Oral-diadochokinesis between Parkinson's disease and neurotypical elderly among Malaysian-Malay speakers.

BACKGROUND: Parkinson's disease (PD) has an impact on speech production, manifesting in various ways including alterations in voice quality, challenges in articulating sounds and a decrease in speech rate. Numerous investigations have been conducted to ascertain the oral-diadochokinesis (O-DDK) rate in individuals with PD. However, the existing literature lacks exploration of such O-DDK rates in Malaysia and does not provide consistent evidence regarding the advantage of real-word repetition. AIMS: To explore the effect of gender, stimuli type and PD status and their interactions on the O-DDK rates among Malaysian-Malay speakers. METHODS & PROCEDURES: O-DDK performance of 62 participants (29 individuals with PD and 33 healthy elderly) using a non-word ('pataka'), a Malay real-word ('patahkan') and an English real-word ('buttercake') was audio recorded. The number of syllables produced in 8 s was counted. A hierarchical linear modelling was performed to investigate the effects of stimuli type (non-word, Malay real-word, English real-word), PD status (yes, no), gender (male, female) and their interactions on the O-DDK rate. The model accounted for participants' age as well as the nesting of repeated measurements within participants, thereby providing unbiased estimates of the effects. OUTCOMES & RESULTS: The stimuli effect was significant (p < 0.0001). Malay real-word showed the lowest O-DDK rate (5.03 ± 0.11 syllables/s), followed by English real-word (5.25 ± 0.11 syllables/s) and non-word (5.42 ± 0.11 syllables/s). Individuals with PD showed a significantly lower O-DDK rate compared to healthy elderly (4.73 ± 0.15 syllables/s vs. 5.74 ± 0.14 syllables/s, adjusted p < 0.001). A subsequent analysis indicated that the O-DDK rate declined in a quadratic pattern. However, neither gender nor age effects were observed. Additionally, no significant two-way interactions were found between stimuli type, PD status and gender (all p > 0.05). Therefore, the choice of stimuli type has no or only limited effect considering the use of O-DDK tests in clinical practice for diagnostic purposes. CONCLUSIONS & IMPLICATIONS: The observed slowness in O-DDK among individuals with PD can be attributed to the impact of the movement disorder, specifically bradykinesia, on the physiological aspects of speech production. Speech-language pathologists can gain insights into the impact of PD on speech production and tailor appropriate intervention strategies to address the specific needs of individuals with PD according to disease stages. WHAT THIS PAPER ADDS: What is already known on this subject The observed slowness in O-DDK rates among individuals with PD may stem from the movement disorder's effects on the physiological aspects of speech production, particularly bradykinesia. However, there is a lack of consistent evidence regarding the influence of real-word repetition and how O-DDK rates vary across different PD stages. What this study adds to existing knowledge The O-DDK rates decline in a quadratic pattern as the PD progresses. The research provides insights into the advantage of real-word repetition in assessing O-DDK rates, with Malay real-word showing the lowest O-DDK rate, followed by English real-word and non-word. What are the potential or actual clinical implications of this work? Speech-language pathologists can better understand the evolving nature of speech motor impairments as PD progresses. This insight enables them to design targeted intervention strategies that are sensitive to the specific needs and challenges associated with each PD stage. This finding can guide clinicians in selecting appropriate assessment tools for evaluating speech motor function in PD patients.

Electrochemical genosensor based on RNA-responsive human telomeric G-quadruplex DNA: A proof-of-concept with SARS-CoV-2 RNA.

