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BackgroundDiabetes mellitus is a significant global health issue, and alternative treatments from natural products like Garcinia mangostana L. [Clusiaceae] or GM are being explored for their potential benefits. This study focused on evaluating the hypoglycemic effects of GM on diabetic rodent models.MethodsA comprehensive search was conducted in PubMed, Scopus, and Embase for studies reporting blood glucose levels within 2 weeks as the primary outcome and changes in total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) as secondary outcomes. A network meta-analysis (NMA) was performed to determine the pooled effectiveness of each intervention, estimating the weighted mean difference (WMD) and 95% confidence interval (CI) from both direct and indirect evidence. The surface under the cumulative ranking curve (SURCA) was used to rank the interventions.ResultsTen articles were identified, with nine included for quantitative analysis. All GM extracts showed greater effectiveness than the control in decreasing blood glucose levels within 2 weeks. GM at 200 mg/kg (GM200) was the top-ranked extract for reducing glucose levels beyond 2 weeks and increasing HDL-C levels. The ethanol extract of GM at 200 mg/kg (GME200) was the most effective for blood glucose reduction within 2 weeks and for TC and TG reductions. The methanol extract of GM at 200 mg/kg (GMM200) was the top-ranked extract for LDL-C reductions.ConclusionGM and its extracts demonstrated significant hypoglycemic activity and improvements in lipid profiles in diabetic rodent models, highlighting their potential as therapeutic agents for the prevention and treatment of diabetes mellitus. Further research in human trials is warranted to confirm these findings and establish clinical applications.Clinical trial registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023426254.

Background: Burkholderia pseudomallei, a Gram-negative pathogen, causes melioidosis. Although various clinical laboratory identification methods exist, culture-based techniques lack comprehensive evaluation. Thus, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of culture-based automation and non-automation methods. Methods: Data were collected via PubMed/MEDLINE, EMBASE, and Scopus using specific search strategies. Selected studies underwent bias assessment using QUADAS-2. Sensitivity and specificity were computed, generating pooled estimates. Heterogeneity was assessed using I2 statistics. Results: The review encompassed 20 studies with 2988 B. pseudomallei samples and 753 non-B. pseudomallei samples. Automation-based methods, particularly with updating databases, exhibited high pooled sensitivity (82.79%; 95% CI 64.44–95.85%) and specificity (99.94%; 95% CI 98.93–100.00%). Subgroup analysis highlighted superior sensitivity for updating-database automation (96.42%, 95% CI 90.01–99.87%) compared to non-updating (3.31%, 95% CI 0.00–10.28%), while specificity remained high at 99.94% (95% CI 98.93–100%). Non-automation methods displayed varying sensitivity and specificity. In-house latex agglutination demonstrated the highest sensitivity (100%; 95% CI 98.49–100%), followed by commercial latex agglutination (99.24%; 95% CI 96.64–100%). However, API 20E had the lowest sensitivity (19.42%; 95% CI 12.94–28.10%). Overall, non-automation tools showed sensitivity of 88.34% (95% CI 77.30–96.25%) and specificity of 90.76% (95% CI 78.45–98.57%). Conclusion: The study underscores automation's crucial role in accurately identifying B. pseudomallei, supporting evidence-based melioidosis management decisions. Automation technologies, especially those with updating databases, provide reliable and efficient identification.

Background: Significant errors of activated partial thromboplastin time (aPTT) ratio were frequently observed in blood sampling from central venous dialysis catheter (CVC) of hemodialysis (HD) patients. Following the draw-and-return methods, initial blood withdrawal from the catheter before sampling can reduce the error, but the optimal withdrawal volume remains undetermined. Aim: The objective of this study is to determine the optimal blood withdrawal volume for the draw-and-return methods to improve aPTT ratio accuracy in hemodialysis patients with CVC. Methods: A prospective study was conducted in patients receiving HD via CVC. Four blood samples were collected from each patient, involving a peripheral venipuncture and three draw-and-return samples (10 ml, 20 ml and 25 ml groups). The aPTT ratio of a peripheral sample was used as a reference to determine the aPTT ratio accuracy for each draw-and-return group. Subsequently, the agreement was illustrated using modified Bland-Altman plot. Results: A total of 1,000 samples were obtained from 250 patients. The patients had a mean age of 59.6 ± 15.4 years, with 17.2% using citrate as the CVC's locking agent. The adjusted accuracies of the aPTT ratio varied significantly among the three withdrawal volumes (p-value

