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  1. Goh SL, Jaafar Z, Gan YN, Choong A, Kaur J, Kundakci B, et al.
    PLoS One, 2021;16(5):e0252204.
    PMID: 34038486 DOI: 10.1371/journal.pone.0252204
    INTRODUCTION: Prolotherapy and other injections, primarily acting on pathways associated with maladaptive tissue repair, are recommended for recalcitrant chronic soft tissue injuries (CSTI). However, selection of injection is challenging due to mixed results. This network meta-analysis (NMA) aimed to compare prolotherapy with other therapies, particularly injections, for CSTI and establish robustness of the results.

    METHODOLOGY: Pubmed, Medline, SPORTDiscus and Google scholar were searched from inception to 4th January 2021 for randomised controlled trials (RCTs) involving injection therapies (e.g. blood derivatives, corticosteroid, hyaluronic acid, botulinum toxin) for CSTI. The primary and secondary outcomes were pain and function, respectively, at (or nearest to) 6 months. Effect size (ES) was presented as standardised mean difference with 95% confidence interval (CI). Frequentist random effect NMA was used to generate the overall estimates, subgroup estimates (by region and measurement time point) and sensitivity analyses.

    RESULTS: A total of 91 articles (87 RCTs; 5859 participants) involving upper limb (74%), lower limb (23%) and truncal/hip (3%) injuries were included. At all time points, prolotherapy had no statistically significant pain benefits over other therapies. This observation remained unchanged when tested under various assumptions and with exclusion of studies with high risk of bias. Although prolotherapy did not offer statistically significant functional improvement compared to most therapies, its ES was consistently better than non-injections and corticosteroid injection for both outcomes. At selected time points and for selected injuries, prolotherapy demonstrated potentially better pain improvement over placebo (<4 months: shoulder [ES 0.65; 95% CI 0.00 to 1.30]; 4-8 months: elbow [ES 0.91; 95% CI 0.12 to 1.70]; >8 months: shoulder [ES 2.08; 95% CI 1.49, to 2.68]). Injections generally produced greater ES when combined with non-injection therapy.

    CONCLUSION: While clinical outcomes were generally comparable across types of injection therapy, prolotherapy may be used preferentially for selected conditions at selected times.

