Browse Articles

Access to lactation consult services during the COVID-19 pandemic and the impact on breastfeeding outcome variables

Background: Inpatient lactation consultation and social influences affect breastfeeding (BF) choices and sustainability. The COVID-19 pandemic introduced barriers to BF initiation and continuation including access to lactation support and social connection. Equitable access to lactation support can reduce health disparities. Research aim: The study aimed to (1) determine the prevalence of professional lactation support during the COVID-19 pandemic, (2) explore the influence of this pandemic on the equitable accessibility to lactation support services, and (3) identify changes in BF rates and access to lactation support at three different phases of the pandemic (early, middle, and late). Methods: Patients receiving prenatal care at a mid-sized academic medical institution in Central Pennsylvania were recruited and surveyed and this data was collected and combined with data from the electronic medical record. Results: 88% of patients received a lactation consultation during birth hospitalization. Having COVID-19 during pregnancy did not change access to lactation consultation post-partum (p = 0.0961). Neither BF exclusivity during the three phases of the pandemic nor the number of lactation consult visits were statistically different (p = 0.2263; p = 0.0958 respectively). Multiple regression models assessing BF exclusivity in the hospital found significant associations with having a lactation consult (OR 2.50, 95% CI 1.04, 6.04), having an infant in the neonatal intensive care unit (OR 0.29, 95% CI 0.11, 0.73), and having reported social support during pregnancy (OR 1.09, 95% CI 1.01,1.18). Conclusions: Social support during pregnancy and having a lactation consult visit during birth hospitalization remained critical factors for BF exclusivity. This study highlights the importance of having professional lactation support on both BF exclusivity and continuation during the COVID-19 pandemic.

Research on establishment decision of medical equipment measurement standard based on GDM-AHP

A global estimate of multiecosystem photosynthesis losses under microplastic pollution

Understanding how ecosystems respond to ubiquitous microplastic (MP) pollution is crucial for ensuring global food security. Here, we conduct a multiecosystem meta-analysis of 3,286 data points and reveal that MP exposure leads to a global reduction in photosynthesis of 7.05 to 12.12% in terrestrial plants, marine algae, and freshwater algae. These reductions align with those estimated by a constructed machine learning model using current MP pollution levels, showing that MP exposure reduces the chlorophyll content of photoautotrophs by 10.96 to 12.84%. Model estimates based on the identified MP-photosynthesis nexus indicate annual global losses of 4.11 to 13.52% (109.73 to 360.87 MT·y −1 ) for main crops and 0.31 to 7.24% (147.52 to 3415.11 MT C·y −1 ) for global aquatic net primary productivity induced by MPs. Under scenarios of efficient plastic mitigation, e.g., a ~13% global reduction in environmental MP levels, the MP-induced photosynthesis losses are estimated to decrease by ~30%, avoiding a global loss of 22.15 to 115.73 MT·y −1 in main crop production and 0.32 to 7.39 MT·y −1 in seafood production. These findings underscore the urgency of integrating plastic mitigation into global hunger and sustainability initiatives.

Longitudinal trajectories of muscle impairments in growing boys with Duchenne muscular dystrophy

