The Intergenerational Transfer of Mental Disorders: A Population-Based Multigenerational Linkage Study: Le transfert intergénérationnel des troubles mentaux : une étude sur les liens multigénérationnels basée sur la population.

OBJECTIVES: The aetiology of mental disorders involves genetic and environmental factors, both reflected in family health history. We examined the intergenerational transmission of multiple mental disorders from parents and grandparents using population-based, objectively measured family histories. METHODS: This population-based retrospective cohort study used administrative healthcare databases in Manitoba, Canada and included adults living in Manitoba from 1977 to 2020 with linkages to at least one parent and one grandparent. Index date was when individuals turned 18 or 1 April 1977, whichever occurred later. Mental disorder diagnoses (mood and anxiety, substance use and psychotic disorders) were identified in individuals, parents and grandparents from hospitalization and outpatient records. Cox proportional hazards regression models included sociodemographic characteristics, individual's comorbidity and mental disorder history in a grandparent, mother and father. RESULTS: Of 109,359 individuals with no mental disorder prior to index date, 47.1% were female, 36.3% had a mental disorder during follow-up, and 90.9% had a parent or grandparent with a history of a mental disorder prior to the index date. Both paternal and maternal history of a mental disorder increased the risk of the disorder in individuals. Psychotic disorders had the strongest association with parental history and were mostly influenced by paternal (hazards ratio [HR] 3.73, 95% confidence interval [CI] 2.99 to 4.64) compared to maternal history (HR 2.23, 95% CI, 1.89 to 2.64). Grandparent history was independently associated with the risk of all mental disorders but had the strongest influence on substance use disorders (HR 1.42, 95% CI, 1.34 to 1.50). CONCLUSIONS: Parental history of mental disorders was associated with an increased risk of all mental disorders. Grandparent history of mental disorders was associated with a small risk increase of the disorders above and beyond parental history influence. This three-generation study further highlights the need for family-based interventional programs in families affected by mental disorders. PLAIN LANGUAGE SUMMARY TITLE: The Intergenerational Transfer of Mental Illnesses.

ObjectivesBoth genetics and environmental factors, such as poverty, maltreatment and parental education, have a role in the development of mental illnesses. Some genetic and environmental risk factors for mental illnesses are shared within families. We conducted a large study to test the extent to which mental illnesses are passed down through generations.MethodsThis study used healthcare data from Manitoba, Canada captured during the delivery of healthcare services for administrative purposes. These data included all adults from 1977 to 2020 who had at least one parent and one grandparent with linked data. Mental illnesses were diagnosed in individuals, parents and grandparents by doctors during hospitalizations or physician visits. The illnesses included mood and anxiety, substance use, and psychotic illnesses. We estimated the likelihood of developing a mental illness when parents and/or grandparents had a mental illness as well.ResultsThe study included 109,359 individuals; a third developed a mental illness during the study period. The majority had a history of a mental illness in a parent or grandparent. We found that a history of mental illness in a mother and father increased the chance of developing the illness. Psychotic illnesses had the strongest relation with parental history. In particular, having a father with a psychotic illness increased the chance of developing the illness by four times. The likelihood of developing a mental illness was higher if a grandparent had a mental illness, above and beyond parental history influence, particularly for substance use disorders.ConclusionsHaving a parent or grandparent with a mental illness increases an individual's chance of developing a mental illness. Family-based intervention programs are needed to support families affected by mental illnesses in coping with their heavy burden.

The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study.

BACKGROUND: Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures. METHODS: A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 - 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges. RESULTS: Relative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes. CONCLUSIONS: Multimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures.

Extraneous fibre traces brought by river water - A case study.