  2. Kundakci B, Kaur J, Goh SL, Hall M, Doherty M, Zhang W, et al.
    Pain, 2022 Aug 01;163(8):1432-1445.
    PMID: 34813518 DOI: 10.1097/j.pain.0000000000002500
    Fibromyalgia is a highly heterogeneous condition, but the most common symptoms are widespread pain, fatigue, poor sleep, and low mood. Nonpharmacological interventions are recommended as first-line treatment of fibromyalgia. However which interventions are effective for the different symptoms is not well understood. The objective of this study was to assess the efficacy of nonpharmacological interventions on symptoms and disease-specific quality of life. Seven databases were searched from their inception until June 1, 2020. Randomised controlled trials comparing any nonpharmacological intervention to usual care, waiting list, or placebo in people with fibromyalgia aged >16 years were included without language restriction. Fibromyalgia Impact Questionnaire (FIQ) was the primary outcome measure. Standardised mean difference and 95% confidence interval were calculated using random effects model. The risk of bias was evaluated using the modified Cochrane tool. Of the 16,251 studies identified, 167 randomised controlled trials (n = 11,012) assessing 22 nonpharmacological interventions were included. Exercise, psychological treatments, multidisciplinary modality, balneotherapy, and massage improved FIQ. Subgroup analysis of different exercise interventions found that all forms of exercise improved pain (effect size [ES] -0.72 to -0.96) and depression (ES -0.35 to -1.22) except for flexibility exercise. Mind-body and strengthening exercises improved fatigue (ES -0.77 to -1.00), whereas aerobic and strengthening exercises improved sleep (ES -0.74 to -1.33). Psychological treatments including cognitive behavioural therapy and mindfulness improved FIQ, pain, sleep, and depression (ES -0.35 to -0.55) but not fatigue. The findings of this study suggest that nonpharmacological interventions for fibromyalgia should be individualised according to the predominant symptom.
  3. Zaghlul N, Goh SL, Razman R, Danaee M, Chan CK
    PLoS One, 2023;18(1):e0280361.
    PMID: 36649257 DOI: 10.1371/journal.pone.0280361
    The validity and reliability of the Lafayette stability platform are well-established for double leg testing. However, no evaluation of single leg (SL) stance on the platform was discovered yet. Therefore, this study aimed to investigate the reliability of conducting the SL stance on the Lafayette platform. Thirty-six healthy and active university students (age 23.2 ± 3.2 years; BMI 21.1 ± 3.1 kg/m2) were tested twice, one week apart (week 1; W1, week 2; W2). They stood on their dominant leg with eyes-open (EO) and eyes-closed (EC) in random order. Three successful trials of 20 seconds each were recorded. The duration during which the platform was maintained within 0° of tilt was referred to as time in balance (TIB). At all-time points, TIB was consistently longer in EO (EOW1: 17.02 ± 1.04s; EOW2: 17.32 ± 1.03s) compared to EC (ECW1: 11.55 ± 1.73s; ECW2: 13.08 ± 1.82s). A ±10 seconds difference was demonstrated in the Bland-Altman analysis in both EO and EC. Lower standard error of measurement (SEM) and coefficient of variation (CV) indicated consistent output. High intraclass correlation coefficient (ICC) values were seen between weeks (EO = 0.74; EC = 0.76) and within weeks (EOW1 = 0.79; EOW2 = 0.86; ECW1 = 0.71; ECW2 = 0.71). Although statistical measures (i.e., SEM, CV, and ICC) indicated good reliability of Lafayette for SL tasks, the wide agreement interval is yet to be clinically meaningful. Factors underlying the wide variation need to be identified before Lafayette is used for TIB assessment.
  4. Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Hum YC, Tee YK, et al.
    Comput Intell Neurosci, 2021;2021:4931437.
    PMID: 34804143 DOI: 10.1155/2021/4931437
    Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.
  5. Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, et al.
    J Healthc Eng, 2022;2022:4138666.
    PMID: 35222885 DOI: 10.1155/2022/4138666
    Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.
  6. Teoh YX, Alwan JK, Shah DS, Teh YW, Goh SL
    Clin Biomech (Bristol, Avon), 2024 Mar;113:106188.
    PMID: 38350282 DOI: 10.1016/j.clinbiomech.2024.106188
    BACKGROUND: Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains.

    METHODS: Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used.

    FINDINGS: Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies.

    INTERPRETATIONS: The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research.

  7. Kwan MSK, Tan SL, Keowmani T, Goh SL, Voo JYH, Tan HS, et al.
    Asia Pac Allergy, 2023 Sep;13(3):121-126.
    PMID: 37744958 DOI: 10.5415/apallergy.0000000000000115
    BACKGROUND: Given the deficits in allergists and testing capacity, the diagnosis of drug allergy is largely dependent on the clinician's and pharmacist's judgment. The ability to recognize drug allergies and respond appropriately is crucial to patient safety. Currently, there is a void in the evidence that limits the ability to recommend comprehensive and swift improvements on this front.

    OBJECTIVE: This study thus aimed to evaluate the knowledge, attitude, and practice toward drug allergy among doctors and pharmacists working in public healthcare facilities in Sabah, Malaysia.

    METHODS: This cross-sectional study was conducted in 24 hospitals and 11 clinics in Sabah. A validated Drug Allergy Knowledge, Attitude, and Practice Questionnaire was adapted from a published study and developed on an online survey platform. The questionnaire was distributed to all listed eligible respondents via email and personal messenger service.

    RESULTS: A total of 549 doctors and pharmacists responded, with an overall response rate of 18.2%. The total mean knowledge, attitude, and practice scores were 8.3 (SD, 1.98), 18.9 (SD, 2.55), and 17.3 (SD, 4.4), respectively. It was found that pharmacists performed significantly poorer than both medical officers (mean score difference = -0.5; P = 0.006) and specialists (mean score difference = -0.9; P = 0.020) in the knowledge domain. As the time in service doubles, the knowledge score increases significantly by 0.3 (P = 0.015).

    CONCLUSION: Knowledge, attitude, and practice on drug allergy among doctors and pharmacists in Sabah were poor. It is thus timely for advanced educational programs on drug allergy to be formalized and implemented.