Background Insights into the progression of muscle impairments in growing boys with Duchenne muscular dystrophy (DMD) remain incomplete due to the frequent oversight of normal maturation as a confounding factor, thereby restricting the delineation of sole pathological processes. Objective To establish longitudinal trajectories for a comprehensive integrated set of muscle impairments, including muscle weakness, contractures and muscle size alterations, while correcting for normal maturation, in DMD. Methods Thirty-three boys with DMD (aged 4.3–17 years) were included. Fixed dynamometry, goniometry, and 3D freehand ultrasound were used to repeatedly assess lower limb muscle strength, passive range of motion (ROM) and muscle size, resulting in 161, 178 and 64 assessments for the strength, ROM and ultrasound dataset, respectively. To account for natural strength development, ROM reduction, and muscle growth in growing children, muscle outcomes were converted to unit-less z-scores calculated in reference to typically developing (TD) peers. This allows the interpretation of the muscle outcomes as deficits or alterations with respect to TD. Mixed-effect models estimated the longitudinal change in muscle impairments. Results At 4.3–4.9 years of age, all muscle strength outcomes and several ROMs (i.e., dorsiflexion, hamstrings, and hip extension) showed deficits relative to TD, while m. medial gastrocnemius size was increased. Most muscle outcomes remained stable or slightly improved until the ages of 6.6–9.4 years (except knee flexion strength). After this period, muscle strength (−0.27 to −0.45 z-score/year; p < 0.0044), dorsiflexion ROM (−0.23 to −0.33 z-score/year; p < 0.0007), m. medial gastrocnemius size (−0.56 z-score/year; p = 0.0022), and m. rectus femoris size (−0.36 z-score/year; p = 0.0054) declined. Conclusions The current study established longitudinal trajectories of muscle impairments in boys with DMD. The results provided enriched history data and revealed promising outcome measures that could enhance the detection of the efficacy of novel therapeutic strategies. Future studies are necessary to validate these outcomes.

Association between air pollution exposure and lower urinary tract symptoms in Korean men

How nidoviruses evolved the largest known RNA genomes

Identifying healthcare needs with patient experience reviews using ChatGPT

Background Valuable findings can be obtained through data mining in patients’ online reviews. Also identifying healthcare needs from the patient’s perspective can more accurately improve the quality of care and the experience of the visit. Thereby avoiding unnecessary waste of health care resources. The large language model (LLM) can be a promising tool due to research that demonstrates its outstanding performance and potential in directions such as data mining, healthcare management, and more. Objective We aim to propose a methodology to address this problem, specifically, the recent breakthrough of LLM can be leveraged for effectively understanding healthcare needs from patient experience reviews. Methods We used 504,198 reviews collected from a large online medical platform, haodf.com. We used the reviews to create Aspect Based Sentiment Analysis (ABSA) templates, which categorized patient reviews into three categories, reflecting the areas of concern of patients. With the introduction of thought chains, we embedded ABSA templates into the prompts for ChatGPT, which was then used to identify patient needs. Results Our method has a weighted total precision of 0.944, which was outstanding compared to the direct narrative tasks in ChatGPT-4o, which have a weighted total precision of 0.890. Weighted total recall and F1 scores also reached 0.884 and 0.912 respectively, surpassing the 0.802 and 0.843 scores for “direct narratives in ChatGPT.” Finally, the accuracy of the three sampling methods was 91.8%, 91.7%, and 91.2%, with an average accuracy of over 91.5%. Conclusions Combining ChatGPT with ABSA templates can achieve satisfactory results in analyzing patient reviews. As our work applies to other LLMs, we shed light on understanding the demands of patients and health consumers with novel models, which can contribute to the agenda of enhancing patient experience and better healthcare resource allocations effectively.

The associations between the FAGR and all-cause and cardiovascular mortality in patients with STEMI

Abstract Although the fibrinogen-to-albumin-to-globulin ratio (FAGR) has been proven to be related to coronary artery disease (CAD), the association between the FAGR and acute ST-segment elevation myocardial infarction (STEMI) has not been adequately investigated. This study aimed to evaluate the prognostic potential of the FAGR for STEMI. A total of 1042 patients with STEMI after emergency PCI admitted to the First Affiliated Hospital of Kunming Medical University from June 2018 to January 2023 were enrolled in the study. Patients were divided into a low FAGR group and a high FAGR group according to the median FAGR (2.44). We used Kaplan–Meier plots, restricted cubic spline regression, Cox survival analyses and time-dependent ROC analyses to explore the predictive value of the FAGR for all-cause and cardiovascular mortality. Kaplan‒Meier analysis revealed that the cumulative incidence rates of all-cause and cardiovascular mortality in patients with STEMI were greater in the high FAGR group. Multivariate Cox proportional hazard analysis revealed that the FAGR was an independent predictor of both all-cause and cardiovascular death. In terms of the prediction of all-cause mortality, the FAGR had an area under the ROC curve of 0.720, which was better than that for fibrinogen (AUC = 0.687). In terms of the prediction of cardiovascular mortality, the area under the ROC curve for the FAGR was 0.726, which was also better than that for Fib (AUC = 0.698). The present results suggest that the FAGR may serve as a potential prognostic indicator in patients with STEMI after emergency PCI.