The fibre traces on a young victim found underwater were mostly single fibre traces besides small amounts of fibre collectives indistinguishable from his parents clothes (mainly wool). Most of those single fibre traces were blue-grey polyester fibres showing tiny differences among each other. They were unexpected according to known population fibre studies. One year after the victim's discovery experiments were conducted to evaluate the possible contamination with fibres from river water. A small amount of extraneous fibres were collected among which blue and grey-black cotton and man-made (mainly polyester) fibres. All man-made fibres were single fibre traces and small fibre collectives were only observed for cotton. These results confirmed the frequent occurrence of blue and grey-black cotton fibres as background, but also highlighted the possible contamination with single blue and grey-black man-made fibres from river water. No wool was found, strengthening the significance of the wool fibre collectives present on the victim.

Investigation of an association between onset of narcolepsy and vaccination with pandemic influenza vaccine, Ireland April 2009-December 2010.

In 2011, the Irish Medicines Board received reports of onset of narcolepsy following vaccination against influenza A(H1N1)pdm09 with Pandemrix. A national steering committee was convened to examine the association between narcolepsy and pandemic vaccination. We conducted a retrospective population-based cohort study. Narcolepsy cases with onset from 1 April 2009 to 31 December 2010 were identified through active case finding. Narcolepsy history was gathered from medical records. Pandemic vaccination status was obtained from vaccination databases. Two independent experts classified cases using the Brighton case definition. Date of onset was defined as date of first healthcare contact for narcolepsy symptoms. Incidence of narcolepsy in vaccinated and non-vaccinated individuals was compared. Of 32 narcolepsy cases identified, 28 occurred in children/adolescents and for 24 first healthcare contact was between April 2009 and December 2010. Narcolepsy incidence was 5.7 (95% confidence interval (CI): 3.4­8.9) per 100,000 children/adolescents vaccinated with Pandemrix and 0.4 (95% CI: 0.1­1.0) per 100,000 unvaccinated children/adolescents (relative risk: 13.9; absolute attributable risk: 5.3 cases per 100,000 vaccinated children/adolescents). This study confirms the crude association between Pandemrix vaccination and narcolepsy as observed in Finland and Sweden. The vaccine is no longer in use in Ireland. Further studies are needed to explore the immunogenetic mechanism of narcolepsy.

Surveillance for outbreaks of gastroenteritis in elderly long-term care facilities in France, November 2010 to May 2012.

This article describes outbreaks of gastroenteritis in elderly long-term care facilities (LTCF) in France from November 2010 to May 2012 reported through the surveillance system for gastroenteritis outbreaks in LTCF. A total of 1,072 outbreaks were reported, causing 26,551 episodes of illness and 60 deaths. The median attack rate (AR) among residents was 32%. Norovirus and person-to-person transmission were the most frequently reported aetiology and mode of transmission. Control measures were implemented in 1,054 (98%) outbreaks and for 928 outbreaks, the timing of such measures could be inferred. Of these, 799 (86%) had put control measures into effect within three days of the occurrence of the first case. Outbreaks of gastroenteritis in LTCF cause substantial morbidity and mortality among elderly people in France. LTCF are encouraged to develop infection prevention and control plans and to notify any gastroenteritis outbreak to health authorities to ensure rapid control.

Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment.

