Little is known about the role of Sox11 in the regulation of mammary progenitor cells. Sox11 is expressed by mammary bud epithelial cells during embryonic mammary gland development and is not detected in mammary epithelial cells after birth. As Sox11 is an oncofetal gene, we investigated the effects of reducing Sox11 levels in embryonic mammary progenitor cells and found that Sox11 regulates proliferative state, stem cell activity and lineage marker expression. We also investigated the effect of reducing Sox11 levels in two transplantable Brca1-deficient oestrogen receptor-negative mouse mammary tumour cell lines, to assess whether Sox11 regulates similar functions in tumour progenitor cells. When Sox11 levels were reduced in one Brca1-deficient mammary tumour cell line that expressed both epithelial and mesenchymal markers, similar effects on proliferation, stem cell activity and expression of lineage markers to those seen in the embryonic mammary progenitor cells were observed. Orthotopic grafting of mammary tumour cells with reduced Sox11 levels led to alterations in tumour-initiating capacity, latency, expression of lineage markers and metastatic burden. Our results support a model in which tumours expressing higher levels of Sox11 have more stem and tumour-initiating cells, and are less proliferative, whereas tumours expressing lower levels of Sox11 become more proliferative and capable of morphogenetic/metastatic growth, similar to what occurs during embryonic mammary developmental progression.Sporadic colorectal cancer (CRC) is a leading cause of worldwide cancer mortality. It arises from a complex milieu of host and environmental factors, including genetic and epigenetic changes in colon epithelial cells that undergo mutation, selection, clonal expansion, and transformation. The gut microbiota has recently gained increasing recognition as an additional important factor contributing to CRC. Several gut bacteria are known to initiate CRC in animal models and have been associated with human CRC. In this Review, we discuss the factors that contribute to CRC and the role of the gut microbiota, focusing on a recently described mechanism for cancer initiation, the so-called microbiota-induced bystander effect (MIBE). In this cancer mechanism, microbiota-driven parainflammation is believed to act as a source of endogenous mutation, epigenetic change and induced pluripotency, leading to the cancerous transformation of colon epithelial cells. This theory links the gut microbiota to key risk factors and common histologic features of sporadic CRC. MIBE is analogous to the well-characterized radiation-induced bystander effect. Both phenomena drive DNA damage, chromosomal instability, stress response signaling, altered gene expression, epigenetic modification and cellular proliferation in bystander cells. Myeloid-derived cells are important effectors in both phenomena.  https://www.selleckchem.com/peptide/avexitide.html  A better understanding of the interactions between the gut microbiota and mucosal immune effector cells that generate bystander effects can potentially identify triggers for parainflammation, and gain new insights into CRC prevention.
  Hospital-acquired diarrhoea (HAD) and Clostridioides difficile infection (CDI) may be triggered by antibiotic use.
 
  To determine the effect of specific antibiotic agents and duration of therapy on the risk of HAD and CDI.
 
  A single-centre retrospective cohort study was conducted between May 2012 and December 2014 in the internal medicine division. HAD was defined based on documentation of diarrhoea in the medical record or an uncancelled C. difficile test in the laboratory database. CDI was diagnosed using a two-step test (initial glutamate dehydrogenase and toxin A/B EIA, with PCR for discrepant results). Outcomes first occurred on hospital Day 4 or later. Treatment with antibiotics and days of therapy were modelled.
 
  In 29 063 hospitalizations there were 970 HAD events [incidence rate per 10 000 patient days (IR) = 38.5] and 105 CDI events (IR = 3.9). Any antibiotic treatment increased the risk of HAD [adjusted relative risk (aRR) 2.79; 95% CI 2.27-3.43] and CDI (aRR 5.31; 95% CI 2.23-12.69). Each day of β-lactam/β-lactamase inhibitors (βL/βLIs), carbapenems, IV glycopeptides and metronidazole increased the risk of HAD. Each day of βL/βLIs, third- and fourth-generation cephalosporins and carbapenems increased the risk of CDI by over 2%.
 
  Preventing HAD and CDI should focus on reducing the overall use of antibiotics and shortening antibiotic exposure, rather than focusing on specific agents.
 Preventing HAD and CDI should focus on reducing the overall use of antibiotics and shortening antibiotic exposure, rather than focusing on specific agents.
  To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.
 
  We evaluated models for stroke risk at varying intervals of follow-up (<9 years, 0-3 years, 3-6 years, 6-9 years) in 503842 adults without prior history of stroke recruited from 10 areas in China in 2004-2008. Inputs included sociodemographic factors, diet, medical history, physical activity, and physical measurements. We compared discrimination and calibration of Cox regression, logistic regression, support vector machines, random survival forests, gradient boosted trees (GBT), and multilayer perceptrons, benchmarking performance against the 2017 Framingham Stroke Risk Profile. We then developed an ensemble approach to identify individuals at high risk of stroke (>10% predicted 9-yr stroke risk) by selectively applying either a GBT or Cox model based on individual-level characteristics.
 
  For 9-yr stroke risk prediction, GBT provided the best discrimination (AUROC 0.833 in men, 0.836 in women) and calibration, with consistent results in each interval of follow-up. The ensemble approach yielded incrementally higher accuracy (men 76%, women 80%), specificity (men 76%, women 81%), and positive predictive value (men 26%, women 24%) compared to any of the single-model approaches.
 
  Among several approaches, an ensemble model combining both GBT and Cox models achieved the best performance for identifying individuals at high risk of stroke in a contemporary study of Chinese adults. The results highlight the potential value of expanding the use of ML in clinical practice.
 Among several approaches, an ensemble model combining both GBT and Cox models achieved the best performance for identifying individuals at high risk of stroke in a contemporary study of Chinese adults. The results highlight the potential value of expanding the use of ML in clinical practice.