Antibody reactivity against EBNA1 and GlialCAM differentiates multiple sclerosis patients from healthy controls

Multiple sclerosis (MS) is an autoimmune demyelinating disorder of the central nervous system (CNS), which is linked to Epstein–Barr virus (EBV) infection, preceding the disease. The molecular mechanisms underlying this connection are only partially understood. We previously described molecular mimicry between the EBV transcription factor EBV nuclear antigen 1 (EBNA1) and three human CNS proteins: anoctamin-2 (ANO2), alpha-B crystallin (CRYAB), and glial cellular adhesion molecule (GlialCAM). Here, we investigated antibody responses against EBNA1 and GlialCAM in a large cohort of 650 MS patients and 661 matched population controls and compared them to responses against CRYAB and ANO2. We confirmed that elevated IgG responses against EBNA1 and all three CNS-mimic antigens associate with increased MS risk. Blocking experiments confirmed the presence of cross-reactive antibodies and molecular mimicry between EBNA1 and GlialCAM, and accompanying antibody responses against adjacent peptide regions of GlialCAM suggest epitope spreading. Antibody responses against EBNA1, GlialCAM, CRYAB, and ANO2 are elevated in MS patients carrying the main risk allele HLA-DRB1*15:01, and combinations of HLA-DRB1*15:01 with anti-EBNA1 and anti-GlialCAM antibodies increase MS risk significantly and in an additive fashion. In addition, antibody reactivities against more than one EBNA1 peptide and more than one CNS-mimic increase the MS risk significantly but modestly. Overall, we show that molecular mimicry between EBNA1 and GlialCAM is likely an important molecular mechanism contributing to MS pathology.

Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system

This study proposes an improved YOLOv8 model for vehicle and pedestrian detection in urban traffic monitoring systems. In order to improve the detection performance of the model, we introduced a multi-scale feature fusion module and an improved non-maximum suppression (NMS) algorithm based on the YOLOv8 model. The multi-scale feature fusion module enhances the model’s detection ability for targets of different sizes by combining feature maps of different scales; the improved non-maximum suppression algorithm effectively reduces repeated detection and missed detection by optimizing the screening process of candidate boxes. Experimental results show that the improved YOLOv8 model exhibits excellent detection performance on the VisDrone2019 dataset, and outperforms other classic target detection models and the baseline YOLOv8 model in key indicators such as precision, recall, F1 score, and mean average precision (mAP). In addition, through visual analysis, our method demonstrates strong target detection capabilities in complex urban traffic environments, and can accurately identify and label targets of multiple categories. Finally, these results prove the effectiveness and superiority of the improved YOLOv8 model, providing reliable technical support for urban traffic monitoring systems.

A new approach to MADM problem using interval-valued hesitant Fermatean fuzzy Hamacher operators and statistical variance

Populations of large-diameter trees are increasing across the United States

Large-diameter trees provide vital ecological functions in forested ecosystems. Old, large-diameter trees may also be vulnerable to climate-driven mortality events, but past work on large tree populations has been geographically limited. Here, we characterize the population of large-diameter trees from two size categories, 50 to 100 cm diameter at breast height (DBH) (medium) and >100 cm DBH (big), within the United States using Forest Inventory and Analysis data. Although populations of big trees are concentrated along the west coast, populations of medium trees are more evenly distributed across the nation. In the western United States, trees >50 cm DBH comprise ~75% of the total carbon stored in live trees, while in the eastern United States they comprise ~20%. Plot remeasurement data indicate that populations of big trees are increasing at an annual rate of 0.49% in the west and 2.9% in the east, and populations of medium trees are increasing at an annual rate of 0.5% in the west and 2.4% in the east. One exception is the Sierra Nevada region, where big trees are declining. Additionally, we observed declines for several individual species. While the overall population trend for large-diameter trees is positive, declines in these species could have localized impacts for the environments in which they occur.

