METHODS: In this study, plasma miRNA profiles from eight early-stage breast cancer patients and nine age-matched (± 2 years) healthy controls were characterized by miRNA array-based approach, followed by differential gene expression analysis, Independent T-test and construction of Receiver Operating Characteristic (ROC) curve to determine the capability of the assays to discriminate between breast cancer and the healthy control.
RESULTS: Based on the 372-miRNAs microarray profiling, a set of 40 differential miRNAs was extracted regarding to the fold change value at 2 and above. We further sub grouped 40 miRNAs of breast cancer patients that were significantly expressed at 2-fold change and higher. In this set, we discovered that 24 miRNAs were significantly upregulated and 16 miRNAs were significantly downregulated in breast cancer patients, as compared to the miRNA expression of healthy subjects. ROC curve analysis revealed that seven miRNAs (miR-125b-5p, miR-142-3p, miR-145-5p, miR-193a-5p, miR-27b-3p, miR-22-5p and miR-423-5p) had area under curve (AUC) value > 0.7 (AUC p-value < 0.05). Overlapping findings from differential gene expression analysis, ROC analysis, and Independent T-Test resulted in three miRNAs (miR-27b-3p, miR-22-5p, miR-145-5p). Cohen's effect size for these three miRNAs was large with d value are more than 0.95.
CONCLUSION: miR-27b-3p, miR-22-5p, miR-145-5p could be potential biomarkers to distinguish breast cancer patients from healthy controls. A validation study for these three miRNAs in an external set of samples is ongoing.
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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.
Methods: This cohort study was designed to screen the hearing of newborns using transiently evoked otoacoustic emission and auditory brain stem response, and to determine the risk factors associated with hearing loss of newborns in 3 tertiary hospitals in Northern Thailand. Data were prospectively collected from November 1, 2010 to May 31, 2012. To develop the risk score, clinical-risk indicators were measured by Poisson risk regression. The regression coefficients were transformed into item scores dividing each regression-coefficient with the smallest coefficient in the model, rounding the number to its nearest integer, and adding up to a total score.
Results: Five clinical risk factors (Craniofacial anomaly, Ototoxicity, Birth weight, family history [Relative] of congenital sensorineural hearing loss, and Apgar score) were included in our COBRA score. The screening tool detected, by area under the receiver operating characteristic curve, more than 80% of existing hearing loss. The positive-likelihood ratio of hearing loss in patients with scores of 4, 6, and 8 were 25.21 (95% confidence interval [CI], 14.69-43.26), 58.52 (95% CI, 36.26-94.44), and 51.56 (95% CI, 33.74-78.82), respectively. This result was similar to the standard tool (The Joint Committee on Infant Hearing) of 26.72 (95% CI, 20.59-34.66).
Conclusion: A simple screening tool of five predictors provides good prediction indices for newborn hearing loss, which may motivate parents to bring children for further appropriate testing and investigations.
METHODS: The system typically consists of segmenting the calcium region from the CT scan into slices based on Hounsfield Unit-based threshold, and subsequently computing the summation of the calcium areas in each slice. However, when the carotid volume has intermittently higher concentration of contrast agent, a dependable approach is adapted to correct the calcium region using the neighboring slices, thereby estimating the correct volume. Furthermore, mitigation is provided following the regulatory constraints by changing the system to semi-automated criteria as a fall back solution. We evaluate the automated and semi-automated techniques using completely manual calcium volumes computed based on the manual tracings by the neuroradiologist.
RESULTS: A total of 64 patients with calcified plaque in the internal carotid artery were analyzed. Using the above algorithm, our automated and semi-automated system yields correlation coefficients (CC) of 0.89 and 0.96 against first manual readings and 0.90 and 0.96 against second manual readings, respectively. Using the t-test, there was no significant difference between the automated and semi-automated methods against manual. The intra-observer reliability was excellent with CC 0.98.
CONCLUSIONS: Compared to automated method, the semi-automated method for calcium volume is acceptable and closer to manual strategy for calcium volume. Further work evaluating and confirming the performance of our semi-automated protocol is now warranted.
METHODS: We recruited 164 healthy controls (HC) and 120 cognitively impaired (CI) subjects- 47 mild cognitive impairment (MCI) and 73 mild Alzheimer's disease (AD) dementia participants, from four countries between January 2015 and August 2016 to determine the usefulness of a single version of the VCAT, without translation or adaptation, in a multinational, multilingual population. The VCAT was administered along with established cognitive evaluation.
RESULTS: The VCAT, without local translation or adaptation, was effective in discriminating between HC and CI subjects (MCI and mild AD dementia). Mean (SD) VCAT scores for HC and CI subjects were 22.48 (3.50) and 14.17 (5.05) respectively. Areas under the curve for Montreal Cognitive Assessment (0.916, 95% CI 0.884-0.948) and the VCAT (0.905, 95% CI 0.870-0.940) in discriminating between HCs and CIs were comparable. The multiple languages used to administer VCAT in four countries did not significantly influence test scores.
CONCLUSIONS: The VCAT without the need for language translation or cultural adaptation showed satisfactory discriminative ability and was effective in a multinational, multilingual Southeast Asian population.
OBJECTIVE: To compare the ability of the prehospital GCS and GCS-M to predict 30-day mortality and severe disability in trauma patients.
