OBJECTIVE: The aim of this systematic review and meta-analysis is to compare the effectiveness of amiodarone, dexmedetomidine and magnesium in preventing JET following congenital heart surgery.
METHODS: This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement, where 11 electronic databases were searched from date of inception to August 2020. The incidence of JET was calculated with the relative risk of 95% confidence interval (CI). Quality assessment of the included studies was assessed using the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement.
RESULTS: Eleven studies met the predetermined inclusion criteria and were included in this meta-analysis. Amiodarone, dexmedetomidine and magnesium significantly reduced the incidence of postoperative JET [Amiodarone: risk ratio 0.34; I2= 0%; Z=3.66 (P=0.0002); 95% CI 0.19-0.60. Dexmedetomidine: risk ratio 0.34; I2= 0%; Z=4.77 (P<0.00001); 95% CI 0.21-0.52. Magnesium: risk ratio 0.50; I2= 24%; Z=5.08 (P<0.00001); 95% CI 0.39-0.66].
CONCLUSION: All three drugs show promise in reducing the incidence of JET. Our systematic review found that dexmedetomidine is better in reducing the length of ICU stays as well as mortality. In addition, dexmedetomidine also has the least pronounced side effects among the three. However, it should be noted that this conclusion was derived from studies with small sample sizes. Therefore, dexmedetomidine may be considered as the drug of choice for preventing JET.
METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel.
RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy.
CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.
RESULTS: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community.
CONCLUSIONS: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.
METHODS: Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system.
RESULTS: Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines.
CONCLUSION: The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.
METHOD: The APLC cohort is an ongoing, prospective longitudinal cohort. Adult patients who meet either the American College of Rheumatology (ACR) Modified Classification Criteria for systemic lupus erythematosus (SLE), or the Systemic Lupus International Collaborating Clinics (SLICC) Classification Criteria, and provide informed consent are recruited into the cohort. Patients are routinely followed up at 3- to 6-monthly intervals. Information on demographics, clinical manifestations, treatment, pathology results, outcomes, and patient-reported quality of life (Short-form 36 version 2) are collected using a standardized case report form. Each site is responsible for obtaining local ethics and governance approval, patient recruitment, data collection, and data transfer into a centralized APLC database.
RESULTS: The latest APLC cohort comprises 2160 patients with >12 000 visits from Australia, China, Hong Kong, Indonesia, Japan, Malaysia, Philippines, Singapore, Taiwan and Thailand. The APLC has proposed the Lupus Low Disease Activity State (LLDAS) as a treat-to-target (T2T) endpoint, and reported several retrospective and cross-sectional analyses consistent with the validity of LLDAS. Longitudinal validation of LLDAS as a T2T endpoint is currently underway.
CONCLUSION: The APLC cohort is one of the largest contemporary SLE patient cohorts in the world. It is the only cohort with substantial representation of Asian patients. This cohort represents a unique resource for future clinical research including evaluation of other endpoints and quality of care.
DESIGN/METHODOLOGY/APPROACH: All inpatient referrals to the CLiP team were recorded over a three-month period and compared to previous audit data from 2017. Four audit standards were assessed: the reporting of referrals, timeliness of response indication of reason for referral and presence of a management plan.
FINDINGS: The compliance of reporting using the CLiP form was 70.1 per cent compared to 28 per cent in the audit data from 2017 after interventions were conducted. Analysis of the completed CLiP form reveals that 89 per cent of referrals were seen within the same working day. All referrals included the reason for referral. The most common reason for referral was for depressive disorders, but post-assessment, delirium was the most common diagnosis. In total, 87.8 per cent satisfied the audit criteria for a completed written care plan.
ORIGINALITY/VALUE: Specialised CLiP services are relatively new in Malaysia and this is the first paper to examine the quality of such services in the country. Interventions were effective in improving the compliance of reporting using the CLiP database. The findings suggest that the CLiP services are on par with international audit standards. Furthermore, data from this clinical audit can serve as a benchmark for the development of national operating policies in similar settings.
METHODS: This is a cross-sectional descriptive study that was conducted to evaluate perception and experience of pharmacists with the use of Internet-based medication information by their patients. During the study period, 200 pharmacists were approached to participate in the study using a paper-based survey to assess their perceptions and current experience with the use of Internet-based medication information by their patients. Data were analyzed using descriptive statistics (mean/standard deviation for continuous variables, and frequency/percentages for qualitative variables). Also, simple linear regression was utilized to screen factors affecting pharmacists' perception scores of the use of Internet-based medication information.
RESULTS: Among 161 recruited pharmacists, the majority (n = 129, 80.1%) reported receiving inquiries from patients about Internet-based medication information within the last year. Among them, only 22.6% (n = 29) of pharmacists believed that Internet-based medication information is somewhat or very accurate. Unfortunately, only 24.2% (n = 31) of them stated that they always had enough time for their patient to discuss their Internet-based medication information. Regarding pharmacists' perception of the use of Internet-based medication information by their patients, more than half of the pharmacists (>50%) believe that Internet-based medication information could increase the patient's role in taking responsibility. On the other hand, 54.7% (n = 88) of the pharmacists believed that Internet-based medication information would contribute to rising the healthcare cost by obtaining unnecessary medications by patients. Finally, pharmacists' educational level was found to significantly affect their perception scores toward patient use of Internet-based medication information where those with higher educational level showed lower perception score (r = -0.200, P-value = 0.011).