In this report, a facile and label-free electrochemical RNA biosensor is developed by exploiting methylene blue (MB) as an electroactive positive ligand of G-quadruplex. The electrochemical response mechanism of the nucleic acid assay was based on the change in differential pulse voltammetry (DPV) signal of adsorbed MB on the immobilized human telomeric G-quadruplex DNA with a loop that is complementary to the target RNA. Hybridization between synthetic positive control RNA and G-quadruplex DNA probe on the transducer platform rendered a conformational change of G-quadruplex to double-stranded DNA (dsDNA), and increased the redox current of cationic MB π planar ligand at the sensing interface, thereby the electrochemical signal of the MB-adsorbed duplex is proportional to the concentration of target RNA, with SARS-CoV-2 (COVID-19) RNA as the model. Under optimal conditions, the target RNA can be detected in a linear range from 1 zM to 1 µM with a limit of detection (LOD) obtained at 0.59 zM for synthetic target RNA and as low as 1.4 copy number for positive control plasmid. This genosensor exhibited high selectivity towards SARS-CoV-2 RNA over other RNA nucleotides, such as SARS-CoV and MERS-CoV. The electrochemical RNA biosensor showed DPV signal, which was proportional to the 2019-nCoV_N_positive control plasmid from 2 to 200000 copies (R2 = 0.978). A good correlation between the genosensor and qRT-PCR gold standard was attained for the detection of SARS-CoV-2 RNA in terms of viral copy number in clinical samples from upper respiratory specimens.

The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring-A Review.

Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model.

Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.

Speech Performance among Healthy Malay Female Speakers during Dual Tasks and Sentence Complexity.

OBJECTIVES: This study aimed to identify how different dual tasks influence speech performance among young Malay speakers by manipulating the complexity of sentences. Fifteen female speakers (20-30 years old) were audio recorded while executing a series of simple and complex sentence repetition tasks. METHODS: Hierarchical linear modeling was conducted to examine the effects of task (speech task only, speech + cognitive tasks, speech + linguistic tasks, and speech + manual tasks) and sentence complexity (simple vs. complex), as well as their interaction on 3 outcome measures (word accuracy rate [WAR], response reaction time [RRT], and duration of sentence [DoS]). RESULTS: Significantly higher WAR were found when participants read the sentences only compared to when they completed another task simultaneously. The pairwise comparison further revealed that the conditions of speech + manual tasks produced the lowest WAR. For RRT, participants responded significantly faster when they only read the sentences (mean = 1.36, SE = 0.17) than when they read the sentences and conducted another task at the same time (mean = 1.66, SE = 0.14; p < 0.0001). A pairwise comparison showed that the conditions of speech + manual tasks produced the longest DoS. Neither the effect of sentence complexity nor the interaction between task and sentence complexity was statistically significant across WAR, RRT, and DoS. CONCLUSIONS: The results augment what is known about interference between speech and other concurrent tasks and provides evidence that divided attention affects manual task performance more than other tasks. In addition, sentence complexity did not affect speech performance in healthy adults. This paradigm could be used in future studies to serve as a clinical marker that can potentially distinguish mild cognitive impairment from normal aging.

Comparison of sPLA2IIA performance with high-sensitive CRP neutrophil percentage PCT and lactate to identify bacterial infection.

Early bacterial infection (BI) identification in resource-limiting Emergency Departments (ED) is challenging, especially in low- and middle-income counties (LMIC). Misdiagnosis predisposes to antibiotic overuse and propagates antimicrobial resistance. This study evaluates new emerging biomarkers, secretory phospholipase A2 group IIA (sPLA2-IIA) and compares with other biomarkers on their performance characteristic of BI detection in Malaysia, an LMIC. A prospective cohort study was conducted involving 151 consecutive patients admitted to the ED. A single measurement was taken upon patient arrival in ED and was analysed for serum levels of sPLA2-IIA, high-sensitive C-reactive protein (CRP), procalcitonin (PCT), neutrophil percentage (N%), and lactate. All biomarkers' performance was compared for the outcomes using area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The performance of sPLA2-IIA (AUROC 0.93 [95% CI: 0.89-0.97]; Sn 80% [95% CI: 72-87]; Sp 94% [95% CI: 81-89]) was the highest among all. It was comparable with high-sensitive CRP (AUROC 0.93 [95% CI: 0.88-0.97]; Sn 75% [95% CI: 66-83]; Sp 91 [95% CI: 77-98]) but had a higher Sn and Sp. The sPLA2-IIA was also found superior to N%, PCT, and lactate. This finding suggested sPLA2-IIA was recommended biomarkers for BI detection in LMIC.

Utilization of second derivative photoplethysmographic features for myocardial infarction classification.