Whether simultaneous automated ascertainments of prevalent vertebral fracture (auto-PVFx) and abdominal aortic calcification (auto-AAC) on vertebral fracture assessment (VFA) lateral spine bone density (BMD) images jointly predict incident fractures in routine clinical practice is unclear. We estimated the independent associations of auto-PVFx and auto-AAC primarily with incident major osteoporotic and secondarily with incident hip and any clinical fractures in 11 013 individuals (mean [SD] age 75.8 [6.8] years, 93.3% female) who had a BMD test combined with VFA between March 2010 and December 2017. Auto-PVFx and auto-AAC were ascertained using convolutional neural networks (CNNs). Proportional hazards models were used to estimate the associations of auto-PVFx and auto-AAC with incident fractures over a mean (SD) follow-up of 3.7 (2.2) years, adjusted for each other and other risk factors. At baseline, 17% (n = 1881) had auto-PVFx and 27% (n = 2974) had a high level of auto-AAC (≥ 6 on scale of 0 to 24). Multivariable-adjusted hazard ratios (HR) for incident major osteoporotic fracture (95% CI) were 1.85 (1.59, 2.15) for those with compared with those without auto-PVFx, and 1.36 (1.14, 1.62) for those with high compared with low auto-AAC. The multivariable-adjusted HRs for incident hip fracture were 1.62 (95% CI, 1.26 to 2.07) for those with compared to those without auto-PVFx, and 1.55 (95% CI, 1.15 to 2.09) for those high auto-AAC compared with low auto-AAC. The 5-year cumulative incidence of major osteoporotic fracture was 7.1% in those with no auto-PVFx and low auto-AAC, 10.1% in those with no auto-PVFx and high auto-AAC, 13.4% in those with auto-PVFx and low auto-AAC, and 18.0% in those with auto-PVFx and high auto-AAC. While physician manual review of images in clinical practice will still be needed to confirm image quality and provide clinical context for interpretation, simultaneous automated ascertainment of auto-PVFx and auto-AAC can aid fracture risk assessment.

Individuals with calcification of their abdominal aorta (AAC) and vertebral fractures seen on lateral spine bone density images (easily obtained as part of a bone density test) are much more likely to have subsequent fractures. Prior studies have not shown if both AAC and prior vertebral fracture both contribute to fracture prediction in routine clinical practice. Additionally, a barrier to using these images to aid fracture risk assessment at the time of bone density testing has been the need for expert readers to be able to accurately detect both AAC and vertebral fractures. We have developed automated computer methods (using artificial intelligence) to accurately detect vertebral fracture (auto-PVFx) and auto-AAC on lateral spine bone density images for 11 013 older individuals having a bone density test in routine clinical practice. Over a 5-year follow-up period, 7.1% of those with no auto-PVFx and low auto-AAC, 10.1% of those with no auto-PVFx and high auto-AAC, 13.4% of those with auto-PVFx and low auto-AAC, and 18.0% of those with auto-PVFx and high auto-AAC had a major osteoporotic fracture. Auto-PVFx and auto-AAC, ascertained simultaneously on lateral spine bone density images, both contribute to the risk of subsequent major osteoporotic fractures in routine clinical practice settings.

Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images.

BACKGROUND: Lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to assess cardiovascular disease risk. However, this process is laborious and requires careful training. METHODS: Training and testing of model performance of the convolutional neural network (CNN) algorithm for automated AAC-24 scoring utilised 5012 lateral spine images (2 manufacturers, 4 models of bone density machines), with trained imaging specialist AAC scores. Validation occurred in a registry-based cohort study of 8565 older men and women with images captured as part of routine clinical practice for fracture risk assessment. Cox proportional hazards models were used to estimate the association between machine-learning AAC (ML-AAC-24) scores with future incident Major Adverse Cardiovascular Events (MACE) that including death, hospitalised acute myocardial infarction or ischemic cerebrovascular disease ascertained from linked healthcare data. FINDINGS: The average intraclass correlation coefficient between imaging specialist and ML-AAC-24 scores for 5012 images was 0.84 (95% CI 0.83, 0.84) with classification accuracy of 80% for established AAC groups. During a mean follow-up 4 years in the registry-based cohort, MACE outcomes were reported in 1177 people (13.7%). With increasing ML-AAC-24 scores there was an increasing proportion of people with MACE (low 7.9%, moderate 14.5%, high 21.2%), as well as individual MACE components (all p-trend <0.001). After multivariable adjustment, moderate and high ML-AAC-24 groups remained significantly associated with MACE (HR 1.54, 95% CI 1.31-1.80 & HR 2.06, 95% CI 1.75-2.42, respectively), compared to those with low ML-AAC-24. INTERPRETATION: The ML-AAC-24 scores had substantial levels of agreement with trained imaging specialists, and was associated with a substantial gradient of risk for cardiovascular events in a real-world setting. This approach could be readily implemented into these clinical settings to improve identification of people at high CVD risk. FUNDING: The study was supported by a National Health and Medical Research Council of Australia Ideas grant and the Rady Innovation Fund, Rady Faculty of Health Sciences, University of Manitoba.

