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Airway nitric oxide (NO) deficiency is a hallmark of cystic fibrosis (CF), but the reasons for the reduced NO production in CF airways are unclear. Interleukin (IL)-1 pathway activation plays a role in early CF lung disease and is also involved in the regulation of NO synthase activity. Treatment of CF patients with the CFTR-targeting drug ivacaftor, among other beneficial effects, results in an increase in airway NO levels. In this longitudinal observational trial, we show that ivacaftor therapy leads to a significant reduction in sputum IL-1�� concentration but not in other IL-1- or Th17-associated cytokines. IL-1�� concentrations were closely linked to improvement in pulmonary function, measures of NO metabolism in sputum and exhaled NO. These data therefore suggest a potential interaction between transepithelial chloride conductance, IL-1�� and airway NO production.

This study investigated the relationship between biopsychosocial characteristics (age, sex, self-rated health, mental health, parental socio-economic status, family support, teacher support, peer support) and alcohol consumption (weekly alcohol consumption in the past three months, drunkenness in the past three months, and binge drinking in the past month) in adolescents during a crisis event. The study consisted of 1019 Israeli students aged 11–18. Questionnaires were distributed to the students between May and July 2021 during school. Teacher support among those who presented weekly alcohol consumption and drunkenness in the past three months was lower than in those who did not present such behavior. The effects of parental support differed only for drunkenness behavior, with those who engaged in drunken behavior presenting significantly less parental support. Our findings suggest that teacher support and mental health are the two major factors in preventing risky alcohol consumption behavior during a crisis, such as the COVID-19 pandemic, among adolescents.

Myeloid-derived suppressor cells (MDSCs) represent a heterogeneous population of immature myeloid cells known to play a role in perpetuating a wide range of pathologies, such as chronic infections, autoimmune diseases, and cancer. MDSCs were first identified in mice by the markers CD11b

Myeloid-derived suppressor cells (MDSCs) are heterogenous populations of immature myeloid cells that can be divided into two main subpopulations, polymorphonuclear (PMN) MDSCs and monocytic (M) MDSCs. These cells accumulate during chronic inflammation and induce immunosuppression evident in an array of pathologies such as cancer, inflammatory bowel disease, and infectious and autoimmune diseases. Herein, we describe methods to isolate and characterize MDSCs from various murine tissue, as well as to phenotype blood-derived MDSCs from patients. The protocols describe methods for isolation of total MDSCs and their subpopulations, for characterization, and for evaluation of their distribution within tissue, as well as for assessing their maturation stage by flow cytometry, immunofluorescence analyses, and Giemsa staining. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Single-cell suspension generation from different tissue Alternate Protocol 1: Single-cell suspension generation from subcutaneous melanoma tumors Basic Protocol 2: Characterization of MDSC phenotype Basic Protocol 3: Cell separation using magnetic beads: Separating pan-MDSCs or PMN-MDSC and M-MDSC subpopulations Alternate Protocol 2: Staining and preparing MDSCs for sorting Support Protocol: PMN-MDSC and M-MDSC gating strategy in mouse Basic Protocol 4: Immunofluorescence analysis of MDSCs Basic Protocol 5: Handling human blood samples and characterizing human MDSCs Alternate Protocol 3: Flow cytometry staining of thawed human whole blood samples.

Myeloid-derived suppressor cells (MDSCs) are heterogenous populations of immature myeloid cells that can be divided into two main subpopulations, polymorphonuclear (PMN) MDSCs and monocytic (M) MDSCs. These cells accumulate during chronic inflammation, characterizing an array of pathologies such as cancer, inflammatory bowel disease, and infectious and autoimmune diseases, and induce immunosuppression. The suppressive effects of MDSCs on the immune system are studied mainly when focusing on their features, functions, and impact on target cells such as T cells, natural killer cells, and B cells, among others. Herein, we describe methods for the analysis of MDSC immunosuppressive features and functions, measuring different mediators that contribute to their activities and how they impact on T cell function. The protocols described are a continuation to those in a companion Current Protocols article by Reuven et al. (2022), which uses a generated single-cell suspension and isolated cells to test their activity. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Evaluating MDSC suppressive features Alternate Protocol 1: Dichlorofluorescein diacetate-based reactive oxygen species detection Support Protocol 1: Detection of nitric oxide secretion Support Protocol 2: Measurement of arginase activity Basic Protocol 2: Evaluating MDSC suppressive function Alternate Protocol 2: In vitro effects of MDSCs on expression of T cell receptor complex during activation Support Protocol 3: Effect of MDSCs on interferon γ production Basic Protocol 3: Effect of MDSCs on T cell proliferation Basic Protocol 4: Effect of MDSCs on T cell cytotoxic activity Alternate Protocol 3: In vivo cytotoxicity assay Basic Protocol 5: Analysis of MDSC differentiation.

