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Quality of life in older immigrant adults on hemodialysis

Background In the United States (U.S.), over 34% of individuals with Chronic kidney disease (CKD) are aged 65 or older. Understanding the quality of life (QoL) in this population is essential. While there has been research on the experiences of U.S.-born older adults on hemodialysis, there is limited data on the experiences of older immigrant adults with CKD. Purpose This study aims to explore the perceived QoL of older immigrant adults living with stage 5 CKD, with a focus on how the disease impacts their cultural beliefs, practices, and overall well-being. Methods A qualitative, basic interpretive approach was employed to capture the lived experiences of older immigrant adults undergoing hemodialysis. Participants were selected based on predefined inclusion and exclusion criteria. The Short Blessed Test (SBT) was used to screen for cognitive impairment. Participants were recruited in the Mid-Hudson Valley Region of New York. Data were collected through semi-structured interviews. Thematic analysis was applied to the data to identify key themes and patterns in the participants’ experiences. Results The findings revealed that the QoL of older immigrant adults on hemodialysis is shaped by complex interactions between family and social support, cultural and religious practices, balancing independence and dependence, health and illness perceptions, life adjustments to hemodialysis, emotional responses, and immigration adjustment. Participants highlighted the significance of spiritual and cultural beliefs in maintaining resilience and managing the emotional stress of the disease. Conclusion The study underscores the need for culturally competent care that integrates social, emotional, and spiritual support to improve the QoL of older immigrant adults on hemodialysis. The findings suggest that CKD care must extend beyond biomedical factors to include sociocultural and spiritual dimensions. The results emphasize the importance of holistic healthcare approaches that respect cultural values and provide support systems to enhance the QoL of this vulnerable group.

The associations between female fecundability and postpartum breastfeeding: A prospective cohort study

Objective This study aimed to investigate the relationship between women’s fecundability and postpartum breastfeeding. Methods We used a prospective cohort study design to recruit pregnant women who came to the hospital for antenatal checkups before 20 weeks’ gestation between April 2019 and March 2020 at the Maternal and Child Health Hospital of Gulou District, Nanjing, China. Women were categorized into prolonged time to pregnancy (TTP) group (>3 months) and shorten TTP (≤3 months) groups. A telephone follow-up was conducted 42 days postpartum to collect breastfeeding practice data. Logistic regression models were employed to analyze the association between TTP and exclusive breastfeeding. Results A total of 535 pregnant women were initially included in the study cohort and 478(89.35%) completed the follow-up, among 79 (16.5%) in the prolonged TTP group, and the rest in the short TTP group (n = 399, 83.5%). According to the follow-up, 271 (56.7%) were in the exclusive breastfeeding group and the rest in the non-exclusive breastfeeding group (n = 207, 43.3%). A significant decrease in exclusive breastfeeding rate was observed in the prolonged TTP group compared to the short TTP group (OR=0.46, 95% CI: 0.27–0.74). After adjusting for potential confounders such as age, husband’s age, BMI, and regularity of menstruation, the negative association between TTP and exclusive breastfeeding remained (OR=0.50, 95% CI: 0.29–0.84). In stratified analyses, the results were generally consistent. Conclusion The probability of postpartum breastfeeding is lower in women with lower fecundability. These findings suggest that women with lower fecundability may benefit from targeted breastfeeding support.

