Population-based study of the reproductive risk factors for transvaginal ultrasound diagnosed uterine fibroids in Nigerian women

There has been no previous systematic, epidemiological study of the reproductive risk factors for uterine fibroids (UF) in African populations despite African women having the highest burden of UF in the world. Improved knowledge of the associations between UF and reproductive factors would contribute to better understanding of the etiology of UF and may suggest novel opportunities for prevention and therapeutic interventions. We used nurse administered questionnaires to survey the demographic and reproductive risk factors of UF among 484 women who are members of the African Collaborative Center for Microbiome and Genomics Research (ACCME) Study Cohort in central Nigeria, and who had transvaginal ultrasound diagnosis (TVUS). We used logistic regression models to the evaluate associations between reproductive risk factors and UF, adjusted for significant covariates. In our multivariable logistic regression models, we found inverse associations with number of children (OR = 0.83, 95%CI = 0.74-0.93, p-value = 0.002), parity (OR = 0.41, 95%CI = 0.24-0.73, p-value = 0.002), history of any type of abortion (OR = 0.53, 95%CI = 0.35-0.82, p-value = 0.004), duration of use of Depot Medroxyprogesterone Acetate (DMPA) (p-value for trend = 0.02), menopausal status (OR = 0.48, 95%CI = 0.27-0.84, p-value = 0.01), and a non-linear positive association with age (OR = 1.04, 95%CI = 1.01-1.07, p-value = 0.003). Other reproductive risk factors that have been reported in other populations (age at menarche and menopause, and oral contraceptives) were not associated with UF in this study. Our study confirms some of the reproductive risk factors for UF that have been found in other populations and shows that some of them are stronger in the Nigerian population. The associations we found with DMPA suggest opportunities for further research to understand the mechanisms of action of progesterone and its analogues in the etiology of UF, their potential use for prevention and treatment of UF.

Selling sex in the context of substance use: social and structural drivers of transactional sex among men who use opioids in Maryland

BACKGROUND

Transactional sex is an important driver of HIV risk among people who use drugs in the USA, but there is a dearth of research characterizing men's selling and trading of sex in the context of opioid use. To identify contextually specific factors associated with selling or trading sex in a US population of men who use drugs, we cross-sectionally examined social and structural correlates of transactional sex among men who use opioids (MWUO) in Anne Arundel County and Baltimore City, Maryland.

METHODS

Between July 2018 and March 2020, we used targeted sampling to recruit men reporting past-month opioid use from 22 street-level urban and suburban recruitment zones. MWUO completed a 30-min self-administered interview eliciting substance use histories, experiences with hunger and homelessness, criminal justice interactions, and transactional sex involvement. We identified correlates of recent (past 3 months) transactional sex using multivariable log-binomial regression with cluster-robust standard errors.

RESULTS

Among 422 MWUO (mean age 47.3 years, 73.4% non-Hispanic Black, 94.5% heterosexual), the prevalence of recent transactional sex was 10.7%. In multivariable analysis, younger age (adjusted prevalence ratio [aPR] 0.98, 95% confidence interval [95% CI] 0.97-0.99, p < 0.001), identifying as gay/bisexual (aPR = 5.30, 95% CI 3.81-7.37, p < 0.001), past-month food insecurity (aPR = 1.77, 95% CI 1.05-3.00, p = 0.032), and injection drug use in the past 3 months (aPR = 1.75, 95% CI 1.02-3.01, p = 0.043) emerged as statistically significant independent correlates of transactional sex.

CONCLUSIONS

Synergistic sources of social and structural marginalization-from sexuality to hunger, homelessness, and injection drug use-are associated with transactional sex in this predominantly Black, heterosexual-identifying sample of MWUO. Efforts to mitigate physical and psychological harms associated with transactional sex encounters should consider the racialized dimensions and socio-structural drivers of transactional sex among MWUO.

Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy

BACKGROUND

Despite treatment advancements with immunotherapy, our understanding of response relies on tissue-based, static tumor features such as tumor mutation burden (TMB) and programmed death-ligand 1 (PD-L1) expression. These approaches are limited in capturing the plasticity of tumor-immune system interactions under selective pressure of immune checkpoint blockade and predicting therapeutic response and long-term outcomes. Here, we investigate the relationship between serial assessment of peripheral blood cell counts and tumor burden dynamics in the context of an evolving tumor ecosystem during immune checkpoint blockade.

METHODS

Using machine learning, we integrated dynamics in peripheral blood immune cell subsets, including neutrophil-lymphocyte ratio (NLR), from 239 patients with metastatic non-small cell lung cancer (NSCLC) and predicted clinical outcome with immune checkpoint blockade. We then sought to interpret NLR dynamics in the context of transcriptomic and T cell repertoire trajectories for 26 patients with early stage NSCLC who received neoadjuvant immune checkpoint blockade. We further determined the relationship between NLR dynamics, pathologic response and circulating tumor DNA (ctDNA) clearance.

RESULTS

Integrated dynamics of peripheral blood cell counts, predominantly NLR dynamics and changes in eosinophil levels, predicted clinical outcome, outperforming both TMB and PD-L1 expression. As early changes in NLR were a key predictor of response, we linked NLR dynamics with serial RNA sequencing deconvolution and T cell receptor sequencing to investigate differential tumor microenvironment reshaping during therapy for patients with reduction in peripheral NLR. Reductions in NLR were associated with induction of interferon-γ responses driving the expression of antigen presentation and proinflammatory gene sets coupled with reshaping of the intratumoral T cell repertoire. In addition, NLR dynamics reflected tumor regression assessed by pathological responses and complemented ctDNA kinetics in predicting long-term outcome. Elevated peripheral eosinophil levels during immune checkpoint blockade were correlated with therapeutic response in both metastatic and early stage cohorts.

CONCLUSIONS

Our findings suggest that early dynamics in peripheral blood immune cell subsets reflect changes in the tumor microenvironment and capture antitumor immune responses, ultimately reflecting clinical outcomes with immune checkpoint blockade.

Harnessing data science to advance radiation oncology

Radiation oncology, a major treatment modality in the care of patients with malignant disease, is a technology‐ and computer‐intensive medical specialty. As such, it should lend itself ideally to data science methods, where computer science, statistics, and clinical knowledge are combined to advance state‐of‐the‐art care. Nevertheless, data science methods in radiation oncology research are still in their infancy and successful applications leading to improved patient care remain scarce. Here, we discuss data interoperability issues within and across organizational boundaries that hamper the introduction of big data and data science techniques in radiation oncology. At the semantic level, creating common underlying models and codification of the data, including the use of data elements with standardized definitions, an ontology, remains a work in progress. Methodological issues in data science and in the use of large population‐based health data registries are identified. We show that data science methods and big data cannot replace randomized clinical trials in comparative effectiveness research by reviewing a series of instances where the outcomes of big data analyses and randomized trials are at odds. We also discuss the modern wave of machine learning and artificial intelligence as represented by deep learning and convolutional neural networks. Finally, we identify promising research avenues and remain optimistic that the data sources in radiation oncology can be linked to yield important insights in the near future. We argue that data science will be a valuable complement to, but not a replacement of, the traditional hypothesis‐driven translational research chain and the randomized clinical trials that form the backbone of evidence‐based medicine.