METHODS: A total of 28 critically ill patients were included in this study. All data were collected from medical, microbiology and pharmacokinetic records. The clinical response was evaluated on the basis of clinical and microbiological parameters. The 24-h area under the curve (AUC0-24) was estimated from a single trough level using established equations.
RESULTS: Out of the 28 patients, 46% were classified as responders to vancomycin treatment. The trough vancomycin concentration did not differ between the responders and non-responders (15.02 ± 6.16 and 14.83 ± 4.80 μg mL-1; P = 0.929). High vancomycin minimum inhibitory concentration (MIC) was observed among the non-responders (P = 0.007). The ratio between vancomycin trough concentration and vancomycin MIC was significantly lower in the non-responder group (8.76 ± 3.43 vs. 12.29 ± 4.85 μg mL-1; P = 0.034). The mean ratio of estimated AUC0-24 and vancomycin MIC was 313.78 ± 117.17 μg h mL-1 in the non-responder group and 464.44 ± 139.06 μg h mL-1 in the responder group (P = 0.004). AUC0-24/MIC of ≥ 400 μg h mL-1 was documented for 77% of the responders and 27% of the non-responders (c2 = 7.03; P = 0.008).
CONCLUSIONS: Ratio of trough concentration/MIC and AUC0-24/MIC of vancomycin are better predictors for MRSA treatment outcomes than trough vancomycin concentration or AUC0-24 alone. The single trough-based estimated AUC may be sufficient for the monitoring of treatment response with vancomycin.
Materials and Methods: The primary analysis was based on population control studies. Data were pooled by means of a random-effects model, and sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratios (DOR), and areas under the summary receiver operating characteristic curve (AUC) were calculated.
Results: In 23 population control studies, HLA-B*15:02 was measured in 373 patients with CBZ-induced TEN/SJS and 3452 patients without CBZ-induced TEN/SJS. The pooled sensitivity, specificity, LR+, LR-, DOR, and AUC were 0.67 (95% confidence interval [CI] = 0.63-0.72), 0.98 (95% CI = 0.98-0.99), 19.73 (95% CI = 10.54-36.92), 0.34 (95% CI = 0.23-0.49), 71.38 (95% CI = 34.89-146.05), and 0.96 (95% CI = 0.92-0.98), respectively. Subgroup analyses for Han Chinese, Thai, and Malaysian populations yielded similar findings. Specifically, racial/ethnic subgroup analyses revealed similar findings with respect to DOR for Han Chinese (99.28; 95% CI = 22.20-443.88), Thai (61.01; 95% CI = 23.05-161.44), and Malaysian (30; 95% CI = 7.08-126.68) populations, which are similar to the pooled DOR for the relationship between the HLA-B*15:02 allele and CBZ-induced TEN/SJS across all populations (71.38; 95% CI = 34.89-146.05).
Conclusions: The present study reveals that CBZ is the leading cause of TEN/SJS in many countries. Screening of HLA-B*15:02 may help patients to prevent the occurrence of CBZ-induced TEN/SJS, especially in populations with a higher (≥5%) risk allele frequency.
METHODS: Healthy participants consumed pure forms of a non-nutritive sweetener (NNS) mixed with water that were standardized to doses of 14% (0.425 g) of the acceptable daily intake (ADI) for aspartame and 20% (0.136 g) of the ADI for sucralose every day for two weeks. Blood samples were collected and analysed for glucose, insulin, active glucagon-like peptide-1 (GLP-1), and leptin.
RESULTS: Seventeen participants (10 females and 7 males; age 24 ± 6.8 years; BMI 22.9 ± 2.5 kg/m2) participated in the study. The total area under the curve (AUC) values of glucose, insulin, active GLP-1 and leptin were similar for the aspartame and sucralose treatment groups compared to the baseline values in healthy participants. There was no change in insulin sensitivity after NNS treatment compared to the baseline values.
CONCLUSIONS: These findings suggest that daily repeated consumption of pure sucralose or aspartame for 2 weeks had no effect on glucose metabolism among normoglycaemic adults. However, these results need to be tested in studies with longer durations. Novelty: • Daily consumption of pure aspartame or sucralose for 2 weeks had no effect on glucose metabolism. • Daily consumption of pure aspartame or sucralose for 2 weeks had no effect on insulin sensitivity among healthy adults.
PURPOSE: Osteoporosis self-assessment tool for Asians (OSTA) is a convenient screening algorithm used widely to identify patients at risk of osteoporosis. Currently, the number of studies validating OSTA in Malaysian population is limited. This study aimed to validate the performance of OSTA in identifying subjects with osteoporosis determined with DXA.
METHODS: This cross-sectional study recruited 786 Malaysians in Klang Valley, Malaysia. Their bone health status was assessed by DXA and OSTA. The association and agreement between OSTA and bone mineral density assessment by DXA were determined by Pearson's correlation and Cohen's kappa, respectively. Receiver operating characteristics (ROC) curves were used to determine the sensitivity, specificity, and area under the curve (AUC) for OSTA.
