Poor Quality Research Articles

Sleep quality disparities in different pregnancy trimesters in low- and middle-income countries: a systematic review and meta-analysis

IntroductionSleep is a crucial determinant of maternal and fetal health, significantly impacting the well-being of both the mother and her developing fetus. Poor sleep quality, characterized by difficulties in falling asleep or staying asleep, can cause poor pregnancy outcome. Conversely, studies came with inconsistent result in the prevalence of poor sleep quality in different trimester of pregnancy. Therefore, this systematic review and meta-analysis study aimed to compare the prevalence of poor sleep quality in different trimesters.MethodA systematic review and meta-analysis were done on published studies. Electronic data base search was done from PubMed, Hinari, Medline and Google Scholar. Data were extracted with Excel and the analysis were done using STATA version 17. Publication bias was assessed both graphically and statistically. I-square test was used to identify heterogeneity.ResultIn this meta-analysis, 38 studies that measured poor sleep quality using the Pittsburg Sleep Quality Index (PSQI ≥ 5) were included. The pooled prevalence of poor sleep quality was identified as 37.46% (95% CI: 29.26, 45.67) in the first trimester, 47.62% (95% CI: 42.23, 53.02) in the second trimester, and 60.05% (95% CI: 51.32, 68.78) in the third trimester.ConclusionThis study identified a significant discrepancy in the prevalence of poor sleep quality, which increases as gestational age advances. Therefore, this discrepancy should be addressed, and additional support should be provided to pregnant women to help them achieve adequate sleep, especially as gestational age advances.

Emotional intelligence: a novel predictor of quality of life in patients with systemic sclerosis.

Systemic sclerosis (SSc), a chronic systemic autoimmune disease, affects skin and internal organs compromising organ function and leading to significant morbidity and poor health-related quality of life (HrQoL). This cross-sectional study investigated whether HrQoL is influenced by trait emotional intelligence (TEI). Sixty patients with SSc (Female: 86.67%) completed the socio-demographic characteristics form, TEI Questionnaire Short-Form (TEIQue-SF), and Short-Form Health Survey (SF-36). Sixty healthy controls were also completed the TEIQue-SF. A series of multiple linear regression analyses with correlation matrix was used to analyze SF-36 domains as dependent variables with TEI domains (well-being, self-control, emotionality, sociability) as independent variables. The average age of participants was 57.3 ± 12.9 years with a mean disease duration of 7.7 ± 6.7 years. Patients differed from controls in the sociability domain of TEI. TEI global was found to affect the physical and mental component summaries (p < .001), and all 8 dimensions of the HrQoL (p < .001). Age, disease duration, and gastrointestinal manifestations were negatively associated with various components of SF-36. TEI was positively associated with all dimensions of HrQoL. Understanding the relationship between TEI and HrQoL dimensions is important for the support and empowerment of SSc patients, as well as the establishment and implementation of appropriate psychotherapeutic interventions.

Caregiver Wellness after Traumatic Brain Injury (CG-Well): Protocol for a randomized clinical trial

IntroductionAfter injury, survivors of moderate to severe traumatic brain injury (msTBI) depend on informal family caregivers. Upwards of 77% of family caregivers experience poor outcomes, such as adverse life changes, poor health-related quality of life, and increased depressive symptoms. Caregivers frequently report minimal support or training to prepare them for their new role. The majority of previously developed caregiver and caregiver/survivor dyad interventions after msTBI focus on providing information to either survivors only, or to long-term caregivers, rather than to the new caregiver. This manuscript describes the protocol of an ongoing randomized control trial, Caregiver Wellness after TBI (CG-Well), developed to provide education, support, and skill-building to caregivers of adults with msTBI, beginning when the survivor is early in the clinical course. MethodsWithin two weeks of admission to the ICU, participants are randomized to CG-Well online modules (intervention group, n = 50 dyads) or information, support, and referral (ISR) e-bulletins that exist in the public domain (control group, n = 50 dyads) over the first six months after their family member’s msTBI. Both groups receive regular phone calls. The primary outcome is intervention satisfaction at six months. ResultsEnrollment began in March, 2022 and is projected to complete October 2024. We have enrolled approximately 70% of participants at this time. Primary analysis completion is anticipated April, 2025. DiscussionThis RCT is designed to evaluate caregiver satisfaction by addressing the need for tailored supportive care for caregivers of msTBI beginning during the ICU admission. Trial RegistrationClinicaltrials.gov Registration Number: NCT05307640

Data Fusion for Enhancing Urban Air Quality Modeling using Large-Scale Citizen Science

Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality management by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the priori dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.

