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The present study aims to determine the potential benefits of PNF on balance and gait function in patients with chronic stroke by using a systematic review and meta-analysis. Systematic review in the following databases: MEDLINE/PubMed, Physiotherapy Evidence Database (PEDro), Cochrane Library and Google Scholar. Studies up to September 2020 are included. A systematic database search was conducted for randomized control trials (RCTs) that investigated the effects of PNF intervention in patients with chronic stroke using balance and gait parameters as outcome measures. The primary outcomes of interest were Berg Balance Scale (BBS), Functional Reach Test (FRT), Timed Up and Go Test (TUG) and 10-Meter Walking Test (10MWT). Nineteen studies with 532 participants were included, of which twelve studies with 327 participants were included for meta-analysis. When the data were pooled, PNF made statistically significant improvements in balance with BBS, FRT and TUG (p < 0.05) or gait velocity with 10MWT (p < 0.001) when compared to the control. This review indicates that PNF is a potential treatment strategy in chronic stroke rehabilitation on balance and gait speed. Further high-quality research is required for concluding a consensus of intervention and research on PNF.

Purpose The effect of nocturnal wear of denture on sleep quality and integrity is still not well understood. Therefore, this systematic review was conducted to provide evidence on this topic. Methods Electronic searches were conducted from 1964 up to September 2015, using MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials. Only publications in English or French, in which sleep quality of edentate adult individuals wearing dentures at night was compared to those not wearing were included in the review. Random effects models were used to pool the effect sizes. Results A total of 10 studies were included in the systematic review and 5 in the meta-analysis. No statistically significant difference between sleeping with denture and without denture was found for the Apnea-Hypopnea Index (AHI; Standard Mean Difference = −0.60, 95 % CI −1.67–0.47; Z  = −1.10; p  = 0.27). However, there was considerable heterogeneity in the studies included in the meta-analysis (Tau 2  = 1.34; Q-value = 59.32, df  = 4 ( P  < 0.0001); I 2  = 93.3 %). When results from randomized controlled trials (RCTs) were pooled in subgroup analyses of AHI, there was a tendency towards favoring sleeping without dentures ( P  = 0.059) and no evidence of heterogeneity between studies (Tau 2  = 0.000; Q-value = 0.06, df  = 1 ( P  = 0.80); I 2  = 0.000 %). Conclusion The current evidence suggests that there is no difference in the sleep quality and integrity of individuals wearing or not wearing their denture during sleep. However, the results of randomized controlled trials favoring sleeping without dentures and the likely presence of bias in the previous studies indicate the need for further randomized controlled trials for the development of clinical guideline.

Background Edentulism and sleep disturbance are chronic conditions that are common in older people and have serious adverse consequences for their functioning and quality of life. Edentulism can disturb sleep through the alteration of the craniofacial structure and surrounding soft tissue. However, the effect of prosthetic rehabilitation of edentulism on sleep quality is still not well understood. The objectives of this study are to test whether nocturnal denture wear affects sleep quality, daytime sleepiness, and the oral health related quality of life of edentate older people with moderate to severe sleep apnea, and to identify modifiers of effect of nocturnal denture wear. Methods/design We will carry out a single-blind randomized cross-over trial. Seventy edentate older people with moderate to severe obstructive sleep apnea will be enrolled. The study participants will be assigned to wear and not wear their dentures on alternate periods of 30 days. The outcome measures will be sleep quality (assessed by portable polysomnography), daytime sleepiness (assessed by the Epworth Sleepiness Scale), and oral health related quality of life (assessed by validated questionnaire). A number of characteristics (sociodemographic, oropharyngeal morphology, oral and prosthesis characteristics, and perceived general health quality of life) will be assessed by means of clinical examination, 3D imaging of the craniofacial structure, and validated questionnaires at baseline. Linear mixed effects regression models for repeated measures will be fitted to test the study hypotheses. The main analyses will be based on the intention-to-treat principle. To assess the robustness of the findings to potential incomplete adherence, sensitivity analyses will be conducted while applying the per-protocol principle. Discussion This practice-relevant evidence could represent a preventive approach to improve sleep characteristics of the older population and improve their well-being and quality of life. Trial registration ClinicalTrials.gov NCT01868295 .

Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set. Measurement(s) diseases and abnormal findings from chest X-ray scans Technology Type(s) AI is used to detect diseases and abnormal findings Sample Characteristic - Location Vietnam

IntroductionImplementation of low-intensity, evidence-based psychological interventions can help meet the mental health and psychosocial needs of people with cancer, especially in low-resource settings where there is a dearth of mental health specialists. In this study, we will conduct a feasibility randomised controlled trial (RCT) of the stress management intervention Self-Help Plus, which has been translated and adapted to Vietnamese, vSH+, among people newly diagnosed with breast or gynaecological cancer in Viet Nam.Methods and analysisAt six participating hospitals, individuals diagnosed with breast or gynaecologic cancer within the past year will be recruited, consented and randomised into either enhanced usual care (EUC) or EUC plus the vSH+ intervention, which consists of four sessions each lasting approximately 75 min. Quantitative surveys will be administered at three time points: enrolment/baseline (T0), after 6 weeks (T1) and after 4 months (T2). A qualitative evaluation component, which will include in-depth interviews with patients, implementers and healthcare staff and managers, as well as focus group discussions with caregivers, will assess the acceptability and feasibility of the vSH+ intervention.Ethics and disseminationEthical reviews for the study were obtained from Boston University, Hanoi University of Public Health (HUPH) and all the participating hospital sites. On completion of data collection and analyses, the research team will prepare and submit abstracts to scientific conferences as well as manuscripts to peer-reviewed journals. We will also conduct dissemination events to report the trial results to relevant stakeholders.Trial registration numberNCT06398067.