Myocardial infarction (MI) is a common disease that causes morbidity and mortality. The current tools for diagnosing this disease are improving, but still have some limitations. This study utilised the second derivative of photoplethysmography (SDPPG) features to distinguish MI patients from healthy control subjects. The features include amplitude-derived SDPPG features (pulse height, ratio, jerk) and interval-derived SDPPG features (intervals and relative crest time (RCT)). We evaluated 32 MI patients at Pusat Perubatan Universiti Kebangsaan Malaysia and 32 control subjects (all ages 37-87 years). Statistical analysis revealed that the mean amplitude-derived SDPPG features were higher in MI patients than in control subjects. In contrast, the mean interval-derived SDPPG features were lower in MI patients than in the controls. The classifier model of binary logistic regression (Model 7), showed that the combination of SDPPG features that include the pulse height (d-wave), the intervals of "ab", "ad", "bc", "bd", and "be", and the RCT of "ad/aa" could be used to classify MI patients with 90.6% accuracy, 93.9% sensitivity and 87.5% specificity at a cut-off value of 0.5 compared with the single features model.

Analysis of myocardial infarction signals using optical technique.

The risk of heart attack or myocardial infarction (MI) may lead to serious consequences in mortality and morbidity. Current MI management in the triage includes non-invasive heart monitoring using an electrocardiogram (ECG) and the cardic biomarker test. This study is designed to explore the potential of photoplethysmography (PPG) as a simple non-invasive device as an alternative method to screen the MI subjects. This study emphasises the usage of second derivative photoplethysmography (SDPPG) intervals as the extracted features to classify the MI subjects. The statistical analysis shows the potential of "a-c" interval and the corrected "a-cC" interval to classify the subject. The sensitivity of the predicted model using "a-c" and "a-cC" is 90.6% and 81.2% and the specificity is 87.5% and 84.4%, respectively.

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.

Development of a computer-based automated pure tone hearing screening device: a preliminary clinical trial.

Hearing screening is important for the early detection of hearing loss. The requirements of specialized equipment, skilled personnel, and quiet environments for valid screening results limit its application in schools and health clinics. This study aimed to develop an automated hearing screening kit (auto-kit) with the capability of realtime noise level monitoring to ensure that the screening is performed in an environment that conforms to the standard. The auto-kit consists of a laptop, a 24-bit resolution sound card, headphones, a microphone, and a graphical user interface, which is calibrated according to the American National Standards Institute S3.6-2004 standard. The auto-kit can present four test tones (500, 1000, 2000, and 4000 Hz) at 25 or 40 dB HL screening cut-off level. The clinical results at 40 dB HL screening cut-off level showed that the auto-kit has a sensitivity of 92.5% and a specificity of 75.0%. Because the 500 Hz test tone is not included in the standard hearing screening procedure, it can be excluded from the auto-kit test procedure. The exclusion of 500 Hz test tone improved the specificity of the auto-kit from 75.0% to 92.3%, which suggests that the auto-kit could be a valid hearing screening device. In conclusion, the auto-kit may be a valuable hearing screening tool, especially in countries where resources are limited.

Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results.

In obstetrics, fetal heart rate (FHR) detection remains the standard for intrapartum assessment of fetal well-being. In this paper, a low-power (< 55 mW) optical technique is proposed for transabdominal FHR detection using near-infrared photoplesthysmography (PPG). A beam of IR-LED (890 nm) propagates through to the maternal abdomen and fetal tissues, resulting in a mixed signal detected by a low-noise detector situated at a distance of 4 cm. Low-noise amplification and 24-bit analog-to-digital converter resolution ensure minimum effect of quantization noise. After synchronous detection, the mixed signal is processed by an adaptive filter to extract the fetal signal, whereas the PPG from the mother's index finger is the reference input. A total of 24 datasets were acquired from six subjects at 37 +/- 2 gestational weeks. Results show a correlation coefficient of 0.96 (p-value < 0.001) between the proposed optical and ultrasound FHR, with a maximum error of 4%. Assessment of the effect of probe position on detection accuracy indicates that the probe should be close to fetal tissues, but not necessarily restricted to head or buttocks.