Pituitary-hormone secretion by thyrotropinomas

Hormone secretion by somatotropinomas, corticotropinomas and prolactinomas exhibits increased pulse frequency, basal and pulsatile secretion, accompanied by greater disorderliness. Increased concentrations of growth hormone (GH) or prolactin (PRL) are observed in about 30% of thyrotropinomas leading to acromegaly or disturbed sexual functions beyond thyrotropin (TSH)-induced hyperthyroidism. Regulation of non-TSH pituitary hormones in this context is not well understood. We there therefore evaluated TSH, GH and PRL secretion in 6 patients with up-to-date analytical and mathematical tools by 24-h blood sampling at 10-min intervals in a clinical research laboratory. The profiles were analyzed with a new deconvolution method, approximate entropy, cross-approximate entropy, cross-correlation and cosinor regression. TSH burst frequency and basal and pulsatile secretion were increased in patients compared with controls. TSH secretion patterns in patients were more irregular, but the diurnal rhythm was preserved at a higher mean with a 2.5 h phase delay. Although only one patient had clinical acromegaly, GH secretion and IGF-I levels were increased in two other patients and all three had a significant cross-correlation between the GH and TSH. PRL secretion was increased in one patient, but all patients had a significant cross-correlation with TSH and showed decreased PRL regularity. Cross-ApEn synchrony between TSH and GH did not differ between patients and controls, but TSH and PRL synchrony was reduced in patients. We conclude that TSH secretion by thyrotropinomas shares many characteristics of other pituitary hormone-secreting adenomas. In addition, abnormalities in GH and PRL secretion exist ranging from decreased (joint) regularity to overt hypersecretion, although not always clinically obvious, suggesting tumoral transformation of thyrotrope lineage cells

Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression

Risk-taking behaviour is an important component of several psychiatric disorders, including attention-deficit hyperactivity disorder, schizophrenia and bipolar disorder. Previously, two genetic loci have been associated with self-reported risk taking and significant genetic overlap with psychiatric disorders was identified within a subsample of UK Biobank. Using the white British participants of the full UK Biobank cohort (n = 83,677 risk takers versus 244,662 controls) for our primary analysis, we conducted a genome-wide association study of self-reported risk-taking behaviour. In secondary analyses, we assessed sex-specific effects, trans-ethnic heterogeneity and genetic overlap with psychiatric traits. We also investigated the impact of risk-taking-associated SNPs on both gene expression and structural brain imaging. We identified 10 independent loci for risk-taking behaviour, of which eight were novel and two replicated previous findings. In addition, we found two further sex-specific risk-taking loci. There were strong positive genetic correlations between risk-taking and attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia. Index genetic variants demonstrated effects generally consistent with the discovery analysis in individuals of non-British White, South Asian, African-Caribbean or mixed ethnicity. Polygenic risk scores comprising alleles associated with increased risk taking were associated with lower white matter integrity. Genotype-specific expression pattern analyses highlighted DPYSL5, CGREF1 and C15orf59 as plausible candidate genes. Overall, our findings substantially advance our understanding of the biology of risk-taking behaviour, including the possibility of sex-specific contributions, and reveal consistency across ethnicities. We further highlight several putative novel candidate genes, which may mediate these genetic effects

Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs

Early detection of pneumoconiosis in X-Rays has been a challenging task that leads to high inter- and intra-reader variability. Motivated by the success of deep learning in general and medical image classification, this paper proposes an approach to automatically detect pneumoconiosis using a deep feature based binary classifier. The features are extracted from X-rays using deep transfer learning, comprising both low and high-level feature sets. For this, a CNN model pre-trained with a transfer learning from a CheXNet model was initially used to extract deep features from the X-Ray images, then the deep features were mapped to higher-dimensional feature spaces for classification using Support Vector Machine (SVM) and CNN based feature aggregation methods. In order to cross validate the proposed method, the training and testing images were randomly split into three folds before each experiment. Nine evaluation metrics were employed to compare the performance of the proposed method and state-of-the-art methods from the literature that used the same datasets. The experimental results show that the proposed framework outperformed others, achieving an accuracy of 92.68% in the automated detection of pneumoconiosis

