We aimed to evaluate the histopathological characteristics of chronic gastritis in dyspeptic patients without visible mucosal lesions in different age groups and different biopsy sites.Patients who...

Background: Malnutrition harms treatment outcomes, QoL, and survival in lung cancer patients. Effective dietary counseling can improve nutrition, but few randomized controlled trials have focused on lung cancer patients. The objective of this study was to determine if dietary counseling improves nutritional and treatment outcomes when compared to routine care. Methods: This open-label parallel RCT was conducted at Maharaj Nakorn Chiang Mai Hospital in Thailand. The investigators used computer-generated blocked randomization to assign patients to dietary counseling by a nutritionist or routine care. The nutritionist sessions occurred before treatment, with follow-ups at 3–4 weeks and 12 weeks. The primary outcome was the mean percentage change in the body weight of patients at 12 weeks. Secondary outcomes included changes in the BMI, nutrition score, QoL, serum albumin level, lymphocyte count, energy and protein intake, treatment response, PFS, and OS. Results: Between April 2020 and May 2022, after completing recruitment, 80 lung cancer patients were randomized: 43 to dietary counseling and 37 to routine care. The dietary counseling group showed significant benefits, with smaller decreases in body weight at 3–4 weeks (−0.8% vs. −2.6%, p = 0.05) and 12 weeks (−1.1% vs. −4.3%, p = 0.05). They also had higher energy and protein intake levels and better treatment response rates. The secondary outcomes and significant adverse events did not differ significantly between the groups. Conclusions: Dietary counseling helps to maintain body weight, maintain dietary intake, and enhance treatment responses in lung cancer patients. Although not all nutritional markers or survival outcomes were affected, these findings highlight the importance of early nutritional interventions.

Background: The Clinical Frailty Scale (CFS) is widely used for frailty assessment, but has not yet been formally validated for use in Thai populations. This study evaluated the reliability and validity of the Thai versions of the CFS (CFS-Thai) and its Classification Tree (CFS-CT-Thai). Methods: In this cross-sectional study, 213 participants aged ≥ 65 years (127 outpatients and 86 inpatients) were enrolled from two tertiary care hospitals in Thailand. The CFS and CFS-CT were translated into Thai using standard procedures. Inter-rater reliability was evaluated in a subsample of 53 inpatients. Concurrent validity was examined using the Thai version of the FRAIL scale (T-FRAIL), the Eastern Cooperative Oncology Group Performance Status (ECOG PS), and the modified Thai Frailty Index (mTFI). Results: The CFS-Thai showed strong inter-rater reliability (κ = 0.80, p < 0.001) and excellent agreement with the CFS-CT-Thai (κ = 0.94, p < 0.001). It demonstrated moderate correlation with T-FRAIL (ρ = 0.53) and strong correlation with ECOG PS (ρ = 0.76) and mTFI (ρ = 0.73). Using mTFI as the reference standard, the CFS-Thai showed high sensitivity (92.7%) and lower specificity (55.0%) at cut-off ≥ 4 (AUC = 0.74, 95% CI: 0.62–0.86), while cut-off ≥ 5 improved specificity (79.3%) and retained high sensitivity (93.5%) (AUC = 0.86, 95% CI: 0.81–0.92). ECOG PS ≥ 2 provided balanced diagnostic performance (sensitivity 83.9%, specificity 93.3%, AUC = 0.89, 95% CI: 0.82–0.95). Conclusions: The CFS-Thai and CFS-CT-Thai are reliable and valid instruments for frailty assessment in Thai older adults. Their diagnostic accuracy supports integration into clinical practice, especially in settings with limited geriatric expertise. Further studies should examine their implementation across diverse populations and their predictive value for clinical outcomes.

Background: Home blood pressure monitoring is currently a gold standard for guiding an adjustment of antihypertensive medications. According to the American Heart Association (AHA), the self-automated blood pressure (BP) measurement may represent accurate home BP. However, there is a limited data in real-world practice Objective: To explore the accuracy of a self-automated BP measurement in representing the actual home BP monitoring, in comparison with a single spot-arm automated BP measurement, which is routinely used in the clinical setting. Methods: We conducted a single center, randomized cross-over trial. At the Cardiology clinic, hypertensive patients aged between 18 and 80 years, who were able to perform self-automated BP measurement, were included in the study. The patients were categorized into two groups using different BP measurement methods: a self-automated BP measurement according to the AHA Recommendation 2017 and a single spot-arm automated BP measurement, and then crossed over to alternative methods. All participants were instructed to rest at least 5 minutes prior to undergo BP measurement in both methods. The data of BP measurements were subsequently compared with the standard home BP monitoring. Results: Overall, 190 patients underwent randomization. Mean differences of systolic blood pressure (SBP) in the self-automated AHA measurement and the single spot-arm measurement, compared with the standard home BP monitoring, were 3.57 mmHg (95% confidence interval (CI); 1.39 to 5.74 mmHg) and 9.44 mmHg (95% CI; 7.03 to11.86 mmHg), respectively (P<0.001) (table1). Mean differences of diastolic blood pressure (DBP) in the self-automated AHA and the single spot-arm measurements, compared with the standard home BP were −1.51 mmHg (95% CI; −2.76 to −0.25) and 2.69 mmHg (95% CI; 1.07–4.31), respectively (P<0.001) (table 1). White coat hypertension was detected in 7 (3.72%) of patients by AHA measurement and 17 (9.04%) by the single-spot arm measurement (P=0.008). Conclusion: The self-automated BP measurement according to the AHA Recommendation 2017 was more accurate than the single spot-arm automated BP measurement in representing the actual home BP monitoring and reduced the possibility of the white coat effect. Funding Acknowledgement: Type of funding sources: None. Table 1