METHODS: Methadone-maintained therapy (MMT) users from three centers in Malaysia had their exhaled carbon monoxide (eCO) levels recorded via the piCO+ and iCOTM Smokerlyzers®, their nicotine dependence assessed with the Malay version of the Fagerström Test for Nicotine Dependence (FTND-M), and daily tobacco intake measured via the Opiate Treatment Index (OTI) Tobacco Q-score. Pearson partial correlations were used to compare the eCO results of both devices, as well as the corresponding FTND-M scores.
RESULTS: Among the 146 participants (mean age 47.9 years, 92.5% male, and 73.3% Malay ethnic group) most (55.5%) were moderate smokers (6-19 cigarettes/day). Mean eCO categories were significantly correlated between both devices (r=0.861, p<0.001), and the first and second readings were significantly correlated for each device (r=0.94 for the piCO+ Smokerlyzer®, p<0.001; r=0.91 for the iCOTM Smokerlyzer®, p<0.001). Exhaled CO correlated positively with FTND-M scores for both devices. The post hoc analysis revealed a significantly lower iCOTM Smokerlyzer® reading of 0.82 (95% CI: 0.69-0.94, p<0.001) compared to that of the piCO+ Smokerlyzer®, and a significant intercept of -0.34 (95% CI: -0.61 - -0.07, p=0.016) on linear regression analysis, suggesting that there may be a calibration error in one or more of the iCOTM Smokerlyzer® devices.
CONCLUSIONS: The iCOTM Smokerlyzer® readings are highly reproducible compared to those of the piCO+ Smokerlyzer®, but calibration guidelines are required for the mobile-phone-based device. Further research is required to assess interchangeability.
METHOD: A multicenter cross-sectional observational study was conducted in 388 diabetes patients attending daily diabetes clinics and teaching hospitals in Pakistan's twin city between August 2019 and February 2020. The chi-square test and linear regression were used to detect RLS-related factors in type 2 diabetes mellitus.
RESULTS: The prevalence of RLS found was; 3.1% patients with diabetes were suffering from very severe RLS, 23.5% from severe RLS, 34% from moderate RLS, 21.1% from mild RLS and 18.3% from non-RLS. Gender, age, education, blood glucose fasting (BSF), blood glucose random (BSR) and HBA1c were found to be significant predictors of RLS in patients with diabetes.
CONCLUSION: Policy makers can develop local interventions to curb the growing RLS prevalence by keeping in control the risk factors of RLS in people living with type 2 diabetes.
METHODS: A cross-sectional study was conducted among 148 children with SLD and their caregivers. The evaluation consisted of the Movement Assessment Battery for Children-2 (MABC-2) and the Functional Mobility domain from Pediatric Evaluation of Disability Inventory-Computer Adaptive Test (PEDI-CAT). The level of motor performances and functional mobility were determined. A linear regression was then conducted to assess the influence of motor performances that could be accounted for functional mobility scores.
RESULTS: More than half of the children with SLD showed motor performance difficulty in manual dexterity subscale (54.7%). For functional mobility, the mean standard T-score indicated an average level of capability (49.49±15.96). A regression analysis revealed that both manual dexterity and balance were significant predictors for functional mobility. According to the regression coefficients, manual dexterity (B=1.37, β=0.303, sr2=0.077) was found to be a stronger predictor compared to balance (B=0.85, β=0.178, sr2=0.028).
CONCLUSION: Manual dexterity was found to influence functional mobility among children with SLD. Therefore, fine motor skills intervention for children with SLD should emphasize on manual dexterity training. Future studies that involve dual tasks and inclusion of typical children would give useful additional information on motor performances issues in children with SLD.
Materials and Methods: This cross-sectional study was conducted among 75 residents in the family medicine residency programs in Al Madina, Saudi Arabia. A self-administered questionnaire was used that includes questions on sociodemographic characteristics and sources of stress and burnout. T test, analysis of variance (ANOVA) test, and multiple linear regression analysis were employed.
Results: Majority were female (54.7%) and aged 26 to 30 years (84.0%). The significant predictors of burnout in the final model were "tests/examinations" (P = 0.014), "large amount of content to be learnt" (P = 0.016), "unfair assessment from superiors" (P = 0.001), "work demands affect personal/home life" (P = 0.001), and "lack of support from superiors" (P = 0.006).