The COVID-19 pandemic spreads worldwide, causing devastating consequences. BioNTech and Pfizer's BNT162b2 vaccine was one of the first vaccines to receive emergency-use authorization. However, its impact on women's fertility has not been primarily assessed leading to spread of unfounded rumors, causing vaccine hesitancy. We investigate the possible impact of BNT162b2 COVID-19 vaccine on in vitro fertilization (IVF) outcomes.We compared data from 42 women undergoing their first IVF following 2 doses of BNT162b2 vaccination to 42 unvaccinated women undergoing their first IVF. The first outcome consisted of the pregnancy based on first hCG value; secondary outcomes were IVF cycle outcomes. A second analysis was done on 29 paired patients from both groups based on age, FSH, and the indication for IVF.We report different levels of the pregnancy formation; oocyte's development: numbers of oocytes retrieved (9.3±6.8 vs. 11.5 ± 7.9, p=0.19) and matured (6.9±4.8 vs. 9.1 ± 6.6, p=0.14), and embryonal stage: fertilization rates (64.1±26.6 vs. 66.3±23.3, p=0.14), the quantity (4.3 ± 3.2 vs. 5.7±4.4, p=0.23) and qualities of embryos (good/fair/poor) at day 3 were comparable. The analysis showed no significant difference between the groups, and the odds for pregnancy were similar (OR=0.9, 95% CL[0.3-2.8] p.value 0.833).From our results, this vaccine does not affect IVF performance and outcomes from the early stage of oocyte development through to the early beginning of pregnancy; therefore, it seems that the BNT162b2 vaccine does not compromise women's fertility.

Epidural analgesia alleviates pain during normal labour but women who undergo medical abortion procedures using epidural analgesia continue to express high pain levels. To understand this we assessed if patients undergoing medical abortions, treated with epidural analgesia, use their pain for psychological benefits.This study comprised 105 patients over 13 weeks of gestational age diagnosed with foetal abnormalities after selecting a medical abortion procedure using epidural analgesia. Three questionnaires were handed-out: 1) 'Need for Affect', assessed the motivation to react to emotions; 2) 'Pain-Levels' 3) PANA- 'Positive Affect' (PA), 'Negative Affect' (NA) assessed emotions pre-abortion and post-abortion.Patients with a strong Need for Affect and high Pain-Levels expressed a stronger PA post-abortion (b = .69, se = .11, β = .68, p .001, 95%CI [.48,.90]). Patients with a strong NA pre-abortion and high Pain-Levels expressed a higher NA post-abortion (b = .48, se = .11, β = .53, p .001, 95%CI [.26, .70]).Patients with a strong Need for Affect who express a strong PA pre-abortion intensify their pain to fulfill their Need for Affect, which then helps recovery. Patients with a strong NA pre-abortion and high Pain-Levels indicate a less favourable outcome.

Study objective To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments. Methods Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Results There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

Objective Recent studies suggest many patients with non-specific low back pain presenting to public hospital EDs receive low-value care. The primary aim was to describe management of patients presenting with low back pain to the ED of a private hospital in Melbourne, Australia, and received a final ED diagnosis of non-specific low back pain. We also determined predictors of hospital admission. Methods Retrospective review of patients who presented with low back pain and received a final ED diagnosis of non-specific low back pain to Cabrini Malvern ED in 2015. Demographics, lumbar spinal imaging, pathology tests and medications were extracted from hospital records. Multivariate logistic regression was used to determine independent predictors of hospital admission. Results Four hundred and fifty presentations were included (60% female); 238 (52.9%) were admitted to hospital. One hundred and seventy-seven (39.3%) patients received lumbar spine imaging. Two hundred and eighty (62.2%) patients had pathology tests and 391 (86.9%) received medications, which included opioids (n = 298, 66.2%), paracetamol (n = 219, 48.7%), NSAIDs (n = 161, 35.8%), benzodiazepines (n = 118, 26.2%) and pregabalin (n = 26, 5.8%). Predictors of hospital admission included older age (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.02-1.05), arrival by ambulance (OR 2.03, 95% CI 1.06-3.90) and receipt of pathology tests (OR 3.32, 95% CI 2.01-5.49) or computed tomography scans (OR 1.86, 95% CI 1.12-3.11). Conclusion We observed high rates of imaging, pathology tests and hospital admissions compared with previous public hospital studies, while medication use was similar. Implementation of strategies to optimise evidence-based ED care is needed to reduce low-value care and improve patient outcomes.

