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Fulltext fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses

Chong JS, Chan YL, Ebenezer EGM, Chen HY, Kiguchi M, Lu CK, et al.

Sci Rep, 2020 12 16;10(1):22041.
PMID: 33328535 DOI: 10.1038/s41598-020-79053-z

This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students' cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.
Eating Disorder Among Malaysian University Students and Its Associated Factors

Chan YL, Samy AL, Tong WT, Islam MA, Low WY

Asia Pac J Public Health, 2020 08 13;32(6-7):334-339.
PMID: 32787612 DOI: 10.1177/1010539520947879

Eating disorder is highly prevalent among university students worldwide. However, in Malaysia, studies on eating disorder is scanty and were mostly conducted among medical students. A stratified cluster sampling was used to recruit participants in a university based in Kuala Lumpur. This cross-sectional study was conducted among 1017/1132 students (response rate: 89.8%). The questionnaires administered was a combination of the Eating Attitude Test-26 and items related to perceived body weight, body mass index, trying to weight loss, tobacco use, posttraumatic stress disorder, and depression. Descriptive analyses were performed to provide background information of at-risk students by gender. Multiple logistic regressions were used to identify associated factors of eating disorder. The results showed that 13.9% of the university students were at risk of eating disorder. Students who were trying to lose weight and had posttraumatic stress disorder predicted eating disorder. Hence, eating disorder among university students merits attention and requires implementations of public health policies.
Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

Ho CS, Chan YL, Tan TW, Tay GW, Tang TB

J Psychiatr Res, 2022 Jan 12;147:194-202.
PMID: 35063738 DOI: 10.1016/j.jpsychires.2022.01.026

BACKGROUND: Given that major depressive disorder (MDD) is both biologically and clinically heterogeneous, a diagnostic system integrating neurobiological markers and clinical characteristics would allow for better diagnostic accuracy and, consequently, treatment efficacy.
OBJECTIVE: Our study aimed to evaluate the discriminative and predictive ability of unimodal, bimodal, and multimodal approaches in a total of seven machine learning (ML) models-clinical, demographic, functional near-infrared spectroscopy (fNIRS), combinations of two unimodal models, as well as a combination of all three-for MDD.
METHODS: We recruited 65 adults with MDD and 68 matched healthy controls, who provided both sociodemographic and clinical information, and completed the HAM-D questionnaire. They were also subject to fNIRS measurement when participating in the verbal fluency task. Using the nested cross validation procedure, the classification performance of each ML model was evaluated based on the area under the receiver operating characteristic curve (ROC), balanced accuracy, sensitivity, and specificity.
RESULTS: The multimodal ML model was able to distinguish between depressed patients and healthy controls with the highest balanced accuracy of 87.98 ± 8.84% (AUC = 0.92; 95% CI (0.84-0.99) when compared with the uni- and bi-modal models.
CONCLUSIONS: Our multimodal ML model demonstrated the highest diagnostic accuracy for MDD. This reinforces the biological and clinical heterogeneity of MDD and highlights the potential of this model to improve MDD diagnosis rates. Furthermore, this model is cost-effective and clinically applicable enough to be established as a robust diagnostic system for MDD based on patients' biosignatures.
Fulltext Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder

Ho CSH, Tan TWK, Khoe HCH, Chan YL, Tay GWN, Tang TB

J Clin Med, 2024 Feb 21;13(5).
PMID: 38592058 DOI: 10.3390/jcm13051222

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models. Methods: In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. Serum amino acid profiling was conducted by means of chromatography-mass spectrometry. A total of 21 metabolites were analysed, with 17 from a preset amino acid panel and the remaining 4 from a preset kynurenine panel. Logistic regression was applied to differentiate MDD patients from HCs. Results: The best-performing model utilised both feature selection and hyperparameter optimisation and yielded a moderate area under the receiver operating curve (AUC) classification value of 0.76 on the testing data. The top five metabolites identified as potential biomarkers for MDD were 3-hydroxy-kynurenine, valine, kynurenine, glutamic acid, and xanthurenic acid. Conclusions: Our study highlights the potential of using an interpretable machine learning analysis model based on amino acids to aid and increase the diagnostic accuracy of MDD in clinical practice.
Exploring naphthoquinone and anthraquinone derivatives as antibiotic adjuvants against Staphylococcus aureus biofilms: Synergistic effects of menadione