Performance analysis of particulate matter priority control algorithm for outdoor Air-Introduced ventilation Cleaner using prediction model

Growing concerns about health risks associated with poor indoor air quality, such as respiratory illnesses and allergies, highlight the need for improved management strategies. This study proposes a novel approach that combines an artificial neural network (ANN)-based indoor PM2.5 prediction model with a PM-priority control algorithm. This combined approach addresses the limitations of traditional methods by enabling proactive control measures based on predictive PM2.5 levels. With increased global attention to indoor air quality following the COVID-19 pandemic and the stringent 2021 WHO air quality guidelines, this research focuses on real-time management of PM2.5 and CO2 concentrations to maintain a healthy indoor environment. Two elementary school classrooms were equipped as living labs to analyze the performance of the developed system. The PM-priority control algorithm uses predictive data to dynamically regulate ventilation and air purification processes. Continuous retraining of the prediction model ensures adaptability to environmental changes by evaluating the algorithm’s efficiency in real time. The findings demonstrate that maintaining PM2.5 levels within comfortable ranges and reducing energy consumption can enhance the sustainability of buildings and improve the health and productivity of occupants. This approach provides a scalable solution applicable to schools and potentially other public buildings, emphasizing the preventive value of enhancing occupant health and productivity.

Obesity and poor sleep quality were negative factors for better dietary practices in Brazil

Obesity and poor sleep quality were negative factors for better dietary practices in Brazil

Restriction of Mobile Phone Usage at Bed Time: Effect on Sleep Quality, Mood and Cognitive Function

Introduction: Mobile phones are integral to modern life, but excessive use, particularly at night, can lead to disrupt well-being. Limiting mobile phone use before bedtime may improve individual well-being. This study aimed to evaluate whether restricting mobile phone use at bedtime enhances sleep quality, mood, and cognitive function. Methodology: Undergraduate students from a selected engineering college were assessed for bedtime mobile phone use. Sixty-eight students were chosen via simple random sampling. A self-reported questionnaire including the Pittsburgh Sleep Quality Index, Positive and Negative Affect Scale, and Montreal Cognitive Assessment Scale evaluated sleep quality, mood, and cognitive function before implementing restrictions. The "Lock My Phone" app was used to enforce these restrictions. Post-intervention assessments were conducted on the 15th and 30th days. Results: Before the intervention, all students reported poor sleep quality, 80.8% had reduced positive affect, 91.1% experienced high negative affect, and only 23.5% had normal cognitive function. Significant improvements were observed in sleep quality, mood, and cognitive function post-restriction (p&lt;0.001). Conclusion: Restricting mobile phone use before bedtime significantly improved sleep quality, mood, and cognitive function among undergraduate students.

Using optimization approach to design dental implant in three types of bone quality - A finite element analysis

Background/PurposeThe use of finite element (FE) analysis in implant biomechanics offers many advantages over other approaches in simulating the complexity of clinical situations. The aim of this study was to perform an optimization analysis of dental implants with different thread designs in three types of bone quality. Materials and methodsThe three-dimensional FE model of a mandibular bone block with a screw-shaped dental implant and superstructure was simulated. In the optimization analysis, the design variables included the thread pitch and the thread depth of the implant. The objective was to minimize the displacement of the implant to the target value. Three FE models with different bone qualities (D2: better bone quality; D3: ordinary bone quality; D4: poor bone quality) were created. ResultsThe FE results showed that the displacement of the implant and the stress of the cortical bone increased, while the Young's modulus of the cancellous bone decreased. In the D2 bone, changing the thread pitch and thread depth had little effect on cortical stress and implant displacement. However, in D3 and D4 bone, increasing thread depth reduced cortical stress by 40 % and implant displacement by at least 9 %. ConclusionAdjusted thread depth for D3 and D4 bone would reduce crestal bone stress and increase implant stability, but only a little alteration on crestal bone stress and implant stability for D2 bone.

How Should Clinicians Partner With Families to Promote Children's Sleep Health?

Short sleep duration, poor sleep quality, and irregular timing of sleep are prevalent sleep troubles for children, but fully assessing children's sleep environments and effectively promoting children's overall sleep health is nearly impossible during brief clinical encounters. This commentary on a case suggests strategies for navigating this problem with a patient- and family-centered approach that prioritizes identifying family sleep-related beliefs, values, and goals and maintaining flexibility when offering evidence-based recommendations to improve children's sleep.