Effectiveness of 2010/2011 seasonal influenza vaccine in Ireland.

We conducted a case-control study to estimate the 2010/2011 trivalent influenza vaccine effectiveness (TIVE) using the Irish general practitioners' influenza sentinel surveillance scheme. Cases were influenza-like illness (ILI) patients with laboratory-confirmed influenza. Controls were ILI patients who tested negative for influenza. Participating sentinel general practitioners (GP) collected swabs from patients presenting with ILI along with their vaccination history and other individual characteristics. The TIVE was computed as (1 - odds ratiofor vaccination) x100%. Of 60 sentinel GP practices, 22 expressed interest in participating in the study and 17 (28%) recruited at least one ILI patient. In the analysis, we included 106 cases and 85 controls. Seven controls (8.2%) and one influenza case (0.9%) had been vaccinated in 2010/2011. The estimated TIVE against any influenza subtype was 89.4% [95% CI: 13.8; 99.8%], suggesting a protective effect against GP-attended laboratory confirmed influenza. This study design could be used to monitor influenza vaccine effectiveness annually but sample size and vaccination coverage should be increased to obtain precise and adjusted estimates.

Development of a manufacturer-independent convolutional neural network for the automated identification of vertebral compression fractures in vertebral fracture assessment images using active learning.

BACKGROUND: Convolutional neural networks (CNNs) can identify vertebral compression fractures in GE vertebral fracture assessment (VFA) images with high balanced accuracy, but performance against Hologic VFAs is unknown. To obtain good classification performance, supervised machine learning requires balanced and labeled training data. Active learning is an iterative data annotation process with the ability to reduce the cost of labeling medical image data and reduce class imbalance. PURPOSE: To train CNNs to identify vertebral fractures in Hologic VFAs using an active learning approach, and evaluate the ability of CNNs to generalize to both Hologic and GE VFA images. METHODS: VFAs were obtained from the OsteoLaus Study (labeled Hologic Discovery A, n = 2726), the Manitoba Bone Mineral Density Program (labeled GE Prodigy and iDXA, n = 12,742), and the Canadian Longitudinal Study on Aging (CLSA, unlabeled Hologic Discovery A, n = 17,190). Unlabeled CLSA VFAs were split into five equal-sized partitions (n = 3438) and reviewed sequentially using active learning. Based on predicted fracture probability, 17.6% (n = 3032) of the unlabeled VFAs were selected for expert review using the modified algorithm-based qualitative (mABQ) method. CNNs were simultaneously trained on Hologic, GE dual-energy and GE single-energy VFAs. Two ensemble CNNs were constructed using the maximum and mean predicted probability from six separately trained CNNs that differed due to stochastic variation. CNNs were evaluated against the OsteoLaus validation set (n = 408) during the active learning process; ensemble performance was measured against the OsteoLaus test set (n = 819). RESULTS: The baseline CNN, prior to active learning, achieved 55.0% sensitivity, 97.9% specificity, 57.9% positive predictive value (PPV), F1-score 56.4%. Through active learning, 2942 CLSA Hologic VFAs (492 fractures) were added to the training data-increasing the proportion of Hologic VFAs with fractures from 4.2% to 12.5%. With active learning, CNN performance improved to 80.0% sensitivity, 99.7% specificity, 94.1% PPV, F1-score 86.5%. The CNN maximum ensemble achieved 91.9% sensitivity (100% for grade 3 and 95.5% for grade 2 fractures), 99.0% specificity, 81.0% PPV, F1-score 86.1%. CONCLUSION: Simultaneously training on a composite dataset consisting of both Hologic and GE VFAs allowed for the development of a single manufacturer-independent CNN that generalized to both scanner types with good classification performance. Active learning can reduce class imbalance and produce an effective medical image classifier while only labeling a subset of available unlabeled image data-thereby reducing the time and cost required to train a machine learning model.