International Conference Image and Vision Computing New Zealand

Coal Worker Pneumoconiosis (CWP), commonly called Black Lung (BL), is an incurable respiratory disease caused by long-term inhalation of respirable dust. Privacy restrictions and disease incidence placed limits on the available BL datasets, which introduces significant challenges for training deep learning (DL) models. Recently, transfer learning has been seen as an efficient DL method for automatic disease detection with small datasets. This paper investigates BL detection in chest X-rays using transfer DL knowledge from a CheXNet model on a small dataset. A training image set of real, segmented lung X-ray images, with and without BL, was used as a benchmark for detection accuracy. The training data set was then augmented using a Cycle-Consistent Adversarial Networks (CycleGAN) and Keras Image Data Generator, to generate training data with real, augmented and synthetic CWP radiographs to the CheXNet model (with and without pre-trained weights). The effects of different dropout nodes as a blocking factor was also investigated. The accuracy, sensitivity (recall or true positive rate), specificity (true negative rate) and error rate (ERR or incorrect prediction rate) using 3-fold cross-validation experiments was compared for each transfer learning experiment. The total execution time for binary classification of our model also measured. While no definitive conclusion could be reached regarding the effect of dropout rates, results indicated an improvement of classification accuracy from transfer learning

ANALYZING PASSENGER'S SATISFACTION ON RECENTLY LAUNCHED BUS SERVICES IN DHAKA CITY THROUGH DISCRETE CHOICE MODEL

Proceedings of the 7th International Conference on Civil Engineering for Sustainable Development (ICCESD 2024), 7~9 February 2024, KUET, Khulna, BangladeshIn Dhaka, the bus transportation system deals with the issues of insecurity, lack of dependability, inefficiency, and challenges in managing the daily mobility needs of its substantial populace. As a result, it becomes imperative to assess the effectiveness of bus service quality (SQ) through the lens of customer experiences. Despite numerous studies delving into the performance of public transportation systems in Bangladesh, there has been limited focus on comprehending how service quality attributes intersect with passenger satisfaction. In this context, this paper undertakes a comparative examination of user perceptions surrounding two recently launched public transport services in Dhaka city. To unravel the relationship of key indicators, a discrete choice model has been used. Constructed from a comprehensive survey of 1140 questionnaires, which assessed passengers' travel experiences and feedback, the choice model serves as the analytical framework. The outcomes of the model analysis sort out the SQ of both Bus Service-1 (BS-1) and Bus Service-2 (BS-2). Particularly, the assessment indicates that BS-1 accumulates an overall rating of "good," while BS-2 attains a "satisfactory" rating, based on a spectrum of preference levels encompassing "excellent," "satisfactory," "good," and "poor." Pertaining to attributes of service, such as travel time, BS-1 receives a positive rating with a coefficient of 0.413, whereas BS-2's rating stands at 0.168. The analysis also accentuates waiting time and mode availability as pivotal contributors to passenger satisfaction. The study's conclusions offer transportation planners a valuable opportunity to formulate strategic transport policies and regulations aimed at elevating service quality to meet specific goal

Evaluation of Genotypic Performances in Native Rice Landraces of Bangladesh

Field experiments were carried out at Dinajpur, Nilphamari and Faridpur from July, 2017 to March, 2018 to evaluate the performance of native land races of rice viz., Chinigura, Kataribhog, Radhunipagol, Badshabhog, Kalozira, Uknimadhu, Dudshar, Salna, Shitabhog and Zirashail to assess G x E interaction against five quantitative characters, plant height (cm), productive tillers/hill, 1000-grain weight (g), grain yield/m2 and days to maturity, and three qualitative characters, proline (%) as µmol/g fresh weight, aroma from green leaves and cooked rice. The field experiment was conducted in Randomized Complete Block Design with three replications. The highest grain yield (390.25 g/m2) was obtained from Radhunipagol at Dinajpur. Next to Radhunipagol, Kataribhog produced higher grain yield (350.00 g/m2) which was significantly higher than that of Nilphamari and Faridpur but Radhunipagol was suited both for Dinajpur and Nilphamari. The cultivar, Kalozira was adapted to three locations as reflected by its regression coefficient very close to unity (b=0.92) and deviation from the coefficient estimated very near to zero (s2d=0.16). Maximum proline was estimated (18.7 µmol/g fresh weight) from Chinigura cultivated at Dinajpur. The proline (%) estimated average from Kalozira at three locations and the range varied from 14.00 -15.90 µmol/g fresh weight. Dinajpur appeared as the best and Faridpur as an unfavorable location for local aromatic rice cultivars. Since, aroma was assessed through sensory method the maximum aroma was assessed from Chinigura under Dinajpur but its content gradually decreased at Nilphamari and Faridpur. The aroma assessed from cooked rice ranged from 7.05-8.90 over three locations but maximum aroma was assessed under Dinajpur. Chinigura, Radhunipagol and Kataribhog found suitable for Dinajpur, and Kalozira and Badshabhog might suggest cultivating over the locations of Bangladesh