Effects of velocity regularization on neural network performance in processing particle images

Recent studies have witnessed remarkable progress in harnessing convolutional neural networks (CNNs) to overcome the inherent limitations of conventional particle image velocimetry (PIV) methods. Traditional PIV techniques often suffer from compromised resolution and precision, hindering their ability to capture the complexities of fluid dynamics within the observation frame. While CNNs offer promise in addressing these challenges, they face obstacles such as limited accuracy, weak generalization, and a dearth of physical interpretability. In our prior research, we presented a CNN architecture that incorporates optical flow algorithms as supplementary physical constraints, thereby bolstering the model interpretability and precision. Nevertheless, the practical implications of this approach, especially when dealing with multi-dimensional, low-quality particle image data and restricted training sets, have yet to be fully explored. To address this knowledge gap, we have assembled a comprehensive dataset that simulates a wide array of experimental scenarios. We have systematically assessed the influence of velocity regularization on neural network performance, taking into account variations in image quality and the size of training datasets. The results underscore the pivotal importance of velocity regularization in enhancing the predictive prowess of neural networks, particularly when dealing with poor image quality and smaller data sizes. This study provides useful insights into the effective application of CNNs with velocity regularization in the field of experimental fluid dynamics.

Impact of Pemphigus on Sleep Quality: A Prospective Observational Single-Center Case-Control Study.

Pemphigus, an autoimmune disorder, significantly impacts the quality of life for those affected. This study examined the sleep quality in patients with pemphigus, a domain for which the existing literature provides limited data without a focused analysis. A total of 156 individuals, 52 in the case and 104 in the control group, completed several questionnaires (Pittsburgh Sleep Quality Index [PSQI], General Health Questionnaire-12 [GHQ-12], and ABQOL [Autoimmune Bullous Disease Quality of Life]). Disease severity was evaluated using the Pemphigus disease area index (PDAI index). The case group exhibited significantly higher levels of psychological distress, reflected in GHQ-12 scores (P = 0.00), and notably poorer sleep quality compared to controls across various parameters (sleep latency [SL], disturbances [SDi], medication [SM], daytime sleep dysfunction [DSD]), and the global PSQI score (GS)] (P = 0.00)). Significant correlations were observed between PDAI scores and sleep duration (SDu) (P = 0.01), SM (P=0.03), SL (P =0.03), and GS (P=0.00). An association was found between the equivalent steroid intake and SDu (P=0.00) as well as GS (P=0.02). No statistically significant correlation emerged between disease duration and PSQI scores. Our findings indicated a correlation between poor sleep quality and cutaneous rather than mucosal manifestations (p=0.01). Pemphigus affects sleep quality. Severe disease showed heightened SDi, correlating with higher steroid doses, and in the chronic phase, the impact seems to have been more influenced by the pro-inflammatory stage of the disease. Patients with cutaneous pemphigus exhibited reduced sleep quality compared to those with oral pemphigus. Understanding that pemphigus worsens sleep quality can be beneficial for the holistic management of individuals with this condition.

Self-supervised learning-based re-parameterization instance segmentation for hazardous object in terahertz image

Recently, terahertz imaging technology has shown widespread potential applications in the public security screening field. Previous terahertz hazardous object detection techniques primarily relied on manual recognition and image processing methods. Some solutions incorporating deep learning technologies depend on large quantities of high-quality data, making it challenging to achieve low-cost and high-performance detection. To solve the issue of dependency on large amounts of high-quality data, we propose a diversified dynamic structure You Only Look Once (DDS-YOLO) model based on masked image modeling and structural reparameterization for the instance segmentation of hazardous objects in terahertz security inspection images. To address the scarcity and annotating difficulty problems of terahertz security inspection image samples, we combine automatic data strategies with masked image modeling for self-supervised learning. We propose a multilevel feature refinement fusion mechanism to enhance the quality of learned feature representations. The backbone parameter transfer and fine-tuning training strategies are employed to achieve hazardous object instance segmentation on the terahertz dataset. To address the low detection accuracy issue caused by the poor terahertz image quality, we develop a reparameterizable hierarchical structure for the backbone, improve a multi-scale feature-integrating neck, and design a dynamically decoupled head with lower computational requirements to enhance the performance of the instance segmentation model. Experimental results demonstrate that the proposed model accurately outputs detection boxes, categories, and segmentation masks for hazardous objects with minimal training samples. The comparative experimental results indicate that the proposed model outperforms existing state-of-the-art methods in terms of detection performance. The proposed DDS-YOLO model achieves 59.3 % in mask mean Average Precision (mAP) and 61.3 % in box mAP, and the model parameters and computational requirements also meet practical application scenarios.

