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Background: Estimating changes in fine fuel loads (FFL) is essential for carbon monitoring and fire management. Field measurements of post-fire fuel response are challenging, leading to reliance on generalised fuel types in operational models. Aims: This study presents a proof-of-concept for estimating fine fuel consumption and accumulation by integrating forest inventory and fire records, aiming to refine fuel dynamics estimates and enhance current practices. Methods: We estimated FFL changes across vertical strata in southeast Australian eucalypt forests, considering burn severity, fire type and forest cover. Fuel consumption was estimated by correlating pre-fire observations with combustion factors defined by burn severity. Fuel accumulation was predicted using modified Olson models with dynamic input parameters. Key results: Wildfires typically occurred in forests with higher FFL and consumed more fuels than prescribed burns. Closed forests experienced greater fuel loss compared with open and woodland forests. Increasing fire severity led to lower decomposition rates and a longer time to reach pre-fire FFL, with denser forests showing higher accumulation rates. Conclusions: Integrating forest inventory and fire history data offers valuable insights into fuel dynamics, potentially enhancing existing fuel hazard models. Implications: The approach is applicable in regions with mature forest inventories and advanced fire severity mapping. This study presents a proof-of-concept for estimating fine fuel consumption and accumulation by integrating forest inventory and fire records, aiming to refine fuel dynamics estimates and enhance current practices. We estimated FFL changes across different vertical strata in southeast Australian eucalypt forests, considering burn severity, fire type and forest cover. [ABSTRACT FROM AUTHOR]

Context: Pasture dieback has emerged as a significant threat to the health and productivity of sown pastures in eastern Queensland and northern New South Wales, Australia. Aims: We aimed to address knowledge gaps on spatial spread patterns, recovery trajectories and floristic changes using remote sensing and ground surveys. Methods: We used a time series of high-resolution (12–25 cm) aerial imagery to quantify and compare pasture dieback spread over 7 years in three land-use areas: ungrazed pasture, grazed pasture and rehabilitation following mining. The green leaf index was applied using supervised random forest algorithms to classify areas affected between 2015 and 2021. Flora surveys were conducted to compare impacted and unimpacted areas for the three land uses and validate classifications. Key results: The first emergence of pasture dieback was in ungrazed pasture, and these areas recorded the highest rate of dieback spread at 1.88 ha month−1, compared with 0.54 and 0.19 ha month−1 in rehabilitated and grazed pastures respectively. Field validation showed that dieback-impacted pastures shifted from buffel grass (Cenchrus ciliaris L.), to forb-dominated communities with significantly different species mix, biomass and cover conditions. An analysis of local climate data showed that winter night-time temperatures and rainfall were notably higher than long-term means in the year preceding the first detection of pasture dieback. Conclusions: High resolution aerial imagery and ground surveys can be used to monitor pasture health by employing vegetation indices and random forest classifiers. Implications: Ungrazed pastures and roadside areas should be managed to protect the region from further outbreaks. Pasture dieback has been affecting the health and productivity of pastures in eastern Queensland and northern NSW. We used aerial imagery to map and compare pasture dieback spread patterns in three land use areas: grazed pasture, ungrazed pasture and rehabilitated pasture following coal mining. The fastest rate of spread was recorded in ungrazed pasture followed by rehabilitation and grazed pastures. Flora surveys showed that dieback-impacted sites shifted from buffel grass to forb dominated pastures. [ABSTRACT FROM AUTHOR]

Background: Determining the clinical and subclinical characteristics related to the recurrence status in patients with a thyroid carcinoma has great significance for prognosis, prediction of recurrence and monitoring of treatment outcomes. This study aimed to determine the association between recurrence rate and some characteristics in patients with thyroid carcinoma. Patients and Methods: The study was conducted by descriptive method with longitudinal follow-up on 102 thyroid carcinoma patients at 103 Military Hospital, Hanoi, Vietnam, from July 2013 to December 2016. Results: Univariate analysis showed that there was a relationship between the recurrence characteristics in the studied patients and the characteristics of lymph node metastasis (P = 0.026; OR = 15; 95% CI = 1.4-163.2) and BRAF V600E mutation status (P = 0.01; OR = 3.41; 95% CI = 1.31-8.88). When analysing the multivariable Logistic regression model, there was a positive correlation between the occurrence of BRAF V600E gene mutation (P = 0.032; OR = 17.649; 95% CI = 1.290-241.523) and male sex (P = 0.036; OR = 12.788; 95% CI = 1.185-137.961) and the occurrence of recurrence in study patients. The mean time to relapse was earlier in male patients than in female patients (P = 0.02). The mean time to relapse in patients with the BRAF V600E mutation (31.81 ± 1.14 months) was shorter than the mean time to relapse in the group without the mutation (57.82 ± 2.08 months) (P = 0.01). The group of patients with mutations in the BRAF V600E gene increased the risk of recurrence compared with the group without the mutation (HR = 9.14, P = 0.04). Conclusion: There is a positive correlation between recurrence and masculinity, lymph node metastasis and the occurrence of BRAF V600E mutations in thyroid carcinoma patients. [ABSTRACT FROM AUTHOR]

An empirical scale-up model of the pan-coating process for controlled-release urea fertilizer (CRUF) was novel performed via a pan-coating process using the biodegradable polymer solution. The similarities in the geometry, kinetics, dynamics, and thermodynamics during the scaling-up of the pan-coating process were analyzed. The dimensionless numbers and a dimensionless number equation were determined and derived, respectively. The thickness of the formed coating related almost to the air velocity and the pan speed. This model showed the significant applications in the design and optimization of industrial particle-coating systems. [ABSTRACT FROM AUTHOR]

Eucalypt forests are vital for biodiversity and fire dynamics, with fine fuels serving as the primary ignition source and critical factors in determining fire intensity. Accurate measurement of fine fuel via destructive field methods is location and time-restricted, hindering the tracking of their dynamic variations across diverse landscapes. In response, this study utilised forest inventory data collected by the Victorian Forest Monitoring Program (VFMP) in Victoria, southeastern Australia, along with allometric models, to estimate fine fuel loads across four vertical layers (surface, near-surface, elevated, and canopy). The average total fine fuel load in Victorian Eucalypt forests was estimated at 18.45 t ha−1 (ranging from 2.23 to 39.54 t ha−1), with surface litter contributing 58.2% of this total, followed by canopy (22.1%), near-surface (13.4%), and elevated (6.6%) fine fuels. We also conducted a comprehensive investigation and found distinct variations in fine fuel loads across different bioregions, forest covers, and vegetation types. Furthermore, stepwise multiple regression was employed to develop predictive models for estimating fine fuel loads from forest structural and environmental variables. Results show that total and canopy fine fuels can be reasonably explained by forest height, density, and climate-related variables (with R 2 = 0.38 and 0.51, respectively). However, the explanatory power of these models diminished when applied to elevated, near-surface, and surface fine fuels. This study underscores the intricacies of fine fuel distribution in Eucalypt forests, emphasising the necessity for comprehensive factors in fire management planning. Further research is needed to better comprehend the relationship between forest characteristics and fine fuel dynamics to enhance wildfire risk assessment and mitigation strategies. • Forest inventory data was utilised to estimate and analyse fine fuel loads across large areas. • Fine fuels were estimated across four vertical layers (surface, near-surface, elevated, canopy). • Fine fuel loads exhibit significant variations across different spatial strata. • Fine fuel loads show moderate correlations with forest structural and environmental variables. • Modelling fine fuel loads from forest structural and environmental variables. [ABSTRACT FROM AUTHOR]

In 2016, extreme drought and saline intrusion led to widespread damage and livelihood disruption for many people in the coastal region of the Vietnamese Mekong Delta. Through an integrated multi-step approach of literature and stakeholder analysis, focus group discussions and interviews with local governments and farmers, an understanding of the local water governance systems and their functioning is gained, providing insight into the power, interest and responsibility of multiple agencies. Development of more responsive and inclusive water governance systems, with improved early warnings and information flows during extreme events, is needed to improve the resilience and adaptation of the region. [ABSTRACT FROM AUTHOR]

Influenza virus A is a significant agent involved in the outbreak of worldwide epidemics, causing millions of fatalities around the world by respiratory diseases and seasonal illness. Many projects had been conducting to investigate recovered infected patients for therapeutic vaccines that have broad-spectrum activity. With the aid of the computational approach in biology, the designation for a vaccine model is more accessible. We developed an in silico protocol called iBRAB to design a broad-reactive Fab on a wide range of influenza A virus. The Fab model was constructed based on sequences and structures of available broad-spectrum Abs or Fabs against a wide range of H1N1 influenza A virus. As a result, the proposed Fab model followed iBRAB has good binding affinity over 27 selected HA of different strains of H1 influenza A virus, including wild-type and mutated ones. The examination also took by computational tools to fasten the procedure. This protocol could be applied for a fast-designed therapeutic vaccine against different types of threats. [ABSTRACT FROM AUTHOR]

The spray coating process is an important process of the coating particle technology for the production of controlled-release fertilisers (CRFs). Its modelling would be helpful in the production process such as increase material usage efficiency, save production costs, and reduce dispersal waste produced that will cause environmental pollution. This article presents the results of an experimental study to establish an empirical model of the spray coating process with a biodegradable polymer solution. The shape and size of the spray area, droplet size distribution (DSD) and Sauter mean diameter (SMD) were determined by using high-speed camera 1000 frames per second (fps), laser transmitter and the image processing toolbox of MATLAB software. The results showed that the spray area had a cone shape, its mean width, corresponding to a spray distance of 300 mm, was 451 mm at a pressure of 0.18 MPa, viscosity of 50 mPa.s, spray flow of 1.5 mL/min and the droplet size distributions were noted 0.153 to 1.449 mm and droplet SMD was 0.876 mm. The model also showed that droplet SMD was calculated by four dimensionless numbers, will be applied to calculate, optimize and simulate coating particle technology. [ABSTRACT FROM AUTHOR]

Across the world, millions of hectares of forest are burned by wildfires each year. Satellite remote sensing, particularly when used in time series, can describe complex disturbance‐recovery processes, but is underutilized by ecologists. This study examines whether a greater disturbance magnitude equates to a longer recovery length, in the fire‐adapted forests of south‐east Australia. Using Landsat time series, spectral disturbance and recovery maps were first created, for 2.3 million hectares of forest, burned between 2002 and 2009. To construct these maps, a piecewise linear model was fitted to each pixel's Normalized Burn Ratio (NBR) temporal trajectory, and used to extract the disturbance magnitude (change in NBR) and the spectral recovery length (number of years for the NBR trajectory to return to its pre‐fire state). Pearson's correlations between disturbance magnitude and spectral recovery length were then calculated at a state level, bioregion level and patch level (600 m × 600 m, or 36 hectares). Our results showed overall correlation at the state level to be inconclusive, due to confounding factors. At the bioregion level, correlations were predominantly positive (i.e. a greater disturbance equals a longer recovery). At the patch level, both positive and negative correlations occurred, with clear evidence of spatial patterns. This suggests that the relationship between disturbance magnitude and recovery length is dependent on forest type. This was further explored by investigating the major vegetation divisions within one bioregion, which provided further evidence that relationships varied by vegetation type. In Heathy Dry Forests, for example, a greater disturbance magnitude usually led to a longer recovery length, while in Tall Mist Forests, the opposite behaviour was evident. Results of the patch‐level analysis were particularly promising, demonstrating the utility of satellite remote sensing in producing landscape scale information to inform policy and management. [ABSTRACT FROM AUTHOR]

Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass's delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy 'greenness', are not as reliable, with values returning to pre-fire levels in 3-5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8-10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and limitations of the various indices that these methods rely on. [ABSTRACT FROM AUTHOR]

In a study to identify strategies to deal with rapid urbanization and threatening climate change, in Can Tho City, Vietnam, a survey of 1200 households was undertaken to investigate water access and sanitation services. The survey targeted three different groups based on their access to water services: (a) those with piped water supply, (b) without piped water, and (c) a mix of (a) and (b). Socio-economic factors and level of urbanization significantly influenced the different water sources accessed by households and their type of sanitation. Results indicated that householders often employed multiple water sources in an attempt to match source with intended use. The use of multiple water sources, levels of sanitation and the quality of water were found to be good indicators of reported rates of illness. Results also showed that the provision of piped water does not always provide the desired health outcomes, as the definition of adequate water supply does not include water quality objectives. Therefore, more detailed assessments may be required to understand how people use and access water and sanitation, and the impacts on public health. [ABSTRACT FROM PUBLISHER]

• We evaluated the Forest Vegetation Simulator (FVS) Central Rockies Variant. • Bakuzis matrix exhibited qualitatively reasonable default model behavior. • Chronosequence validation found default over-prediction of stand biomass. • Equivalence testing found unrealistic growth of non-lodgepole pine species. • Sensitivity analysis identified initial species composition drives output. The Forest Vegetation Simulator (FVS) growth and yield model is widely used throughout the United States, but recent studies have reported unexpectedly large bias for some regional model variants. Here we propose a general framework for model evaluation, designed to highlight model strengths and weaknesses and inform calibration efforts. We apply the framework to evaluate the Lodgepole Pine (LP) model of the FVS Central Rockies Variant (FVS-CR), which has rarely been evaluated in the literature despite its widespread use in the western US. We started with a qualitative verification of the structure and logic of the FVS-CR LP model against a modified Bakuzis matrix, determining that it adequately reproduces known patterns of stand dynamics. We then compared stand-level growth simulations to a chronosequence developed from 554 Forest Inventory and Analysis (FIA) plots measured in Colorado and Wyoming lodgepole pine forests. This quantitative validation exercise revealed that the default model settings substantially over-predict basal area and total stand carbon after 50 years for both pure lodgepole pine stands and mixed lodgepole pine stands containing a minor proportion of Engelmann spruce, subalpine fir, and quaking aspen. Using equivalence testing to validate the large-tree diameter increment model against a separate dataset of 3,790 remeasured trees on 124 lodgepole pine FIA plots, we found that the default FVS–CR LP model adequately predicts lodgepole diameter growth, though we observed large variation in model errors. Equivalence tests also revealed systematic under-estimation of spruce–fir and over-estimation of aspen productivity. Finally, we conducted a sensitivity analysis to identify the most important model parameters and data inputs driving simulated stand structure and carbon accumulation in both the short- (50 years) and long-term (200 years). The model shows the greatest sensitivity to initial species composition; a small proportion of aspen, spruce, or fir seedlings led to long-term stand re-structuring and greatly increased carbon accumulation. Other sensitive parameters included site index, the fixed large-tree diameter increment adjustment factor, and the parameter controlling the point at which density-dependent self-thinning begins (maximum stand density index). This evaluation leads us to conclude that the FVS-CR LP model tends to under-estimate tree mortality (particularly in young stands) and the default site index represents relatively high productivity stands, such that re-calibration may often be necessary to capture realistic long-term stand behaviors. Our sensitivity analysis provides guidance for future efforts to re-calibrate the FVS-CR LP model and highlights the importance of collecting site index and seedling species composition data wherever possible to produce the most realistic simulations. [ABSTRACT FROM AUTHOR]

Vietnam's forests have experienced a notable transformation over the past 20 years from net deforestation to reforestation and expanding forests. Continued reforestation that aims to achieve further economic and environmental benefits remains a national priority and strategy. We explore the current status of plantation forests and highlight possible means to facilitate their expansion in the uplands of Vietnam. We employ mixed method triangulation to empirically explore plantation forests and their economic role in household livelihood, to quantify trade-offs between plantation forests and shifting cultivation, and to assess the constraints on plantation forest expansion in Nghe An province, north-central Vietnam. Results show that forest in the study area expanded by 406,000 ha (71.1%) between 1990 and 2016. Plantation forests increased by nearly 500% (from 32,000 ha to 190,000 ha), while natural forests expanded by 48.1% (from 538,000 ha to 797,000 ha). Plantation forests contributed an average of 35.1 percent of total household income in wealthier households and 27.9 percent of income in poor households. Switching from shifting cultivation to plantation forests would increase total household income and average carbon stock but decrease food provision. Total Economic Value would be higher for plantation forest scenarios if increased carbon stocks in plantations can be monetized. This carbon income might drive conversion of shifting cultivation to plantation forests. Constraints on further expansion of plantation forest are low external cooperation, education, market stability, and agroforestry extension services. Our empirical results inform national plantation forest development, sustainable upland livelihood development, and climate change mitigation programs to ultimately facilitate forest transition and improve the resilience and sustainability of socio-ecological systems. [ABSTRACT FROM AUTHOR]

The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for estimating forest biomass from remote sensing data and forest inventory plots, there is no general agreement on which approach is most appropriate for biomass estimation across large areas. In this study, we compared several imputation approaches for estimating forest biomass using Landsat time-series and inventory plot data. We evaluated 18 kNN models to impute three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees). These models were developed using different distance techniques (Random Forest or RF, Gradient Nearest Neighbour or GNN, and Most Similar Neighbour or MSN) and different combinations of response variables (model scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees). We also assessed the ability of our imputation method to spatially predict biomass variables across large areas in relation to a forest disturbance history over a 30-year period (1987–2016). Our results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The lowest error rates were achieved by RF-based models with generalized root mean squared difference (gRMSD, RMSE divided by the standard deviation of the observed values) ranging from 0.74 to 1.24. Whereas gRMSD associated with MSN-based and GNN-based models ranged from 0.92 to 1.36 and from 1.04 to 1.42, respectively. The indirect imputation method generally achieved better biomass predictions than the direct imputation method. In particular, the kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass. This model reported a gRMSD of 0.89, 0.95 and 1.08 for total AGB, AGB of live trees and AGB of dead trees, respectively. In addition, spatial predictions of biomass showed relatively consistent trends with disturbance severity and time since disturbance across the time-series. As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this work helps those using these methods ensure their modelling and mapping practices are optimized. [ABSTRACT FROM AUTHOR]

Cotton is an economically important crop in Australia that requires high resource application, particularly that of nitrogen (N) fertilizers. Determining optimal N fertilizer rates that reach both economic and environmental objectives is a key challenge in cotton systems because of the inherent within-field variability and relatively low N fertilizer use efficiency (NFUE). This study aimed to model optimal N fertilizer rates by accounting for within-field variability through management zone (MZ) delineation across a cotton field in Queensland, Australia. MZs were delineated using satellite-derived normalized difference vegetation index (NDVI) and the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), was automatically calibrated with a grid-search optimization algorithm and validated across two cotton seasons. A total of 336 different N fertilizer scenarios were subsequently evaluated at pre-planting and top-dressing to observe the effect on profit margin and NFUE. The MZ delineation analysis determined that within-field variability could be best represented by two MZs, one of which displaying subsoil constraints due to high carbonate concentrations. The use of the auto-calibration algorithm led to a successful validation of the model with a Wilmott d-index of agreement ranging between 0.75 (soil nitrate) and 0.96 (aboveground biomass and plant N), respectively. The subsequent N scenario simulations indicated that by reducing N fertilizer rates by 80 and 30 kg N ha−1 across the two MZs, respectively, compared to the current industry average, profits could be maximized through maintaining yields while reducing N inputs. Overall, these results demonstrate the potential of combining remote sensing-derived MZs and crop model auto-calibration techniques to support the cotton industry in achieving improved resource efficiency and profit margins. Relationships between preplant and top-up nitrogen (N) fertilizer rates and their effect on lint yield (kg ha−1) and profit (AUD ha−1) across two management zones (MZs) (a-b); and when restricted within the optimal N fertilizer use efficiency range for cotton across the same two management zones (c-d). The red points indicate the best trade-off between maximum profit margin and the minimum amount of total N applied. [Display omitted] • Determining optimal nitrogen (N) fertilizer rates in cotton is difficult due to spatial variability and low use efficiency. • An auto-calibrated crop simulation model (DSSAT) was used to determine optimal N rates across management zones. • Fertilizer scenarios determined N rates for each management zone that maximized profit and optimized N use efficiency. • Combined use of management zones and crop models can improve profit margins and N fertilizer use efficiency in cotton. [ABSTRACT FROM AUTHOR]

Purpose Understanding the relevant risk factors for patellar instability and the clinical and radiographic tests necessary to determine optimal treatment. This case series intends to evaluate arthroscopic indications in the treatment of patellar instability in children. Methods From 2013 to 2021, 33 patients (seven to 16 years of age) with 35 knees sustaining first-time dislocation with loose bodies, recurrent dislocation or subluxation, and habitual dislocation were arthroscopically operated on according to the flow chart. Periods of follow-up were two to 10 years (avg. 5.5 years). Follow-up assessment included the recurrence, complications (joint stiffness and excessive reduction), and the final function outcomes by using the Kujala score. Results Among 35 knees, there were two (5.7%) first dislocations, 30 (85.7%) recurrent dislocations, and three (8.6%) habitual dislocations, lateral release 27/35 (77.1%), medial reefing 23/35 (65.7%), reconstruction of the medial patellofemoral ligament (MPFL) 12/35 (34.3%). The major complication was a knee of extensive stiffness after medial reefing and lateral release. Recurrence was in 4/35 (11.4%) of knees, not correlated to lateral release (p=0.21), medial reefing, or reconstruction of MPFL (p=0.07); in about 23 knees of medial reefing, recurrence was significantly correlated to number of knots (p=0.045). The final functional results according to Kujala were 88-100 (avg. 95.5). Conclusions This study showed the role of arthroscopy in both medial reefing and reconstruction of MPFL in children by low recurrence rate and high Kujala score at final follow-up. There was no significant correlation between recurrence and the procedures as arthroscopic indications counted on the flow chart., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Vo et al.)