Feasibility of a generalized convolutional neural network for automated identification of vertebral compression fractures: The Manitoba Bone Mineral Density Registry.

BACKGROUND: Vertebral fracture assessment (VFA) images are acquired in dual-energy (DE) or single-energy (SE) scan modes. Automated identification of vertebral compression fractures, from VFA images acquired using GE Healthcare scanners in DE mode, has achieved high accuracy through the use of convolutional neural networks (CNNs). Due to differences between DE and SE images, it is uncertain whether CNNs trained on one scan mode will generalize to the other. PURPOSE: To evaluate the ability of CNNs to generalize between GE DE and GE SE VFA scan modes. METHODS: 12,742 GE VFA images from the Manitoba Bone Mineral Density Program, obtained between 2010 and 2017, were exported in both DE and SE modes. VFAs were classified by imaging specialists as fracture present or absent using the modified algorithm-based qualitative (mABQ) method. VFA scans were randomly divided into independent training (60%), validation (10%), and test (30%) sets. Three CNN models were constructed by training separately on DE only, SE only, and a composite dataset comprised of both SE and DE VFAs. All three trained CNN models were separately evaluated against both SE and DE test datasets. RESULTS: Good performance was seen for CNNs trained and evaluated on the same scan mode. DE scans used for both training and evaluation (DE/DE) achieved 87.9% sensitivity, 87.4% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.94. SE scans used for both training and evaluation (SE/SE) achieved 78.6% sensitivity, 90.6% specificity, AUC = 0.92. Conversely, CNNs performed poorly when evaluated on scan modes that differed from their training sets (AUC = 0.58). However, a composite CNN trained simultaneously on both SE and DE VFAs gave performance comparable to DE/DE (82.4% sensitivity, 94.3% specificity, AUC = 0.95); and provided improved performance over SE/SE (82.2% sensitivity, 92.3% specificity, AUC = 0.94). Positive predictive value was higher with the composite CNN compared with models trained solely on DE (74.5% vs. 58.7%) or SE VFAs (68.6% vs. 62.9%). CONCLUSION: CNNs for vertebral fracture identification are highly sensitive to scan mode. Training CNNs on a composite dataset, comprised of both GE DE and GE SE VFAs, allows CNNs to generalize to both scan modes and may facilitate the development of manufacturer-independent machine learning models for vertebral fracture detection.

Machine learning for automated abdominal aortic calcification scoring of DXA vertebral fracture assessment images: A pilot study.

BACKGROUND: Abdominal aortic calcification (AAC) identified on dual-energy x-ray absorptiometry (DXA) vertebral fracture assessment (VFA) lateral spine images is predictive of cardiovascular outcomes, but is time-consuming to perform manually. Whether this procedure can be automated using convolutional neural networks (CNNs), a class of machine learning algorithms used for image processing, has not been widely investigated. METHODS: Using the Province of Manitoba Bone Density Program DXA database, we selected a random sample of 1100 VFA images from individuals qualifying for VFA as part of their osteoporosis assessment. For each scan, AAC was manually scored using the 24-point semi-quantitative scale and categorized as low (score < 2), moderate (score 2 to <6), or high (score ≥ 6). An ensemble consisting of two CNNs was developed, by training and evaluating separately on single-energy and dual-energy images. AAC prediction was performed using the mean AAC score of the two models. RESULTS: Mean (SD) age of the cohort was 75.5 (6.7) years, 95.5% were female. Training (N = 770, 70%), validation (N = 110, 10%) and test sets (N = 220, 20%) were well-balanced with respect to baseline characteristics and AAC scores. For the test set, the Pearson correlation between the CNN-predicted and human-labelled scores was 0.93 with intraclass correlation coefficient for absolute agreement 0.91 (95% CI 0.89-0.93). Kappa for AAC category agreement (prevalence- and bias-adjusted, ordinal scale) was 0.71 (95% CI 0.65-0.78). There was complete separation of the low and high categories, without any low AAC score scans predicted to be high and vice versa. CONCLUSIONS: CNNs are capable of detecting AAC in VFA images, with high correlation between the human and predicted scores. These preliminary results suggest CNNs are a promising method for automatically detecting and quantifying AAC.

