Corrigendum: Variation of photosynthetic induction in major horticultural crops is mostly driven by differences in stomatal traits.

[This corrects the article DOI: 10.3389/fpls.2022.860229.].

Plasma hPG80 (Circulating Progastrin) as a Novel Prognostic Biomarker for early-stage breast cancer in a breast cancer cohort.

BACKGROUND: Recurrence and metastases are still frequent outcomes after initial tumour control in women diagnosed with breast cancer. Although therapies are selected based on tumour characteristics measured at baseline, prognostic biomarkers can identify those at risk of poor outcomes. Circulating progastrin or hPG80 was found to be associated with survival outcomes in renal and hepatocellular carcinomas and was a plausible prognostic biomarker for breast cancer. METHODS: Women with incident breast cancers from Calgary, Alberta, Canada enrolled in the Breast to Bone (B2B) study between 2010 to 2016 and provided blood samples prior to any treatment initiation. Plasma from these baseline samples were analysed for circulating progastrin or hPG80. Participant characteristics as well as tumour ones were evaluated for their association with hPG80 and survival outcomes (time to recurrence, recurrence - free survival, breast cancer specific survival and overall survival) in Cox proportional hazards regression models. RESULTS: The 464 participants with measurable hPG80 in this study had an average age of 57.03 years (standard deviation of 11.17 years) and were predominantly diagnosed with Stage I (52.2%) and Stage II (40.1%) disease. A total of 50 recurrences and 50 deaths were recorded as of June 2022. In Cox PH regression models adjusted for chemotherapy, radiation therapy, cancer stage and age at diagnosis, log hPG80 (pmol/L) significantly increased the risks for recurrence (Hazard Ratio (HR) = 1.330, 95% Confidence Interval (CI) = (0.995 - 1.777, p = 0.054)), recurrence-free survival (HR = 1.399, 95% CI = (1.106 - 1.770), p = 0.005) and overall survival (HR = 1.385, 95% CI = (1.046 - 1.834), = 0.023) but not for breast cancer specific survival (HR = 1.015, 95% CI = (0.684 - 1.505), p = 0.942). CONCLUSIONS: hPG80 levels measured at diagnosis were significantly associated with the risk of recurrence or death from any cause in women with breast cancer. Since the recurrence rates of breast cancer are still relatively high amongst women diagnosed at an early stage, identifying women at high risk of recurrence at their time of diagnosis is important. hPG80 is a promising new prognostic biomarker that could improve the identification of women at higher risk of poor outcomes.

A small dynamic leaf-level model predicting photosynthesis in greenhouse tomatoes.

The conversion of supplemental greenhouse light energy into biomass is not always optimal. Recent trends in global energy prices and discussions on climate change highlight the need to reduce our energy footprint associated with the use of supplemental light in greenhouse crop production. This can be achieved by implementing "smart" lighting regimens which in turn rely on a good understanding of how fluctuating light influences photosynthetic physiology. Here, a simple fit-for-purpose dynamic model is presented. It accurately predicts net leaf photosynthesis under natural fluctuating light. It comprises two ordinary differential equations predicting: 1) the total stomatal conductance to CO2 diffusion and 2) the CO2 concentration inside a leaf. It contains elements of the Farquhar-von Caemmerer-Berry model and the successful incorporation of this model suggests that for tomato (Solanum lycopersicum L.), it is sufficient to assume that Rubisco remains activated despite rapid fluctuations in irradiance. Furthermore, predictions of the net photosynthetic rate under both 400ppm and enriched 800ppm ambient CO2 concentrations indicate a strong correlation between the dynamic rate of photosynthesis and the rate of electron transport. Finally, we are able to indicate whether dynamic photosynthesis is Rubisco or electron transport rate limited.

Variation of Photosynthetic Induction in Major Horticultural Crops Is Mostly Driven by Differences in Stomatal Traits.

Under natural conditions, irradiance frequently fluctuates, causing net photosynthesis rate (A) to respond slowly and reducing the yields. We quantified the genotypic variation of photosynthetic induction in 19 genotypes among the following six horticultural crops: basil, chrysanthemum, cucumber, lettuce, tomato, and rose. Kinetics of photosynthetic induction and the stomatal opening were measured by exposing shade-adapted leaves (50 µmol m-2 s-1) to a high irradiance (1000 µmol m-2 s-1) until A reached a steady state. Rubisco activation rate was estimated by the kinetics of carboxylation capacity, which was quantified using dynamic A vs. [CO2] curves. Generally, variations in photosynthetic induction kinetics were larger between crops and smaller between cultivars of the same crop. Time until reaching 20-90% of full A induction varied by 40-60% across genotypes, and this was driven by a variation in the stomatal opening rather than Rubisco activation kinetics. Stomatal conductance kinetics were partly determined by differences in the stomatal size and density; species with densely packed, smaller stomata (e.g., cucumber) tended to open their stomata faster, adapting stomatal conductance more rapidly and efficiently than species with larger but fewer stomata (e.g., chrysanthemum). We conclude that manipulating stomatal traits may speed up photosynthetic induction and growth of horticultural crops under natural irradiance fluctuations.