Using eDNA tools to examine the impact of kelp farming on underlying sediments

Using environmental DNA (eDNA)-based tools, we examined sediments underlying a ~ 1.25 hectare commercial kelp farm in the Gulf of Maine growing sugar kelp (Saccharina latissima) for two farming seasons, post-harvest. Two eDNA methods were used: a newly designed S. latissima-specific digital polymerase chain reaction (dPCR) assay targeting the cytochrome oxidase subunit I (COI) mitochondrial gene, as well as metabarcoding for the 16S and 18S ribosomal RNA (rRNA) genes, to examine overall bacterial, archaeal, and eukaryotic diversity. Sediment carbon and nitrogen content was analyzed using isotope ratio mass spectrometry (IRMS) as more traditional indicators of potential kelp biomass-derived nutrient enrichment in the benthos. When targeted sampling sites were added inside the footprint of the farm lease area in year two of the study, dPCR data showed subtle but significant differences between sediment samples inside and outside of the farm, with mean S. latissima COI gene copies from cores taken inside the farm being ~8% greater than mean values outside the farm. The highest COI copy numbers in marine sediments were from sites with observed accumulation of kelp biomass, while there was no conclusive difference in carbon and nitrogen content of those same sediment samples. Metabarcoding data also revealed subtle differences in taxa associated with sediments inside and outside the farm. For example, microbial taxa that correlated with kelp eDNA from cores within the farm included the families Rhodothermaceae, Rubritaleaceae, Flavobacteriaceae, Prolixibacteraceae, Nitrosomonadaceae, Nitrincolaceae and Rubinisphaeraceae. However, the majority of the above taxa were low in relative abundance, with only Flavobacteriaceae ranking among the top 30 most abundant and prevalent families in these sediments. In summary, this study demonstrates the sensitivity and specificity of eDNA tools to detect potential ecological and anthropogenic effects in marine sediments, beyond that of bulk nutrient and stable isotope analyses.

Optimizing diesel engine performance and emissions with mahua biodiesel blends using taguchi methodology

This study investigates how adjusting operational parameters influences the performance and emission characteristics of a diesel engine operating on a blend of traditional diesel fuel and mahua biodiesel. The biodiesel was obtained using the transesterification method, and fuel blends were formulated with diesel proportions ranging from 80% to 100% and biodiesel content from 0% to 20%. Key engine parameters such as engine load (20 –100%), mahua biodiesel blend (0 –20%), and engine speed (1300 –1450 rpm) were varied systematically during the experiments, while the compression ratio was held constant at 18:1. The aim was to determine the most effective combination of settings to enhance combustion efficiency and reduce harmful exhaust emissions. The findings demonstrated a modest yet meaningful improvement in engine efficiency, with gains of approximately 2–3%. While seemingly incremental, this enhancement becomes significant when coupled with concurrent reductions in harmful emissions, aligning with global efforts to transition toward sustainable fuel alternatives. The optimized biodiesel blends not only improved fuel utilization but also advanced environmental objectives by mitigating pollutant emissions, underscoring their dual role in enhancing performance and ecological sustainability. The outcomes of this study support the potential of mahua biodiesel as a viable and eco-friendly supplement to petroleum diesel. Overall, the results contribute meaningful data toward improving diesel engine performance while reducing environmental pollution.

Indolicidin derivatives as potent dual-action antifungal and antibacterial agents for the treatment of skin infections: A comprehensive study from in vitro to in vivo evaluation

As listed by the WHO, the emergence of antibiotic-resistant pathogens, particularly Staphylococcus aureus and Pseudomonas aeruginosa, highlights the urgent need for novel antimicrobial agents. In parallel, fungal infections, especially those caused by Candida albicans, have also become increasingly prevalent and clinically challenging, further emphasizing the necessity for broad-spectrum therapeutic strategies. In particular, skin and soft tissue infections (SSTIs) caused by these bacteria and fungal are common in humans and can lead to severe complications if left untreated. Antimicrobial peptides (AMPs) have gained significant attention due to their broad-spectrum antimicrobial activity and immunomodulatory effects. Indolicidin, a naturally occurring cationic AMP, has demonstrated potent antibacterial and antifungal properties. In this study, we designed and synthesized a novel Indolicidin-derivative peptide IND-4,11K to enhance their antimicrobial activity and stability. The peptide was evaluated in vitro for their antimicrobial efficacy against Gram-positive, Gram-negative bacteria and Candida albicans. IND-4,11K was then prepared in 0–2% cream formula for in vivo testing in rabbit models. These findings suggest that modified Indolicidin-derivatives have potential as novel dual-action antimicrobial agents for treating resistant skin infections and warrant further preclinical investigations.