Chan YL, Tang SN, Osman CP, Chee CF, Tay ST

Microb Pathog, 2024 Aug 31;195:106886.
PMID: 39182855 DOI: 10.1016/j.micpath.2024.106886

Given the ability of Staphylococcus aureus to form biofilms and produce persister cells, making infections difficult to treat with antibiotics alone, there is a pressing need for an effective antibiotic adjuvant to address this public health threat. In this study, a series of quinone derivatives were evaluated for their antimicrobial and antibiofilm activities against methicillin-susceptible and methicillin-resistant S. aureus reference strains. Following analyses using broth microdilution, growth curve analysis, checkerboard assay, time-kill experiments, and confocal laser scanning microscopy, menadione was identified as a hit compound. Menadione exhibited a notable antibacterial profile (minimum inhibitory concentration, MIC = 4-16 μg/ml; minimum bactericidal concentration, MBC = 256 μg/ml) against planktonic S. aureus and its biofilms (minimum biofilm inhibitory concentration, MBIC50 = 0.0625-0.25 μg/ml). When combined with oxacillin, erythromycin, and vancomycin, menadione exhibited a synergistic or additive effect against planktonic cells and biofilms of two S. aureus reference strains and six clinical isolates, highlighting its potential as a suitable adjuvant for further development against S. aureus biofilm-associated infections.
Fulltext Assessing seroprevalence and associated risk factors for multiple infectious diseases in Sabah, Malaysia using serological multiplex bead assays

Chan YL, Patterson CL, Priest JW, Stresman G, William T, Chua TH, et al.

Front Public Health, 2022;10:924316.
PMID: 36388287 DOI: 10.3389/fpubh.2022.924316

BACKGROUND: Infectious diseases continue to burden populations in Malaysia, especially among rural communities where resources are limited and access to health care is difficult. Current epidemiological trends of several neglected tropical diseases in these populations are at present absent due to the lack of habitual and efficient surveillance. To date, various studies have explored the utility of serological multiplex beads to monitor numerous diseases simultaneously. We therefore applied this platform to assess population level exposure to six infectious diseases in Sabah, Malaysia. Furthermore, we concurrently investigated demographic and spatial risk factors that may be associated with exposure for each disease.
METHODS: This study was conducted in four districts of Northern Sabah in Malaysian Borneo, using an environmentally stratified, population-based cross-sectional serological survey targeted to determine risk factors for malaria. Samples were collected between September to December 2015, from 919 villages totaling 10,100 persons. IgG responses to twelve antigens of six diseases (lymphatic filariasis- Bm33, Bm14, BmR1, Wb123; strongyloides- NIE; toxoplasmosis-SAG2A; yaws- Rp17 and TmpA; trachoma- Pgp3, Ct694; and giardiasis- VSP3, VSP5) were measured using serological multiplex bead assays. Eight demographic risk factors and twelve environmental covariates were included in this study to better understand transmission in this community.
RESULTS: Seroprevalence of LF antigens included Bm33 (10.9%), Bm14+ BmR1 (3.5%), and Wb123 (1.7%). Seroprevalence of Strongyloides antigen NIE was 16.8%, for Toxoplasma antigen SAG2A was 29.9%, and Giardia antigens GVSP3 + GVSP5 was 23.2%. Seroprevalence estimates for yaws Rp17 was 4.91%, for TmpA was 4.81%, and for combined seropositivity to both antigens was 1.2%. Seroprevalence estimates for trachoma Pgp3 + Ct694 were 4.5%. Age was a significant risk factors consistent among all antigens assessed, while other risk factors varied among the different antigens. Spatial heterogeneity of seroprevalence was observed more prominently in lymphatic filariasis and toxoplasmosis.
CONCLUSIONS: Multiplex bead assays can be used to assess serological responses to numerous pathogens simultaneously to support infectious disease surveillance in rural communities, especially where prevalences estimates are lacking for neglected tropical diseases. Demographic and spatial data collected alongside serosurveys can prove useful in identifying risk factors associated with exposure and geographic distribution of transmission.
Fulltext Denoising of neuronal signal from mixed systemic low-frequency oscillation using peripheral measurement as noise regressor in near-infrared imaging

Sutoko S, Chan YL, Obata A, Sato H, Maki A, Numata T, et al.

