Health Information Seeking Behavior Among Perinatal Women: A Systematic Review

The health of perinatal women is critical to maternal well-being and infant development. Understanding how perinatal women seek health information is essential for providing effective antenatal and postnatal care. However, there is a lack of systematic reviews on this topic. This study aimed to conduct a comprehensive literature review of existing research following the PRISMA-SCR guidelines. The results showed that perinatal women commonly seek pregnancy-related health information. Interpersonal sources were identified as the primary and most trusted sources of information. The study also identified various factors and barriers that influence health information seeking, including individual, socio-cultural, structural, and information-related factors, with personal responsibility for health being an influential factor that has not been emphasized previously. It provides valuable insights into the health information seeking behavior of perinatal women. The influence of personal health responsibility, social power dynamics and socio-cultural norms on health information seeking needs to be further investigated

An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning

Worldwide, water security is adversely affected by factors such as population growth, rural–urban migration, climate, hydrological conditions, over-abstraction of groundwater, and increased per-capita water use. Water security modeling is one of the key strategies to better manage water safety and develop appropriate policies to improve security. In view of the growing global demand for safe water, intelligent methods and algorithms must be developed. Therefore, this paper proposes an integrated interval type-2 Fuzzy VIseKriterijumska Optimizcija I Kompromisno Resenje (IT2FVIKOR) with unsupervised machine learning (ML). This includes IT2FVIKOR for ranking and selecting a set of alternatives. Unsupervised machine learning includes hierarchical clustering, self-organizing map, and autoencoder for clustering, silhouette analysis and elbow method to find the most optimal cluster count, and finally Adjusted Rank Index (ARI) to find the best comparison within two clusters. This proposed integrated method can be divided into a two-phase fuzzy-machine learning-based framework to select the best water security strategies and categorize the polluted area using the water datasets from the Terengganu River, one of Malaysia’s rivers. Phase 1 focuses on the IT2FVIKOR method to select five different strategies with five different criteria using five decision makers for finding the best water security strategies. Phase 2 continues the unsupervised machine learning where three different clustering algorithms, namely, hierarchical clustering, self-organizing map, and autoencoder, are used to cluster the polluted area in the Terengganu River. Silhouette analysis is applied along with the clustering algorithms to estimate the number of optimal clusters in a dataset. Then, the ARI is applied to find the best comparison within the original data with hierarchical clustering, self-organizing map, and autoencoder. Next, the elbow method is applied to double-confirm the best clusters for each clustering algorithm. Last, lists of polluted areas in each cluster are retrieved. Finally, this 2-phase fuzzy-Machine learning–based framework offers an alternative intelligent model to solve the water security problems and find the most polluted area

A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario

River quality classification using different distances in k-nearest neighbors algorithm

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality

Mindfulness-based stress reduction combined with early cardiac rehabilitation improves negative mood states and cardiac function in patients with acute myocardial infarction assisted with an intra-aortic balloon pump: a randomized controlled trial

ObjectiveTo investigate the clinical effects of mindfulness-based stress reduction (MBSR) intervention combined with early cardiac rehabilitation (CR) on patients with acute myocardial infarction (AMI) assisted with an intra-aortic balloon pump (IABP).MethodsA total of 100 AMI patients with IABP assistance due to hemodynamic instability at Wuhan Asia Heart Hospital were enrolled in the study. The participants were divided into two groups using the random number table method (n = 50 each group). Patients receiving routine CR were assigned to the CR control group, while patients receiving MBSR plus CR were assigned to the MBSR intervention group. The intervention was performed twice a day until the removal of the IABP (5–7 days). Each patient's level of anxiety/depression and negative mood state were evaluated before and after intervention using the self-rating anxiety scale (SAS), self-rating depression scale (SDS), and profiles of mood state scale (POMS). The results of the control and intervention groups were compared. IABP-related complications and left ventricular ejection fraction (LVEF), measured with echocardiography, were also assessed and compared between the two groups.ResultsThe SAS, SDS, and POMS scores were lower in the MBSR intervention group than in the CR control group (P < 0.05). There were also less IABP-related complications in the MBSR intervention group. LVEF was significantly improved in both groups, but the degree of LVEF improvement was more significant in the MBSR intervention group than in the CR control group (P < 0.05).ConclusionsMBSR combined with early CR intervention can assist in alleviating anxiety, depression, and other negative mood states, reduce IABP-related complications, and further improve cardiac function in AMI patients with IABP assistance

Pattern of prefrontal cortical activation and network revealed by task-based and resting-state fNIRS in Parkinson’s disease’s patients with overactive bladder symptoms