DESIGN: We used the Pan-Asia Trauma Outcomes Study registry to enroll all trauma patients >18 years of age who presented to hospitals via emergency medical services from 1 January 2016 to November 30, 2018.
SETTINGS AND PARTICIPANTS: A total of 16,218 patients were included in the analysis of 30-day mortality and 11 653 patients in the analysis of functional outcomes.
OUTCOME MEASURES AND ANALYSIS: The primary outcome was 30-day mortality after injury, and the secondary outcome was severe disability at discharge defined as a Modified Rankin Scale (MRS) score ≥4. Areas under the receiver operating characteristic curve (AUROCs) were compared between GCS and GCS-M for these outcomes. Patients with and without traumatic brain injury (TBI) were analyzed separately. The predictive discrimination ability of logistic regression models for outcomes (30-day mortality and MRS) between GCS and GCS-M is illustrated using AUROCs.
MAIN RESULTS: The primary outcome for 30-day mortality was 1.04% and the AUROCs and 95% confidence intervals for prediction were GCS: 0.917 (0.887-0.946) vs. GCS-M:0.907 (0.875-0.938), P = 0.155. The secondary outcome for poor functional outcome (MRS ≥ 4) was 12.4% and the AUROCs and 95% confidence intervals for prediction were GCS: 0.617 (0.597-0.637) vs. GCS-M: 0.613 (0.593-0.633), P = 0.616. The subgroup analyses of patients with and without TBI demonstrated consistent discrimination ability between the GCS and GCS-M. The AUROC values of the GCS vs. GCS-M models for 30-day mortality and poor functional outcome were 0.92 (0.821-1.0) vs. 0.92 (0.824-1.0) ( P = 0.64) and 0.75 (0.72-0.78) vs. 0.74 (0.717-0.758) ( P = 0.21), respectively.
CONCLUSION: In the prehospital setting, on-scene GCS-M was comparable to GCS in predicting 30-day mortality and poor functional outcomes among patients with trauma, whether or not there was a TBI.
METHODS: Patients undergoing curative resection for oesophageal cancer were identified from the international Oesophagogastric Anastomosis Audit (OGAA) from April 2018-December 2018. Definitions for AL and CN were those set out by the Oesophageal Complications Consensus Group. Univariate and multivariate analyses were performed to identify risk factors for both AL and CN. A risk score was then produced for both AL and CN using the derivation set, then internally validated using the validation set.
RESULTS: This study included 2247 oesophagectomies across 137 hospitals in 41 countries. The AL rate was 14.2% and CN rate was 2.7%. Preoperative factors that were independent predictors of AL were cardiovascular comorbidity and chronic obstructive pulmonary disease. The risk scoring model showed insufficient predictive ability in internal validation (area under the receiver-operating-characteristic curve [AUROC] = 0.618). Preoperative factors that were independent predictors of CN were: body mass index, Eastern Cooperative Oncology Group performance status, previous myocardial infarction and smoking history. These were converted into a risk-scoring model and internally validated using the validation set with an AUROC of 0.775.
CONCLUSION: Despite a large dataset, AL proves difficult to predict using preoperative factors. The risk-scoring model for CN provides an internally validated tool to estimate a patient's risk preoperatively.
MATERIALS AND METHODS: A prospective study was conducted at the single centre ICU in Hospital Sultanah Aminah (HSA) Malaysia. External validation of APACHE IV involved a cohort of 916 patients who were admitted in 2009. Model performance was assessed through its calibration and discrimination abilities. A first-level customisation using logistic regression approach was also applied to improve model calibration.
RESULTS: APACHE IV exhibited good discrimination, with an area under receiver operating characteristic (ROC) curve of 0.78. However, the model's overall fit was observed to be poor, as indicated by the Hosmer-Lemeshow goodness-of-fit test (Ĉ = 113, P <0.001). Predicted in-ICU mortality rate (28.1%) was significantly higher than the actual in-ICU mortality rate (18.8%). Model calibration was improved after applying first-level customisation (Ĉ = 6.39, P = 0.78) although discrimination was not affected.
CONCLUSION: APACHE IV is not suitable for application in HSA ICU, without further customisation. The model's lack of fit in the Malaysian study is attributed to differences in the baseline characteristics between HSA ICU and APACHE IV datasets. Other possible factors could be due to differences in clinical practice, quality and services of health care systems between Malaysia and the United States.
MATERIALS AND METHODS: We retrospectively analyzed the clinical and imaging data of cervical cancer patients diagnosed pathologically at our hospital from January 2021 to June 2024. All patients underwent routine magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and APT imaging before treatment. Apparent diffusion coefficient (ADC) and APT values were measured. Based on the pathological results, patients were categorized into LVSI (+) and LVSI (-) groups, and PMI (+) and PMI (-) groups. Independent sample t-tests were used to compare the ADC and APT values between these groups. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, and ADC + APT in predicting PMI and LVSI. The Delong test was employed to compare the diagnostic performance among these measures.
RESULTS: A total of 83 patients were included, with 56 in the LVSI (-) group, 27 in the LVSI (+) group, 35 in the PMI (-) group, and 16 in the PMI (+) group. The ADC values for the LVSI (+) and PMI (+) groups were significantly lower than those for the LVSI (-) and PMI (-) groups (P