Neurophotonics, 2019 Jan;6(1):015001.
PMID: 30662924 DOI: 10.1117/1.NPh.6.1.015001

Functional near-infrared spectroscopy (fNIRS) is a noninvasive functional imaging technique measuring hemodynamic changes including oxygenated (

O

2

Hb

) and deoxygenated (HHb) hemoglobin. Low frequency (LF; 0.01 to 0.15 Hz) band is commonly analyzed in fNIRS to represent neuronal activation. However, systemic physiological artifacts (i.e., nonneuronal) likely occur also in overlapping frequency bands. We measured peripheral photoplethysmogram (PPG) signal concurrently with fNIRS (at prefrontal region) to extract the low-frequency oscillations (LFOs) as systemic noise regressors. We investigated three main points in this study: (1) the relationship between prefrontal fNIRS and peripheral PPG signals; (2) the denoising potential using these peripheral LFOs, and (3) the innovative ways to avoid the false-positive result in fNIRS studies. We employed spatial working memory (WM) and control tasks (e.g., resting state) to illustrate these points. Our results showed: (1) correlation between signals from prefrontal fNIRS and peripheral PPG is region-dependent. The high correlation with peripheral ear signal (i.e.,

O

2

Hb

) occurred mainly in frontopolar regions in both spatial WM and control tasks. This may indicate the finding of task-dependent effect even in peripheral signals. We also found that the PPG recording at the ear has a high correlation with prefrontal fNIRS signal than the finger signals. (2) The systemic noise was reduced by 25% to 34% on average across regions, with a maximum of 39% to 58% in the highly correlated frontopolar region, by using these peripheral LFOs as noise regressors. (3) By performing the control tasks, we confirmed that the statistically significant activation was observed in the spatial WM task, not in the controls. This suggested that systemic (and any other) noises unlikely violated the major statistical inference. (4) Lastly, by denoising using the task-related signals, the significant activation of region-of-interest was still observed suggesting the manifest task-evoked response in the spatial WM task.
Fulltext Molecular modulators of celastrol as the keystones for its diverse pharmacological activities

Ng SW, Chan Y, Chellappan DK, Madheswaran T, Zeeshan F, Chan YL, et al.

Biomed Pharmacother, 2019 Jan;109:1785-1792.
PMID: 30551432 DOI: 10.1016/j.biopha.2018.11.051

In the recent years, much attention has been focused on identifying bioactive compounds from medicinal plants that could be employed in therapeutics, which is attributed to their potent pharmacological actions and better toxicological profile. One such example that has come into the light with considerable interest is the pentacyclic triterpenoid, celastrol, which has been found to provide substantial therapeutic properties in a variety of diseases. In an effort to further accelerate its potential to be utilized in clinical practice in the future; along with advancing technologies in the field of drug discovery and development, different researchers have been investigating on the various mechanisms and immunological targets of celastrol that underlie its broad spectrum of pharmacological properties. In this review, we have collated the various research findings related to the molecular modulators responsible for different pharmacological activities shown by celastrol. Our review will be of interest to the herbal, biological, molecular scientist and by providing a quick snapshot about celastrol giving a new direction in the area of herbal drug discovery and development.
Fulltext Comparison of the Mortality in Emergency Department Sepsis Score, Modified Early Warning Score, Rapid Emergency Medicine Score and Rapid Acute Physiology Score for predicting the outcomes of adult splenic abscess patients in the emergency department

Hung SK, Ng CJ, Kuo CF, Goh ZNL, Huang LH, Li CH, et al.