Abstract Objective To examine the clinical efficacy of prophylactic metoclopramide in reducing the incidence of nausea and vomiting in emergency department (ED) patients with acute pain who were treated with intravenous tramadol. Results We conducted a single-center randomized, double-blinded, placebo-controlled trial. A total of 99 ED patients presented with acute pain were recruited. Sixty-four patients were randomized, 31 patients in the treatment arm and 33 in the control arm. Overall, there were no significant differences in baseline characteristics between treatment arm and control arm. Only one patient within each arm reported having nausea symptom. No patients reported vomiting episode. There was no statistically significant difference in the proportion of patients with nausea or vomiting symptoms between the two groups (3.2% in the treatment arm vs. 3.0% in the control arm, p = 1.000). The administration of prophylactic metoclopramide may not provide additional benefit in reducing the occurrence of nausea and/or vomiting episode in ED patients with acute pain treated with intravenous tramadol. Trial registration Randomized clinical trial TCTR20220525001; registration date: 21 October 2021. Retrospectively registered.

Introduction/objectiveExtubation failure in pediatric patients with congenital or acquired heart diseases increases morbidity and mortality. This study aimed to develop a clinical risk score for predicting extubation failure to guide proper clinical decision-making and management.MethodsWe conducted a retrospective study. This clinical prediction score was developed using data from the Pediatric Cardiac Intensive Care Unit (PCICU) of the Faculty of Medicine, Chiang Mai University, Thailand, from July 2016 to May 2022. Extubation failure was defined as the requirement for re-intubation within 48 h after extubation. Multivariable logistic regression was used for modeling. The score was evaluated in terms of discrimination and calibration.ResultsA total of 352 extubation events from 270 patients were documented. Among these, 40 events (11.36%) were extubation failure. Factors associated with extubation failure included history of pneumonia (OR: 4.14, 95% CI: 1.83–9.37, p = 0.001), history of re-intubation (OR: 5.99, 95% CI: 2.12–16.98, p = 0.001), and high saturation in physiologic cyanosis (OR: 5.94, 95% CI: 1.87–18.84, p = 0.003). These three factors were utilized to develop the risk score. The score showed acceptable discrimination with an area under the curve (AUC) of 0.77 (95% CI: 0.69–0.86), and good calibration.ConclusionThe derived Pediatric CMU Extubation Failure Prediction Score (Ped-CMU ExFPS) could satisfactorily predict extubation failure in pediatric cardiac patients. Employing this score could promote proper personalized care. We suggest conducting further external validation studies before considering implementation in practice.

The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient's presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model's predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76-0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.

Background: Diagnosing tuberculous pleural effusion (TPE) in patients presenting with Lymphocyte-Predominant Exudative pleural effusion (LPE) is challenging, due to the poor clinical utility of TB culture. Adenosine deaminase (ADA) has been recommended for diagnosis, but its high cost and limited availability hinder its clinical utility. We aim to develop diagnostic prediction tools for Thai patients with LPE in scenarios where pleural fluid ADA is available but yields negative results and in situations where pleural fluid ADA is not available. Methods: Two diagnostic prediction tools were developed using retrospective data from patients with LPE at Surin Hospital. Model 1 is for ADA-negative results, and Model 2 is for situations where pleural fluid ADA testing is unavailable. The models were derived using multivariable logistic regression and presented as two clinical scoring systems: round-up and count scoring. The score cut-point that achieves a positive predictive value (PPV) comparable to the post-test probability of a pleural fluid ADA at a cut-point of 40 U/L was used as a threshold for initiating anti-TB treatment. Results: A total of 359 patients were eligible for analysis, with 166 diagnosed with TPE and 193 diagnosed with non-TPE. Age