Pertussis outbreak in northwest Ireland, January - June 2010.

We report a community pertussis outbreak that occurred in a small town located in the northwest of Ireland. Epidemiological investigations suggest that waning immunity and the absence of a booster dose during the second year of life could have contributed to the outbreak. The report also highlights the need to reinforce the surveillance of pertussis in Ireland and especially to improve the clinical and laboratory diagnosis of cases.

Cluster of serogroup W invasive meningococcal disease in a university campus.

INTRODUCTION: In France, the expansion of an hypervirulent strain causing serogroup W invasive meningococcal disease (MenW) has been observed since 2015/16. We describe a cluster of three MenW cases, causing two deaths, at the end of 2016 in a university campus, and the vaccination campaign which was consequently organized. METHODS: Epidemiological and microbiological analyses led a multidisciplinary expertise group to recommend the organization of a mass vaccination campaign using ACWY vaccine targeting more than 30,000 students and staff in the university campus. Individual data on vaccination was collected using the lists of students and staff registered at the university to estimate vaccine coverage. RESULTS: Three MenW cases occurred within a 2-month period among students in different academic courses. All three isolates were identical and belonged to the "UK-2013 strain" phylogenetic branch. The attack rate was 10.8/100,000 students. The vaccination campaign was organized only 15 days after the third case occurred. In total, 13,198 persons were vaccinated. Vaccine coverage was estimated at 41% for students of the university and 35% for university staff. CONCLUSION: Timely notification of cases to health authorities was essential for the detection of the cluster and the rapid implementation of the vaccination campaign. No further cases occurred in the campus in the year following the vaccination campaign. This episode is the second cluster of MenW caused by the "UK-2013 strain" in a university since 2016.

Maternal use of antibiotics and cancer incidence risk in offspring: A population-based cohort study in Manitoba, Canada.

Several epidemiological studies have found an association between maternal antibiotics use during pregnancy and increased risk of certain cancer types, although conclusions differ between studies. We examined this association in a cohort study including 262 116 mother-child pairs of Manitoba births between 1996 and 2013. Maternal antibiotics use during prepregnancy (6 months prior to pregnancy) and pregnancy periods was assessed. Children's cancer incidence was tracked up to the end of the follow-up period (December 2015). We calculated incidence rate and used Cox regression to estimate adjusted hazard ratios (HRs). Antibiotics use during pregnancy was not associated with overall cancer (HR = 1.1, 95% confidence interval 0.9-1.4), leukemias (1.3, 0.9-1.8), or acute lymphocytic leukemia (1.1, 0.7-1.6). The association between antibiotics use and overall cancer risk differed by trimester: 1.5 (1.1-1.9) in the first, 0.8 (0.6-1.0) in the second, and 1.1 (0.8-1.5) in the third trimester. Further research is necessary to confirm the association between first-trimester exposure and cancer risk after a better controlling of confounding factors.

Evidence for high molecular weight immunoreactive thyrotropin-releasing hormone (TRH) precursor forms in the developing mouse hypothalamus. Simultaneous immunolocalization with TRH in cultured neurons.