Mowing management favors primary productivity and carbon sequestration without changing species diversity in a temperate hayfield in Central Interior British Columbia, Canada

We examined the effects of different mowing heights on the plant and soil characteristics of an irrigated and fertilized perennial cropping system in the central interior of British Columbia, Canada primarily composed of Medicago sativa, Phleum pratense, and Trifolium pratense. Mowing treatments included cutting heights of 0 cm, 5 cm, 10 cm, 15 cm, 20 cm, 25 cm, 30 cm, and an unmowed control treatment. Mowing treatments were applied three times throughout the study duration, followed by a final harvest. Data were collected on aboveground plant productivity, plant community diversity, and levels of soil carbon, nitrogen, and organic matter. Results showed plant productivity to be greatest at lower cutting heights, decreasing as cutting height increased. M0, M5, and M10 treatments produced over 300% more cumulative biomass than the control treatment. There were no differences across mowing treatments for measures of species diversity. The ten-centimetre treatment produced highest values of soil carbon, nitrogen, and organic matter than many other mowing treatments after three treatment applications (p <  0.05). Results indicate that lower cutting heights produced higher levels of aboveground biomass, did not alter crop species composition throughout the course of the study, and have potential to contribute towards the carbon pool. These results provide insight on the use of mowing within perennial cropping systems, and the effects on aboveground productivity and levels of soil carbon. The implications of this study allow agricultural producers to make informed decisions on how to manage their land for optimum productivity and environmental sustainability.

DNA color image encryption based on conservative chaotic system

Insulation between adjacent TADs is controlled by the width of their boundaries through distinct mechanisms

Topologically associating domains (TADs) are sub-Megabase regions in vertebrate genomes with enriched intradomain interactions that restrict enhancer–promoter contacts across their boundaries. However, the mechanisms that separate TADs remain incompletely understood. Most boundaries between TADs contain CTCF binding sites (CBSs), which individually contribute to the blocking of Cohesin-mediated loop extrusion. Using genome-wide classification, here we show that the width of TAD boundaries forms a continuum from narrow to highly extended and correlates with CBSs distribution, chromatin features, and gene regulatory elements. To investigate how these boundary widths emerge, we modified the random crosslinker polymer model to incorporate specific boundary configurations, enabling us to evaluate the differential impact of boundary composition on TAD insulation. Our analysis, using three generic boundary categories, identifies differential influence on TAD insulation, with varying local and distal effects on neighboring domains. Notably, we find that increasing boundary width reduces long-range inter-TAD contacts, as confirmed by Hi-C data. While blocking loop extrusion at boundaries indirectly promotes spurious intermingling of neighboring TADs, extended boundaries counteract this effect, emphasizing their role in establishing genome organization. In conclusion, TAD boundary width not only enhances the efficiency of loop extrusion blocking but may also modulate enhancer–promoter contacts over long distances across TAD boundaries, providing a further mechanism for transcriptional regulation.

Exposure to violence and associated factors among university students in Ethiopia: A cross-sectional study

Background Violence is a major public health concern with a significant impact on the health and well-being of individuals, families, and communities. Living in a new environment without parental control and experimenting with new lifestyles may increase the risk of violence among university students. Therefore, this study aimed to assess exposure to violence and its associated factors among university students in Ethiopia. Method A cross-sectional study was conducted among 2988 university students from six randomly selected universities in Ethiopia. A two-stage stratified sampling method was used to recruit the study participants. A self-administered questionnaire was utilized to collect information regarding exposure to emotional, physical, and sexual violence. Bivariable and multivariable logistic regression analyses were used to identify factors associated with violence exposure in the last 12 months. Results The prevalence of exposure to any type of violence in the last 12 months was 17.6% (n = 525) (17.9% among males, 16.5% among females). The adjusted odds ratio (AOR) of violence was 2.9 times higher (95% CI 1.6-5.0) among students older than 25 years than those aged 18-20 years. Those students who were in a relationship had 1.4 times higher odds of violence (95% CI 1.0-2.0) than those who were not in a relationship. In addition, those students who were from rural residences before coming to the university had 1.4 times higher odds of violence (95% CI 1.1-1.8) than those from urban residences. The odds of violence among those who consumed alcohol once a week or more in the past month were 2.2 times higher (95% CI 1.3-3.6) than those who did not consume alcohol. Furthermore, the likelihood of violence was 1.6 times higher (95% CI 1.0-2.4) among those who chewed khat and 2 times higher (95% CI 1.3-3.1) among those who used other drugs in the last 12 months. Conclusion Exposure to violence is a challenge for both male and female university students in Ethiopia. Several socio-demographic and behavioral factors were significantly associated with exposure to violence. Therefore, it is crucial for universities and stakeholders to raise awareness about contributing factors to minimize violence, regardless of gender.

Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors

Abstract The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA). Precision improvements are also considered when creating a model. Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. The linear relationship between temperature and electricity consumption makes the relationship noteworthy. Comparing the temperature effect in a working day and a temperature effect on a weekend day where at night, the marginal effects of temperature on the demand in a working day for power are likewise at their highest. However, there are significant effects of temperature on the demand for a holiday, even a weekend or special holiday. Two scenarios are used to get the results by using machine and deep learning techniques in four seasons. The first scenario is to forecast a working day, and the second scenario is to forecast a holiday (weekend or special holiday) under the effect of the temperature in each of the four seasons and the cost of electricity. To clarify the four techniques’ performance and effectiveness, the results were compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE) values. The forecasting model shows that the four highlighted algorithms perform well with minimal inaccuracy. Where the highest and the lowest accuracy for the first scenario are (99.90%) in the winter by simulating an Adaptive Neural-based Fuzzy Inference System and (70.20%) in the autumn by simulating Artificial Neural Network. For the second scenario, the highest and the lowest accuracy are (96.50%) in the autumn by simulating Adaptive Neural-based Fuzzy Inference System and (68.40%) in the spring by simulating Long Short-Term Memory. In addition, the highest and the lowest values of Mean Absolute Error (MAE) for the first scenario are (46.6514, and 24.759 MWh) in the spring, and the summer by simulating Artificial Neural Networks. The highest and the lowest values of Mean Absolute Error (MAE) for the second scenario are (190.880, and 45.945 MWh) in the winter, and the autumn by simulating Long Short-Term Memory, and Adaptive Neural-based Fuzzy Inference System.

Quantifying compositional variability in microbial communities with FAVA

Microbial communities vary across space, time, and individual hosts, generating a need for statistical methods capable of quantifying variability across multiple microbiome samples at once. To understand heterogeneity across microbiome samples from different host individuals, sampling times, spatial locations, or experimental replicates, we present FAVA ( F ST -based Assessment of Variability across vectors of relative Abundances), a framework for characterizing compositional variability across two or more microbiome samples. FAVA quantifies variability across many samples of taxonomic or functional relative abundances in a single index ranging between 0 and 1, equaling 0 when all samples are identical and 1 when each sample is entirely composed of a single taxon (and at least two distinct taxa are present across samples). Its definition relies on the population-genetic statistic F ST , with samples playing the role of “populations” and taxa playing the role of “alleles.” Its mathematical properties allow users to compare datasets with different numbers of samples and taxonomic categories. We introduce extensions that incorporate phylogenetic similarity among taxa and spatial or temporal distances between samples. We demonstrate FAVA in two examples. First, we use FAVA to measure how the taxonomic and functional variability of gastrointestinal microbiomes across individuals from seven ruminant species changes along the gastrointestinal tract. Second, we use FAVA to quantify the increase in temporal variability of gut microbiomes in healthy humans following an antibiotic course and to measure the duration of the antibiotic’s influence on temporal microbiome variability. We have implemented this tool in an R package, FAVA , for use in pipelines for the analysis of microbial relative abundances.