Study question Are there specific morphokinetic time points which can be used to determine whether an embryo should be discarded? Summary answer Morphokinetic ranges where embryos will be discarded rather than transferred or cryopreserved, can be defined using time-lapse annotations automatically generated with artificial intelligence (AI). What is known already Time-lapse incubation has changed the way embryos are selected. Instead of static daily observations, continuous monitoring of embryos allows for generation of morphokinetic parameters which quantify the pace of development. However, annotations by humans have been shown to incur operator variations and are time-consuming to perform. AI can automatically annotate embryos with equivalence in accuracy to experienced embryologists. Although most embryo selection methods are designed to identify the embryo with the highest chance of becoming a healthy live birth baby, the ability to identify embryos that will not be suitable for treatment is equally important for clinical decision making. Study design, size, duration This is a prospective, observational, cohort study. Time-lapse videos from 142 embryos from a private fertility clinic in Spain were automatically annotated using CHLOE (Fairtility), an AI-based software. CHLOE automatically generated the following morphokinetic parameters: tPNa, tPNf, t2, t3, t4, t5, t6, t7, t8, t9+, tM, tSB, tB, tEB. Participants/materials, setting, methods Embryos analysed were from donor and own oocyte’s treatments. Selected embryos were analysed using CHLOE, to automatically identify morphokinetic parameters. The distribution for each morphokinetic parameter was compared between fates (data presented for transferred + frozen vs discarded as mean+-standard deviation, 2-sided t-test). Each continuous morphokinetic parameter was categorised according to the ranges where embryo utilisation was futile ( Main results and the role of chance For every morphokinetic parameter the difference in event time between frozen+transferred vs discarded embryos was statistically significant(p tPNa (7.68(2.03)vs22.04(27.15),p tPNf (21.71(2.86)vs34.63(24.11),p t2 (24.92(2.71)vs33.78(16.17),p t3 (34.62(4.03)vs42.58(22),p=0.0024), t4 (37.29(4.31)vs48.29(20.29,p t5 (47.03(6.47)vs55.32(22.63),p=0029), t6 (49.54(5.63)vs60.56(22.20),p t7 (53.1(7.86)vs69.13(24.54),p t8 (57.78(9.78)vs77.33(25.79),p t9+ (69.14(7.39)vs81.9(21.96),p tM (83.9(8.72)vs96.08(16.88),p tSB (97.89(7.55)vs105.38(11.38),p=0.0005), tB (105.74(7)vs113.25(15.53),p=0.0002), eEB (110.65(7.58)vs120.47(11.36),p=0.0031). When looking at the exact distribution of these embryos according to time, it became apparent that a goldilocks zone appeared whereby the proportion of embryos transferred or frozen peaked, and the number discarded was at its minimum. The converse was true when looking at the more extreme values of a particular parameter. Thus, we were able to determine the (optimal vs futile time ranges): tPNa (4.4-8.8 hours, where the utilization rate was at its maximum vs 13.7, where the utilization rate was at its minimum), tPNf (19.1-23.2vs28.9), t2 (23.-36.4vs33.6), t3 (32.1-37.4vs>24.6,>43), t4 (34-40.2vs55), t5 (42.7-52vs63.5), t6 (45-4-54.2vs63.70), t7 (47.8-56.7vs77.5), t8 (49.2-64.5 vs 82.5), t9+ (64.1-74.2vs90), tM (76.6-92.6 vs 104.2), tSB (91.2-105vs113.8), tB(97.2-111.2 vs 118.7), tEB (103.4-116.7,122.5). A 60% of the embryos were in the futile range in at least 1 parameter, from which only 1 in 3 were utilised. Limitations, reasons for caution This is a single centre study. Further work will (i) test the limits across different clinics, with different geographical demographic variations, and varied clinical practices, to understand how these factors affect the limits between futile and optimal ranges of morphokinetics, and (ii) assess clinical outputs (implantation, ploidy, live birth). Wider implications of the findings Identifying objective ranges for determining when an embryo is not suitable for treatment will help reduce variation between and within embryologists and clinics; will avoid overly optimistic decisions which waste time and resources and increase patient’s emotional burden, and increase professional confidence when selecting embryos for discarding, transfer or freezing Trial registration number not applicable