PLoS One, 2017;12(11):e0187495.
PMID: 29091954 DOI: 10.1371/journal.pone.0187495

BACKGROUND: Splenic abscess is rare but has mortality rates as high as 14% even with recent improvements in management. Early and appropriate intervention may improve patient outcomes, yet at present there is no identified method that can predict mortality risk rapidly and accurately for emergency physicians, surgeons, and intensivists to decide on the ideal course of action.
OBJECTIVE: This study aims to evaluate the performance of Mortality in Emergency Department Sepsis Score (MEDS), Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS) and Rapid Acute Physiology Score (RAPS) for predicting the mortality risk of adult splenic abscess patients. This will expedite decision making in the emergency department (ED) to increase survival rates and help avoid unnecessary splenectomies.
METHODS: Data of 114 adult patients admitted to the EDs of 4 research and training hospitals who had undergone an abdominal contrast CT scan and diagnosed with splenic abscess between Jan 2000 and April 2015 were analyzed. The MEDS, MEWS, REMS, and RAPS and their corresponding mortality risks were calculated, with their abilities to predict patient mortality assessed through receiver operating characteristic curve analysis and calibration analysis.
RESULTS: MEDS was found to be the best performing scoring system across all indicators, with sensitivity, specificity, and accuracy of 92.86%, 88.00%, and 88.60% respectively; its area under curve for AUROC analysis was 0.92. With a cutoff value of 8, negative predictive value of MEDS was 98.88%.
CONCLUSION: Our series is the largest multicenter study in adult ED patients with splenic abscess. The results from the present study show that MEDS is superior to MEWS, REMS and RAPS in predicting mortality, thus allowing earlier detection of critically ill adult ED splenic abscess patients. Therefore, we recommend that MEDS be used for predicting severity of illness and risk stratification in these patients.
Molecular and Immunological Mechanisms Underlying the Various Pharmacological Properties of the Potent Bioflavonoid, Rutin

Yong DOC, Saker SR, Chellappan DK, Madheswaran T, Panneerselvam J, Choudhury H, et al.

Endocr Metab Immune Disord Drug Targets, 2020;20(10):1590-1596.
PMID: 32359343 DOI: 10.2174/1871530320666200503053846

The application of medicinal plants has captured the interest of researchers in recent times due to their potent therapeutic properties and a better safety profile. The prominent role of herbal products in treating and preventing multiple diseases dates back to ancient history and most of the modern drugs today originated from their significant sources owing to their ability to control multiple targets via different signalling pathways. Among them, flavonoids consist of a large group of polyphenols, which are well known for their various therapeutic benefits. Rutin is considered one of the attractive phytochemicals and important flavonoids in the pharmaceutical industry due to its diverse pharmacological activities via various underlying molecular mechanisms. It is usually prescribed for various disease conditions such as varicosities, haemorrhoids and internal haemorrhage. In this review, we have discussed and highlighted the different molecular mechanisms attributed to the various pharmacological activities of rutin, such as antioxidant, anti-inflammatory, anticancer, anti-allergic and antidiabetic. This review will be beneficial to herbal, biological and molecular scientists in understanding the pharmacological relevance of rutin at the molecular level.
Fulltext Pre-extinction Demographic Stability and Genomic Signatures of Adaptation in the Woolly Rhinoceros

Lord E, Dussex N, Kierczak M, Díez-Del-Molino D, Ryder OA, Stanton DWG, et al.

Curr Biol, 2020 10 05;30(19):3871-3879.e7.
PMID: 32795436 DOI: 10.1016/j.cub.2020.07.046