  8. Faisal A, Ng SC, Goh SL, Lai KW
    Med Biol Eng Comput, 2018 Apr;56(4):657-669.
    PMID: 28849317 DOI: 10.1007/s11517-017-1710-2
    Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.
  9. Tan LP, Tan GW, Sivanesan VM, Goh SL, Ng XJ, Lim CS, et al.
    Int J Cancer, 2020 04 15;146(8):2336-2347.
    PMID: 31469434 DOI: 10.1002/ijc.32656
    Nasopharyngeal carcinoma (NPC) is originated from the epithelial cells of nasopharynx, Epstein-Barr virus (EBV)-associated and has the highest incidence and mortality rates in Southeast Asia. Late presentation is a common issue and early detection could be the key to reduce the disease burden. Sensitivity of plasma EBV DNA, an established NPC biomarker, for Stage I NPC is controversial. Most newly reported NPC biomarkers have neither been externally validated nor compared to the established ones. This causes difficulty in planning for cost-effective early detection strategies. Our study systematically evaluated six established and four new biomarkers in NPC cases, population controls and hospital controls. We showed that BamHI-W 76 bp remains the most sensitive plasma biomarker, with 96.7% (29/30), 96.7% (58/60) and 97.4% (226/232) sensitivity to detect Stage I, early stage and all NPC, respectively. Its specificity was 94.2% (113/120) against population controls and 90.4% (113/125) against hospital controls. Diagnostic accuracy of BamHI-W 121 bp and ebv-miR-BART7-3p were validated. Hsa-miR-29a-3p and hsa-miR-103a-3p were not, possibly due to lower number of advanced stage NPC cases included in this subset. Decision tree modeling suggested that combination of BamHI-W 76 bp and VCA IgA or EA IgG may increase the specificity or sensitivity to detect NPC. EBNA1 99 bp could identify NPC patients with poor prognosis in early and advanced stage NPC. Our findings provided evidence for improvement in NPC screening strategies, covering considerations of opportunistic screening, combining biomarkers to increase sensitivity or specificity and testing biomarkers from single sampled specimen to avoid logistic problems of resampling.
  10. Goh SL, Mokhtar AH, Mohamad Ali MR
    J Sports Med Phys Fitness, 2013 Feb;53(1):65-70.
    PMID: 23470913
    The aim of the study was to examine sports injury pattern and establish cost of injuries in relation to training of 58 competitive badminton players in a Malaysian National Sports School.
  11. Lim CS, Goh SL, Kariapper L, Krishnan G, Lim YY, Ng CC
    Clin Chim Acta, 2015 Aug 25;448:206-10.
    PMID: 26164385 DOI: 10.1016/j.cca.2015.07.008
    Development of indirect enzyme-linked immunosorbent assays (ELISAs) often utilizes synthetic peptides or recombinant proteins from Escherichia coli as immobilized antigens. Because inclusion bodies (IBs) formed during recombinant protein expression in E. coli are commonly thought as misfolded aggregates, only refolded proteins from IBs are used to develop new or in-house diagnostic assays. However, the promising utilities of IBs as nanomaterials and immobilized enzymes as shown in recent studies have led us to explore the potential use of IBs of recombinant Epstein-Barr virus viral capsid antigen p18 (VCA p18) as immobilized antigens in ELISAs for serologic detection of nasopharyngeal carcinoma (NPC).
  12. Lim CS, Goh SL, Krishnan G, Ng CC
    Protein Expr Purif, 2014 Mar;95:8-12.
    PMID: 24291446 DOI: 10.1016/j.pep.2013.11.007
    This paper describes the recombinant production of a biologically active Epstein-Barr virus BZLF1 trans-activator, i.e., Z-encoded broadly reactive activator (ZEBRA), that recognized specific DNA motifs. We used auto-induction for histidine-tagged BZLF1 expression in Escherichia coli and immobilized cobalt affinity membrane chromatography for protein purification under native conditions. We obtained the purified BZLF1 at a yield of 5.4mg per gram of wet weight cells at 75% purity, in which 27% of the recombinant BZLF1 remained biologically active. The recombinant BZLF1 bound to oligonucleotides containing ZEBRA response elements, either AP-1 or ZIIIB, but not a ZIIIB mutant. The recombinant BZLF1 showed a specific DNA-binding activity which could be useful for functional studies.
  13. Persson MS, Fu Y, Bhattacharya A, Goh SL, van Middelkoop M, Bierma-Zeinstra SM, et al.
    Syst Rev, 2016 Sep 26;5(1):165.
    PMID: 27686859
    BACKGROUND: Pain is the most troubling issue to patients with osteoarthritis (OA), yet current pharmacological treatments offer only small-to-moderate pain reduction. Current guidelines therefore emphasise the need to identify predictors of treatment response. In line with these recommendations, an individual patient data (IPD) meta-analysis will be conducted. The study aims to investigate the relative treatment effects of topical non-steroidal anti-inflammatory drugs (NSAIDs) and topical capsaicin in OA and to identify patient-level predictors of treatment response.
    METHODS: IPD will be collected from randomised controlled trials (RCTs) of topical NSAIDs and capsaicin in OA. Multilevel regression modelling will be conducted to determine predictors for the specific and the overall treatment effect.
    DISCUSSION: Through the identification of treatment responders, this IPD meta-analysis may improve the current understanding of the pain mechanisms in OA and guide clinical decision-making. Identifying and prescribing the treatment most likely to be beneficial for an individual with OA will improve the efficiency of patient management.
    SYSTEMATIC REVIEW REGISTRATION:
    CRD42016035254.
    KEYWORDS: Capsaicin; Individual patient data meta-analysis; NSAIDs; Osteoarthritis; Topical
  14. Jacob SA, Chong EY, Goh SL, Palanisamy UD
    Mhealth, 2021;7:29.
    PMID: 33898598 DOI: 10.21037/mhealth.2020.01.04
    Background: Deaf and hard-of-hearing (DHH) patients have trouble communicating with community pharmacists and accessing the healthcare system. This study explored the views on a proposed mobile health (mHealth) app in terms of design and features, that will be able to bridge the communication gap between community pharmacists and DHH patients.

