Head Injury due to Cracker Blast

Abstract Death arising from fireworks is not an uncommon phenomenon in India. Deaths commonly arise during the manufacturing process and improper handling of chemicals for fireworks. Here, we would like to highlight a rare case in which a 12-year-old child sustained a head injury due to a cracker blast, which resulted in a depressed fracture over the vault of her skull. In this paper, we tried to emulate the same pattern of fracture on a human skull bone experimentally using the same type of cracker which caused the injury. This was done to give us an insight as to whether the cracker was powerful enough to produce a fracture and to rule out suspicion of blunt force trauma due to a weapon on the head. The subsequent explosion caused by the blast produced a distinctly similar pattern of fracture in comparison to the fracture observed in our case. The study also highlights the dangers of country-made crackers handled by children

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

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

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

Evidence of Antimicrobial Resistance in Bats and Its Planetary Health Impact for Surveillance of Zoonotic Spillover Events: A Scoping Review

As a result of the COVID-19 pandemic, as well as other outbreaks, such as SARS and Ebola, bats are recognized as a critical species for mediating zoonotic infectious disease spillover events. While there is a growing concern of increased antimicrobial resistance (AMR) globally during this pandemic, knowledge of AMR circulating between bats and humans is limited. In this paper, we have reviewed the evidence of AMR in bats and discussed the planetary health aspect of AMR to elucidate how this is associated with the emergence, spread, and persistence of AMR at the human–animal interface. The presence of clinically significant resistant bacteria in bats and wildlife has important implications for zoonotic pandemic surveillance, disease transmission, and treatment modalities. We searched MEDLINE through PubMed and Google Scholar to retrieve relevant studies (n = 38) that provided data on resistant bacteria in bats prior to 30 September 2022. There is substantial variability in the results from studies measuring the prevalence of AMR based on geographic location, bat types, and time. We found all major groups of Gram-positive and Gram-negative bacteria in bats, which are resistant to commonly used antibiotics. The most alarming issue is that recent studies have increasingly identified clinically significant multi-drug resistant bacteria such as Methicillin Resistant Staphylococcus aureus (MRSA), ESBL producing, and Colistin resistant Enterobacterales in samples from bats. This evidence of superbugs abundant in both humans and wild mammals, such as bats, could facilitate a greater understanding of which specific pathways of exposure should be targeted. We believe that these data will also facilitate future pandemic preparedness as well as global AMR containment during pandemic events and beyond