METHODS: This study included 965 Chinese community-dwelling older adults. Pearson correlation coefficient was conducted to assess the relationship between readiness toward ACP, death anxiety, and family cohesion. Structural equation model was used to examine the study hypothetical model.
RESULTS: 965 valid questionnaires were collected. Death anxiety is significantly related to the readiness toward ACP (r = -0.437, P < 0.01) and family cohesion (r = -0.444, P < 0.01), and family cohesion exhibited a positive correlation with readiness toward ACP (r = 0.499, P < 0.01). Family cohesion partially mediated the effect of death anxiety on readiness toward ACP, accounting for 35.94 % of the total effect.
CONCLUSIONS: Family cohesion mediates the relationship between death anxiety and readiness toward ACP. Healthcare professionals should implement measures to alleviate death anxiety and promote family cohesion in older adults, thereby enhancing their readiness toward ACP.
METHODS: Electronic databases were searched for studies investigating IL-37 and SLE. Data on IL-37 levels, SLE Disease Activity Index (SLEDAI) score, genetic polymorphisms, and its therapeutic effects from pre-clinical studies were extracted.
RESULTS: Previous studies presented conflicting findings on IL-37 levels in SLE patients. Some reported positive correlations with disease activity, while others observed associations between lower IL-37 and increased activity. Genetic variations in the IL-37 gene linked to SLE susceptibility have been reported. Pre-clinical studies using engineered mesenchymal stem cells or direct IL-37 treatment showed promise in reducing disease severity in mouse models and cell cultures of SLE. The analysis of multiple studies reveals that IL-37 expression varies significantly across different SLE subtypes.
CONCLUSIONS: While a potential link exists between IL and 37 and disease activity, genetic predisposition, and therapeutic benefit, further research is needed. Future studies with standardized designs, larger and more diverse populations, and mechanistic investigations are crucial to determine the therapeutic potential of IL-37 for SLE. This review highlights the need for well-designed clinical trials to evaluate the safety and efficacy of IL-37 therapy in patients with SLE.
METHODS: Antimicrobial activity was determined with disc diffusion and broth microdilution assays against eight skin colonising microorganisms including Staphylococcus aureus, Staphylococcus epidermidis, Salmonella enterica, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumonia followed by further fractionation of the pods ethyl acetate fraction by column chromatography along with preparative thin-layer chromatography. Quantification of bacterial death mechanism was elucidated by the measurement of hole size in cell wall that has been induced by extract constituents via field-emission scanning electron microscopy (FESEM).
RESULTS: Four fractions showed significant antimicrobial activity against the six microorganisms tested (p<0.01), with inhibition zones ranging from 35.67 to 17.00 mm, and minimum inhibitory concentration ranging from 6.25 to 50.00 mg/ml in which the pods ethyl acetate fraction was the most effective. The methanol fraction isolated from the pods ethyl acetate fraction was much more effective with a four-fold increase from 6.25 to 1.25 mg/ml against S. epidermidis. The disintegration of S. aureus was due to chronic cell wall alterations with pore creation, invaginations and morphological disorganisation. Autolysis in bacterial cells via the expression of peptidoglycan-disrupting lysozyme or bacterial murein hydrolase was postulated. A significantly large pore with a mean diameter of 293.7 nm was detected in the cell wall of S. aureus.
CONCLUSION: P. speciosa fraction could be a potential novel source for the development of a natural antibacterial agent.
METHODS: This was a cross-sectional study. An online self-administered questionnaire was distributed to Malaysian citizens aged 18-37 years. The questionnaire consisted of 11 questions that investigated their awareness of non-dentists offering orthodontic treatment, the harmful effects of braces fitted by non-dentists, and potential strategies to mitigate this phenomenon.
RESULTS: The study was completed by 426 participants, predominantly Malay, with a mean age of 22.9 years. A total of 76.1% reported awareness of braces fixed by non-dentists, primarily through social media platforms such as Instagram and Facebook. Lower cost emerged as the predominant motive (83.6%) for opting for non-dentist orthodontic treatment, followed by no waiting list (48.8%). Notably, the majority of participants acknowledged the illegality (70%) and potential harm (77%) associated with non-dentists providing orthodontic treatment. Legal enforcement (53.1%) was identified as the preferred method for mitigating this practice. Occupation significantly influenced knowledge of illegal orthodontic treatment (p 0.05).