Two antisera (Anti-P7 and Anti-P10) were raised against (-Gln-His-Pro-Gly-) elongated peptides: P7 Gln-His-Pro-Gly-Lys-Arg-Phe) and P10 (Ser-Lys-Arg-Gln-His-Pro-Gly-Lys-Arg-Phe). They recognized TRH extended peptides but not TRH. A RIA against P7 and a highly sensitive enzyme immunoassay against P10 were used to identify two major high mol wt forms of 25-35 K and 6-8 K in chromatography fractions of adult and fetal mouse as well as adult rat hypothalami. The existence of the largest form was confirmed by immunoblotting with Anti-P7. During mouse hypothalamus development in vivo and in vitro, the ratio of TRH content vs. P10-associated immunoreactivity increased several times. This suggests that these Pro-TRH peptides are precursors of TRH biosynthesis and indicate an acceleration of TRH processing during development. Double immunostaining with A-TRH and A-P7 of hypothalamic cells taken on the 16th fetal day and cultured for 6, 12, and 18 days in vitro (DIV) revealed three populations of neurons: 1) a very minor population (approximately 2%) of small round cells positive with A-TRH only; 2) a major population of neurons positive with both A-TRH and A-P7. 3) multipolar neurons positive with A-P7 only (up to approximately 45% after 18 DIV). The respective distribution of TRH and P7 along neurites also varied with time in culture. Whatever perikarya staining, TRH was restricted to short neurites and growth cones before synapse formation and, during synapse development, to varicosities and terminal boutons. However even at the latest stage examined some varicosities and terminal boutons were positive with A-P7 only. These results suggest a preferential processing of pro-TRH at a post-Golgi step during axonal transport to growth cones and synaptic boutons.

Altered balance between thyrotropin-releasing hormone and dopamine in prolactinomas and other pituitary tumors compared to normal pituitaries.

We measured TRH and dopamine (DA) concentrations in prolactinomas and other pituitary tumors in order to further understand the roles of these two factors in the hormone hypersecretion and growth of these tumors. The mean TRH concentration (by RIA) in 16 prolactinomas was 247 +/- 92 (+/- SE) fmol/mg cell protein (range, 10-1297), near that found in normal pituitary tissue. The prolactinoma TRH content did not correlate with the patient's tumor size or plasma PRL level. By contrast, DA assayed by high pressure liquid chromatography was present in normal pituitary tissue (7.3 +/- 3.5 pmol/mg cell protein), but was very low or undetectable in the prolactinomas (23 fmol/mg cell protein or less). 3,4-Dihydroxyphenylacetic acid, also assayed by high pressure liquid chromatography, was undetectable in both normal pituitary tissue and prolactinomas. This imbalance between TRH and DA content also was found in GH-secreting and nonsecreting adenomas. The TRH content in 18 GH-secreting tumors (24 +/- 6 fmol/mg) was considerably lower than that in the prolactinomas (P less than 0.001). In 8 nonsecreting adenomas, the mean TRH concentration was 109 +/- 28 fmol/mg, about half of that in the prolactinomas. In those 2 types of adenomas, DA also was nearly undetectable (less than or equal to 73 fmol/mg cell protein). We conclude that the imbalance between TRH and DA contents in prolactinomas compared to those in normal pituitary tissue might participate in the mechanisms leading to hypersecretion of PRL and the growth of all types of pituitary adenomas.

Normal and adenomatous human pituitaries secrete thyrotropin-releasing hormone in vitro: modulation by dopamine, haloperidol, and somatostatin.

