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Discovery of intestinal microorganisms that affect the improvement of muscle strength

Synergistic effects of surfactant blends on lignite dust wettability

In this study, we employed a combination of theoretical and experimental analyses to explore the effects of the physico-chemical properties of lignite samples and surfactants on lignite dust’s wettability, thereby improving dust control in coal mines. First, we measured and analysed the coal samples’ industrial composition, elemental composition and chemical structure. It was found that the selected lignite dust has high ash and low moisture content and contains many hydrophobic functional groups, resulting in poor wettability by water. Next, we conducted surface tension tests, contact angle tests and lignite dust settling experiments to screen 12 monomer surfactants, exploring the impact of these solutions on lignite dust wettability. Finally, considering all monomer surfactants’ abilities to reduce surface tension, decrease contact angles and promote dust settling in solutions, we selected five surfactants (AES, MES-30, AEO-9, CDEA and CHSB) for blending based on their excellent performance in tests. We prepared the blends of these five surfactants, each with a mass fraction of 0.5 wt%, in a 1:1 ratio, resulting in 10 blended solutions. We measured the performance of these solutions and revealed that the AES + AEO-9 blend demonstrated a significant synergistic effect, markedly enhancing the capture efficiency of water for lignite dust.

The Advantage of Early and Progressive Rehabilitation over Conventional Physiotherapy in Noninvasive Ventilation Patients Must Be Confirmed by Appropriate Studies

Evaluation of automated streaking patterns in urine culture for clinical workflow optimization

Abstract Automated streaking systems improve the standardization of microbiological workflows; however, the optimal streaking pattern for single-colony isolation remains uncertain. Seven preinstalled streaking patterns were systematically evaluated using the Copan WASP system to identify the most effective pattern for urine culture. We assessed seven pre-installed streaking patterns using five standard bacterial strains at varying bacterial loads from 10² to10⁷ CFU/mL. The top three patterns were further tested on clinical urine samples collected over three months. Single-colony isolation efficiencies were compared across different bacterial loads and polymicrobial samples. Among standard strains, Single Streak Type 6 (SST6) exhibited the highest single-colony isolation efficiency, followed by Four Quadrants Streak Type 5 and Five Quadrants Streak Type 1. SST6 consistently performed the best under moderate bacterial loads (10–10 CFU/mL). However, in high bacterial load samples (≥ 10⁷ CFU/mL), Four Quadrants Streak Type 5 outperformed SST6 (p = 0.003). Additionally, SST6 failed to isolate single colonies of the five species in the polymicrobial samples. Five Quadrants Streak Type 1 showed the lowest coefficient of variation, suggesting greater consistency across different bacterial loads. This study highlighted the critical role of streaking pattern selection in automated streaking systems. While the performance varied depending on the bacterial load and sample complexity, certain patterns demonstrated superior single-colony isolation and consistency. Optimizing streaking patterns is essential for improving reproducibility, enhancing diagnostic accuracy, and standardizing automated microbiological workflows.

Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease

Background Diabetic kidney disease (DKD) is a severe global complication of diabetes, yet its molecular mechanisms remain incompletely understood. This study aimed to investigate the role of protein glycosylation in DKD pathogenesis and its association with gene expression changes, with the goal of identifying diagnostic biomarkers and personalized therapeutic targets. Methods Integrated bioinformatics and machine learning approaches were applied to analyze multiple gene expression datasets. Differentially expressed glycosylation-related genes were identified, followed by unsupervised clustering to define molecular subtypes. Functional enrichment, immune cell infiltration analysis, and machine learning algorithms (including feature selection for hub genes) were employed. qPCR validation was performed on clinical DKD and normal kidney tissues, and ROC curves were generated to assess diagnostic potential. Results Unsupervised clustering of glycosylation-related genes revealed two distinct DKD molecular subtypes with differential pathway activation (e.g., extracellular matrix remodeling) and immune infiltration patterns. Six hub genes (S100A12, EXT1, SBSPON, ADAMTS1, FMOD, SPTB) were identified as critical to DKD pathogenesis through machine learning. Immune infiltration analysis showed significant differences in macrophage and neutrophil activity between DKD and controls and Immunohistochemical results confirmed the occurrence of immune infiltration. qPCR validation confirmed dysregulation of hub genes in DKD tissues compared to normal samples. ROC analysis demonstrated high diagnostic accuracy for these genes. Conclusions This study highlights abnormal protein glycosylation as a key player in DKD and identifies six hub genes with potential as diagnostic biomarkers. The molecular subtypes and immune infiltration patterns provide insights into disease heterogeneity, paving the way for personalized therapies. Future studies should validate these findings in larger cohorts with explicit sample sizes to strengthen clinical applicability.