Background: The COVID-19 pandemic has underscored the significance of adopting healthy lifestyles to mitigate the risk of severe outcomes and long-term consequences., Objective: This study focuses on assessing the prevalence and clustering of 5 unhealthy lifestyle behaviors among Vietnamese adults after recovering from COVID-19, with a specific emphasis on sex differences., Methods: The cross-sectional data of 5890 survivors of COVID-19 in Vietnam were analyzed from December 2021 to October 2022. To examine the sex differences in 5 unhealthy lifestyle behaviors (smoking, drinking, unhealthy diet, physical inactivity, and sedentary behavior), the percentages were plotted along with their corresponding 95% CI for each behavior. Latent class analysis was used to identify 2 distinct classes of individuals based on the clustering of these behaviors: the "less unhealthy" group and the "more unhealthy" group. We examined the sociodemographic characteristics associated with each identified class and used logistic regression to investigate the factors related to the "more unhealthy" group., Results: The majority of individuals (male participants: 2432/2447, 99.4% and female participants: 3411/3443, 99.1%) exhibited at least 1 unhealthy behavior, with male participants being more susceptible to multiple unhealthy behaviors. The male-to-female ratio for having a single behavior was 1.003, but it escalated to 25 for individuals displaying all 5 behaviors. Male participants demonstrated a higher prevalence of combining alcohol intake with sedentary behavior (949/2447, 38.8%) or an unhealthy diet (861/2447, 35.2%), whereas female participants tended to exhibit physical inactivity combined with sedentary behavior (1305/3443, 37.9%) or an unhealthy diet (1260/3443, 36.6%). Married male participants had increased odds of falling into the "more unhealthy" group compared to their single counterparts (odds ratio [OR] 1.45, 95% CI 1.14-1.85), while female participants exhibited lower odds (OR 0.65, 95% CI 0.51-0.83). Female participants who are underweight showed a higher likelihood of belonging to the "more unhealthy" group (OR 1.11, 95% CI 0.89-1.39), but this was not observed among male participants (OR 0.6, 95% CI 0.41-0.89). In both sexes, older age, dependent employment, high education, and obesity were associated with higher odds of being in the "more unhealthy" group., Conclusions: The study identified notable sex differences in unhealthy lifestyle behaviors among survivors of COVID-19. Male survivors are more likely to engage in unhealthy behaviors compared to female survivors. These findings emphasize the importance of tailored public health interventions targeting sex-specific unhealthy behaviors. Specifically, addressing unhealthy habits is crucial for promoting post-COVID-19 health and well-being., (©Lan T H Le, Thi Ngoc Anh Hoang, Tan T Nguyen, Tien D Dao, Binh N Do, Khue M Pham, Vinh H Vu, Linh V Pham, Lien T H Nguyen, Hoang C Nguyen, Tuan V Tran, Trung H Nguyen, Anh T Nguyen, Hoan V Nguyen, Phuoc B Nguyen, Hoai T T Nguyen, Thu T M Pham, Thuy T Le, Thao T P Nguyen, Cuong Q Tran, Ha-Linh Quach, Kien T Nguyen, Tuyen Van Duong. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 02.04.2024.)

Purpose We investigated the impact of intravascular ultrasound guidance on reducing the incidence of contrast-induced acute kidney injury (CI-AKI) in patients undergoing percutaneous coronary intervention (PCI). Methods Ninety-nine patients were enrolled in this prospective cohort who were not randomly assigned to angiography-guided percutaneous coronary intervention or intravascular ultrasound-guided percutaneous coronary intervention. The patients were hospitalized at the Vietnam National Heart Institute - Bach Mai Hospital between 2019 and 2020. Acute kidney injury incidence during hospitalization was the primary endpoint. Results A total of 99 patients were divided into two groups: the intravascular ultrasound-guided group (33 participants) and the angiography-guided group (66 participants). The mean ± SD contrast volume of each group was 95.2 ± 37.1 mL and 133.0 ± 36.0 mL for the ultrasound-guided and angiography-guided groups, with P < 0.0001. Intravascular imaging-guided percutaneous coronary intervention (IVUS-guided PCI) was associated with reduced acute kidney injury incidence during hospitalization: 0.0% vs. 12.12% and P = 0.049. Conclusions Intravascular ultrasound is a safe imaging tool that guides percutaneous coronary intervention and significantly reduces the rate of acute kidney injury compared to angiography alone. Patients who have a high chance of experiencing acute kidney injury benefit from using intravascular ultrasound., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Tran Duc et al.)