A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals

Amide proton transfer imaging in stroke

Amide proton transfer (APT) imaging, a variant of chemical exchange saturation transfer MRI, has shown promise in detecting ischemic tissue acidosis following impaired aerobic metabolism in animal models and in human stroke patients due to the sensitivity of the amide proton exchange rate to changes in pH within the physiological range. Recent studies have demonstrated the possibility of using APT-MRI to detect acidosis of the ischemic penumbra, enabling the assessment of stroke severity and risk of progression, monitoring of treatment progress, and prognostication of clinical outcome. This paper reviews current APT imaging methods actively used in ischemic stroke research and explores the clinical aspects of ischemic stroke and future applications for these methods

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Quantitative measurement of pH in stroke using chemical exchange saturation transfer magnetic resonance imaging

Stroke is one of the leading causes of death and adult disability worldwide. The major therapeutic intervention for acute ischemic stroke is the administration of recombinant tissue plasminogen activator (rtPA) to help to restore blood flow to the brain. This has been shown to increase the survival rate and to reduce the disability of ischemic stroke patients. However, rtPA is associated with intracranial haemorrhage and thus its administration is currently limited to only about 5% of ischemic stroke patients. More advanced imaging techniques can be used to better stratify patients for rtPA treatment. One new imaging technique, chemical exchange saturation transfer (CEST) magnetic resonance imaging, can potentially image intracellular pH and since tissue acidification happens prior to cerebral infarction, CEST has the potential to predict ischemic injury and hence to improve patient selection. Despite this potential, most studies have generated pH-weighted rather than quantitative pH maps; the most widely used metric to quantify the CEST effect is only able to generate qualitative contrast measurements and suffers from many confounds. The greatest clinical benefit of CEST imaging lies in its ability to non-invasively measure quantitative pH values which may be useful to identify salvageable tissue. The quantitative techniques and work presented in this thesis thus provide the necessary analysis to determine whether a threshold for the quantified CEST effect or for pH exists to help to define tissue outcome following stroke; to investigate the potential of CEST for clinical stroke imaging; and subsequently to facilitate clinical translation of CEST for acute stroke management.</p

Investigation of possible rickettsial infection in patients with malaria

Rickettsioses are a common health problem in many geographical areas, including rural areas in Southeast Asia. Co-infection of rickettsioses and malaria has been reported in Africa, where common reservoir and vectors are available. In this study, blood samples of Malaysian patients microscopically positive (n=148) and negative (n=88) for malaria parasites (Plasmodium knowlesi, Plasmodium malariae, Plasmodium falciparum, and Plasmodium vivax) were screened for the presence of rickettsial DNA, using PCR assays targeting specific genes. A partial fragment of rickettsial ompB gene was successfully amplified and sequenced from a patient microscopically positive for Plasmodium spp. and PCR-positive for P. vivax. BLAST analysis of the ompB sequence demonstrated the highest sequence similarity (99.7% similarity, 408/409nt) with Rickettsia sp. RF2125 (Genbank accession no. JX183538) and 91.4% (374/409 nt) similarity with Rickettsia felis URRWXCal2 (Genbank accession no. CP000053). This study reports rickettsial infection in a malaria patient for the first time in the Southeast Asia region. © 2019, Malaysian Society for Parasitology. All rights reserved

Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images

Abstract Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN

Assessing the efficacy of machine learning algorithms for syncope classification: A systematic review

Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4–96.1%), specificity of 81.5% (95% CI: 69.8–92.8%) and accuracy of 85.8% (95% CI: 78.6–92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients

Sensitisation to recombinant Aspergillus fumigatus allergens and clinical outcomes in COPD

Background Variable clinical outcomes are reported with fungal sensitisation in chronic obstructive pulmonary disease (COPD), and it remains unclear which fungi and what allergens associate with the poorest outcomes. The use of recombinant as opposed to crude allergens for such assessment is unknown. Methods A prospective multicentre assessment of stable COPD (n=614) was undertaken in five hospitals across three countries: Singapore, Malaysia and Hong Kong. Clinical and serological assessment was performed against a panel of 35 fungal allergens including crude and recombinant Aspergillus and non-Aspergillus allergens. Unsupervised clustering and topological data analysis (TDA) approaches were employed using the measured sensitisation responses to elucidate if sensitisation subgroups exist and their related clinical outcomes. Results Aspergillus fumigatus sensitisation was associated with increased exacerbations in COPD. Unsupervised cluster analyses revealed two “fungal sensitisation” groups. The first was characterised by Aspergillus sensitisation and increased exacerbations, poorer lung function and worse prognosis. Polysensitisation in this group conferred even poorer outcome. The second group, characterised by Cladosporium sensitisation, was more symptomatic. Significant numbers of individuals demonstrated sensitisation responses to only recombinant (as opposed to crude) A. fumigatus allergens f 1, 3, 5 and 6, and exhibited increased exacerbations, poorer lung function and an overall worse prognosis. TDA validated these findings and additionally identified a subgroup within Aspergillus-sensitised COPD of patients with frequent exacerbations. Conclusion Aspergillus sensitisation is a treatable trait in COPD. Measuring sensitisation responses to recombinant Aspergillus allergens identifies an important patient subgroup with poor COPD outcomes that remains overlooked by assessment of only crude Aspergillus allergens.Ministry of Education (MOE)Ministry of Health (MOH)National Medical Research Council (NMRC)Published versionThis research is supported by the Singapore General Hospital Research Grant (SRG-OPN-06-2021) (P.Y. Tiew) and the Singapore Ministry of Health's National Medical Research Council under its Clinician-Scientist Individual Research Grant (MOH-000141) (S.H. Chotirmall) and Clinician-Scientist Award (MOH-000710) (S.H. Chotirmall). K. Tsaneva-Atanasova gratefully acknowledges the financial support of the EPSRC via grant EP/T017856/1. F.T. Chew (Singapore) received grants from the National University of Singapore (N-154-000-038-001), Singapore Ministry of Education Academic Research Fund (R-154-000-191-112, R-154-000-404-112, R-154-000-553-112, R-154-000-565-112, R-154-000-630-112, R-154-000-A08-592, R-154-000-A27-597, R-154-000-A91-592, R-154-000-A95-592, R154-000-B99-114), Biomedical Research Council (Singapore) (BMRC/01/1/21/18/077, BMRC/04/1/21/19/315, BMRC/APG2013/108), Singapore Immunology Network (SIgN-06-006, SIgN-08-020), National Medical Research Council (Singapore) (NMRC/1150/2008), and the Agency for Science Technology and Research (Singapore) (H17/01/a0/008 and APG2013/108). Funding information for this article has been deposited with the Crossref Funder Registry