Familial Otosclerosis Linked to Osteogenesis Imperfecta A Case Record

This study examined whether Parkinson's disease (PD1) and schizophrenia (SCZ2) share a hypo dopaminergic dysfunction of the prefrontal cortex leading to cognitive impairments in decision processing. 24 medicated PD patients and 28 matched controls performed the Eriksen flanker two-choice reaction time (RT3) task while brain activity was measured throughout, using functional Magnetic Resonance Imaging (fMRI4). Results were directly compared to those of 30 SCZ patients and 30 matched controls. Significant differences between SCZ and PD were found, through directly comparing the z-score deviations from healthy controls across all behavioral measures, where only SCZ patients showed deviances from controls. Similarly a direct comparison of z-score activation deviations from controls indicated significant differences in prefrontal and cingulate cortical activation between SCZ and PD, where only SCZ patients showed hypo-activation of these areas compared to controls. The hypo-activation of the dorsolateral prefrontal cortex was related to larger RT variability (ex-Gaussian tau) in SCZ but not PD patients. Overall, the concluding evidence does not support a shared neural substrate of cognitive dysfunction, since the deficit in speeded decision processing and the related cortical hypo-activation observed in SCZ were absent in PD.Automated Teller Machine bombings are an increasing societal problem that are often committed using Improvised Explosive Devices. The evolution in IEDs and the negative consequences for society require new security measures to prevent these crimes. Ink staining and security smoke devices are added to cash cassettes, in order to protect ATMs and prevent ATM bombings. Traces found at crime scenes, such as fingerprints and DNA, can contribute to the identification of perpetrators. However, the effect of ink staining and security smoke devices on dactyloscopy and DNA profiling is still unknown. In the current study, we demonstrate that procedures using Citrus Cleaner or sulfosalicylic acid were successful in removing ink and security smoke deposited on plastic plates but did result in the massive loss of fingerprint information as only a low number (4%) of good quality fingerprints were recovered after smoke contamination. Secondly, security ink Sun Blue ES2, but not SICPA Green and Sun Blue ES1, had a significant impact on DNA profiling success. Osimertinib DNA concentrations obtained from blood spiked swabs decreased with increasing ink concentration resulting in a complete loss of genotype information with the addition of ≥10 μl Sun Blue ES2. No noticeable PCR inhibition or DNA degradation was detected during quantification, but a decreased efficiency of DNA extraction could not be excluded. Security smoke, on the other hand, does not seem to have a significant influence on DNA analysis. Precautions must therefore be taken in order to avoid contaminating DNA swabs with ink during sampling. Thirdly, only a single negative impression of a glove in ink and a single glove print were able to be visualized with white fingerprint powder on detonated cash cassettes. In conclusion, the detection of glove prints and fingerprints is limited and security ink, contrary to smoke, after detonation can have a negative influence on downstream DNA analysis procedures.
Frailty marks an increased risk for adverse health outcomes. Since childhood trauma is associated with the onset of physical and mental health diseases during the lifespan, we examined the link between childhood trauma and multidimensional frailty.
A cross-sectional study embedded in a clinical cohort study (ROM-GPS) of older (≥60 years) patients (n=182) with a unipolar depressive-, anxiety- and/or somatic symptom disorder according to DSM-criteria referred to specialized geriatric mental health care. Frailty was assessed with the Tilburg Frailty Indicator (TFI), comprising a physical, psychological, and social dimension. Physical, sexual and psychological abuse and emotional neglect before the age of 16 years was measured with a structured interview.
Of 182 patients, 103 (56.6%) had experienced any childhood trauma and 154 (84.6%) were frail (TFI sum score ≥5). Linear regression analyses, adjusted for lifestyle, psychological and physical-health factors, showed that the presence of any type of childhood trauma was not associated with the TFI sum score, however when considered separately, physical abuse was (ß=0.16, p=.037). Regarding the specific frailty dimensions, any childhood trauma was associated with social frailty (ß=0.18, p=.019), with emotional neglect as main contributor.
These findings demonstrate a complex link between different types of childhood trauma and multidimensional frailty among older psychiatric patients. Regarding the three dimensions of frailty, social frailty seems most affected by childhood trauma. This may have been underestimated until now and should receive more attention in clinical care and future research.
These findings demonstrate a complex link between different types of childhood trauma and multidimensional frailty among older psychiatric patients. Regarding the three dimensions of frailty, social frailty seems most affected by childhood trauma. This may have been underestimated until now and should receive more attention in clinical care and future research.Multiple sclerosis (MS) is a chronic autoimmune, inflammatory neurological disease of the central nervous system. Its diagnosis nowadays commonly includes performing an MRI scan, as it is the most sensitive imaging test for MS. MS plaques are commonly identified from fluid-attenuated inversion recovery (FLAIR) images as hyperintense regions that are highly varying in terms of their shapes, sizes and locations, and are routinely classified in accordance to the McDonald criteria. Recent years have seen an increase in works that aimed at development of various semi-automatic and automatic methods for detection, segmentation and classification of MS plaques. In this paper, we present an automatic combined method, based on two pipelines a traditional unsupervised machine learning technique and a deep-learning attention-gate 3D U-net network. The deep-learning network is specifically trained to address the weaker points of the traditional approach, namely difficulties in segmenting infratentorial and juxtacortical plaques in real-world clinical MRIs.