    Methods: A community-based participatory research method was utilized. Two focus group discussions (FGDs) were conducted in Malaysian sign language (BIM) with a total of 10 DHH individuals. Respondents were recruited using purposive sampling. Video-recordings were transcribed and analyzed using a thematic approach.

    Results: Two themes emerged: (I) challenges and scepticism of the healthcare system; and (II) features of the mHealth app. Respondents expressed fears and concerns about accessing healthcare services, and stressed on the need for sign language interpreters. There were also concerns about data privacy and security. With regard to app features, the majority preferred videos instead of text to convey information about their disease and medication, due to their lower literacy levels.

    Conclusions: For an mHealth app to be effective, app designers must ensure the app is individualised according to the cultural and linguistic diversity of the target audience. Pharmacists should also educate patients on the potential benefits of the app in terms of assisting patients with their medicine-taking.

  15. Chaw SH, Lo YL, Goh SL, Cheong CC, Tan WK, Loh PS, et al.
    Obes Surg, 2021 10;31(10):4305-4315.
    PMID: 34282569 DOI: 10.1007/s11695-021-05564-x
    BACKGROUND: Transversus abdominis plane (TAP) block and intraperitoneal local anesthetics (IPLA) are widely investigated techniques that potentially improve analgesia after bariatric surgery. The analgesic efficacy of TAP block has been shown in previous studies, but the performance of TAP block can be difficult in patients with obesity. We performed a systematic review and meta-analysis to compare the analgesic efficacy of TAP block and IPLA. An alternative technique is useful in clinical setting when TAP block is not feasible.

    METHODS: We searched PubMed, Embase, and CENTRAL from inception until August 2020 for randomized controlled trials comparing both techniques. The primary outcome was cumulative morphine consumption at 24 h. Secondary pain-related outcomes included pain score at rest and on movement at 2, 6, 12, and 24 h; postoperative nausea and vomiting; and length of hospital stay.

    RESULTS: We included 23 studies with a total of 2,178 patients. TAP block is superior to control in reducing opioid consumption at 24 h, improving pain scores at all the time points and postoperative nausea and vomiting. The cumulative opioid consumption at 24 h for IPLA is less than control, while the indirect comparison between IPLA with PSI and control showed a significant reduction in pain scores at rest, at 2 h, and on movement at 12 h, and 24 h postoperatively.

