PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks

Cardiovascular diseases are one of the most severe causes of mortality, taking a heavy toll of lives annually throughout the world. The continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, bringing about several layers of complexities. This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals. In addition we explore the advantage of deep learning as it would free us from sticking to ideally shaped PPG signals only, by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present, PPG2ABP, a deep learning based method, that manages to predict the continuous ABP waveform from the input PPG signal, with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of DBP, MAP and SBP from the predicted ABP waveform outperforms the existing works under several metrics, despite that PPG2ABP is not explicitly trained to do so

ACC-ViT : Atrous Convolution's Comeback in Vision Transformers

Transformers have elevated to the state-of-the-art vision architectures through innovations in attention mechanism inspired from visual perception. At present two classes of attentions prevail in vision transformers, regional and sparse attention. The former bounds the pixel interactions within a region; the latter spreads them across sparse grids. The opposing natures of them have resulted in a dilemma between either preserving hierarchical relation or attaining a global context. In this work, taking inspiration from atrous convolution, we introduce Atrous Attention, a fusion of regional and sparse attention, which can adaptively consolidate both local and global information, while maintaining hierarchical relations. As a further tribute to atrous convolution, we redesign the ubiquitous inverted residual convolution blocks with atrous convolution. Finally, we propose a generalized, hybrid vision transformer backbone, named ACC-ViT, following conventional practices for standard vision tasks. Our tiny version model achieves $\sim 84 \%$ accuracy on ImageNet-1K, with less than $28.5$ million parameters, which is $0.42\%$ improvement over state-of-the-art MaxViT while having $8.4\%$ less parameters. In addition, we have investigated the efficacy of ACC-ViT backbone under different evaluation settings, such as finetuning, linear probing, and zero-shot learning on tasks involving medical image analysis, object detection, and language-image contrastive learning. ACC-ViT is therefore a strong vision backbone, which is also competitive in mobile-scale versions, ideal for niche applications with small datasets

NLRP3インフラマソームはRANKLで誘導したマウス骨髄マクロファージの破骨細胞形成を負に調節するが、リポ多糖の存在下では正に調節する

長崎大学学位論文 [学位記番号]博（医歯薬）甲第1488号 [学位授与年月日]令和5年3月20

RamanNet: A generalized neural network architecture for Raman Spectrum Analysis

Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kind of materials. This sort of fingerprinting molecules has thus led to widespread application of Raman spectrum in various fields like medical dignostics, forensics, mineralogy, bacteriology and virology etc. Despite the recent rise in Raman spectra data volume, there has not been any significant effort in developing generalized machine learning methods for Raman spectra analysis. We examine, experiment and evaluate existing methods and conjecture that neither current sequential models nor traditional machine learning models are satisfactorily sufficient to analyze Raman spectra. Both has their perks and pitfalls, therefore we attempt to mix the best of both worlds and propose a novel network architecture RamanNet. RamanNet is immune to invariance property in CNN and at the same time better than traditional machine learning models for the inclusion of sparse connectivity. Our experiments on 4 public datasets demonstrate superior performance over the much complex state-of-the-art methods and thus RamanNet has the potential to become the defacto standard in Raman spectra data analysi

IN-VITRO COMPARATIVE STUDY OF ANTI-INFLAMMATORY AND ANTI-ARTHRITIC EFFECTS OF FLEMINGIA STRICTA ROXB AND NYMPHAEA NOUCHALI LEAF

Objective: To evaluate the comparative study of anti-inflammatory, anti-arthritic activity of methanol extract of Flemingia stricta and Nymphaea nouchali leaf.Methods: Human Red Blood Cell (HRBC) membrane stabilization method was evaluated for anti-inflammatory activity. Anti-denaturation method was performed by using Bovine Serum Albumin (BSA) to evaluate the anti-arthritic potential.Results: The in vitro anti-inflammatory activity of the methanol extracts of Flemingia stricta and Nymphaea nouchali showed 81.85±0.67% (P<0.01) and 85.59±0.58% (P<0.01) of membrane stabilization at 1000µg/ml conc. and 51.85±0.49% (P<0.01) and 70.63±0.50% (P<0.01) at 31.25µg/ml respectively. All the results were compared with standard Diclofenac which showed 93.15±1.03% protection at 1000µg/ml conc. The in vitro study on both leaves also showed the presence of significant anti-arthritic activity. Here the extracts showed 70.43±1.42% (P<0.01) and 83.33±0.54% of protein denaturation at the highest conc. (1000 µg/ml) and 39.25±1.08% (P<0.01) and 38.71±0.93% (P<0.01) at the lowest conc. (31.25µg/ml), in where the standard drug displayed the 86.56±2.15% at 1000ug/ml and 51.08±1.42% at 31.25 µg/ml.Conclusion: These results suggest that both the methanol extract of Flemingia stricta and Nymphaea nouchali possess significant anti-inflammatory and anti-arthritic activity.Â

Anti-oxidant effect of Flemingia stricta Roxb. leaves methanolic extract

Aim of the study was to evaluate the possible anti-oxidant activity of Flemingia stricta leaf extract. In antioxidant study, plant crude methanol extract was evaluated for 1,1-diphenyl,2-picrylhydrazyl (DPPH) and reducing power capacity. Moreover, total phenolic and total flavonoid content of plant extracts were determined and expressed in mg of gallic acid equivalent per gram of dry sample (mg GAE/g dry weight). In the DPPH free radical scavenging assay, methanol extract showed concentration dependent inhibition of the free radicals. IC50 of ascorbic acid and F. stricta leaves were 4.25 µg/ml and 320.47 µg/ml respectively. In case of reducing capacity, the methanol extract at concentrations of 25, 50, 100, 200, 400 µg/ml, the absorbances were 0.56, 0.92, 1.41, 1.76, 2.23, respectively. Total phenolic content was estimated by gallic acid and expressed as milligrams of gallic acid equivalent (GAE). The methanol extracts contained a considerable amount of phenolic contents of 482±8.72 of GAE/g of extract and the total flavonoid content of the F. stricta leaf was estimated by using aluminium chloride colorimetric technique and found that the extract contained flavonoid content 340.625±4.50 of GAE/g of extract. These results suggested that the methanol extract of F. stricta Roxb. possess anti-oxidant activity. DOI: http://dx.doi.org/10.5281/zenodo.146976