CONCLUSION: The survey revealed that young adults are aware of and informed about non-dentists offering orthodontic treatment. While they identified cost as the primary reason for seeking such services, they also recognized legislation and public awareness through campaigns and social media as effective strategies to address this issue. Additionally, significant differences in legal awareness were observed among different occupational levels.
PURPOSE: To examine the accuracy of AI-based radiomics in diagnosis, prognosis assessment and predicting the diagnostic value of radiomics for pelvic LN metastasis in cervical cancer patients.
MATERIAL AND METHODS: The study included 118 female patients with 660 LNs and 118 merged LNs. Four imaging histology models-decision tree, random forest, logistic regression, and support vector machine (SVM)-were created in this study. The imaging histology features were extracted from both the independent and merged LN groups. The AUC values for the test sets and the training sets of the four imaging histology models were compared for the independent LN group and the merged LN group. The DeLong test was used to compare the models.
RESULT: The imaging histology prediction model developed in the merged LN group outperformed the independent LN group in terms of test set AUC (0.668 vs. 0.535 for decision tree, 0.841 vs. 0.627 for logistic regression, 0.785 vs. 0.637 for random forest, 0.85 vs. 0.648 for SVM) and accuracy (0.754 vs. 0.676 for decision tree, 0.780 vs. 0.671 for random forest, 0.848 vs. 0.685 for logistic regression, 0.822 vs. 0.657 for SVM).
CONCLUSION: The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.
METHODS: This retrospective cohort study was conducted on 866 patients from the Gulf Left Main Registry who presented between 2015 and 2019. The study outcome was hospital all-cause mortality. Various machine learning models [logistic regression, random forest (RF), k-nearest neighbor, support vector machine, naïve Bayes, multilayer perception, boosting] were used to predict mortality, and their performance was measured using accuracy, precision, recall, F1 score, and area under the receiver operator characteristic curve (AUC).
RESULTS: Nonsurvivors had significantly greater EuroSCORE II values (1.84 (10.08-3.67) vs. 4.75 (2.54-9.53) %, P<0.001 for survivors and nonsurvivors, respectively). The EuroSCORE II score significantly predicted hospital mortality (OR: 1.13 (95% confidence interval: 1.09-1.18), P<0.001), with an AUC of 0.736. RF achieved the best ML performance (accuracy=98, precision=100, recall=97 and F1 score=98). Explainable artificial intelligence using SHAP demonstrated the most important features as follows: preoperative lactate level, emergency surgery, chronic kidney disease (CKD), NSTEMI, nonsmoking status, and sex. QLattice identified lactate and CKD as the most important factors for predicting hospital mortality this patient group.
CONCLUSION: This study demonstrates the potential of ML, particularly the Random Forest, to accurately predict hospital mortality in patients undergoing CABG for LMCA disease and its superiority over traditional methods. The key risk factors identified, including preoperative lactate levels, emergency surgery, chronic kidney disease, NSTEMI, nonsmoking status, and sex, provide valuable insights for risk stratification and informed decision-making in this high-risk patient population. Additionally, incorporating newly identified risk factors into future risk scoring systems can further improve mortality prediction accuracy.
AIM: To determine the changes in frequency and pattern of anticholinergic drug use within a low- and middle-income country.
METHOD: Comparisons were made between population-based datasets collected from Malaysian residents aged 55 years and older in 2013-15 and 2020-22. Anticholinergic exposure was determined using the anticholinergic cognitive burden (ACB) tool. Drugs with ACB were categorised according to the Anatomical Therapeutic Chemical (ATC) classification.
RESULTS: A total number of 5707 medications were recorded from the 1616 participants included in the 2013-15 dataset. A total number of 6175 medications were recorded from 2733 participants in 2020-22. Two hundred and ninety-three (18.1%) and 280 (10.2%) participants consumed ≥ 1 medication with ACB ≥ 1 in 2013-15 and 2020-22 respectively. The use of nervous system drugs with ACB had increased (27 (0.47%) versus 39 (0.63%). The use of ACB drugs in the cardiovascular (224 (3.9%) versus 215 (3.4%)) and alimentary tract and metabolism (30 (0.52%) versus 4 (0.06%)) classes had reduced over time. Participants in 2020-22 were significantly less likely than those in 2013-15 to have total ACB = 1 - 2 (odds ratio [95% confidence interval] = 0.473[0.385-0.581]) and ACB ≥ 3 (0.251[0.137 - 0.460]) compared to ACB = 0 after adjustment for potential confounders (p