Challenges in higher education: Differences between students with and without specific learning disorder and the moderating role of executive functions

Students with Specific Learning Disorders (SLD) face difficulties not only in academic skills but also in the social, emotional, and executive function (EF) domains. These challenges may increase vulnerability to rumination—a repetitive and maladaptive focus on distress, which is strongly linked to emotional difficulties. This study explores differences in academic, social, emotional, and EF challenges between students with and without SLD and investigates whether these challenges moderate the relationship between SLD and rumination. A sample of 95 college students (46 with SLD, 49 without SLD) completed questionnaires assessing rumination and emotional distress. They also completed a writing task in which they provided an upsetting event related to their studies. The event were coded to four categories related to students’ challenges: academic, social, emotional, and EF challenges. Results indicated that students with SLD reported significantly higher levels of rumination, greater EF challenges, and higher levels of perceived stress than their peers. A moderation analysis further revealed that EF difficulties significantly moderated the link between SLD and rumination, with students experiencing both SLD and EF challenges showing the highest rumination levels. Findings emphasize the critical role of EF in the emotional experiences of students with SLD. Addressing EF deficits among students with SLD can reduce emotional distress and improve academic outcomes.

Machine learning for predicting the diagnosis of tuberculous versus malignant pleural effusion: External validation and accuracy in two different settings

Objective To perform an external validation of a previously reported machine learning (ML) approach for predicting the diagnosis of pleural tuberculosis. Patients and Methods We defined two cohorts: a Training group, comprising 273 out of 1,220 effusions from our prospective study (2013–2022); and a Testing group, from a retrospective analysis of 360 effusions from 832 consecutive patients in Bajo Deba health district (1996–2012). All the effusions included were exudative and lymphocytic. In Training and Testing groups respectively, 49 and 104 cases were tuberculous, 143 and 92 were malignant, and 81 and 164 were diagnosed with “other diseases”; pre-test probabilities of pleural tuberculosis were 4% and 12.7%. Variables included were: age, pH, adenosine deaminase, glucose, protein, and lactate dehydrogenase levels, and white cell counts (total and differential) in pleural fluid. We used two ML classifiers: binary (tuberculous and non-tuberculous), and three-class (tuberculous, malignant, and others); and compared them with Bayesian analysis. Results The best binary classifier yielded a sensitivity of 88%, specificity of 98%, and accuracy of 95%. The best three-class classifier achieved the same accuracy and correctly classified 83% (77/92) of malignant cases. The ML models yielded higher positive predictive values than Bayesian analysis based on ADA > 40 U/l and lymphocyte percentage ≥ 50% (92%). Conclusions This external validation confirms the good performance of the previously reported ML approach for predicting the diagnosis of pleural tuberculosis based on exudative and lymphocytic pleural effusions, and for discriminating the cases most likely to be malignant. Additionally, ML was more accurate than the Bayesian approach in our study.

Upscaling, toxicity and efficacy of multifaceted dressing embedded with dsirna-loaded gold nanoparticles for enhancing diabetic wound treatment

Poor vascularization and infections hinder diabetic wound healing, posing challenges in therapy development. A multi-action approach incorporating Dicer-substrate small interfering RNA (DsiRNA) against the prostaglandin transporter (PGT) gene and gold nanoparticles (AuNPs) into a Pluronic F-127 (PF127) gel was developed. This study aimed to upscale AuNP biosynthesis using Lignosus rhinocerotis (tiger milk mushroom, TMM) extract and chitosan as stabilizers. Response Surface Methodology (RSM) optimized the synthesis with 0.6 mL chloroauric acid (HAuCl₄) and 4.4 mL TMM extract, producing upscaled AuNPs (152.93 ± 1.56 nm, + 30 mV) with a Surface Plasmon Resonance peak at 538 nm. Both lab-scale and upscaled AuNPs exhibited antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa (MIC: 250 μg/mL). The gel formulation demonstrated favourable gelling properties for wound dressing. A 28-day toxicity study confirmed no adverse effects on haematology, biochemistry, or organ morphology. In diabetic Wistar rats, only the wounds treated with Pluronic gels containing AuNPs-DsiRNA showed no signs of infection, while the other groups exhibited infection and pustules in the wounded areas. These findings highlight the potential of AuNP-DsiRNA thermoresponsive gels as an innovative, safe, and effective therapy for diabetic wound healing.