RESULTS: OSTA and DXA showed a fair association in the study (r = 0.382, κ = 0.159, p
METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.
RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.
CONCLUSION: The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.
METHODS: The AD8 was translated into Malay for Malay-speaking participants. A correlation analysis and a receiver operator characteristic curve were generated to establish the psychometric properties of the AD8 in relation to the MoCA.
RESULTS: One hundred fifty patients and their caretakers completed the AD8 and MoCA. Using a cutoff score of 1/8, the AD8 had 81% sensitivity and 59% specificity for the detection of cognitive impairment in PD. With a cutoff score of 2/8, the AD8 had 83% specificity and 64% sensitivity. The area under the receiver operator characteristic curve was 80%, indicating good-to-excellent discriminative ability.
DISCUSSION: These findings suggest that the AD8 can reliably differentiate between cognitively impaired and cognitively normal patients with PD and is a useful caregiver screening tool for PD.
METHODS: Subjects were recruited among those responding to a social media announcement or patients attending the SEGi Oral Health Care Centre between May and December 2019, and among some staff at the centre. Five ml of unstimulated whole saliva was collected and salivary LDH enzyme activity levels were measured with a LDH colorimetric assay kit. Salivary LDH activity level was determined for each group and compared statistically.
RESULTS: Eighty-eight subjects were categorized into three groups (control n=30, smokers n=29, and vapers n=29). The mean ± standard deviation (SD) values for salivary LDH activity levels for vapers, smokers, and control groups were 35.15 ± 24.34 mU/ml, 30.82 ± 20.73 mU/ml, and 21.45 ± 15.30 mU/ml, respectively. The salivary LDH activity levels of smoker and vaper groups were significantly higher than in the control group (p = 0.031; 0.017). There was no significant difference of salivary LDH activity level in vapers when compared with smokers (p= 0.234).
CONCLUSION: Our findings showed higher LDH levels in the saliva of vapers when compared with controls, confirming cytotoxic and harmful effects of e-cigarettes on the oral mucosa.
RESULTS: We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data.
CONCLUSIONS: Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .
METHODS: Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).
RESULTS: The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).
CONCLUSIONS: The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.
METHOD: This is a retrospective cohort study of confirmed severe dengue patients that were admitted in 2014 to Hospital Kuala Lumpur. Data on baseline characteristics, clinical parameters, and laboratory findings at diagnosis of severe dengue were collected. The outcome of interest is death among patients diagnosed with severe dengue.
RESULTS: There were 199 patients with severe dengue included in the study. Multivariate analysis found lethargy, OR 3.84 (95% CI 1.23-12.03); bleeding, OR 8.88 (95% CI 2.91-27.15); pulse rate, OR 1.04 (95% CI 1.01-1.07); serum bicarbonate, OR 0.79 (95% CI 0.70-0.89) and serum lactate OR 1.27 (95% CI 1.09-1.47), to be statistically significant predictors of death. The regression equation to our model with the highest AUROC, 83.5 (95% CI 72.4-94.6), is: Log odds of death amongst severe dengue cases = - 1.021 - 0.220(Serum bicarbonate) + 0.001(ALT) + 0.067(Age) - 0.190(Gender).
CONCLUSION: This study showed that a large proportion of severe dengue occurred early, whilst patients were still febrile. The best prediction model to predict death at recognition of severe dengue is a model that incorporates serum bicarbonate and ALT levels.
METHODS: Patient data was obtained retrospectively through the Ministry of Health, Malaysia, from 2011 to 2016. Patients with incomplete data were excluded. A total of 2044 clinical P. vivax malaria cases treated with primaquine were included. Data collected were patient, disease, and treatment characteristics. Two-thirds of the cases (n = 1362) were used to develop a clinical risk score, while the remaining third (n = 682) was used for validation.
RESULTS: Using multivariate analysis, age (p = 0.03), gametocyte sexual count (p = 0.04), indigenous transmission (p = 0.04), type of treatment (p = 0.12), and incomplete primaquine treatment (p = 0.14) were found to be predictors of recurrence after controlling for other confounding factors; these predictors were then used in developing the final model. The beta-coefficient values were used to develop a clinical scoring tool to predict possible recurrence. The total scores ranged between 0 and 8. A higher score indicated a higher risk for recurrence (odds ratio [OR]: 1.971; 95% confidence interval [CI]: 1.562-2.487; p ≤ 0.001). The area under the receiver operating characteristic (ROC) curve of the developed (n = 1362) and validated model (n = 682) was of good accuracy (ROC: 0.728, 95% CI: 0.670-0.785, p value area under the ROC curves showed no significant difference in predicting recurrence based on the constructed scoring mechanism (p = 0.399; Z-value: -0.8441; standard error: 0.045).
CONCLUSIONS: The developed model to predict recurrence was found to be of good accuracy and could be a useful tool in targeting patients at a higher risk for recurrence for closer monitoring during follow-up, after treatment with primaquine.