CONCLUSION: Although pharmacists felt that usage of Internet-based data by patients is beneficial, they also have believed that it has a negative impact in terms of rising the healthcare cost, and it promotes unnecessary fear or concern about medications. We suggest that pharmacists be trained on principles of critical appraisal to become professional in retrieval information on the Internet that might improve their delivery of healthcare information and their recommendations to patients.
OBJECTIVE: In our present study, we explored patents associated with various biomedical applications of polyhydroxyalkanoates.
METHOD: Patent databases of European Patent Office, United States Patent and Trademark Office and World Intellectual Property Organization were mined. We developed an intensive exploration approach to eliminate overlapping patents and sort out significant patents.We demarcated the keywords and search criterions and established search patterns for the database request. We retrieved documents within the recent 6 years, 2010 to 2016 and sort out the collected data stepwise to gather the most appropriate documents in patent families for further scrutiny.
RESULTS: By this approach, we retrieved 23,368 patent documents from all the three databases and the patent titles were further analyzed for the relevance of polyhydroxyalkanoates in biomedical applications. This ensued in the documentation of approximately 226 significant patents associated with biomedical applications of polyhydroxyalkanoates and the information was classified into six major groups. Polyhydroxyalkanoates has been patented in such a way that their applications are widely distributed in the medical industry, drug delivery system, dental material, tissue engineering, packagingmaterial as well as other useful products.
CONCLUSION: There are many avenues through which PHA & PHB could be used. Our analysis shows patent information can be used to identify various applications of PHA and its representatives in the biomedical field. Upcoming studies can focus on the application of PHA in the different field to discover the related topics and associate to this study.We believe that this approach of analysis and findings can initiate new researchers to undertake similar kind of studies in their represented field to fill the gap between the patent articles and research publications.
METHODS: Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well.
RESULTS: Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%.
CONCLUSION: The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals.
METHODS: This was a comparative cross-sectional study. We recruited children aged 8-11 years from eight primary schools in Kota Bharu, Kelantan, Malaysia. The children were divided into two groups: those with LBW (< 2,500 g) and those with normal birth weight (≥ 2,500 g). Parents of the enrolled children were asked to complete a translated version of the International Study of Asthma and Allergies in Childhood questionnaire. Lung function tests, done using a MicroLoop Spirometer, were performed for the children in both groups by a single investigator who was blinded to the children's birth weight.
RESULTS: The prevalence of 'ever wheezed' among the children with LBW was 12.9%. This value was significantly higher than that of the children with normal birth weight (7.8%). Forced vital capacity (FVC), forced expiratory volume in one second, and forced expiratory flow when 50% and 75% of the FVC had been exhaled were significantly lower among the children with LBW as compared to the children with normal birth weight.
CONCLUSION: LBW is associated with an increased prevalence of asthma-like symptoms and impaired lung function indices later in life. Children born with LBW may need additional follow-up so that future respiratory problems can be detected early.
METHODS: A total of three databases were searched on September 15, 2020: PubMed, Web of Science, and Science Direct. The searches were conducted using a pre-specified search strategy to record studies reported the reproductive number of coronavirus from its inception in December 2019. It includes keywords of coronavirus and its reproductive number, which were combined using the Boolean operators (AND, OR). Based on the included studies, we estimated a summary reproductive number by using the meta-analysis. We used narrative synthesis to explain the results of the studies where the reproductive number was reported, however, were not possible to include in the meta-analysis because of the lack of data (mostly due to confidence interval was not reported).
RESULTS: Total of 42 studies included in this review whereas 29 of them were included in the meta-analysis. The estimated summary reproductive number was 2.87 (95% CI, 2.39-3.44). We found evidence of very high heterogeneity (99.5%) of the reproductive number reported in the included studies. Our sub-group analysis was found the significant variations of reproductive number across the country for which it was estimated, method and model that were used to estimate the reproductive number, number of case that was considered to estimate the reproductive number, and the type of reproductive number that was estimated. The highest reproductive number was reported for the Diamond Princess Cruise Ship in Japan (14.8). In the country-level, the higher reproductive number was reported for France (R, 6.32, 95% CI, 5.72-6.99) following Germany (R, 6.07, 95% CI, 5.51-6.69) and Spain (R, 3.56, 95% CI, 1.62-7.82). The higher reproductive number was reported if it was estimated by using the Markov Chain Monte Carlo method (MCMC) method and the Epidemic curve model. We also reported significant heterogeneity of the type of reproductive number- a high-value reported if it was the time-dependent reproductive number.
CONCLUSION: The estimated summary reproductive number indicates an exponential increase of coronavirus infection in the coming days. Comprehensive policies and programs are important to reduce new infections as well as the associated adverse consequences including death.