Physical model test on soil slopes under the effect of bedrock fault dislocations

Bedrock fault dislocations significantly influence the rupture instability of rock and soil slopes adjacent to fault zones. Understanding the dynamic processes, kinematic characteristics, and genesis mechanisms of landslides induced by strong seismic fault dislocations is crucial for advancing the theoretical framework of landslide studies. This paper presents a representative experiment simulating the emergence of seismic faults (internal rupture belts within the soil mass) at the shoulders and toes of slopes due to bedrock fault dislocations. Two failure modes were examined: soil mass rupture at the shoulders and the toe of the slope. The rupture at the shoulders was found to pose a greater threat to the stability of the soil slopes under intense fault dislocations. Furthermore, the genesis mechanisms and dynamic characteristics of sandy soil slopes destabilized by reverse fault dislocations can be categorized into three stages: Stage I, the gestation stage of slope soil mass rupture; Stage II, the development stage of the slope soil mass rupture zone; and Stage III, the stage of rupture-induced landslides. The study findings can be useful in slope stability analysis under bedrock dislocation conditions as well as for slope protection and management near fault zones. Both model tests and field cases of earthquake damage demonstrate a strong correlation between the location of the main fracture zone on the slope surface (at the slope shoulder and slope toe) and the displacement of the two walls of the bedrock fault.

The impact of prior online gaming experience on the migration of offline gamblers to online gambling platforms

The proliferation of online gambling platforms has heightened concerns over their potential to intensify problematic gambling behaviors. While previous research has examined various risk factors, the influence of prior online gaming experience on gambling transitions remains underexplored. This study investigates whether and how engagement with online gaming facilitates the migration from offline to online gambling. Using survey data from 742 adults in South Korea, the analysis demonstrates that individuals with prior online gaming experience are significantly more likely to engage in online gambling, place bets more frequently, and spend larger amounts on real-money online betting. Among this group, higher expenditures on in-game purchases are positively associated with increased gambling frequency and spending, indicating a behavioral link between financial investment in online gaming and subsequent online gambling behavior. The study further identifies two prominent features of online gambling—platform anonymity and the perceived advantage of data-driven decision-making—as especially appealing to experienced online gamers. These findings reveal key psychological and behavioral mechanisms that connect online gaming to online gambling engagement. They also underscore the need for regulatory frameworks that address the evolving risks posed by digitally mediated gambling environments.

Correction: Isolation and purification of DNA double-strand break repair intermediates for understanding complex molecular mechanisms

Artificial vision models for the identification of Mediterranean flora: An analysis in four ecosystems

Object identification has been widely used in several applications, utilising the annotated data with bounding boxes to specify each object’s exact location and category in images and videos. However, relatively little research has been conducted on identifying plant species in their natural environments. Natural habitats play a crucial role in preserving biodiversity, ecological balance, and overall ecosystem health. So, effective monitoring of habitats is necessary for safeguarding them, and one way of doing this is by identifying the typical and early warning plant species. Our study quantitatively evaluates the performance of six popular object detection models on our dataset collected in the wild, comprising various plant species from four habitats: screes, dunes, grasslands, and forests. The dataset employed in this work includes the data collected by human operators and the quadrupedal robot ANYmal C. The pre-trained object detection models have been chosen for experiments, and they are fine-tuned on our dataset to achieve better performance. These models incorporate two one-stage (RetinaNet and YOLOv8n), two two-stage (Faster RCNN and Cascade RCNN), and two transformer-based detectors (DETR and Deformable DETR). Extensive experimentation has been performed on the four habitat datasets by applying class balancing and hyperparameter tuning, and the obtained results are discussed.

Research on the optimization of cold chain logistics distribution routes considering time-dependent networks and simultaneous pick-up and delivery from the perspective of sustainability

In today’s economic globalization, the cold chain logistics industry is a fundamental sector supporting the development of the national economy, with sustainable development and a people-oriented approach being crucial. This paper investigates the path optimization problem of cold chain logistics vehicles that simultaneously pick up and deliver goods within a time window, aiming to achieve sustainable development in real-world cold chain logistics distribution. Additionally, the paper takes into account the varying speeds of vehicles over time to more accurately simulate real-world traffic conditions. The objective of the mathematical model is to minimize the total cost, which includes economic, environmental, social, and equity costs. To address this problem, a Heuristic Cross Brainstorm Optimization Algorithm (HCBSO) has been selected, and strategies such as segment-based initial solution construction and heuristic crossover have been designed to enhance the algorithm’s performance. Furthermore, a departure time optimization strategy has been developed to avoid traffic congestion. The effectiveness of the model and algorithm has been verified through multiple sets of comparative tests. The test results indicate that the constructed model and designed algorithm can scientifically optimize departure times, plan vehicle routes to avoid traffic congestion, effectively reduce total costs, and promote the sustainable development of the cold chain logistics system.

Optimizing noise control in flexible shells with bridging membrane discs variations

This study explores the acoustic behavior of flexible cylindrical shells incorporating membrane discs at structural interfaces, focusing on their influence on wave propagation characteristics. The dynamics of the embedded membrane discs are modeled at the junctions between different shell segments, and the resulting boundary value problem is addressed using a combination of the Mode-Matching (MM) and Galerkin methods. The governing equations comprise the Helmholtz equation in the fluid domain and the Donnell–Mushtari shell equations in the elastic guiding regions. To ensure the accuracy and convergence of the semi-analytical solution, generalized orthogonality conditions are employed. A truncated modal expansion is used to reconstruct the matching conditions and enforce physical conservation laws at the interfaces. Numerical simulations are conducted to examine the effects of geometric parameters—such as the radii of adjacent shell segments, the size of the membrane discs, and the excitation frequency—providing valuable insights for the design and optimization of waveguide-based acoustic attenuation systems.

A novel mitovirus associated with the fungal entomopathogen Zoophthora radicans

Metatranscriptome sequencing has emerged as a powerful tool for uncovering viral diversity in insects and their associated microbes. To explore viruses linked to the pea aphid (Acyrthosiphon pisum), we performed metatranscriptome sequencing on field-collected samples. In addition to several known plant viruses, we assembled the genome of a new virus homologous to species in the family Mitoviridae, which are positive-sense single-stranded RNA viruses that encode only an RNA-dependent RNA polymerase and typically replicate in mitochondria. Given the frequent association of mitoviruses with fungi and the presence of entomopathogenic fungal reads in our dataset, we conducted 18S amplicon sequencing and PCR screening on individual aphids. These analyses identified two fungal entomopathogens and uncovered an association between the mitovirus and Zoophthora radicans, a generalist aphid pathogen. Our findings shed light on the hidden microbial diversity in natural pea aphid populations and emphasize the utility of metatranscriptomics in identifying novel virus-host associations.

Reinforcement learning-enhanced multi-objective optimization for sustainable coal blending in thermal power plants

Coal blending in thermal power plants is a complex multi-objective challenge involving economic, operational and environmental considerations. This study presents a Q-learning-enhanced NSGA-II (QLNSGA-II) algorithm that integrates the adaptive policy optimization of Q-learning with the elitist selection of NSGA-II to dynamically adjust crossover and mutation rates based on real-time performance metrics. A physics-based objective function takes into account the thermodynamics of ash fusion and the kinetics of pollutant emission, ensuring compliance with combustion efficiency and NOx limits. Benchmark tests on the Walking Fish Group (WFG) and Unconstrained Function (UF) suites show that QLNSGA-II achieves a 12.7% improvement in Inverted Generational Distance (IGD) and a 9.3% improvement in Hypervolume (HV) compared to prevailing algorithms. Industrial validation at the Huaneng Yingkou power plant confirms a 14.7% reduction in fuel cost and a 41% reduction in slagging incidence over conventional blending methods, backed by 12 months of operational data. Other benefits include a 24.8% reduction in sulphur content, a 6.9% increase in the plant’s net heat rate and annual savings of RMB 12.3 million, 2,150 tonnes of limestone and 38,500 tonnes of CO2-equivalent emissions. These results highlight QLNSGA-II as a scalable, robust solution for multi-objective coal blending, offering a promising way to improve the efficiency and sustainability of coal-fired power generation.