How to improve hospital admission screening for patients at risk of multidrug-resistant organism carriage: a before-and-after interventional study and cost-effectiveness analysis.

BACKGROUND: Infection prevention and control (IPC) is a prioritised task for healthcare workers in emergency department (ED). Here, we examined compliance with admission screening (AS) and additional precautions (AP) measures for patients at risk of infection with multidrug-resistant organisms (MDROs) by using a two-stage, multifaceted educational intervention, also comparing the cost of a developed automated indicator for AS and AP compliance and clinical audits to sustain observed findings. METHODS: In the first stage, staff in the ED of the University Hospitals of Geneva, Switzerland, were briefed on IPC measures (AS and AP). A cross-sectional survey was then conducted to assess barriers to IPC measures. In the second stage, healthcare workers underwent training sessions, and an electronic patient record 'order-set' including AS and AP compliance indicators was designed. We compared the cost-benefit of the audits and the automated indicators for AS and AP compliance. RESULTS: Compliance significantly improved after training, from 36.2% (95% CI 23.6% to 48.8%) to 78.8% (95% CI 67.1% to 90.3%) for AS (n=100, p=0.0050) and from 50.2% (95% CI 45.3% to 55.1%) to 68.5% (95% CI 60.1% to 76.9%) for AP (n=125, p=0.0092). Healthcare workers recognised MDRO screening as an ED task (70.2%), with greater acknowledgment of risk factors at AS considered an ED duty. The monthly cost was higher for clinical audits than the automated indicator, with a reported yearly cost of US$120 203. The initial cost of developing the automated indicator was US$18 290 and its return on investment US$3.44 per US$1 invested. CONCLUSION: Training ED staff increased compliance with IPC measures when accompanied by team discussions for optimal effectiveness. An automated indicator of compliance is cheaper and closer to real-time than a clinical audit.

hPG80 (Circulating Progastrin), a Novel Blood-Based Biomarker for Detection of Poorly Differentiated Neuroendocrine Carcinoma and Well Differentiated Neuroendocrine Tumors.

Current blood-based biomarkers for neuroendocrine neoplasms (NENs) lack both sensitivity and specificity. Human circulating progastrin (hPG80) is a novel biomarker that can be easily measured in plasma by ELISA. This study is the first to examine hPG80 in NENs. Plasma hPG80 was quantified from 95 stage IV NEN patients, using DxPG80 technology (ECS Progastrin, Switzerland) and compared with hPG80 concentrations in two cohorts of healthy donor controls aged 50-80 (n = 252) and 18-25 (n = 137). Median hPG80 in NENs patients was 5.54 pM compared to 1.5 pM for the 50-80 controls and 0.29 pM the 18-25 cohort (p < 0.0001). Subgroup analysis revealed median hPG80 levels significantly higher than for either control cohort in neuroendocrine carcinoma (NEC; n = 25) and neuroendocrine tumors (NET; n = 70) including the small-cell lung cancer (SCLC) sub-cohort (n = 13). Diagnostic accuracy, estimated by AUCs, was high for NENs, as well as both sub-groups (NEC/NET) when compared to the younger and older control groups. Plasma hPG80 in NENs may be a diagnostic blood biomarker for both low- and high-grade NENs; further study is warranted. A prospective multi-center trial is ongoing in NET to evaluate hPG80 as a means of monitoring disease (NCT04750954).

Cohort profile: Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS).

PURPOSE: The Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS) is an ongoing prospective cohort created by the Geneva Directorate of Health. It consists of an operational database compiling all SARS-CoV-2 test results recorded in the Geneva area since late February 2020. This article aims at presenting this comprehensive cohort, in light of some of the varying public health measures in Geneva, Switzerland, since March 2020. PARTICIPANTS: As of 1 June 2021, the database included 360 525 patients, among which 65 475 had at least one positive test result for SARS-CoV-2. Among all positive patients, 37.6% were contacted only once, 10.6% had one follow-up call, 8.5% had two and 27.7% had three or more follow-up calls. Participation rate among positive patients is 94%. Data collection is ongoing. FINDINGS TO DATE: ARGOS data illustrates the magnitude of COVID-19 pandemic in Geneva, Switzerland, and details a variety of population factors and outcomes. The content of the cohort includes demographic data, comorbidities and risk factors for poor clinical outcome, self-reported COVID-19 symptoms, environmental and socioeconomic factors, prospective and retrospective contact tracing data, travel quarantine data and deaths. The registry has already been used in several publications focusing on symptoms and long COVID-19, infection fatality rate and re-infection. FUTURE PLANS: The data of this large real-world registry provides a valuable resource for various types of research, such as clinical research, epidemiological research or policy assessment as it illustrates the impact of public health policies and overall disease burden of COVID-19.