Ethanolic duea ching extract in combination with microbial transglutaminase: Strengthening effect on mince gel and cross-linking of myofibrillar proteins from Indian mackerel

Surimi or fish mince gel are popular seafood products worldwide. Lean fish are commonly used as raw material, while dark fleshed fish, especially Indian mackerel, has high fat content and dark color associated with poor gel quality. To improve gel strength, plant extract, particularly from duea ching (Ficus botryocarpa Miq.), rich in polyphenols and calcium could be used as natural protein cross-linker to strengthen gel network. Effect of ethanolic duea ching extract (EDCE) at varying levels in the absence and presence of microbial transglutaminase (MTGase) on gel property of Indian mackerel mince gel prepared from fillets pre-washed for different times (1 and 3 times) was studied. Heat-induced aggregation of myofibrillar protein (MP) from Indian mackerel fillet under different conditions was also investigated. Mince gel prepared from fillets pre-washed for 3 times added with 0.10% EDCE and 0.5 unit/g of MTGase (3-PWM-MT-0.10) showed the highest breaking force, deformation and textural properties (P<0.05). Lower autolysis of mince gel was obtained when EDCE, MTGase or in combination were added. However, addition of ECHE decreased whiteness of mince gel, particularly at higher concentrations (P<0.05). Electrophoretic images revealed the disappearance of myosin heavy chain when MTGase or EDCE in conjunction with MTGase was incorporated. Addition of 0.10% EDCE, MTGase or both could induce aggregation and enhance setting phenomenon of MP solution as indicated by higher absorbance at 660 nm and storage modulus (G'), respectively. Based on microstructure study, 3-PWM-MT-0.10 had more compact, finer and interconnected structure than other gels. Therefore, the use of EDCE and MTGase at the appropriate levels could be used for improving quality of mince gel from pre-washed Indian mackerel fillets.

Interplay Between Health-Promoting Lifestyle, and Cognitive Emotion Regulation with Risk of Cosmetic Surgery Seeking in Young Women.

This study investigated the relationship between lifestyle and cognitive regulation of emotion and the risk of seeking cosmetic surgery in young women. The current study was a cross-sectional study. The outcome variable in this research was cosmetic surgery seeking. The association between sociodemographic and health behaviors with risk of cosmetic surgery seeking investigated. The multinomial logistic regression was used to examine the association between health promotion lifestyle and cognitive emotion regulation with cosmetic surgery seeking. Underweight was associated with cosmetic surgery seeking [5.50; 95% CI 1.90-15.92; P<0.005]. Other risk factors were former smoking, alcohol use, and poor sleep quality. An increase in poor lifestyle is associated with an increased risk of cosmetic surgery seeking [30.41; 95% CI 4.87-189.84; P<0.001]. Adaptive cognitive emotion regulation protects against cosmetic surgery seeking [0.36; 95% CI 0.15-0.84; P<0.001]. Lifestyle and adaptive cognitive emotion regulation played a significant role in people who were looking for cosmetic surgery, and therefore, like in other areas of disease and health, lifestyle and adaptive cognitive emotion regulation are important in cosmetic surgery, and it is necessary to pay attention to the effects of each of them. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Computational ghost imaging using the dilated ghost network

Computational ghost imaging has poor imaging quality at low sampling rates. To solve this issue, a dilated ghost network is proposed to improve the quality of noisy images reconstructed by computational ghost imaging in this paper. The network builds upon the traditional GhostNet framework, which minimizes computational costs while maintaining stable performance. By incorporating dilated convolutions, the network expands the receptive field without adding additional parameters. However, the use of dilated convolutions can cause a gridding problem, which degrades the imaging quality. To solve this problem, a sawtooth wave-like structure is introduced into the network. Simulation experiments, combined with metrics such as SSIM, PSNR, and RMSE, validate both the existence of the gridding problem and the effectiveness of the sawtooth wave-like structure in overcoming it. The experimental results indicate that the proposed method significantly outperforms traditional and compressive-sensing-based CGI methods.

