Genetic Polymorphisms in CYP2E1: Association with Schizophrenia Susceptibility and Risperidone Response in the Chinese Han Population

CYP2E1 is a member of the cytochrome P450 superfamily, which is involved in the metabolism and activation of both endobiotics and xenobiotics. The genetic polymorphisms of CYP2E1 gene (Chromosome 10q26.3, Accession Number NC_000010.10) are reported to be related to the development of several mental diseases and to be involved in the clinical efficacy of some psychiatric medications. We investigated the possible association of CYP2E1 polymorphisms with susceptibility to schizophrenia in the Chinese Han Population as well as the relationship with response to risperidone in schizophrenia patients.In a case-control study, we identified 11 polymorphisms in the 5' flanking region of CYP2E1 in 228 schizophrenia patients and 384 healthy controls of Chinese Han origin. From among the cases, we chose 130 patients who had undergone 8 weeks of risperidone monotherapy to examine the relationship between their response to risperidone and CYP2E1 polymorphisms. Clinical efficacy was assessed using the Brief Psychiatric Rating Scale (BPRS).Statistically significant differences in allele or genotype frequencies were found between cases and controls at rs8192766 (genotype p = 0.0048, permutation p = 0.0483) and rs2070673 (allele: p = 0.0018, permutation p = 0.0199, OR = 1.4528 95%CI = 1.1487-1.8374; genotype: p = 0.0020, permutation p = 0.0225). In addition, a GTCAC haplotype containing 5 SNPs (rs3813867, rs2031920, rs2031921, rs3813870 and rs2031922) was observed to be significantly associated with schizophrenia (p = 7.47E-12, permutation p<0.0001). However, no association was found between CYP2E1 polymorphisms/haplotypes and risperidone response.Our results suggest that CYP2E1 may be a potential risk gene for schizophrenia in the Chinese Han population. However, polymorphisms of the CYP2E1 gene may not contribute significantly to individual differences in the therapeutic efficacy of risperidone. Further studies in larger groups are warranted to confirm our results

South African mouse shrews (Myosorex) feel the heat: using species distribution models (SDMs) and IUCN Red List criteria to flag extinction risks due to climate change.

Five species of mouse or forest shrews (Myosorex) are endemic to South Africa, Lesotho and Swaziland, four of which (Myosorex varius, Myosorex cafer, Myosorex longicaudatus and Myosorex cf. tenuis) are associated with montane or temperate grassland, fynbos and/or forest habitats while a fifth (Myosorex sclateri) is associated with lowland subtropical forests. Due to their small size, specialised habitat, low dispersal capacity, high metabolism and sensitivity to temperature extremes, we predicted that, particularly for montane species, future climate change should have a negative impact on area of occupancy (AOO) and ultimately extinction risks. Species distribution models (SDMs) indicated general declines in AOO of three species by 2050 under the A1b and A2 climate change scenarios (M. cafer, M. varius, M. longicaudatus) while two species (M. sclateri and M. cf. tenuis) remained unchanged (assuming no dispersal) or increased their AOO (assuming dispersal). While temperate species such as M. varius appear to be limited by temperature maxima (preferring cooler temperatures), the subtropical species M. sclateri appears to be limited by temperature minima (preferring warmer temperatures). Evidence for declines in AOO informed the uplisting (to a higher category of threat) of the Red List status of four Myosorex species to either vulnerable or endangered as part of a separate regional International Union for Conservation of Nature (IUCN) Red List assessment

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning

Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (+/- 0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted kappa = 0.747 +/- 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p &amp;lt; 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution

Serum Hormone Levels and Normal Breast Histology Among Premenopausal Women

Purpose: Breast terminal duct lobular units (TDLUs) are the main source of breast cancer (BC) precursors. Higher serum concentrations of hormones and growth factors have been linked to increased TDLU numbers and to elevated BC risk, with variable effects by menopausal status. We assessed associations of circulating factors with breast histology among premenopausal women using artificial intelligence (AI) and preliminarily tested whether parity modifies associations. Methods: Pathology AI analysis was performed on 316 digital images of H&E-stained sections of normal breast tissues from Komen Tissue Bank donors ages ≤ 45 years to assess 11 quantitative metrics. Associations of circulating factors with AI metrics were assessed using regression analyses, with inclusion of interaction terms to assess effect modification. Results: Higher prolactin levels were related to larger TDLU area (p < 0.001) and increased presence of adipose tissue proximate to TDLUs (p < 0.001), with less significant positive associations for acini counts (p = 0.012), dilated acini (p = 0.043), capillary area (p = 0.014), epithelial area (p = 0.007), and mononuclear cell counts (p = 0.017). Testosterone levels were associated with increased TDLU counts (p < 0.001), irrespective of parity, but associations differed by adipose tissue content. AI data for TDLU counts generally agreed with prior visual assessments. Conclusion: Among premenopausal women, serum hormone levels linked to BC risk were also associated with quantitative features of normal breast tissue. These relationships were suggestively modified by parity status and tissue composition. We conclude that the microanatomic features of normal breast tissue may represent a marker of BC risk

Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence

Background Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory process that restores baseline microanatomy after weaning. Dysregulated PPI is implicated in the pathogenesis of postpartum BCs. We propose that assessment of TDLUs in the postpartum period may have value in risk estimation, but characteristics of these tissues in relation to epidemiological factors are incompletely described. Methods Using validated Artificial Intelligence and morphometric methods, we analyzed digitized images of tissue sections of normal breast tissues stained with hematoxylin and eosin from donors &amp;lt;= 45 years from the Komen Tissue Bank (180 parous and 545 nulliparous). Metrics assessed by AI, included: TDLU count; adipose tissue fraction; mean acini count/TDLU; mean dilated acini; mean average acini area; mean "capillary" area; mean epithelial area; mean ratio of epithelial area versus intralobular stroma; mean mononuclear cell count (surrogate of immune cells); mean fat area proximate to TDLUs and TDLU area. We compared epidemiologic characteristics collected via questionnaire by parity status and race, using a Wilcoxon rank sum test or Fishers exact test. Histologic features were compared between nulliparous and parous women (overall and by time between last birth and donation [recent birth: &amp;lt;= 5 years versus remote birth: &amp;gt; 5 years]) using multivariable regression models. Results Normal breast tissues of parous women contained significantly higher TDLU counts and acini counts, more frequent dilated acini, higher mononuclear cell counts in TDLUs and smaller acini area per TDLU than nulliparas (all multivariable analyses p &amp;lt; 0.001). Differences in TDLU counts and average acini size persisted for &amp;gt; 5 years postpartum, whereas increases in immune cells were most marked &amp;lt;= 5 years of a birth. Relationships were suggestively modified by several other factors, including demographic and reproductive characteristics, ethanol consumption and breastfeeding duration. Conclusions Our study identified sustained expansion of TDLU numbers and reduced average acini area among parous versus nulliparous women and notable increases in immune responses within five years following childbirth. Further, we show that quantitative characteristics of normal breast samples vary with demographic features and BC risk factors.Funding Agencies|National Institutes of Health [CA RO1 CA229811, R01 CA262393]; Mayo Clinic Comprehensive Cancer Center [P30CA15083-45]</p