What drives the demand for drought-resilient sorghum varieties? Evidence from moisture-stressed areas in Ethiopia

Sorghum is one of the critical food security crops, particularly in moisture-stressed areas of Ethiopia. However, in the absence of a well-organized formal seed system, public research institutions have continued to promote and disseminate improved sorghum varieties to encourage adoption. On the other hand, the lack of evidence on smallholder farmers’ demand for improved varieties has discouraged the seed industry from investing in marginalized crops, like sorghum, in contrast to more commercialized crops such as wheat and maize. This study assessed producers’ willingness to pay (WTP) for improved sorghum varieties suitable for moisture-stressed sorghum growing agro-ecologies. Data were collected from 659 households selected using probability proportional to size (PPS) sampling techniques. Descriptive statistics, heterogeneity analysis and generalized ordered probit econometric model were employed for data analysis. Farmers’ WTP was, on average, 59% higher than the market price set by the government. In the Amhara and Oromia regions, WTP was 67% and 47% above the official price, respectively. WTP varied significantly by age, farm size, income source, and gender. The inelastic nature of WTP and the observed gender gap—where only 40% of female-headed households exhibited WTP at the market price compared to 60% of male-headed households—highlight the need for gender-responsive, non-price interventions such as targeted subsidies, smaller input packages, and inclusive extension services to promote equitable access and uptake of improved sorghum varieties.

Author Correction: A novel immunochromatographic strips assay for rapid and simple detection of systemic lupus erythematosus

Modulation of cellular adhesion, contractility, and migration by MiuA: A comprehensive analysis of its biomechanical impact

Cellular adhesion and contractility are essential for cell movement. In this study, we investigated the effects of actin stabilization on adhesion properties, contractility, and cell migration. For this, we used the recently synthesized actin stabilizer miuraenamide A (MiuA), which has been discussed as a more reliable alternative to the otherwise commonly used actin stabilizer jasplakinolide. We investigated the number and size of focal adhesions in RPE-1 cells and used single-cell force spectroscopy to evaluate the adhesion properties of those cells after MiuA treatment. We showed that MiuA increases the number of focal adhesions while decreasing their size and reduces adhesion energy and force. Additionally, we investigated its effects on the contractility of RPE-1 cells by measuring their contractile energy using pattern-based contractility screening (PaCS). We found no significant change in contractility after MiuA treatment. Finally, we confined RPE-1 cells in PDMS microchannels and analyzed their migration after treatment with MiuA, showing that neither their speed nor their persistence is affected by MiuA. To check that these effects are not specific to RPE-1 cells, we also analyzed the effects of MiuA treatment in MEF cells and neutrophils. Both MEF cells and neutrophils showed the same results as the RPE-1 cells. Our measurements indicate that, although altering focal adhesions significantly reduces adhesion, it does not impact cell contractility. This finding also clarifies why amoeboid migration, which operates independently of adhesion, remains unaffected. Additionally, it explains the previously observed reduction in mesenchymal migration, which relies on adhesion-based mechanisms.

Effects of one year of extreme isolation in Antarctica on olfactory and gustatory functions

Abstract Taste and smell are critical for food intake and maintaining adequate energy balance, particularly in isolated, confined, and extreme (ICE) environments. Hypoxic conditions, low humidity, and limited chemosensory exposure at Concordia Station in Antarctica may impair taste and smell functions, though research remains scarce. Gustatory and olfactory functions were assessed in 19 participants (39.2 ± 10.9 years) during two overwintering missions at Antarctic Concordia Station. Testing occurred six weeks pre-departure, three times during isolation, and six months post-isolation. Gustatory function was evaluated using ODOFIN Taste Strips; olfactory function using ODOFIN Sniffin’ Sticks. Additionally, subjective sensory reports were collected. Hyposmia increased during isolation, accompanied by a trend toward declining smell identification (p = 0.054), with limited follow-up data offering no clear evidence of recovery. Hypogeusia was primarily reflected in an elevated prevalence of reduced salt sensitivity during mid- to late isolation (p = 0.036), returning to baseline levels post-expedition. Subjective evaluations only partially aligned with psychophysical test results. A one-year stay at Concordia Station revealed individual variability in chemosensory responses, highlighting the sensitivity of taste and smell to environmental extremes. While causality remains unclear, the findings emphasize the need to monitor chemosensory function in extreme settings and constrained environments in everyday life.