functionalities within a single product. This has the potential to increase the value and impact the economic feasibility, justifying higher capital costs for PPB photobioreactors for real-life applications. Artificial illumination is prohibitively expensive, and naturally illuminated, outdoor units are a critical next step. However, information required for informed technoeconomic assessment of single-cell protein from PPB is still missing and can only be determined in dedicated larger-scale, outdoor systems. Larger scale units are also required to supply feed for larger cohort trials. Although data from microalgae research can be used as starting point to estimate costs, they cannot be translated directly for PPB, as the organisms and metabolic growth are fundamentally different.

Data Science in Healthcare: COVID-19 and Beyond.

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available 'big' data [...].

Outdoor demonstration-scale flat plate photobioreactor for resource recovery with purple phototrophic bacteria.

To make purple phototrophic bacteria (PPB)-based technologies a reality for resource recovery, research must be demonstrated outdoors, using scaled reactors. In this study, a 10 m long PPB-enriched flat plate photobioreactor (FPPBR) with a volume of 0.95 m3 was operated for 253 days, fed with poultry processing wastewater. Different operational strategies were tested, including varying influent types, retention times, feeding strategies, and anaerobic/aerobic conditions in a novel mixed metabolic mode concept. The overall results show that regardless of the fermented wastewater fed (raw or after solid removal via dissolved air flotation) and the varying environmental conditions (e.g., light exposure and temperatures), the FPPBR provided effective volatile fatty acids (VFAs), N, and P removals (average efficiencies of >90%, 34-77%, and 28-45%, respectively). The removal of N and P was limited by the availability of biodegradable COD. Biomass (C, N and P) could be harvested at ∼90% VS/TS ratio, 58% crude protein content and a suitable amino acid profile for potential feed applications. During fully anaerobic operation with semicontinuous/day-only feeding, the FPPBR showed biomass productivities between 25 and 84 g VS m-2 d-1 (high due to solid influx; the productivities estimated from COD removal rates were 6.0-24 g VS•m-2•d-1 (conservative values)), and soluble COD removal rates of up to 1.0 g•L-1•d-1 (overall average of 0.34 ± 0.16 g•L-1•d-1). Under these conditions, the relative abundance of PPB in the harvested biomass was up to 56%. A minimum overall HRT of 2-2.4 d (1.0-1.2 d when only fed during the day) is recommended to avoid PPB washout, assuming no biomass retention. A combined daily-illuminated-anaerobic/night-aerobic operation (supplying air during night-time) exploiting photoheterotrophy during the day and aerobic chemoheterotrophy of the same bacteria at night improved the overall removal performance, avoiding VFA accumulation during the night. However, while enabling enhanced treatment, this resulted in a lower relative abundance of PPB and reduced biomass productivities, highlighting the need to balance resource recovery and treatment goals.

Naturally illuminated photobioreactors for resource recovery from piggery and chicken-processing wastewaters utilising purple phototrophic bacteria.

Resource recovery from wastewater, preferably as high value products, has become an integral part of modern wastewater treatment. This work presents the potential to produce single cell protein (SCP) from pre-settled piggery wastewater (PWW) and meat chicken processing wastewater (CWW), utilising anaerobic purple phototrophic bacteria (PPB). PPB were grown as biofilm in outdoors 60 L, 80 L and 100 L flat-plate reactors, operated in sequential batch mode. PPB biofilm was recovered from reactor walls at a total solid (TS) content ∼90 g•L - 1, and the harvested biomass (depending on the wastewater) had a consistent quality, with high protein contents (50-65%) and low ash, potentially applicable as SCP. The COD, N and P removal efficiencies were 71±5.3%, 22±6.6%, 65±5.6% for PWW and 78±1.8%, 67±2.7% and 37±4.0% for CWW, respectively, with biofilm areal productivities up to 14 g TS•m - 2•d - 1. This was achieved at ammonium-N concentrations over 1.0 g•L - 1 and temperatures up to 55 °C and down to 6 °C (daily fluctuations of 20-30 °C). The removal performances and biomass productivities were mostly dependent on the bioavailable COD in the form of volatile fatty acids (VFA). At sufficient VFA availability, the irradiance became limiting, capping biofilm formation. Harvesting of the suspended fraction resulted in increased productivities and recovery efficiencies, but lowered the product quality (e.g., containing undesired inerts). The optimum between quantity and quality of product is dependent on the wastewater characteristics (i.e., organic degradable fraction) and potential pre-treatment. This study shows the potential to utilise sunlight to treat agri-industrial wastewaters while generating protein-rich PPB biomass to be used as a feed, feed additive or feed supplement.

Literature analysis of artificial intelligence in biomedicine.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer.

