Buprenorphine-naloxone, buprenorphine, and methadone throughout pregnancy in maternal opioid use disorder

IntroductionCurrent WHO guidelines recommend using methadone or buprenorphine as maintenance treatments for maternal opioid use disorder. However, buprenorphine-naloxone, with a lower abuse risk than buprenorphine monotherapy or methadone, offers a potentially beneficial alternative, but scientific evidence on its effects on pregnancies, fetuses, and newborns is scarce. This paper compares the outcomes of the pregnancies, deliveries, and newborns of women on buprenorphine-naloxone, buprenorphine, or methadone maintenance treatments. According to the hypothesis, as a maintenance treatment, buprenorphine-naloxone does not have more adverse effects than buprenorphine, whereas methadone is more complicated. Material and methodsIn this population-based study, 172 pregnant women on medical-assisted treatments were followed-up at Helsinki University Women's Hospital (Finland). Women receiving the same opioid maintenance treatment from conception to delivery and their newborns were included. Consequently, 67 mother-child dyads met the final inclusion criteria. They were divided into three groups based on their opioid pharmacotherapy. The outcomes were compared among the groups and, where applicable, with the Finnish population. ResultsThe buprenorphine-naloxone and buprenorphine groups showed similar outcomes and did not significantly differ from each other in terms of maternal health during pregnancies, deliveries, or newborns. Illicit drug use during the pregnancy was common in all groups, but in the methadone group it was most common (p = 0.001). Most neonates (96%) were born full-term with good Apgar scores. They were of relatively small birth size, with those in the methadone group tending to be the smallest. Of the neonates 63% needed pharmacological treatment for neonatal opioid withdrawal syndrome. The need was lower in the buprenorphine-based groups than in the methadone group (p = 0.029). ConclusionsBuprenorphine-naloxone seems to be as safe for pharmacotherapy for maternal opioid use disorder as buprenorphine monotherapy for both mother and newborn. Hence it could be a choice for oral opioid maintenance treatment during pregnancy, but larger studies are needed before changing the official recommendations. Women on methadone treatment carry multifactorial risks and require particularly cautious follow up. Furthermore, illicit drug use is common in all treatment groups and needs to be considered for all patients with opioid use disorder.Peer reviewe

In vitro fertilization does not increase the incidence of de novo copy number alterations in fetal and placental lineages

Although chromosomal instability (CIN) is a common phenomenon in cleavage-stage embryogenesis following in vitro fertilization (IVF)1,2,3, its rate in naturally conceived human embryos is unknown. CIN leads to mosaic embryos that contain a combination of genetically normal and abnormal cells, and is significantly higher in in vitro-produced preimplantation embryos as compared to in vivo-conceived preimplantation embryos4. Even though embryos with CIN-derived complex aneuploidies may arrest between the cleavage and blastocyst stages of embryogenesis5,6, a high number of embryos containing abnormal cells can pass this strong selection barrier7,8. However, neither the prevalence nor extent of CIN during prenatal development and at birth, following IVF treatment, is well understood. Here we profiled the genomic landscape of fetal and placental tissues postpartum from both IVF and naturally conceived children, to investigate the prevalence and persistence of large genetic aberrations that probably arose from IVF-related CIN. We demonstrate that CIN is not preserved at later stages of prenatal development, and that de novo numerical aberrations or large structural DNA imbalances occur at similar rates in IVF and naturally conceived live-born neonates. Our findings affirm that human IVF treatment has no detrimental effect on the chromosomal constitution of fetal and placental lineages

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland

Spatial analyses of immune cell infiltration in cancer: current methods and future directions.:A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers.</p

Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland