Browse Articles

Increased IFN responses drive myeloid cell activation in people living with HIV-1

Antiviral and anti-inflammatory efficacy of nanoencapsulated brazilian green propolis against SARS-CoV-2

Retraction: Induction of Hypoxia-inducible Factor 1 Activity by Muscarinic Acetylcholine Receptor Signaling

Spatial and single cell mapping of castleman disease reveals key stromal cell types and cytokine pathways

High-performance p-type bilayer WSe2 field effect transistors by nitric oxide doping

A normative principle governing memory transfer in cerebellar motor learning

A novel mouse model for studying complications related to type 2 diabetes using a medium-fat diet, fructose, and streptozotocin

Abstract The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal models, the most common of which involves the consumption of high-fat diets comprising 60% calories from fat. Although these models reproduce the onset and most complications associated with T2DM, they do not accurately mimic human dietary patterns, as they lack the addition of carbohydrates such as fructose in drinking water. The aim of this study was to develop a mouse model for studying complications related to T2DM. To this end, male C57BL/6 mice were fed a medium-fat diet (34.5% kcal from fat), given 20% fructose in drinking water, and injected with a single low dose of streptozotocin (STZ; 100 mg/kg) (D + T). At week 20, D + T mice exhibited significant weight gain, elevated fasting blood glucose levels, and the development of insulin resistance compared with control mice. Furthermore, the circulating levels of liver enzymes (GPT, GOT, and alkaline phosphatase), total cholesterol, and LDL increased. Multi-organ damage, including reduced pancreatic islet size and number, severe hepatic steatosis, inflammatory infiltration in visceral adipose tissue, and cardiac and renal dysfunction, was also detected. The proposed model replicates T2DM-associated complications in young mice by combining a medium-fat diet with fructose and STZ.

Enhancement of biodegradation of tar-rich coal through sequential treatment by N. mangyaensis and Ochrobactrum sp.

Development and validation of a modified SOFA score for mortality prediction in candidemia patients

Abstract Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed to enhance the predictive performance of the sequential organ failure assessment (SOFA) score by developing a modified SOFA (mSOFA) score, which is specifically designed for candidemia patients. Using data from MIMIC-III, MIMIC-IV, and ICU-JN databases, we identified key prognostic variables through LASSO regression and integrated into the mSOFA_3 model. The model incorporated respiratory_SOFA, coagulation_SOFA, and circulatory_SOFA along with clinical biomarkers, including lactate, albumin, and blood urea nitrogen. The mSOFA_3 model demonstrated superior predictive performance across multiple machine learning algorithms, with the logistic regression-based model achieving the highest AUC of 0.826 in the internal validation cohort and 0.813 in the test cohort. Kaplan-Meier survival analysis further validated the model’s utility in stratifying patients into high-risk and low-risk groups with distinct survival outcomes. These findings highlight the mSOFA_3 as a robust and clinically relevant tool for early risk stratification, offering potential for improved decision-making and therapeutic management in critically ill patients with candidemia.

Genome-wide analysis of the ammonium transporters gene family in Phaseolus vulgaris and its roles in response to drought and salinity stress

The impact of co-fed plastic diet on Tenebrio molitor gut bacterial community structure

Abstract This study aimed to analyze the long-term impact of co-fed plastic diet on the bacterial community of self-sustaining laboratory populations of T. molitor fed with wheat bran with added polystyrene (PS), and low density polyethylene (LDPE) over a three year period. The most abundant phyla for all three populations were Firmicutes, Bacteroidota and Proteobacteria. PS group microbiota is similar to C group, pointing to a common bacterial species capable for degrading lignocellulose and PS, while consumption of LDPE caused a significant decrease of Bacteroidota and Actinobacteriota compared to both C and PS group, and Campylobacterota compared to PS group. A predictive metabolomics analysis recognized dTDP-L-rhamnose biosynthesis I in PS group as one of five unique pathways, while other five distinctive pathways, like peptidoglycan maturation, were linked to LDPE group. Further studies are needed to determine the plastic degrading properties of the detected bacteria. The results highlight T. molitor’s versatility in biotechnological applications.

Multiclass semantic segmentation for prime disease detection with severity level identification in Citrus plant leaves

Abstract Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food security and the incomes of farmers. Citrus plants, recognized for their nutritional benefits and economic significance, are especially vulnerable to diseases such as citrus greening, Black spot, and Citrus canker. Due to technological advancements, image processing and Deep learning algorithms can now detect and classify plant diseases early on, which assists in preserving crop health and productivity. The proposed work enables farmers to identify and visualize multiple diseases affecting citrus plants. This study proposes an efficient model to detect multiple citrus diseases (canker, black spot, and greening) that may co-occur on the same leaf. It is achieved using the RSL (Residual Squeeze & Excitation LeakyRelu) Linked-TransNet multiclass segmentation model. The proposed model stands out in its ability to address major limitations in existing models, including spatial inconsistency, loss of fine disease boundaries, and inadequate feature representation. The significance of this proposed RSL Linked-Transnet model lies in its integration of hierarchical feature extraction, global context modeling via transformers, and precise feature reconstruction, ensuring superior segmentation accuracy and robustness. The results of the proposed RSL Linked-TransNet architecture reveal average values of 0.9755 for accuracy, 0.0660 for loss, 0.9779 for precision, 0.9738 for recall, and 0.9308 for IoU. Additionally, the model achieves a mean F1 score of 0.7173 and a mean IoU of 0.7567 for each disease class in images from the test dataset. The segmentation results are further utilized to identify the prime disease affecting the leaves and evaluate disease severity using the prime disease classification and severity detection algorithm.