Maternal occupation and risk of adverse fetal outcomes in Tanzania: A hospital-based cross-sectional study

Background Women constitute a large proportion of the workforce in today’s world. Hazardous working environment conditions for these women pose threat to their reproductive health. Despite efforts to address maternal health in Tanzania, the impact of occupational risks during pregnancy remains unclear. We assessed whether maternal occupation during pregnancy is associated with adverse Foetal outcomes. Methods A cross-sectional study was conducted among 400 self-referred post-delivery women at a referral Hospital in Tanzania. Information on socio-demographic characteristics and maternal occupational characteristics was assessed through the use of a pre-tested questionnaire. Questions on physical demanding work and prolonged standing were obtained from the standardized Musculoskeletal Questionnaire. To assess occupational exposure to chemicals, job titles and task descriptions were linked to a job-exposure-matrix, an expert judgment on exposure to chemicals at the workplace. Information relating to obstetric characteristics and pregnancy outcomes was obtained from the medical files and clinic cards. Data was analyzed by using Statistical Package for Social Sciences (SPSS) version 23. Odds ratios >  1 was considered risk while Odds ratios <  1 was considered protective and P value < 0.05 was considered significant. Results The mean age was 28.0 ±  6.3. Out of 400 post-delivery women studied, 174 (43.5%) were engaged in various occupations. Agriculture (22.4%) was the most prevalent occupation followed by tailoring (19.0%). Relative to the referent group of other occupations, agriculture workers, had higher adjusted odds ratios of congenital malformation (AOR = 4.5, 95% CI; 1.6-12.8)preterm babies (AOR = 2.8, 95% CI; 1.3-7.9), low birth weight (AOR = 3.1, 95% CI; 1.4-8.4) and low Apgar score (AOR = 3.5, 95% CI; 1.3-9.5). Food vendors: low birth weight (AOR = 8.6, 95% CI; 2.7-24.8) and low Apgar score (AOR = 13.5, 95% CI; 4.5-39.4). Conclusion Understanding occupational characteristics and their relation to adverse Foetal outcomes is important to formulate appropriate strategies to promote and protect maternal and infant health at work.

Metagenomic analysis of the faecal microbiota and AMR in roe deer in Western Pomerania

Abstract As an integral part of the global wellbeing, the health of wild animals should be regarded just as important as that of humans and livestock. The investigation of wildlife health, however, is limited by the availability of samples. In an attempt to implement a method with little invasiveness and broad areas of application, shotgun metagenomics were utilised to investigate the faecal microbiome and its antimicrobial resistance genes (AMRG) in roe deer. These genes can facilitate antimicrobial resistances (AMR) in bacteria and are therefore of increasing importance in global health. Accordingly, the abundance in potential vectors like wildlife needs to be assessed. The samples were additionally investigated for ESBL-E. coli, an antibiotic resistant pathogen of global concern, via cultivation. Twenty-seven hunt-harvested animals in Western Pomerania were sampled. This study is the first to our knowledge to describe the faecal microbiome of the European roe deer (Capreolus capreolus), providing insights into the bacterial and archaeal composition. Among the animals, the microbiome was mostly similar and showed a comparable composition to what has been reported in related species, with a ratio of 1.76 between Bacillota and Bacteroidota. The normalised abundance of AMR genes was found to be 0.035 on average, which is similar to other investigations on wild ruminants. Selective cultivation found no ESBL-E. coli in the animals. The prevalence of AMRG in roe deer of Western Pomerania was found to be in line with previous results. The use of shotgun metagenomics allowed for the simultaneous investigation of composition and AMR genes in the faecal microbiome of roe deer, which suggests it as a promising method for the health monitoring of wildlife. This study is the first to describe the prokaryotic assemblage in the faeces of roe deer and its differences to the microbiomes published on other cervids were discussed.