Ancient DNA has significantly improved our understanding of the evolution and population history of extinct megafauna. However, few studies have used complete ancient genomes to examine species responses to climate change prior to extinction. The woolly rhinoceros (Coelodonta antiquitatis) was a cold-adapted megaherbivore widely distributed across northern Eurasia during the Late Pleistocene and became extinct approximately 14 thousand years before present (ka BP). While humans and climate change have been proposed as potential causes of extinction [1-3], knowledge is limited on how the woolly rhinoceros was impacted by human arrival and climatic fluctuations [2]. Here, we use one complete nuclear genome and 14 mitogenomes to investigate the demographic history of woolly rhinoceros leading up to its extinction. Unlike other northern megafauna, the effective population size of woolly rhinoceros likely increased at 29.7 ka BP and subsequently remained stable until close to the species' extinction. Analysis of the nuclear genome from a ∼18.5-ka-old specimen did not indicate any increased inbreeding or reduced genetic diversity, suggesting that the population size remained steady for more than 13 ka following the arrival of humans [4]. The population contraction leading to extinction of the woolly rhinoceros may have thus been sudden and mostly driven by rapid warming in the Bølling-Allerød interstadial. Furthermore, we identify woolly rhinoceros-specific adaptations to arctic climate, similar to those of the woolly mammoth. This study highlights how species respond differently to climatic fluctuations and further illustrates the potential of palaeogenomics to study the evolutionary history of extinct species.
Fulltext Genomic insights into the conservation status of the world's last remaining Sumatran rhinoceros populations

von Seth J, Dussex N, Díez-Del-Molino D, van der Valk T, Kutschera VE, Kierczak M, et al.

Nat Commun, 2021 Apr 26;12(1):2393.
PMID: 33896938 DOI: 10.1038/s41467-021-22386-8

Small populations are often exposed to high inbreeding and mutational load that can increase the risk of extinction. The Sumatran rhinoceros was widespread in Southeast Asia, but is now restricted to small and isolated populations on Sumatra and Borneo, and most likely extinct on the Malay Peninsula. Here, we analyse 5 historical and 16 modern genomes from these populations to investigate the genomic consequences of the recent decline, such as increased inbreeding and mutational load. We find that the Malay Peninsula population experienced increased inbreeding shortly before extirpation, which possibly was accompanied by purging. The populations on Sumatra and Borneo instead show low inbreeding, but high mutational load. The currently small population sizes may thus in the near future lead to inbreeding depression. Moreover, we find little evidence for differences in local adaptation among populations, suggesting that future inbreeding depression could potentially be mitigated by assisted gene flow among populations.
Fulltext Early Pleistocene enamel proteome from Dmanisi resolves Stephanorhinus phylogeny

Cappellini E, Welker F, Pandolfi L, Ramos-Madrigal J, Samodova D, Rüther PL, et al.

Nature, 2019 10;574(7776):103-107.
PMID: 31511700 DOI: 10.1038/s41586-019-1555-y

The sequencing of ancient DNA has enabled the reconstruction of speciation, migration and admixture events for extinct taxa1. However, the irreversible post-mortem degradation2 of ancient DNA has so far limited its recovery-outside permafrost areas-to specimens that are not older than approximately 0.5 million years (Myr)3. By contrast, tandem mass spectrometry has enabled the sequencing of approximately 1.5-Myr-old collagen type I4, and suggested the presence of protein residues in fossils of the Cretaceous period5-although with limited phylogenetic use6. In the absence of molecular evidence, the speciation of several extinct species of the Early and Middle Pleistocene epoch remains contentious. Here we address the phylogenetic relationships of the Eurasian Rhinocerotidae of the Pleistocene epoch7-9, using the proteome of dental enamel from a Stephanorhinus tooth that is approximately 1.77-Myr old, recovered from the archaeological site of Dmanisi (South Caucasus, Georgia)10. Molecular phylogenetic analyses place this Stephanorhinus as a sister group to the clade formed by the woolly rhinoceros (Coelodonta antiquitatis) and Merck's rhinoceros (Stephanorhinus kirchbergensis). We show that Coelodonta evolved from an early Stephanorhinus lineage, and that this latter genus includes at least two distinct evolutionary lines. The genus Stephanorhinus is therefore currently paraphyletic, and its systematic revision is needed. We demonstrate that sequencing the proteome of Early Pleistocene dental enamel overcomes the limitations of phylogenetic inference based on ancient collagen or DNA. Our approach also provides additional information about the sex and taxonomic assignment of other specimens from Dmanisi. Our findings reveal that proteomic investigation of ancient dental enamel-which is the hardest tissue in vertebrates11, and is highly abundant in the fossil record-can push the reconstruction of molecular evolution further back into the Early Pleistocene epoch, beyond the currently known limits of ancient DNA preservation.

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