Study question What are the causes and consequences of blastocyst collapse? Summary answer ∼50% of blastocysts collapsed, especially if they are aneuploid and/or morphologically-poor. Yet, no impact on the live-birth-rate (LBR) per vitrified-warmed euploid single-embryo-transfer (SET) was reported. What is known already Time-lapse-microscopy (TLM) is a powerful tool to describe the peculiar dynamics of preimplantation development. Lately, artificial intelligence (AI) has been also implemented to automatize and standardize such description. Here, we adopted AI to comprehensively portray blastocyst collapse, namely the phenomenon of embryo contraction with an efflux of blastocoel fluid and the detachment of the trophectoderm (TE) from the Zona Pellucida (ZP). Although, the causes of this event are still undetermined, small blastocyst contractions have been reported beneficial for the hatching process, while a full collapse has been associated with lower competence. Study design, size, duration Observational study including 1943 blastocysts from 643 couples cultured in the Embryoscope between January-2013 and December-2020. TE biopsy without day3 ZP drilling and comprehensive-chromosome-testing were performed. The Fairtility® software automatically registered: (i)time of starting-blastulation (tSB), (ii)starting and ending time of each collapse (tSC and tEC), (iii)blastocysts’ areas, (iv)shrinkage% [(area at SC – area at EC)/area at SC)], (v)embryo:ZP ratio at EC (area of the collapsed embryo/area of the ZP), and (vi)time of biopsy (t-biopsy). Participants/materials, setting, methods Blastocyst quality was defined according to Istanbul Consensus (11, excellent; 12-21, good; 22-13-31, average; 33-23-32, poor) and with the Fairtility implantation score (IS) as well, i.e., a continuous variable from 0 to 1 generated by the KID+ software based on the TLM videos of preimplantation development. The main outcome was the LBR per euploid SET adjusted for confounders through logistic regressions. All couple and embryo features were also investigated for their association with blastocyst collapse. Main results and the role of chance 47.3% of the blastocysts collapsed 1- to 9-times (interval between collapses: 4-8hr), and 73% of the couples had ≥1 collapsed blastocyst (1.8±1.1, range:1-8). No couple feature, though, was associated with blastocyst collapse. The longest collapses lasted 1.5±1.1 (0.13-5.1)hr, while the largest shrinkage% and embryo:ZP ratio at EC were 35±14% (10-78%) and 81±9% (33-90%), respectively. In ∼50-60% of collapses a 20-40% blastocyst volume reduction was registered, 40-60% or 20-40% in ∼15-30%, 60-80% in 0-4%. In case of multiple collapses, the first three involved smaller shrinkages. Blastocysts undergoing ≥1 collapse showed similar tSB as not-collapsing blastocysts, but progressively longer tEB and t-biopsy. The earlier the first event, the more the consecutive collapses. Notably, the poorer the morphology, the higher the risk (excellent, good, average, and poor not-collapsing blastocysts were 64%,50%,44% and 37%), number (e.g.,≥4 collapses were 0.4%,2%,4% and 8%) and duration (1.2±1.0,1.4±1.0,1.6±1.1 and 1.9±1.3hr) of blastocyst collapse. Collapsing blastocysts were significantly less euploid than non-collapsing (35% vs 47%; multivariate-OR:0.75,95%CI 0.6-0.92,p Limitations, reasons for caution Gestational and perinatal outcomes were not assessed. Other culture strategies and media shall be assessed for their association with blastocyst collapse. Perhaps, future studies from other groups and with a larger sample size might unveil a significant impact on the clinical outcomes. Wider implications of the findings Collapse is common and delays blastocyst full-expansion. Moreover, poor morphology and aneuploidies involve a higher risk of collapse(s); however, no impact was reported on the clinical outcomes after euploid SET. AI appears to increase the throughput of the analysis, but additional data are required to research the causes of collapse. Trial registration number Not applicable