BackgroundOveractive bladder (OAB) symptoms are common in Parkinson’s disease (PD), and negatively contribute to the quality of life (QoL) of patients. To explore the underlying pathophysiological mechanism, we investigated the correlation between the prefrontal cortex (PFC) function and OAB symptoms in PD patients.MethodsOne hundred fifty-five idiopathic PD patients were recruited and classified either as PD-OAB or PD-NOAB candidates based on their corresponding OAB symptom scores (OABSS). A linear regression analysis identified a correlative connection of cognitive domains. Then cortical activation during the performance of the verbal fluency test (VFT) and brain connectivity during resting state were conducted by functional near-infrared spectroscopy (fNIRS) for 10 patients in each group to investigate their frontal cortical activation and network pattern.ResultsIn cognitive function analysis, a higher OABS score was significantly correlated with a lower FAB score, MoCA total score, and sub-scores of visuospatial/executive, attention, and orientation as well. In the fNIRS study, the PD-OAB group exhibited significant activations in 5 channels over the left hemisphere, 4 over the right hemisphere, and 1 in the median during the VFT process. In contrast, only 1 channel over the right hemisphere showed significant activation in the PD-NOAB group. The PD-OAB group revealed hyperactivation, particularly in certain channel in the left dorsolateral prefrontal cortex (DLPFC), compared with PD-NOAB (FDR P < 0.05). In the resting state, there was a significant increase of the resting state functional connectivity (RSFC) strength between the bilateral Broca area, left frontopolar area (FPA-L) and right Broca’s area (Broca-R), between the FPA and Broca’s area if merging the bilateral regions of interest (ROI), and also between the two hemispheres in the PD-OAB group. The Spearman’s correlation confirmed that the OABS scores were positively correlated with RSFC strength between the bilateral Broca area, FPA-L and Broca-R, between the FPA and Broca area if merging the bilateral ROI.ConclusionIn this PD cohort, OAB was related to decreased PFC functions, with particularly hyperactivated left DLPFC during VTF and an enhanced neural connectivity between the two hemispheres in the resting state as observed by fNIRS imaging

Not performing an OGTT results in underdiagnosis, inadequate risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years: a population-based prospective cohort study

Objective: To explore the influence by not performing an oral glucose tolerance test (OGTT) in Han Chinese over 40 years. Design: Overall, 6682 participants were included in the prospective cohort study and were followed up for 3 years. Methods: Fasting plasma glucose (FPG), 2-h post-load plasma glucose (2h-PG), FPG and 2h-PG (OGTT), and HbA1c testing using World Health Organization (WHO) or American Diabetes Association (ADA) criteria were employed for strategy analysis. Results: The prevalence of diabetes is 12.4% (95% CI: 11.6–13.3), while the prevalence of prediabetes is 34.1% (95% CI: 32.9–35.3) and 56.5% (95% CI: 55.2–57.8) using WHO and ADA criteria, respectively. 2h-PG determined more diabetes individuals than FPG and HbA1c. The testing cost per true positive case of OGTT is close to FPG and less than 2h-PG or HbA1c. FPG, 2h-PG and HbA1c strategies would increase costs from complications for false-positive (FP) or false-negative (FN) results compared with OGTT. Moreover, the least individuals identified as normal by OGTT at baseline developed (pre)diabetes, and the most prediabetes individuals identified by HbA1c or FPG using ADA criteria developed diabetes. Conclusions: The prevalence of isolated impaired glucose tolerance and isolated 2-h post-load diabetes were high, and the majority of individuals with (pre)diabetes were undetected in Chinese Han population. Not performing an OGTT results in underdiagnosis, inadequate developing risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years and great consideration should be given to OGTT in detecting (pre)diabetes in this population. Further population-based prospective cohort study of longer-term effects is necessary to investigate the risk assessment and cost of (pre)diabetes

Divergence in Gut Bacterial Community Among Life Stages of the Rainbow Stag Beetle Phalacrognathus muelleri (Coleptera: Lucanidae)

Although stag beetles are popular saprophytic insects, there are few studies about their gut bacterial community. This study focused on the gut bacterial community structure of the rainbow stag beetle (i.e., Phalacrognathus muelleri) in its larvae (three instars) and adult stages, using high throughput sequencing (Illumina Miseq). Our aim was to compare the gut bacterial community structure among different life stages. The results revealed that bacterial alpha diversity increased from the 1st instar to the 3rd instar larvae. Adults showed the lowest gut bacterial alpha diversity. Bacterial community composition was significantly different between larvae and adults (p = 0.001), and 1st instar larvae (early instar) had significant differences with the 2nd (p= 0.007) and 3rd (p = 0.001) instar larvae (final instar). However, there was little difference in the bacterial community composition between the 2nd and 3rd instar larvae (p = 0.059). Our study demonstrated dramatic shifts in gut bacterial community structure between larvae and adults. Larvae fed on decaying wood and adults fed on beetle jelly, suggesting that diet is a crucial factor shaping the gut bacterial community structure. There were significant differences in bacterial community structure between early instar and final instars larvae, suggesting that certain life stages are associated with a defined gut bacterial community

Theory, Method and Practice of Metal Deformation Instability: A Review

Deformation instability is a macroscopic and microscopic phenomenon of non-uniformity and unstable deformation of materials under stress loading conditions, and it is affected by the intrinsic characteristics of materials, the structural geometry of materials, stress state and environmental conditions. Whether deformation instability is positive and constructive or negative and destructive, it objectively affects daily life at all times and the deformation instability based on metal-bearing analysis in engineering design has always been the focus of attention. Currently, the literature on deformation instability in review papers mainly focuses on the theoretical analysis of deformation instability (instability criteria). However, there are a limited number of papers that comprehensively classify and review the subject from the perspectives of material characteristic response, geometric structure response, analysis method and engineering application. Therefore, this paper aims to provide a comprehensive review of the existing literature on metal deformation instability, covering its fundamental principles, analytical methods, and engineering practices. The phenomenon and definition of deformation instability, the principle and viewpoint of deformation instability, the theoretical analysis, experimental research and simulation calculation of deformation instability, and the engineering application and prospect of deformation instability are described. This will provide a reference for metal bearing analysis and deformation instability design according to material deformation instability, structural deformation instability and localization conditions of deformation instability, etc. From the perspective of practical engineering applications, regarding the key problems in researching deformation instability, using reverse thinking to deduce and analyze the characteristics of deformation instability is the main trend of future research