Persistent activation of the ζ isoform of protein kinase C in the maintenance of long-term potentiation

Long-term potentiation in the CA1 region of the hippocampus, a model for memory formation in the brain, is divided into two phases. A transient process (induction) is initiated, which then generates a persistent mechanism (maintenance) for enhancing synaptic strength. Protein kinase C (PKC), a gene family of multiple isozymes, may play a role in both induction and maintenance. In region CA1 from rat hippocampal slices, most of the isozymes of PKC translocated to the particulate fraction 15 sec after a tetanus. The increase of PKC in the particulate fraction did not persist into the maintenance phase of long-term potentiation. In contrast, a constitutively active kinase, PKM, a form specific to a single isozyme (ζ), increased in the cytosol during the maintenance phase. The transition from translocation of PKC to formation of PKM may help to explain the molecular mechanisms of induction and maintenance of long-term potentiation

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

IntroductionArtificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with ‘untrained’ or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process.MethodsThis protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data.ConclusionThe anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer

Relative contribution of various expressions of cAMP excretion to other indices of parathyroid function, as tested by discriminant multivariate linear regression analysis.

Abstract We evaluated the relative contribution to the diagnosis of hyperparathyroid disease from current laboratory indices of parathyroid function--plasma calcium (I), phosphate (II), carboxy-terminal (III) and predominantly amino-terminal (IV) radioimmunoassays of parathyrin, the urinary excretion ratios of cyclic adenosine monophosphate (cAMP) to creatinine (V) or to glomerular filtrate (VI), and the ratio of the nephrogenous fraction of cAMP to glomerular filtrate (VII)--in 224 subjects: 40 with surgically proven hyperparathyroid disease, the others normoparathyroid. The decreasing order of sensitivity was I greater than VI greater than VII greater than V greater than III greater than IV greater than II; all these indices differed significantly between normoparathyroid and hyperparathyroid patients. The decreasing order of specificity was VII, III greater than I greater than IV greater than V, II greater than VI. Discriminant multivariate linear regression analysis was performed in a subset of 58 subjects (17 hyper- and 41 normoparathyroid) from the population studied here, chosen because all of the laboratory indices were determined for each subject. The classification accuracy was 98.3% for combining I, VII, and III (r = 0.908), or I and V (r = 0.893), or I and VII (r = 0.889). The other variables did not add to the precision of classification.</jats:p

Mitochondrial DNA depletion in a patient with long survival

We studied a 29-year-old woman with myopathy since childhood with evidence of mitochondrial DNA (mtDNA) depletion. Muscle biopsy sample showed cytochrome c oxidase (COX)-negative fibers. Biochemistry showed COX deficiency. Southern blot analysis showed 76% depletion of mtDNA as compared with controls. This patient's clinical course suggests that long survival is possible in some patients with mtDNA depletion.</jats:p