    CONCLUSIONS: Transversus abdominis plane block is effective for reducing pain intensity and has superior opioid-sparing effect compared to control. Current evidence is insufficient to show an equivalent analgesic benefit of IPLA to TAP block.

  16. Loh HH, Lim QH, Chai CS, Goh SL, Lim LL, Yee A, et al.
    J Sleep Res, 2023 Feb;32(1):e13726.
    PMID: 36104933 DOI: 10.1111/jsr.13726
    Obstructive sleep apnea is a chronic, sleep-related breathing disorder, which is an independent risk factor for cardiovascular disease. The renin-angiotensin-aldosterone system regulates salt and water homeostasis, blood pressure, and cardiovascular remodelling. Elevated aldosterone levels are associated with excess morbidity and mortality. We aimed to analyse the influence and implications of renin-angiotensin-aldosterone system derangement in individuals with and without obstructive sleep apnea. We pooled data from 20 relevant studies involving 2828 participants (1554 with obstructive sleep apnea, 1274 without obstructive sleep apnea). The study outcomes were the levels of renin-angiotensin-aldosterone system hormones, blood pressure and heart rate. Patients with obstructive sleep apnea had higher levels of plasma renin activity (pooled wmd+ 0.25 [95% confidence interval 0.04-0.46], p = 0.0219), plasma aldosterone (pooled wmd+ 30.79 [95% confidence interval 1.05-60.53], p = 0.0424), angiotensin II (pooled wmd+ 5.19 [95% confidence interval 3.11-7.27], p 
  17. Goh SL, Kee BP, Abdul Jabar K, Chua KH, Nathan AM, Bruyne J, et al.
    Pathog Glob Health, 2020 02;114(1):46-54.
    PMID: 32003298 DOI: 10.1080/20477724.2020.1719325
    Streptococcus pneumoniae (S. pneumoniae) is one of the main causative agents of pneumococcal diseases. To date, more than 90 distinct serotypes have been identified. Implementation of vaccines has caused a drastic reduction in vaccine-serotype pneumococcal diseases but increase in cases due to non-vaccine serotype has been observed in Malaysia. However, further investigation on different serotype incidence in Malaysia is needed and the rate of pneumococcal vaccination for new-born babies in Malaysia remains low. The recent emergence of drug-resistant S. pneumoniae (DRSP) has also been a global concern, especially penicillin resistance. This study determined the serotypes of S. pneumoniae strains (n = 95) isolated from nasopharyngeal specimens from children admitted to UMMC from 2013 to 2015. In accordance with previous studies, PCR result showed 40% of NT isolates were successfully typed as 3 less common serotypes, namely 9N/L, 17A, and 23B. The repetitive-element PCR (REP-PCR) result revealed genetic variations among the strains whereby five major clusters were observed at the similarity of 80% by clustering analysis based on fingerprint data. Penicillin-binding proteins (pbps) of selected isolates were studied by PCR and sequencing. Three strains with ≤19-mm diameter zone for Oxacillin Disc Diffusion (ODD) test previously were recorded to have mutation on all pbp1a, pbp2b, and pbp2x with MIC of 4 µg/ml, which were penicillin-intermediate resistance according to the CLSI breakpoints.
  18. Teoh YX, Othmani A, Lai KW, Goh SL, Usman J
    Comput Methods Programs Biomed, 2023 Dec;242:107807.
    PMID: 37778138 DOI: 10.1016/j.cmpb.2023.107807
    BACKGROUND AND OBJECTIVE: Knee osteoarthritis (OA) is a debilitating musculoskeletal disorder that causes functional disability. Automatic knee OA diagnosis has great potential of enabling timely and early intervention, that can potentially reverse the degenerative process of knee OA. Yet, it is a tedious task, concerning the heterogeneity of the disorder. Most of the proposed techniques demonstrated single OA diagnostic task widely based on Kellgren Lawrence (KL) standard, a composite score of only a few imaging features (i.e. osteophytes, joint space narrowing and subchondral bone changes). However, only one key disease pattern was tackled. The KL standard fails to represent disease pattern of individual OA features, particularly osteophytes, joint-space narrowing, and pain intensity that play a fundamental role in OA manifestation. In this study, we aim to develop a multitask model using convolutional neural network (CNN) feature extractors and machine learning classifiers to detect nine important OA features: KL grade, knee osteophytes (both knee, medial fibular: OSFM, medial tibial: OSTM, lateral fibular: OSFL, and lateral tibial: OSTL), joint-space narrowing (medial: JSM, and lateral: JSL), and patient-reported pain intensity from plain radiography.