A novel method to detect hPG80 (human circulating progastrin) in the blood.

hPG80 (human circulating progastrin) is produced and released by cancer cells. We recently reported that hPG80 is detected in the blood of patients with cancers from different origins, suggesting its potential utility for cancer detection. To accurately measure hPG80 in the blood of patients, we developed the DxPG80 test, a sandwich Enzyme-Linked Immunosorbent Assay (ELISA). This test quantifies hPG80 in EDTA plasma samples. The analytical performances of the DxPG80 test were evaluated using standard procedures and guidelines specific to ELISA technology. We showed high specificity for hPG80 with no cross-reactivity with human glycine-extended gastrin (hG17-Gly), human carboxy-amidated gastrin (hG17-NH2) or the CTFP (C-Terminus Flanking Peptide) and no interference with various endogenous or exogenous compounds. The test is linear between 0 and 50 pM hPG80 (native or recombinant). We demonstrated a trueness of measurement, an accuracy and a variability of hPG80 quantification with the DxPG80 test below the 20% relative errors as recommended in the guidelines. The limit of detection of hPG80 and the limit of quantification were calculated as 1 pM and 3.3 pM respectively. In conclusion, these results show the strong analytical performance of the DxPG80 test to measure hPG80 in blood samples.

Assessing the role of initial conditions in the local structural identifiability of large dynamic models.

Structural identifiability is a binary property that determines whether or not unique parameter values can, in principle, be estimated from error-free input-output data. The many papers that have been written on this topic collectively stress the importance of this a priori analysis in the model development process. The story however, often ends with a structurally unidentifiable model. This may leave a model developer with no plan of action on how to address this potential issue. We continue this model exploration journey by identifying one of the possible sources of a model's unidentifiability: problematic initial conditions. It is well-known that certain initial values may result in the loss of local structural identifiability. Nevertheless, literature on this topic has been limited to the analysis of small toy models. Here, we present a systematic approach to detect problematic initial conditions of real-world systems biology models, that are usually not small. A model's identifiability can often be reinstated by changing the value of such problematic initial conditions. This provides modellers an option to resolve the "unidentifiable model" problem. Additionally, a good understanding of which initial values should rather be avoided can be very useful during experimental design. We show how our approach works in practice by applying it to five models. First, two small benchmark models are studied to get the reader acquainted with the method. The first one shows the effect of a zero-valued problematic initial condition. The second one illustrates that the approach also yields correct results in the presence of input signals and that problematic initial conditions need not be zero-values. For the remaining three examples, we set out to identify key initial values which may result in the structural unidentifiability. The third and fourth examples involve a systems biology Epo receptor model and a JAK/STAT model, respectively. In the final Pharmacokinetics model, of which its global structural identifiability has only recently been confirmed, we indicate that there are still sets of initial values for which this property does not hold.

Prognostic Value of Plasma hPG80 (Circulating Progastrin) in Metastatic Renal Cell Carcinoma.

Precise management of kidney cancer requires the identification of prognostic factors. hPG80 (circulating progastrin) is a tumor promoting peptide present in the blood of patients with various cancers, including renal cell carcinoma (RCC). In this study, we evaluated the prognostic value of plasma hPG80 in 143 prospectively collected patients with metastatic RCC (mRCC). The prognostic impact of hPG80 levels on overall survival (OS) in mRCC patients after controlling for hPG80 levels in non-cancer age matched controls was determined and compared to the International Metastatic Database Consortium (IMDC) risk model (good, intermediate, poor). ROC curves were used to evaluate the diagnostic accuracy of hPG80 using the area under the curve (AUC). Our results showed that plasma hPG80 was detected in 94% of mRCC patients. hPG80 levels displayed high predictive accuracy with an AUC of 0.93 and 0.84 when compared to 18-25 year old controls and 50-80 year old controls, respectively. mRCC patients with high hPG80 levels (>4.5 pM) had significantly lower OS compared to patients with low hPG80 levels (<4.5 pM) (12 versus 31.2 months, respectively; p = 0.0031). Adding hPG80 levels (score of 1 for patients having hPG80 levels > 4.5 pM) to the six variables of the IMDC risk model showed a greater and significant difference in OS between the newly defined good-, intermediate- and poor-risk groups (p = 0.0003 compared to p = 0.0076). Finally, when patients with IMDC intermediate-risk group were further divided into two groups based on hPG80 levels within these subgroups, increased OS were observed in patients with low hPG80 levels (<4.5 pM). In conclusion, our data suggest that hPG80 could be used for prognosticating survival in mRCC alone or integrated to the IMDC score (by adding a variable to the IMDC score or by substratifying the IMDC risk groups), be a prognostic biomarker in mRCC patients.