Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Diabetic Foot Ulcer and Diabetic Microvascular Disease

Background: Diabetic foot ulcer (DFU) is a serious complication of diabetes caused by multiple factors. Diabetic microvascular disease has a close linkage with DFU. However, the inter-relational mechanisms between them are still unclear. This article aimed to explore the shared gene signatures and potential molecular mechanisms in DFU and diabetic microvascular disease. Methods: In the GEO database, DFU microarray datasets (GSE80178, GSE68183) and diabetic microvascular disease microarray datasets (GSE43950) were downloaded. After data standardization processing, we used R software to analyze the transcriptome sequencing data of each data set to find the differentially expressed genes (DEGs) of DFU and diabetic microvascular disease. Then obtained the overlapped DEGs in DFU and diabetic microvascular disease database by Jvenn. Finally, the shared DEGs were enriched by pathway enrichment and protein-protein interaction (PPI) analysis, and the hub gene was found by node analysis. Results: Totally, 1007 DEGs were identified in the GSE80178 dataset, 338 DEGs were identified in the GSE68183 dataset, 1154 were identified in the GSE43950 dataset, Venn diagram analyses showed that there were 14 shared DEGs in these datasets. Enrichment analysis shows that the shared DEGs were mainly associated with chronic inflammatory response, leukocyte migration, cellular transition metal ion homeostasis, vascular wound healing, collagen-containing extracellular matrix and Toll-like receptor binding. Involved pathways were mainly enriched in IL-17 signaling, glycosaminoglycan degradation, and calcium signaling. PPI analysis of these shared DEGs shows that S100A9, S100A8, CSTA, ADAP2, CD34 and FGL2 were the hub gene whose plays a pivotal role in DFU.Conclusion: Our work has identified several new DFU candidate genes that can be used as biomarkers or potential therapeutic targets

Radioisotope identification with neuromorphic methodology: different solutions and evaluations

Early detection of radioisotopes plays an increasingly important role in the modern world. It allows the possibility of quick countermeasures when faced with potentially hazardous radioactive materials like dirty bombs, and nuclear leakage. This could secure the lives of the innocent in populated areas including airports, stadiums or ports. A light-weight compact handheld device could be used in this situation for the patrol team. However, the operating hours for these devices are normally constrained by the batteries they carry. More efficient al- gorithms or solutions are needed for this resource-constraint application to extend the battery life so that security patrol is not frequently interrupted by the recharge. Event-based processing is a novel technique that allows the computing unit to operate only when there is a key event while staying idle otherwise. Spiking neural network (SNN) is a promising candidate for event-based processing and also known as neuromorphic method- ology due to the biomimicry plausibility, which could be easily implemented and still offer comparable accuracy to its counterpart — artificial neural network (ANN), which is notoriously power-hungry. In this research work, it will be demonstrated that using SNN for radioisotope identification (RIID) is possible and capable of achieving the same or even better accuracy when compared with ANNs. Meanwhile, the power consumption of the proposed method on a field program- mable gate array (FPGA) shows that power reduction is highly significant compared with the old software implementation on a smartphone. The task has been delivered in two parts, we first attempted an unsupervised Spike-Timing- Dependent Plasticity (STDP) SNN implementation on SpiNNaker, an emulation platform for SNN. This demonstrates the capability of classifying radioisotopes using purely SNN compat- ible training methods and architecture. We then managed to implement a more complex bin-ratio ensemble SNN (BESNN) on FPGA with better performance. To achieve this implementation, a new SNN conversion method was created to facilitate the digital hardware implementation. This conversion flow allows the highly sparse weight matrix representation without sacrificing overall accuracy. In the meantime, the power consumption of the mentioned design has been characterised, which could be used to estimate the battery life of a handheld system while functioning. Even though this design has been validated on an FPGA, further squeeze for the power saving is possible if an application specific integrated circuits (ASIC) could be delivered. Furthermore, the analogue unit used in the design is a compromise given that the logarithm could not be done by a spiking neuron at the moment. This prevents an end-to-end application, which is preferred for higher integration and potentially more power conservation. According to our knowledge, applying neuromorphic methodology to address RIID represents uncharted territory, especially in the context of power characterisation, an aspect that has not been explored previously. This research work fills the gap that is present in the research field and also offers a functional low-power prototype for the handheld RIID device producer. This project pioneers the use of an event-based processing algorithm for radioisotope identi- fication, marking a significant advancement in the field. Leveraging Spiking Neural Networks (SNNs) on specialised hardware, the project establishes a comprehensive application flow, showcasing the efficacy and potential of SNNs in this domain. The implementation of an unsupervised STDP algorithm for radioisotope identification is also groundbreaking, introducing a local self-learning rule for complex tasks beyond handwritten digit recognition. Additionally, the bin-ratio ensemble project achieves remarkable accuracy, setting new bench- marks in the field. It represents the first ensemble SNN application in radioisotope identifica- tion, further enhanced by an innovative ANN-SNN conversion method with iterative pruning to reduce computational overhead. Furthermore, this research provides detailed insights into sparse SNN construction and char- acterises hardware implementation, shedding light on power and energy consumption con- siderations

Re-evaluation of single nucleotide variants and identification of structural variants in a cohort of 45 sudden unexplained death cases

Sudden unexplained death (SUD) takes up a considerable part in overall sudden death cases, especially in adolescents and young adults. During the past decade, many channelopathy- and cardiomyopathy-associated single nucleotide variants (SNVs) have been identified in SUD studies by means of postmortem molecular autopsy, yet the number of cases that remain inconclusive is still high. Recent studies had suggested that structural variants (SVs) might play an important role in SUD, but there is no consensus on the impact of SVs on inherited cardiac diseases. In this study, we searched for potentially pathogenic SVs in 244 genes associated with cardiac diseases. Whole-exome sequencing and appropriate data analysis were performed in 45 SUD cases. Re-analysis of the exome data according to the current ACMG guidelines identified 14 pathogenic or likely pathogenic variants in 10 (22.2%) out of the 45 SUD cases, whereof 2 (4.4%) individuals had variants with likely functional effects in the channelopathy-associated genes SCN5A and TRDN and 1 (2.2%) individual in the cardiomyopathy-associated gene DTNA. In addition, 18 structural variants (SVs) were identified in 15 out of the 45 individuals. Two SVs with likely functional impairment were found in the coding regions of PDSS2 and TRPM4 in 2 SUD cases (4.4%). Both were identified as heterozygous deletions, which were confirmed by multiplex ligation-dependent probe amplification. In conclusion, our findings support that SVs could contribute to the pathology of the sudden death event in some of the cases and therefore should be investigated on a routine basis in suspected SUD cases