Author Response: High Protein but Normocaloric Diet in the ICU does not Prevent Muscle Atrophy due to Immobility

ProtAlign-ARG: antibiotic resistance gene characterization integrating protein language models and alignment-based scoring

Preservation effect of plant essential oil-KGM composite coating materials on the tomatoes

Tomatoes with rich in nutrients are very popular for humans, but they are extremely perishable during storage and transportation, which may limit their economic and nutritional value. In this research, the suitable concentration of essential oil and soaking time were investigated and selected by measuring the changes of tomatoes’ weight loss ratio and hardness with single factor experiment. Subsequently, the Box-Behnken experimental design model from Response Surface Methodology (RSM) was employed to optimize key process parameters for tomato preservation, with three independent variables selected: cinnamon essential oil concentration, konjac glucomannan (KGM) concentration, and immersion time. As a result, cinnamon essential oil with good preservation effect was selected and combined with KGM to prepare coating materials for the preservation of tomatoes. The optimized preservation condition for tomatoes was in a solution with 0.70 g/L cinnamon essential oil, 8.20 g/L KGM and soaking time for 3.2 min, which had a good preservation effect on tomatoes with 2.5% weight loss ratio and 1.3% hardness after 10 days at room temperature, reduced the consumption of total soluble solid (TSS), delayed the decline of vitamin C (Vc) content, inhibited the increase of malondialdehyde (MDA) content, and enhanced peroxidase (POD) activity. This study demonstrates that the composite coating combines synergistic barrier properties, dual functionality, and economic advantages, establishing a theoretical framework for sustainable tomato preservation while providing transferable strategies for other produce.

High Protein but Normocaloric Diet in the ICU does not Prevent Muscle Atrophy due to Immobility

Genome-wide interaction study of physical activity and genetic susceptibility on colorectal cancer using UK biobank data

Urban morphology and climate vulnerability assessment in Kuwait: A spatio-temporal predictive analysis utilizing deep neural network-enhanced markov chain models for 2050 and 2100

Rapid urban growth in Kuwait creates challenges for adapting to climate change. This study investigates the spatio-temporal dynamics of urban growth in Kuwait and assesses its climate change vulnerability using a Multi-Layer Perceptron Markov Chain Model (MLPMCM) to forecast land use and land cover (LULC) changes for the years 2050 and 2100. Utilizing historical LULC data from 1985, 2005, and 2022, along with various spatial drivers, the research predicts urban expansion patterns for 2050 and 2100. The model achieved high accuracy in predictions, indicating that proximity to coastlines, road networks, and commercial areas are the primary drivers of urban growth in Kuwait. The study projects significant urban expansion, particularly in North-Northwestern and South-Southwestern regions, with urban areas expected to increase from 819 km² in 2022–1,893 km² by 2100. Climate vulnerability analysis, based on RCP 8.5 scenario projections, is assessed using the cross-referencing approach and it suggests temperature increases of up to 17°C in urban and coastal regions by 2100. The research highlights the complex interplay between urban growth and climate change, emphasizing the need for adaptive urban planning strategies. This study contributes to the understanding of urban growth dynamics in rapidly developing, oil-rich nations with arid climates, offering insights for sustainable urban development and climate resilience in Kuwait and similar contexts.

Stress Addiction in Health Care: The Dark Side of the Moon

Dynamic shifts in trophoblast nucleos(t)ide metabolism, transport, and adenosine signaling during gestation and preterm birth

Abstract Nucleos(t)ides are essential for DNA/RNA synthesis, energy metabolism, and signaling, yet their roles in placental development remain poorly understood. The placenta undergoes dynamic metabolic adaptations throughout gestation to support fetal growth. This study investigates gene expression shifts in nucleos(t)ide metabolism, transport, and adenosine signaling during placental development and in the pathological condition of spontaneous preterm birth (PTB). We analyzed gene expression in first-trimester (n = 10) and term (n = 10), and PTB (n = 10) human placentas, and in cytotrophoblast and syncytiotrophoblast stage in primary human trophoblasts (n = 3) and BeWo (n = 5) cells. For developmental context, rat placentas were examined at gestation days (GD) GD12, GD15, and GD20 (n = 5 per group) that correspond to early second trimester in the human placenta. We found that genes involved in nucleos(t)ide metabolism and adenosine signaling were dominantly upregulated from early gestation to term in the human placenta. PTB placentas revealed further elevation compared to the term placenta. Differentiation from cytotrophoblast to syncytiotrophoblast was accompanied by only minor changes. Pearson’s correlation analysis revealed strong gene-metabolite and gene-gene associations, highlighting an integrated metabolic network regulating placental function. Gene expression also differed among the tested GDs in the rat placenta. These findings demonstrate dynamic changes of nucleos(t)ide metabolism during healthy placental development and enhanced expression in PTB placentas, suggesting increasing needs for nucleos(t)ides during placental growth and metabolic shifts in the PTB placenta. Our data also indicate that nucleos(t)ide metabolism is preserved in both proliferative and differentiated states.

