MRI and clinical resolution of a suspected intracranial toxoplasma granuloma with medical treatment in a domestic short hair cat

A two-year-old cat was presented with a left paradoxical vestibular syndrome. MRI of the brain revealed an extra-axial homogenously contrast enhancing mass in the region of the left caudal cerebellar peduncle. Toxoplasma serology was consistent with active infection and the lesion was suspected to be a toxoplasma granuloma. Following eight weeks of tapering oral prednisolone and 11 weeks of oral clindamycin treatment, repeat MRI revealed resolution of the lesion. Eighteen months after initial diagnosis, the cat remained neurologically normal. Differential diagnoses for a solitary, extra-axial, contrast enhancing mass lesion in the feline brain should include toxoplasma granuloma, which can undergo MRI and clinical resolution with medical treatment

Clinical and magnetic resonance imaging characteristics of thoracolumbar intervertenral disk extrusions and protrusions in large breed dogs

It has recently been shown that the fat-derived hormone adiponectin has the ability to decrease hyperglycemia and to reverse insulin resistance. However, bacterially produced full-length adiponectin is functionally inactive. Here, we show that endogenous adiponectin secreted by adipocytes is post-translationally modified into eight different isoforms, as shown by two-dimensional gel electrophoresis. Carbohydrate detection revealed that six of the adiponectin isoforms are glycosylated. The glycosylation sites were mapped to several lysines (residues 68, 71, 80, and 104) located in the collagenous domain of adiponectin, each having the surrounding motif of GXKGE(D). These four lysines were found to be hydroxylated and subsequently glycosylated. The glycosides attached to each of these four hydroxylated lysines are possibly glucosylgalactosyl groups. Functional analysis revealed that full-length adiponectin produced by mammalian cells is much more potent than bacterially generated adiponectin in enhancing the ability of subphysiological concentrations of insulin to inhibit gluconeogenesis in primary rat hepatocytes, whereas this insulin-sensitizing ability was significantly attenuated when the four glycosylated lysines were substituted with arginines. These results indicate that full-length adiponectin produced by mammalian cells is functionally active as an insulin sensitizer and that hydroxylation and glycosylation of the four lysines in the collagenous domain might contribute to this activity.link_to_subscribed_fulltex

Associations between objectively measured and self‐reported sleep with academic and cognitive performance in adolescents: DADOS study

Adequate sleep has been positively related with health and school achievement out-comes during adolescence. The aim of this study was to investigate the associations ofobjectively measured and self‐reported sleep duration and quality with academic andcognitive performance in adolescents. This study was conducted with 257 adolescents(13.9 ± 0.3 years) from the DADOS study (Deporte, ADOlescencia y Salud). Objec-tively measured and self‐reported sleep duration and quality were obtained by a wrist‐worn GENEActiv accelerometer and the Spanish version of Pittsburgh Sleep QualityIndex questionnaire, respectively. Academic performance was analysed through schoolrecords using four indicators: math, language, science and grade point average score.Cognitive performance was measured using the Spanish version of the“SRA Test ofEducational Ability”. After Benjamini–Hochberg correction for the false discovery rate,objectively measured sleep duration was negatively associated with verbal ability (β=−0.179,p= .004), whilst self‐reported sleep quality was positively associated withacademic performance (βranging from 0.209 to 0.273; allp<.001). These associationsremained significant after further controlling for physical fitness and physical activity.Conversely, there were no associations between self‐reported sleep duration andobjective sleep quality with academic and cognitive performance. Our findings fit inline with previous research showing that sleep quality may play an important role onadolescents’academic performance. Further interventional research is needed to clar-ify the mechanisms by which sleep is related to academic performance in youth

Applications of machine and deep learning to thyroid cytology and histopathology: a review.

This review synthesises past research into how machine and deep learning can improve the cyto- and histopathology processing pipelines for thyroid cancer diagnosis. The current gold-standard preoperative technique of fine-needle aspiration cytology has high interobserver variability, often returns indeterminate samples and cannot reliably identify some pathologies; histopathology analysis addresses these issues to an extent, but it requires surgical resection of the suspicious lesions so cannot influence preoperative decisions. Motivated by these issues, as well as by the chronic shortage of trained pathologists, much research has been conducted into how artificial intelligence could improve current pipelines and reduce the pressure on clinicians. Many past studies have indicated the significant potential of automated image analysis in classifying thyroid lesions, particularly for those of papillary thyroid carcinoma, but these have generally been retrospective, so questions remain about both the practical efficacy of these automated tools and the realities of integrating them into clinical workflows. Furthermore, the nature of thyroid lesion classification is significantly more nuanced in practice than many current studies have addressed, and this, along with the heterogeneous nature of processing pipelines in different laboratories, means that no solution has proven itself robust enough for clinical adoption. There are, therefore, multiple avenues for future research: examine the practical implementation of these algorithms as pathologist decision-support systems; improve interpretability, which is necessary for developing trust with clinicians and regulators; and investigate multiclassification on diverse multicentre datasets, aiming for methods that demonstrate high performance in a process- and equipment-agnostic manner