Study question Are automated blastocyst morphometric measurements combined with morphokinetic pattern associated with implantation rate? Summary answer Automated blastocyst morphometric measurements with morphokinetic pattern demonstrated that a larger blastocyst size and a shorter time interval tB-tSB are associated with higher implantation potential. What is known already Optimization of embryo selection is important for increasing implantation potential. Transfer of a high-quality blastocyst based on conventional morphological parameters has been shown to improve IVF clinical outcome. Novel parameters of blastocyst quality, including morphokinetics from time-lapse monitoring and manual analysis of morphometric parameters, have demonstrated promising results regarding implantation potential. However, manual measurement of morphometric parameters is a time-consuming task and is subjected to intra- and inter-observer variations. The introduction of automated morphometric measurements would remove subjective blastocyst analysis and further improve implantation rates. Study design, size, duration A nested retrospective case control analysis of 608 day-5 transferred blastocysts was conducted and included women who underwent IVF treatment in three public IVF units between 2014 and 2017. Participants/materials, setting, methods Automated morphometric blastocyst analysis was measured at the mean time of tEB-tPNf (85.82 h) by training a pixel-wise segmentation model (MaskRCNN) on time-lapse videos. Morphometric blastocyst parameters included the following: blastocyst size (μm), inner cell mass (ICM) size (μm), ICM to blastocyst size ratio, and ICM shape. Annotation variables included all the time intervals (hours) from time of pronuclei fading (tPNf) to the expanded blastocyst (tEB). Main results and the role of chance The mean blastocyst size for implanted embryos was significantly larger compared to non-implanted embryos (152.10 ±19.22μm vs 144.25±18.52μm, respectively, p Limitations, reasons for caution The study's limitations include its retrospective nature and the absence of some patient characteristics. Wider implications of the findings A blastocyst selection based on the combination of automated blastocyst size measurements and manual tB-tSB time interval may double the implantation rate. The inclusion of automated morphometric measurements to the blastocyst selection algorithm may reduce intra- and inter-observer variations and should be incorporated into models for implantation prediction. Trial registration number 0006-20-HMO

Study question Does an extra Gonadotropin (GT) dose on trigger day during a gonadotropin-releasing hormone (GnRH) antagonist cycle for oocyte cryopreservation result in improved cycle outcomes? Summary answer An extra GT dose on trigger day in patients undergoing elective egg freezing is not associated with increased oocyte yield. What is known already The most common controlled ovarian hyperstimulation (COHS) protocol used in elective oocyte cryopreservation cycles is a GnRH antagonist protocol with a GnRH agonist trigger. There is no consensus about the timing of the final gonadotropin dose administration. Some physicians add an extra GT dose during the ovulation trigger day and others advise to receive the last GT dose the day before. The yield of this extra GT dose was not previously evaluated. Study design, size, duration A retrospective cohort study, consisting of cycles of elective oocyte cryopreservation at Hadassah Medical Center from 1.2017 through 5.2021. Patients were divided into 2 groups- women who received their last GT dose a day before the GnRH agonist trigger, and women who received the last GT on the trigger day. Background and gynecologic characteristics, as well as COHS cycle parameters, were compared. The primary outcome was the average number of mature oocytes in each group. Participants/materials, setting, methods Women 30-41 years who were admitted for elective oocyte cryopreservation. Women who were referred due to medical reasons or cycles with a protocol other than GnRH antagonist were excluded. Ovarian stimulation with GT began on the 3rd day of the cycle, and five days later, a GnRH antagonist was added. When more than two mature follicles were demonstrated, a GnRH agonist trigger was administrated and approximately 36 hours later, the oocyte aspiration procedure was performed Main results and the role of chance During the study period, 448 elective egg freezing cycles were performed, 151 cycles with an extra dose on the trigger day and 297 cycles without an extra GT dose. The groups did not differ in their background characteristics, age, BMI, GT used, and the number of induction days. However, the extra GT group had significantly higher day 3 follicular stimulating hormone (FSH), lower anti-Mullerian hormone (AMH), and lower maximal Estradiol (E2) levels. The extra GT dose group had a significantly lower mean number of mature oocytes (11.1±7.1 vs. 6.4±4.5, p To adjust for the maximal E2 parameter, that differed between the groups, an additional analysis was performed, dividing the two groups into subgroups of cycles with maximal E2 above and below 10,000 pmol\l. Interestingly, even in cycles with maximal E2 above 10,000 pmol\l, the extra dose group had poorer results (10.3±4.3 vs. 12.7±7.2, p Limitations, reasons for caution The retrospective design of this study and the differences in cycle characteristics between patients that did or did not receive an extra GT dose on trigger day make it prone to selection bias. Wider implications of the findings Our study shows that administration of an extra GT dose on trigger day in oocyte cryopreservation cycles does not yields more mature oocytes. However, wider prospective studies are needed to thoroughly investigate the implications of this treatment’s effect on cycle outcomes. Trial registration number Not applicable