Introduction. Prostate cancer (PCa) is the most frequent cancer diagnosis in men worldwide. Our ability to identify those men whose cancer will decrease their lifespan and/or quality of life remains poor. The ReIMAGINE Consortium has been established to improve PCa diagnosis. Materials and methods. MRI will likely become the future cornerstone of the risk-stratification process for men at risk of early prostate cancer. We will, for the first time, be able to combine the underlying molecular changes in PCa with the state-of-the-art imaging. ReIMAGINE Screening invites men for MRI and PSA evaluation. ReIMAGINE Risk includes men at risk of prostate cancer based on MRI, and includes biomarker testing. Results. Baseline clinical information, genomics, blood, urine, fresh prostate tissue samples, digital pathology and radiomics data will be analysed. Data will be de-identified, stored with correlated mpMRI disease endotypes and linked with long term follow-up outcomes in an instance of the Philips Clinical Data Lake, consisting of cloud-based software. The ReIMAGINE platform includes application programming interfaces and a user interface that allows users to browse data, select cohorts, manage users and access rights, query data, and more. Connection to analytics tools such as Python allows statistical and stratification method pipelines to run profiling regression analyses. Discussion. The ReIMAGINE Multimodal Warehouse comprises a unique data source for PCa research, to improve risk stratification for PCa and inform clinical practice. The de-identified dataset characterized by clinical, imaging, genomics and digital pathology PCa patient phenotypes will be a valuable resource for the scientific and medical community.

Purple phototrophic bacteria are outcompeted by aerobic heterotrophs in the presence of oxygen.

There is an ongoing debate around the effect of microaerobic/aerobic conditions on the wastewater treatment performance and stability of enriched purple phototrophic bacteria (PPB) cultures. It is well known that oxygen-induced oxidative conditions inhibit the synthesis of light harvesting complexes, required for photoheterotrophy. However, in applied research, several publications have reported efficient wastewater treatment at high dissolved oxygen (DO) levels. This study evaluated the impact of different DO concentrations (0-0.25 mg·L-1, 0-0.5 mg·L-1 and 0-4.5 mg·L-1) on the COD, nitrogen and phosphorus removal performances, the biomass yields, and the final microbial communities of PPB-enriched cultures, treating real wastewaters (domestic and poultry processing wastewater). The results show that the presence of oxygen suppressed photoheterotrophic growth, which led to a complete pigment and colour loss in a matter of 20-30 h after starting the batch. Under aerobic conditions, chemoheterotrophy was the dominant catabolic pathway, with wastewater treatment performances similar to those achieved in common aerobic reactors, rather than those corresponding to phototrophic systems (i.e. considerable total COD decrease (45-57% aerobically vs. ± 10% anaerobically). This includes faster consumption of COD and nutrients, lower nutrient removal efficiencies (50-58% vs. 72-99% for NH4+-N), lower COD:N:P substrate ratios (100:4.5-5.0:0.4-0.8 vs. 100:6.7-12:0.9-1.2), and lower apparent biomass yields (0.15-0.31 vs. 0.8-1.2 g CODbiomass·g CODremoved-1)). The suppression of photoheterotrophy inevitably resulted in a reduction of the relative PPB abundances in all the aerated tests (below 20% at the end of the tests), as PPB lost their main competitive advantage against competing aerobic heterotrophic microbes. This was explained by the lower aerobic PPB growth rates (2.4 d-1 at 35 °C) when compared to common growth rates for aerobic heterotrophs (6.0 d-1 at 20 °C). Therefore, PPB effectively outcompete other microbes under illuminated-anaerobic conditions, but not under aerobic or even micro-aerobic conditions, as shown by continuously aerated tests controlled at undetectable DO levels. While their aerobic heterotrophic capabilities provide some resilience, at non-sterile conditions PPB cannot dominate when growing chemoheterotrophically, and will be outcompeted.

Purple phototrophic bacteria granules under high and low upflow velocities.

The application of granular biomass has enabled energy efficient, high-rate wastewater treatment systems. While initially designed for high-strength wastewater treatment, granular systems can also play a major role in resource recovery. This study focused on the formation of purple phototrophic bacteria (PPB) granular biomass during synthetic wastewater treatment. Liquid upflow velocity was applied as the driving force for granulation. Separate reactors were operated at either low (2-5m h-1) or high (6-9m h-1) upflow velocities, with sludge retention times (SRTs) ranging from 5-15d. Reactors produced anaerobic, photo-granules within ~50d. The sludge volume index (SVI30) of the granules was 10mL g-1 and average settling rates were greater than 30m h-1, both metrics being similar to existing granular technologies. Granule sizes of 2-3mm were recorded, however the particle size distribution was bimodal with a large floc fraction (70-80% volume fraction). The extracellular polymeric substance (EPS) and alginate-like extract (ALE) contents were similar to those in aerobic granular biomass. Fluorescence in-situ hybridisation (FISH) imaging identified PPB bacteria dispersed throughout the granules with very few methanogens and an active core. Outer layer morphology was substantially different in the two reactors. The high-upflow reactor had an outer layer of Chromatiales and an inner layer of Rhodobacteriales, while the low-upflow reactor had lower abundances of both, and limited layering. According to 16s gene sequencing, PPB were a similar fraction of the microbial community in both reactors (40-70%), but the high upflow granules were dominated by Chromatiales (supporting FISH results), while the low upflow velocity reactor had a more diverse PPB community. Methanogens were seen only in the low upflow granules and only in small amounts (≤8%). Granule crude protein content was ~0.60gCP gVS-1 (~0.45gCP gTS-1), similar to that from other PPB production technologies. The growth of a rapid settling and discrete PPB granular biomass on synthetic wastewater suggests methods for resource recovery using PPB can be diversified to also include granular biomass.