Sex differences in the association between social frailty and diet quality among older adults in Spain

ObjectiveThe aim was to examine the association between social frailty and diet quality in adults over 65 years of age, and whether results differed by sex. DesignPopulation-based cross-sectional study using data from the Spanish National Health Survey. Participants5,071 community-dwelling people ≥65 years from Spain. MeasurementsSocial frailty was deemed to exist when the person both lived alone and had low social support, measured with the Duke-UNC scale. Diet was assessed with the Spanish Healthy Eating Index (S-HEI), ranging from 0 to 100 points (highest diet quality). Means and 95% confidence intervals of the S-HEI score for each social frailty group were calculated using linear regressions, with socially robust people as reference. Analyses were adjusted for main confounders, including sociodemographic, lifestyle and morbidity variables. ResultsThere were no differences in the S-HEI adjusted mean of socially frail (74.3 points, 95%CI: 73.4−75.2) compared to socially robust older adults (75.4 points; 95%CI: 75.1−75.7). In sex-stratified analyses, the S-HEI adjusted mean of socially frail men (71.9 points; 95%CI: 70.6−73.2) was lower than robust men (74.8 points; 95%CI: 74.4−75.3). Specifically, social frailty was associated with lower consumption of vegetables, fruits, dairy and lower diet variety in men. Differences were not observed according to social frailty among older women. ConclusionsSocial frailty was associated with poor diet quality in community-dwelling older men, but not in women in Spain. Gender differences in self-care could partly explain this association. Sex-specific interventions are required to minimize the impact of social frailty on diet quality.

Semi-supervised action recognition with dynamic temporal information fusion

The most advanced semi-supervised models available are based on images for innovation, and the use of semi-supervised learning models augmented with temporal data for video-level action recognition still suffers from severe model mismatches, and the models are not sufficiently capable of capturing both local and global information about the action. Secondly the use of constant-threshold pseudo-labeling leads to low utilization of unlabeled data for difficult actions in the early stages of training, poor pseudo-labeling quality and affects recognition accuracy. To make the semi-supervised framework FixMatch more suitable for action recognition, we propose Time-Mixer and Dynamic Threshold, respectively. Time-Mixer explores complementary information between time sequences through the fusion of two-channel temporal context information. Dynamic Threshold utilizes a new core mapping function (Normal Distribution Function) to enhance pseudo-labeling quality. Extensive experiments were conducted on three action recognition datasets (Kinetics-400, UCF-101, and HMDB-51). Comprehensive experiments show that the performance of the semi-supervised model in action recognition improves considerably after using dynamic thresholding and temporal context information fusion, with a 14.4% improvement over the baseline and a 1.8% improvement over the TG (with a labeling rate of 10%) in UCF101, whereas an overall good performance is obtained for DTIF.

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

BackgroundROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. ObjectiveThis study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. MethodsThe pipeline uses different methods, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. ResultsThe pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. ConclusionThe segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

What Teenagers Eat and What Specific Food Recommendations Can Be Made to Improve Their Poor Diet Quality

What Teenagers Eat and What Specific Food Recommendations Can Be Made to Improve Their Poor Diet Quality

Virtual Reality Surgical Planning of a Complex Two-Jaw Defect

Head and neck reconstruction poses challenges requiring stable coverage and optimal appearance and functionality. In complex cases, and beyond the right choice of flap, Virtual Reality (VR) surgical software and preoperative planning, are needed to optimize outcomes. Our patient is a 16-year-old with a history of rhabdomyosarcoma necessitating maxillary-mandibular defect reconstruction. VR-based preoperative planning facilitated the intervention by creating 3D-VR models of facial anatomy and fibula, simulating resection margins, and creating surgical guides for intraoperative use. Our 2-team surgeons opted for a single-staged reconstruction using a vascularized fibula free flap. This case demonstrates successful single-flap reconstruction of a maxillary-mandibular defect, addressing challenges of recipient vessel scarcity, fibrotic scarring, and poor tissue quality. It also underscores the efficacy of a 2-team approach and VR-based preoperative planning in achieving successful outcomes in complex maxillary-mandibular reconstruction. The use of a single fibula flap minimized possible complications and operative time, and the 2-team approach helped by combining the expertise and training of craniofacial and microsurgical trained surgeons. Acknowledging limitations in imaging quality and real-time adaptability, we advocate for continued exploration of VR technologies in enhancing surgical precision in challenging cases.