Reversible Plasticity of Fear Memory-Encoding Amygdala Synaptic Circuits Even after Fear Memory Consolidation

It is generally believed that after memory consolidation, memory-encoding synaptic circuits are persistently modified and become less plastic. This, however, may hinder the remaining capacity of information storage in a given neural circuit. Here we consider the hypothesis that memory-encoding synaptic circuits still retain reversible plasticity even after memory consolidation. To test this, we employed a protocol of auditory fear conditioning which recruited the vast majority of the thalamic input synaptic circuit to the lateral amygdala (T-LA synaptic circuit; a storage site for fear memory) with fear conditioning-induced synaptic plasticity. Subsequently the fear memory-encoding synaptic circuits were challenged with fear extinction and re-conditioning to determine whether these circuits exhibit reversible plasticity. We found that fear memory-encoding T-LA synaptic circuit exhibited dynamic efficacy changes in tight correlation with fear memory strength even after fear memory consolidation. Initial conditioning or re-conditioning brought T-LA synaptic circuit near the ceiling of their modification range (occluding LTP and enhancing depotentiation in brain slices prepared from conditioned or re-conditioned rats), while extinction reversed this change (reinstating LTP and occluding depotentiation in brain slices prepared from extinguished rats). Consistently, fear conditioning-induced synaptic potentiation at T-LA synapses was functionally reversed by extinction and reinstated by subsequent re-conditioning. These results suggest reversible plasticity of fear memory-encoding circuits even after fear memory consolidation. This reversible plasticity of memory-encoding synapses may be involved in updating the contents of original memory even after memory consolidation

Does Collocation Inform the Impact of Collaboration?

Background It has been shown that large interdisciplinary teams working across geography are more likely to be impactful. We asked whether the physical proximity of collaborators remained a strong predictor of the scientific impact of their research as measured by citations of the resulting publications. Methodology/Principal Findings Articles published by Harvard investigators from 1993 to 2003 with at least two authors were identified in the domain of biomedical science. Each collaboration was geocoded to the precise three-dimensional location of its authors. Physical distances between any two coauthors were calculated and associated with corresponding citations. Relationship between distance of coauthors and citations for four author relationships (first-last, first-middle, last-middle, and middle-middle) were investigated at different spatial scales. At all sizes of collaborations (from two authors to dozens of authors), geographical proximity between first and last author is highly informative of impact at the microscale (i.e. within building) and beyond. The mean citation for first-last author relationship decreased as the distance between them increased in less than one km range as well as in the three categorized ranges (in the same building, same city, or different city). Such a trend was not seen in other three author relationships. Conclusions/Significance Despite the positive impact of emerging communication technologies on scientific research, our results provide striking evidence for the role of physical proximity as a predictor of the impact of collaborations.Ewing Marion Kauffman FoundationHarvard University. Office of the Provost (1992-

A Feasibility Study for Diagnosis of Latent Tuberculosis Infection Using an IGRA Point-of-Care Platform in South Korea.

PURPOSE: This study aimed to evaluate ichroma™ IGRA-TB, a novel point-of-care platform for assaying IFN-γ release, and to compare it with QuantiFERON-TB Gold In-Tube (QFT-GIT) for identifying Mycobacterium tuberculosis (M. tb) infection. MATERIALS AND METHODS: We recruited 60 healthy subjects, and blood samples were obtained in QFT-GIT blood collection tubes. The blood collection tubes were incubated at 37°C, and culture supernatant was harvested after 18-24 hours. IFN-γ responses were assessed by the ichroma™ IGRA-TB cartridge and the QFT-GIT IFN-γ enzyme-linked immunosorbent assay. Three active TB patients were recruited as a positive control for M. tb infection. RESULTS: The area under the receiver operating characteristic curve of the ichroma™ IGRA-TB test for differentiating between infected and non-infected individuals was 0.9706 (p<0.001). Inconsistent positivity between the two tests was found in three participants who showed weak positive IFN-γ responses (<1.0 IU/mL) with QFT-GIT. However, the two tests had excellent agreement (95.2%, κ=0.91, p<0.001), and a very strong positive correlation was observed between the IFN-γ values of both tests (r=0.91, p<0.001). CONCLUSION: The diagnostic accuracy demonstrated in this study indicates that the ichroma™ IGRA-TB test could be used as a rapid diagnostic method for detecting latent TB infection. It may be particularly beneficial in resource-limited places that require cost-effective laboratory diagnostics