Study question To identify challenges in choosing a robust AI following comparative validation with data already pre-selected with established embryos selection tools: blastulation, morphology, time-lapse, PGTA. Summary answer Challenges included: bias; assessment against outcomes AI models were not trained on; performance metrics prioritisation; statistical methodology; continuous data cutoffs for binary clinical decision making. What is known already AI is commercially available to be incorporated into routine practice to support embryo selection decision-making. Different clinical practices and demographics are used to train AI models, potentially impacting the prediction efficacy of the same model when used in different clinics. Fertility professionals require robust methods of validation to responsibly implement AI-based tools. Unbiased and robust frameworks for comparing AI systems in the same dataset are needed. Validating AI in a dataset of time-lapse selected euploid blastocysts using all the current methods of embryo selection currently available is the toughest assessment possible and has not previously been performed. Study design, size, duration This study uses a retrospectively timelapse dataset collected from 2018-2021 at a single private fertility clinic. The dataset included 915 blastocysts which underwent PGTA (913 results: 381 euploids, 528 aneuploids, 4 mosaics) and 46 euploids transferred with known bhcg and ongoing clinical outcome (of which 40 resulted to live birth). Following a prospective, comparative, observational, cohort study design, blastocysts were blindly scored using the CHLOE(FAIRTILITY) and another commercially available AI system, referred to as ‘AI-2’. Participants/materials, setting, methods Patients aged 24-47years (average 35.4). Blastocysts selected for biopsy and transfer based on morphology and KIDScore(Vitrolife). Both AI systems were tested in the data set blindly, without any training. Correlation Regression analysis assessed correlation with KIDSCORE and relative to each AI system. Efficacy of prediction (using metrics AUC, Accuracy, Sensitivity, Specificity and Informedness) of outcomes (ploidy, biochemical and clinical pregnancy) were assessed for both AI models (CHLOEvsAI-2) by two independent statisticians to establish significance. Main results and the role of chance Regression analysis demonstrated no correlation between KIDSCORE and AI-2(r2=0.3%,p=0.5) or between CHLOE(FAIRTILITY) and AI-2(r2=0.03%,p=0.9). CHLOE(FAIRTILITY) correlated with KIDSCORE(r2=29%,p AI-2 was not predictive of ploidy (Euploids vs Aneuploids+mosaic: AUC=0.5,p=0.6). CHLOE(Fairtility) was predictive of ploidy(AUC=0.66, p Neither AI-2 or CHLOE(Fairtility) predicted which embryo the human embryologist prioritised for transfer (AI-2 vs CHLOE:accuracy:0.31vs0.49, p CHLOE(Fairtility) was more specific than AI-2 for predicting selection for transfer(0.44/0.80vs0.17/0.93,p Informedness was positive for both CHLOE(Fairtility) and AI-2 in predicting all outcomes assessed. Informedness was greater for AI-2 for predicting morphology(AI-2vsCHLOE:0.16vs0.31,p Limitations, reasons for caution In this single clinic study, both algorithms were assessed against outcomes (live birth following transfer of time-lapse cultured euploid blastocysts) for which they were not trained on: AI-2(designed for ploidy prediction) and CHLOE(FAIRTILITY, implantation prediction of non-PGTA embryos) and no clinic data was used for training. Wider implications of the findings The only way to decide which AI model is more useful is by a direct comparison of two or more models on the same dataset with same outcomes and metrics, as recommended by TRIPOD. To date, this is the first publication comparing multiple commercial AI models on the same dataset. Trial registration number NA