CrustChain: Resolving the blockchain trilemma via decentralized storage and proof-of-capacity consensus

The blockchain trilemma—achieving scalability, security, and decentralization simultaneously—remains an unsolved challenge in distributed systems. This paper introduces CrustChain, a novel framework that advances decentralized storage and consensus via three key innovations: (1) a reputation-weighted (where node influence scales with storage contribution and historical reliability) Proof-of-Capacity mechanism with temporal Sealed-Post (SPoSt) challenges, (2) hybrid erasure-network coding for 82% storage cost reduction, and (3) MDP-optimized sharding for sub-second cross-shard latency. By combining storage resource guarantees with a sharded validation layer, CrustChain processes 1,450 transactions per second (TPS) at sub-second latency while maintaining a chain quality score (fraction of blocks produced by honest nodes) of 0.94 under 40% Byzantine nodes. The system reduces on-chain storage overhead by 82% compared to Bitcoin through decentralized content addressing and Merkle forest compression. Experimental results demonstrate 99.99% data durability across 1,024 global nodes with $150 hardware requirements, achieving energy efficiency of 0.3 Joules per transaction (0.05% of Bitcoin’s consumption), and outperforming Filecoin’s storage costs by 63%. CrustChain’s layered architecture sets a new benchmark for blockchain systems requiring both high throughput and censorship resistance.

Differential Mortality Benefit of Beta-blockers in Septic Shock: A Subgroup Meta-analysis

Susceptibility of the invasive malaria vector Anopheles stephensi in Ethiopia to novel chemical insecticides and insect growth regulator

Influence of microcrack types on macroscopic cracking of sandstone under freeze-thaw erosion

Freeze-thaw erosion is a common hazard in cold region engineering, which is capable of generating a large number of microcracks inside the rock mass. However, the effect of different forms of microcracking on the macrocracking characteristics of sandstones under freeze-thaw erosion conditions has not been elucidated. Hence, the effect of microcracking on macrocracking under freeze-thaw cycling conditions is analysed by means of a combination of acoustic emission tests and numerical simulations. The results show that the peak strength, modulus of elasticity and longitudinal wave velocity of the sandstone produced a decrease with the increasing degree of freeze-thaw erosion. When the freeze-thaw cycle reached 80 times, the ringing counts changes significantly, showing a continuous accumulation trend. The trend of b value shows that microcracking of rock samples with high degree of freeze-thaw erosion is a continuous process of accumulation. The percentage of RA and AF indicates a shift in the cracking pattern from shear to tensile as the rock specimens are subjected to an increasing number of freeze-thaw cycles. Based on a model of sandstone after freeze-thaw erosion, it is concluded that inhomogeneous variations in the displacement and force chain fields of the particles lead to different modes of fracture extension. Finally, the mechanism of the influence of along-crystal microcracking and through-crystal microcracking on the macroscopic fracture of sandstone is discussed.

Flexible Fiberoptic Bronchoscopy for Esophageal Foreign Body Removal in Children

Publisher Correction: A science-based approach to classifying light vehicles in Europe: methodology and case studies

Complete chloroplast genome sequencing of Pseudocodon convolvulaceus, a medicinal herb from Qinghai-Tibet Plateau in China

As a medicinal plant on the Qinghai-Tibet Plateau, Pseudocodon convolvulaceus has garnered significant attention due to its rich medicinal value, demonstrating notable anti-inflammatory and antioxidant activities. To elucidate the characteristics of the chloroplast genome and the phylogenetic position of Pseudocodon convolvulaceus, as well as to explore its genetic structure and evolutionary significance, the complete chloroplast genome was sequenced, assembled, annotated, and compared with the published genomes of the Campanulaceae family. This analysis provides insights into gene content, structural variation, and phylogenetic relationships. A phylogenetic tree was constructed based on the chloroplast genomes of 19 published Campanulaceae species, utilizing two Asteraceae species as outgroups. The results indicated the following: (1) The chloroplast genome of Pseudocodon convolvulaceus is 183,616 bp in length, featuring a typical tetrad structure with a GC content of 38.7%. A total of 134 genes were annotated, comprising 89 protein-coding genes, eight rRNA genes, and 37 tRNA genes, with 12 genes containing one intron and three genes containing two introns. (2) The chloroplast genome includes 67 SSR loci, predominantly single nucleotide repeats, which account for 40% of the total. (3) The genome comprises 64 synonymous codons, including 30 high-frequency codons (RSCU > 1), with 29 of these high-frequency codons ending in A/T, representing 96.7%. This suggests a tendency for high-frequency codons in the chloroplast genome of Pseudocodon convolvulaceus to terminate with A/T. (4) Phylogenetic analysis revealed that Codonopsis minima, Codonopsis lanceolata, Codonopsis pilosula, and Codonopsis tsinlingensis are closely related to Pseudocodon convolvulaceus. The findings of this study enhance our understanding of the genetic basis of this species and its potential applications in drug research, thereby facilitating the use of this genomic resource for conservation strategies and phylogenetic analysis.