Purposes: Coronary artery calcium (CAC) score provides a quantification of atherosclerotic plaque within the coronary arteries. This study aimed to examine the prevalence and CAC score distribution and to evaluate the association of each CAC score classifications with major adverse cardiovascular events (MACE) in a Thai clinical cohort. Methods: This study was a retrospective observational cohort. We included patients aged above 35 years who underwent CAC score testing. The absolute and age-sex specific percentile classifications were categorized as 0, 1 to 10, 11 to 100, 101 to 400, and >400 and 0, 90th, respectively. The endpoint was MACE, including cardiovascular death, myocardial infarction, heart failure hospitalization, coronary artery revascularization procedure, and stroke. Multivariable Cox regression was used to estimate the hazard ratios. The discriminative performance between classifications were compared using Harrell's C-statistics. The agreement was assessed via Cohen's Kappa. Results: This study included 440 patients, with approximately 70% of Thai patients exhibiting a CAC score. CAC score distributed higher in male than female and increased with age. Both CAC score classification demonstrated the acceptable predictive performance. However, fair agreement was observed between classifications (Cohen's kappa 0.51, 95%CI 0.42–0.59). Within the absolute classification, a higher CAC score was associated with increased hazard ratios for MACE across stratified age-sex-specific percentile levels. In contrast, the hazard ratios for MACE did not consistently rise with higher age-sex-specific percentile CAC score when stratified by absolute CAC score levels. Conclusions: Both absolute and age-sex-specific percentile CAC score demonstrated acceptable performance in predicting MACE. However, the absolute CAC score classification may be more suitable for risk stratification within the Thai clinical cohort. Our findings offer supportive information that could inform future recommendations for CAC score testing criteria within national clinical practice guidelines.

Importance: Beta-lactams (BLs) are the most prescribed antibiotics, being the most frequent cause of drug allergy. However, the association between BL allergy and genetic variations is still unclear. Objective: This systematic review and meta-analysis aimed to summarize the genetic effects of BL-induced hypersensitivity using existing evidence. Methods: We searched PubMed, Medline, Scopus, EMBASE, CINAHL, and Cochrane Library from inception to September 15, 2022 with no language restriction. Genetic association studies investigating genetic variant/polymorphism and risk of drug-induced hypersensitivity reactions among individuals receiving BL-antibiotics were included. We excluded studies of acute interstitial nephritis, drug-induced liver injury, serum sickness, and isolated drug fever. Data were comprehensively synthesized and quality of study were assessed using STrengthening the Reporting of Genetic Association Studies (STREGA). The record screening, extraction and quality assessment were performed by two reviewers and discussions were made to resolve discrepancies. The effects of each variant were pooled and evaluated by modified Venice criteria. Results: A total of 9276 records were identified, and 31 studies were eligible for inclusion. Twenty-seven were candidate-gene association studies (5416 cases and 5939 controls), while the others were next-generation sequencing (NGS) or genome-wide association studies (GWASs) (119 838 cases and 1 487 111 controls). Forty-nine polymorphisms were identified and most of them located in allergic reaction pathways. Meta-analyses of 15 candidate variants in a mixture of both immediate and non-immediate reactions revealed weak genetic effects of rs1801275 (8 studies; n = 1,560; odd ratio 0.73; 95%CI: 0.57–0.93) and rs20541 (4 studies; n = 1,482; odd ratio 1.34; 95%CI: 1.07–1.68) in IL4R and IL13, respectively. Results from GWASs and NGS identified, and confirmed associations in HLA regions including HLA-DRA, HLA-B, HLA-DQA, HLA-DRB1, and HLA-DRB3. Conclusion: Our study summarized genetic evidence influencing BL-induced hypersensitivity and estimated effects of potential variants. We postulated that the genomic studies provide better insights to the mechanism of reactions and suggest potential effects of HLA Class II variants. However, results were inconsistent and unable to generalize in different settings. Further high-throughput studies with a well-defined function, epigenetic interaction, incorporated with clinical factors, would be beneficial for risk identification in BL-induced hypersensitivity.

Objective: This study aimed to determine the association between the severity of inflammation at each anatomical sexual activity from gram staining with Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) infections. Materials & Methods: This study was conducted using laboratory test data from patients at the Bangrak Sexually Transmitted Infections Center. The data obtained consisted of gram staining, which was divided by the number of polymorphonuclear leukocytes (PMNL), NG culture, and Nucleic Acid Amplification Test (NAAT) for NG and CT results. Results: For the diagnostic association between PMNL and NG infection, the results revealed that samples with urethral PMNL 3+ or 4+ carried a significant likelihood ratio (LR) for positive infection, LR 5.61 (P

Abstract Prescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines were implemented to reduce the errors but were not found to be highly effective. Machine learning driven clinical decision support systems (CDSS) show a potential solution to address this problem. We developed a HAD screening protocol with a machine learning model using Gradient Boosting Classifier and screening parameters to identify the events of HAD prescription errors from the drug prescriptions of out and inpatients at Maharaj Nakhon Chiang Mai hospital in 2018. The machine learning algorithm was able to screen drug prescription events with a risk of HAD inappropriate use and identify over 98% of actual HAD mismatches in the test set and 99% in the evaluation set. This study demonstrates that machine learning plays an important role and has potential benefit to screen and reduce errors in HAD prescriptions.