    METHODS: We proposed a new feature extraction method by replacing fully-connected layer with global average pooling (GAP) layer. A comparative analysis was conducted to compare the efficacy of 16 different convolutional neural network (CNN) feature extractors and three machine learning classifiers.

    RESULTS: Experimental results revealed the potential of CNN feature extractors in conducting multitask diagnosis. Optimal model consisted of VGG16-GAP feature extractor and KNN classifier. This model not only outperformed the other tested models, it also outperformed the state-of-art methods with higher balanced accuracy, higher Cohen's kappa, higher F1, and lower mean squared error (MSE) in seven OA features prediction.

    CONCLUSIONS: The proposed model demonstrates pain prediction on plain radiographs, as well as eight OA-related bony features. Future work should focus on exploring additional potential radiological manifestations of OA and their relation to therapeutic interventions.

  19. Mohd Sharif NA, Goh SL, Usman J, Wan Safwani WKZ
    Phys Ther Sport, 2017 Nov;28:44-52.
    PMID: 28673759 DOI: 10.1016/j.ptsp.2017.05.001
    BACKGROUND: Knee sleeves are widely used for the symptomatic relief and subjective improvements of knee problems. To date, however, their biomechanical effects have not been well understood.

    OBJECTIVE: To determine whether knee sleeves can significantly improve the biomechanical variables for knee problems.

    METHOD: Systematic literature search was conducted on four online databases - PubMed, Web of Science, ScienceDirect and Springer Link - to find peer-reviewed and relevant scientific papers on knee sleeves published from January 2005 to January 2015. Study quality was assessed using the Structured Effectiveness Quality Evaluation Scale (SEQES).

    RESULTS: Twenty studies on knee sleeves usage identified from the search were included in the review because of their heterogeneous scope of coverage. Twelve studies found significant improvement in gait parameters (3) and functional parameters (9), while eight studies did not find any significant effects of knee sleeves usage.

    CONCLUSION: Most improvements were observed in: proprioception for healthy knees, gait and balance for osteoarthritic knees, and functional improvement of injured knees. This review suggests that knee sleeves can effect functional improvements to knee problems. However, further work is needed to confirm this hypothesis, due to the lack of homogeneity and rigor of existing studies.

  20. Lau PL, Goh SL, Lau EKL, Garry K, Kueh YC, Wong NL
    Malays J Med Sci, 2024 Apr;31(2):170-178.
    PMID: 38694590 DOI: 10.21315/mjms2024.31.2.15
    BACKGROUND: People with disabilities are marginalised in mainstream culture and they also experience increased restrictions in a variety of areas, such as sport. The barriers they encounter may adversely affect their life satisfaction, especially if they have a low perceived sense of autonomy and resilience. The purpose of this study was to investigate the relationship between autonomy, resilience and life satisfaction in para-badminton athletes and the mediating role of resilience in the relationship between autonomy and life satisfaction.

    METHODS: Data were collected from 137 para-badminton athletes (male: 65.0% and female: 35.0%). Self-reported measures were used to assess the participants' autonomy, resilience and life satisfaction levels.

    RESULTS: A structural equation model analysis was performed; the model had sufficient fit indices (comparative fit index [CFI] = 0.94, root mean square error of approximation [RMSEA] = 0.06, standardised root mean square residual [SRMR] = 0.07). The findings showed that autonomy had a significant effect on resilience (β = 0.32, P = 0.001). Further, resilience had a significant effect on life satisfaction (β = 0.19, P = 0.011) and significantly mediated the effect of autonomy on life satisfaction (β = 021, P = 0.033).

    CONCLUSION: This study revealed that autonomy fosters resilience among athletes, in turn allowing them to achieve greater life satisfaction. Therefore, society and sport communities should actively seek ways to improve the autonomy and resilience levels of athletes with disabilities.

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