An assessment of histone-modification antibody quality

We have tested the specificity and utility of more than 200 antibodies raised against 57 different histone modifications in Drosophila melanogaster, Caenorhabditis elegans and human cells. Although most antibodies performed well, more than 25% failed specificity tests by dot blot or western blot. Among specific antibodies, more than 20% failed in chromatin immunoprecipitation experiments. We advise rigorous testing of histone-modification antibodies before use, and we provide a website for posting new test results (http://compbio.med.harvard.edu/antibodies/)

An assessment of histone-modification antibody quality

We have tested the specificity and utility of more than 200 antibodies raised against 57 different histone modifications in Drosophila melanogaster, Caenorhabditis elegans and human cells. Although most antibodies performed well, more than 25% failed specificity tests by dot blot or western blot. Among specific antibodies, more than 20% failed in chromatin immunoprecipitation experiments. We advise rigorous testing of histone-modification antibodies before use, and we provide a website for posting new test results (http://compbio.med.harvard.edu/antibodies/)

Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features

IntroductionPain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a “black box”, where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations.MethodWe focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states.Results and discussionThe ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation

Occupational cancer claims in Korea from 2010 to 2016

Abstract Background Research on carcinogens causing occupational cancer has been updated. Further, social interest in occupational cancer has increased. In addition, the standard for recognizing cancer as a work-related disease has also been revised. The present study aims to describe the distribution of occupational cancer claims or its approval rate and their association with work-related variables. Methods We analyzed 1299 claim cases for occupational cancer from 2010 to 2016 provided by the Korea Workers’ Compensation and Welfare Service (KCOMWEL). The status of approval rate was shown by year, sex, industry, occupation, age of diagnosis, duration from employment to diagnosis, and cancer site. Results The approval rate was 39.0% from 2010 to 2016 and tended to increase annually since 2011. Both the number of claims and the approval rate were higher in men. Mining and quarrying showed the highest approval rate (78.4%). The approval rates by age of diagnosis and duration from employment to diagnosis increased as the time periods increased. Respiratory organ had the highest number of claims and the highest approval rate by cancer site. Conclusions The approval rate of occupational cancer has shown an increasing trend since 2011. The increase of occupational carcinogens and cancer sites and the improvement of social awareness about occupational cancer could have resulted in this trend. The present study provides unique, and the latest and most accurate findings on occupational cancer data of recent 7 years that could be helpful to researchers or policy makers on occupational cancer

A Simple Method for the Design and Development of Flavivirus NS1 Recombinant Proteins Using an In Silico Approach

Even in countries that are currently not facing a flavivirus epidemic, the spread of mosquito-borne flaviviruses presents an increasing public threat, owing to climate change, international travel, and other factors. Many of these countries lack the resources (viral strains, clinical specimens, etc.) needed for the research that could help cope with the threat imposed by flaviviruses, and therefore, an alternative approach is needed. Using an in silico approach to global databases, we aimed to design and develop flavivirus NS1 recombinant proteins with due consideration towards antigenic variation. NS1 genes analyzed in this study included a total of 6,823 sequences, from Dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), Zika virus (ZIKV), and Yellow fever virus (YKV). We extracted and analyzed 316 DENV NS1 sequence types (STs), 59 JEV STs, 75 WNV STs, 30 YFV STs, and 43 ZIKV STs using a simple algorithm based on phylogenetic analysis. STs were reclassified according to the variation of the major epitope by MHC II binding. 78 DENV epitope type (EpT), 29 JEV EpTs, 29 WNV EpTs, 12 YFV EpTs, and 5 ZIKV EpTs were extracted according to their major epitopes. Also, frequency results showed that there were dominant EpTs in all flavivirus. Fifteen STs were selected and purified for the expression of recombinant antigen in Escherichia coli by sodium dodecyl sulfate extraction. Our study details a novel in silico approach for the development of flavivirus diagnostics, including a simple way to screen the important peptide regions