Urinary tract infections (UTIs) are common globally, and are developing increased antibiotic resistance. Despite being the diagnostic \"gold standard,\" urine culture is limited by slow results and a high rate of false negative findings, leading to treatment delays, higher costs, and overuse of empirical antibiotics. Our study aims to develop a rapid and reliable model to predict clinical outcomes. From January 1st to October 31st, 2023, we enrolled patients with symptoms suggesting UTI from the Outpatient Department of our hospital. Inclusion criteria were patients aged ≥18, initially diagnosed with UTI, available urinalysis, flow cytometry, and urinary culture. Exclusion criteria included failed sample collection and cultures, and pregnant women. A case-control study was conducted, with UTI cases defined as ≥ 10^5 CFU/ µ L and controls as < 10^5 CFU/ µ L, matched for age and sex in a 1:1 ratio. For validation, retrospective cases from July to December 2022 were selected with matching controls. Using urine culture as the gold standard, the predictive model was developed with backward stepwise logistic regression. Model discrimination was assessed using area under the curve (AUC). In our discovery cohort, we included 1,335 UTI cases and 1,282 non-UTI controls, with mean ages of 52.9 ± 17.1 years and 51.9 ± 16.4 years, and females of 76.9% and 77.7%. Using 100 cells/uL as a threshold, bacterial counts demonstrated a sensitivity of 91.0% and specificity of 45.7%. Our novel UTIRisk score, developed from urinalysis and flow cytometry parameters, showed strong discrimination for UTI, with a AUC of 0.82 (95% CI: 0.81-0.84). In the validation cohort, the AUC was 0.77 (95% CI: 0.74-0.80). The UTIRisk score exhibited excellent specificity (96.5%) and high positive predictive value (92.6%). The score performed strongly across subgroups, particularly in males and patients aged ≥65. Our UTIRisk score can improve diagnosis, reduce unnecessary urine cultures, optimize antibiotic use, and help control antibiotic resistance in LMICs. Multicenter, and intervention-based studies are warranted before clinical implementation.

Background Non-invasive multi-cancer early detection (MCED) tests have shown promise in enhancing early cancer detection. However, their clinical utility across diverse populations remains underexplored, limiting their routine implementation. This study aims to validate the clinical utility of a multimodal non-invasive circulating tumor DNA (ctDNA)-based MCED test, SPOT-MAS (Screening for the Presence Of Tumor by DNA Methylation And Size). Methods We conducted a multicenter prospective study, K-DETEK (ClinicalTrials.gov identifier: NCT05227261), involving 9057 asymptomatic individuals aged 40 years or older across 75 major hospitals and one research institute in Vietnam. Participants were followed for 12 months. Results Of the 9024 eligible participants, 43 (0.48%) tested positive for ctDNA. Among these, 17 were confirmed with malignant lesions in various primary organs through standard-of-care (SOC) imaging and biopsy, with 9 cases matching our tissue of origin (TOO) predictions. This resulted in a positive predictive value of 39.53% (95%CI 26.37–54.42) and a TOO accuracy of 52.94% (95%CI 30.96–73.83). Among the 8981 participants (99.52%) who tested negative, 8974 were confirmed cancer-free during a 12-month period after testing, yielding a negative predictive value of 99.92% (95% CI 99.84–99.96). The test demonstrated an overall sensitivity of 70.83% (95%CI 50.83–85.09) and a specificity of 99.71% (95% CI 99.58–99.80) for detecting various cancer types, including those without SOC screening options. Conclusions This study presents a prospective validation of a multi-cancer early detection (MCED) test conducted in a lower middle-income country, demonstrating the potential of SPOT-MAS for early cancer detection. Our findings indicate that MCED tests could be valuable additions to national cancer screening programs, particularly in regions where such initiatives are currently limited. Trial registration ClinicalTrials.gov ID: NCT05227261. Date of registration: 07/02/2022.

The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of circulating tumor DNA (ctDNA) in plasma, known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a protocol for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Paired plasma and tumor tissue samples were used to evaluate mutation profiles detected by ultra-deep MPS, which showed 87.5% concordance. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72-0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90-0.98). Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.

The authors describe transmission of 2019-nCoV from a father, who had flown with his wife from Wuhan to Hanoi, to the son, who met his father and mother in central Vietnam and shared a hotel room with them for 3 days. The findings suggest that the incubation period in the son may have been 3 days or less.