Conclusion: Burnout is present among family medicine residents at a relatively high percentage. This situation is strongly triggered by work-related stressors, organizational attributes, and system-related attributes, but not socio-demographics of the respondents. Systemic changes to relieve the workload of family medicine residents are recommended to promote effective management of burnout.
OBJECTIVE: To investigate the relationship between a +ve postoperative Upper Instrumented Vertebra (UIV) (≥0°) tilt angle and the risk of medial shoulder/neck and lateral shoulder imbalance among Lenke 1 and 2 Adolescent Idiopathic Scoliosis (AIS) patients following Posterior Spinal Fusion.
SUMMARY OF BACKGROUND DATA: Current UIV selection strategy has poor correlation with postoperative shoulder balance. The relationship between a +ve postoperative UIV tilt angle and the risk of postoperative shoulder and neck imbalance was unknown.
METHODS: One hundred thirty-six Lenke 1 and 2 AIS patients with minimum 2 years follow-up were recruited. For medial shoulder and neck balance, patients were categorized into positive (+ve) imbalance (≥+4°), balanced, or negative (-ve) imbalance (≤-4°) groups based on T1 tilt angle/Cervical Axis measurement. For lateral shoulder balance, patients were classified into +ve imbalance (≥+3°) balanced, and -ve imbalance (≤-3°) groups based on Clavicle Angle (Cla-A) measurement. Linear regression analysis identified the predictive factors for shoulder/neck imbalance. Logistic regression analysis calculated the odds ratio of shoulder/neck imbalance for patients with +ve postoperative UIV tilt angle.
RESULTS: Postoperative UIV tilt angle and preoperative T1 tilt angle were predictive of +ve medial shoulder imbalance. Postoperative UIV tilt angle and postoperative PT correction were predictive of +ve neck imbalance. Approximately 51.6% of patients with +ve medial shoulder imbalance had +ve postoperative UIV tilt angle. Patients with +ve postoperative UIV tilt angle had 14.9 times increased odds of developing +ve medial shoulder imbalance and 3.3 times increased odds of developing +ve neck imbalance. Postoperative UIV tilt angle did not predict lateral shoulder imbalance.
CONCLUSION: Patients with +ve postoperative UIV tilt angle had 14.9 times increased odds of developing +ve medial shoulder imbalance (T1 tilt angle ≥+4°) and 3.3 times increased odds of developing +ve neck imbalance (cervical axis ≥+4°).
LEVEL OF EVIDENCE: 4.
METHOD: The model was formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear-quadratic with the repopulation model was coupled into the model to account for the cells' population decay due to radiation. The treatment process was then simulated with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which included the proliferation coefficients of the cells, competition coefficients of the cells, and the perturbation constant of the normal cells were obtained from previous literature. The radiation and numerical parameters were obtained from reported clinical data of six patients treated with radiotherapy. The patients had tumor volumes of 24.1cm3, 17.4cm3, 28.4cm3, 18.8cm3, 30.6cm3, and 12.6cm3 with fractionated doses of 2 Gy for the first two patients and 1.8 Gy for the other four. The initial tumor volumes were used to obtain initial populations of cells after which the treatment process was simulated in MATLAB. Subsequently, a global sensitivity analysis was done to corroborate the model with clinical data. Finally, 96 radiation protocols were simulated by using the biologically effective dose formula. These protocols were used to obtain a regression equation connecting the value of the Caputo fractional derivative with the fractionated dose.
RESULTS: The final tumor volumes, from the results of the simulations, were 3.58cm3, 8.61cm3, 5.68cm3, 4.36cm3, 5.75cm3, and 6.12cm3, while those of the normal cells were 23.87cm3, 17.29cm3, 28.17cm3, 18.68cm3, 30.33cm3, and 12.55cm3. The sensitivity analysis showed that the most sensitive model factors were the value of the Caputo fractional derivative and the proliferation coefficient of the cancer cells. Lastly, the obtained regression equation accounted for 99.14% of the prediction.
CONCLUSION: The model can simulate a cancer treatment process and predict the results of other radiation protocols.