TRH is present in human normal pituitaries and in pituitary adenomas. In this study we demonstrated that the same tissues can release TRH in vitro. Fragments from seven normal pituitaries (10-15 mg/syringe) and dispersed cells from eight prolactinomas, four GH-secreting and two nonsecreting adenomas (1-3 x 10(6) cells/syringe) were perifused using a Krebs-Ringer culture medium. After 1 h of equilibration the perifusion medium was collected every 2 min (1 mL/fraction) for 3 h. TRH, PRL, and GH were measured by RIA under basal conditions and in the presence of 10(-10) to 10(-6) mol/L dopamine (DA), alone or concomitant with haloperidol, or in the presence of 10(-10) or 10(-6) mol/L somatostatin. Both normal pituitary fragments and pituitary adenomatous cells (from all types of adenomas studied) spontaneously released TRH in vitro. TRH was detected in the perifusion medium either immediately after the end of the equilibration period or 30-60 min later. The molecular identity of TRH was assessed by high pressure liquid chromatography. There was no difference in the profile and the rate of TRH secretion between normal and tumoral tissues, and no correlation was found between the level of TRH release and that of PRL or GH secretion. DA stimulated TRH release from normal pituitaries and from PRL- and GH-secreting adenomas at doses as low as 10(-10) mol/L. A concomitant decrease in PRL and GH release was observed from adenomatous cells and in one case of normal tissue. Haloperidol (10(-7) mol/L) antagonized the effect of 10(-8) mol/L DA on both TRH and PRL secretion in normal pituitary and in prolactinomas. DA had no effect on TRH release from two nonsecreting tumors. The amounts of TRH released during 1 h of perifusion were 60-1640 pg/2 mg wet wt tissue in normal pituitaries and 54-2174 pg/10(6) cells in adenomas; these values were very high compared to those precedently reported within the tissues. These results indicate that pituitary cells can release TRH in vitro and suggest that TRH might be synthesized in situ. We suggest that TRH could act on pituitary hormone secretion and/or cell proliferation via a paracrine and/or an autocrine mechanism.

Evidence of thyrotropin-releasing hormone (TRH) and TRH-binding sites in human nonsecreting pituitary adenomas.

Immunoreactive TRH (IR-TRH) and TRH-binding sites were sought in nonsecreting pituitary adenomas. [3H]TRH bound specifically to cellular membranes from 11 of 12 such adenomas studied, with a dissociation constant (Kd) of 50 +/- 5 (+/- SEM) nmol/L and a maximum number of binding sites of 76 +/- 16 fmol/mg membrane protein (range, 32-229 fmol/mg protein). IR-TRH was detected in all 8 of the tumors in which it was sought. The identity of the IR-TRH was verified by high pressure liquid chromatography. The tumor IR-TRH concentration varied from 45-248 fmol/mg cell protein (mean, 109 +/- 28 fmol/mg cell protein), about half that in normal human pituitary (229 +/- 55 fmol/mg protein). There was no correlation between the number of binding sites and the IR-TRH content. The role of TRH in nonsecreting pituitary adenomas is unknown at this time.

Differential expression and subcellular localization of secretogranin II and synaptophysin during early development of mouse hypothalamic neurons in culture.

Mature neurons contain two distinct regulated secretory pathways, characterized electron microscopically by so-called large dense core vesicles and small synaptic vesicles, respectively. Each vesicle type is characterized by vesicle-specific proteins, such as the granins (chromogranins/secretogranins) for the matrix of large dense core vesicles and synaptophysin for the membrane of small synaptic vesicles. So far, no data exist on the biogenesis of these two distinct vesicle types during neuronal development. We have used secretogranin II and synaptophysin as markers for the biogenesis of these two vesicle types during the development of mouse hypothalamic neurons in culture, using immunocytochemistry and biochemical analyses. By immunofluorescence, we found that secretogranin II appears as early as synaptophysin, but in a subset of neurons only, and with different subcellular localizations. It was observed in cytoplasmic areas where little or no synaptophysin immunofluorescence was detected, such as lamellipodia, emerging neurites and growth cones. At later stages, the proportion of secretogranin II-containing varicosities remained steady whereas that of synaptophysin-containing varicosities increased dramatically. By quantitative analysis we found that the level of expression of synaptophysin increased several-fold during synaptogenesis whereas that of secretogranin II decreased. These data suggest that large dense core vesicles and small synaptic vesicles can be formed separately and expressed at different levels. They provide evidence for a differential biogenesis of these two distinct vesicle types.