Smart adaptive ensemble model for multiclass imbalanced nonstationary data streams

Reference intervals for coagulation parameters in chinese adults stratified by sex and age

Formulation of low temperature mixed mode crack propagation behavior of crumb rubber modified HMA using artificial intelligence

Abstract Determining mixed mode fracture parameters asphalt concrete mixtures remains an engineering challenge due to non-homogeneity and inelasticity of the material. In this research, a study was conducted to determine the low-temperature R-curves of unmodified and crumb rubber modified Hot Mix Asphalt (HMA) under mode I and mixed-mode (I/II) loading conditions. Single edge notched beam (SE(B)) testing was employed to collect data, and three key fracture parameters—cohesive energy, energy rate, and fracture energy—were extracted to represent different stages of fracture and crack propagation. Within the scope of this study, it was observed that for the AC 85/100 paving grade bitumen, a temperature of − 20 °C serves as a critical temperature, shifting fracture from quasi-brittle to brittle. At this temperature, the stable crack growth region in the R-curves significantly shrinks, causing abrupt specimen failure. The incorporation of 20% crumb rubber demonstrated favorable material characteristics, with a progressively rising R-curve even during the unsfi crack propagation phase. The central goal of this research is to establish prediction models for the mixed-mode (I/II) crack propagation parameters Gb, Gf, and Gi. The features selected for modeling are Gb0, Gf0, and Gi0 (mode I), percentage of crumb rubber, type of aggregate, binder content, nominal maximum aggregate size, temperature, and normalized offset ratio. Two dataset configurations were used: dataset 1 contains all entries, while dataset 2 excludes Gb0, Gf0, and Gi0 (mode I). Five machine learning techniques, Regression, Multi-Gene Genetic Programming (MGGP), Support Vector Regression (SVR), Random Forest, and Artificial Neural Networks were employed to predict three key fracture parameters. Although slightly less accurate than SVR and Random Forest, MGGP offers the key advantage of yielding explicit mathematical expressions for crack propagation prediction. The R2 index for the MGGP model in Dataset 1 was 0.93 for Gb, 0.94 for Gf, and 0.92 for Gi. For dataset 2, the indices were 0.89, 0.93, and 0.88, respectively.

Regulation of NRF2 by stably associated phosphoinositides and small heat shock proteins in response to stress

Boundary complexity of cortical and subcortical areas predicts deep brain stimulation outcomes in Parkinson’s disease

Abstract While deep brain stimulation (DBS) remains an effective therapy for Parkinson’s disease (PD), sources of variance in patient outcomes are still not fully understood, underscoring a need for better prognostic criteria. Here, we leveraged routinely collected T1-weighted (T1-w) magnetic resonance imaging (MRI) data to derive patient-specific measures of brain structure and evaluate their usefulness in predicting changes in PD medications in response to DBS. Preoperative T1-w MRI data from 231 patients with PD were used to extract regional measures of fractal dimension (FD), sensitive to the structural complexities of cortical and subcortical brain. FD was validated as a biomarker of PD progression through comparison of patients with PD and healthy controls (HCs). This analysis revealed significant group differences in FD across nine brain regions, including frontal, occipital, insular, and basal ganglia areas, which supports its utility as a marker of PD. We evaluated the impact of adding imaging features (FD) to a clinical model that included demographics and clinical parameters (age, sex, total number and location of DBS electrodes), and preoperative motor response to levodopa. This model aimed to explain variance and predict changes in medication following DBS. Regression analysis revealed that inclusion of the FD of distributed brain areas correlated with post-DBS reductions in medication burden, explaining an additional 13.6% of outcome variance (R 2  = 0.388) compared to clinical features alone (R 2  = 0.252). Hypergraph-based classification learning tasks achieved an area under the receiver operating characteristic curve of 0.64 when predicting with clinical features alone, versus 0.76 when combining clinical and imaging features. These findings demonstrate that PD effects on brain morphology linked to disease progression influence DBS outcomes. The work also highlights FD as a potentially useful imaging biomarker to enhance DBS candidate selection criteria for optimized treatment planning.

Modulating transformation of DNA origami nanoarray via sequence design

Multidimensional third-generation sequencing of modified DNA bases allows interrogation of complex biological systems

Abstract DNA exists biologically as a highly dynamic macromolecular complex subject to myriad chemical modifications that alter its physiological interpretation, yet most sequencing technologies only measure Watson-Crick base pairing interactions. Third-generation sequencing technologies can directly detect novel and modified bases, yet the difficulty and cost of training these techniques for each novel base has so far limited this potential. Here, we present a method based on barcoded split-pool synthesis to generate reference standard oligonucleotides allowing novel base sequencing. Using novel base detection, we perform multidimensional sequencing to retrieve information, both physiologically stored and experimentally encoded, from DNA, allowing us to characterize the preferential replication of deleterious mitochondrial genome mutations, the infection dynamics of a host-pathogen model, and the effect of chemotherapy on cancer cell DNA at the single molecule level. The low cost and experimental simplicity of this method make this approach widely accessible to the research community, enabling complex experimental interrogation across the biological sciences.

Direct printing of metal oxide nanostructures for wearable electrochemical sensing