Browse Articles

TIP60 promotes chemoresistance by limiting intracellular platinum accumulation and enhancing removal of cisplatin-DNA adducts

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems

Leveraging Stereochemistry to Optimize the Properties of Polyhydroxyalkanoates

Physics-informed gaussian process regression for reproducible and uncertainty-aware CO2 injectivity prediction

Reliable prediction of CO 2 injectivity decline is essential for safe geological carbon storage, yet existing machine learning models often provide deterministic point predictions that lack uncertainty quantification. This paper presents a physics-informed Gaussian process regression (PC-GPR) framework for Relative Injectivity Change (RIC) prediction, embedding constraints derived from two independently grounded physical laws: the Derjaguin–Landau–Verwey–Overbeek (DLVO) colloidal monotonicity condition and the Civan–Kozeny–Carman permeability impairment model. Four GP variants are developed and benchmarked on a curated laboratory dataset ( n  = 44) under a three-tier validation protocol combining Leave-One-Out cross-validation, repeated k -fold cross-validation, and non-parametric bootstrap confidence intervals. Two complementary uncertainty quantification mechanisms are employed: GP posterior calibration via the Expected Calibration Error (ECE) and split-conformal prediction intervals. The GP-Base model achieves strong predictive performance (LOO R 2  = 0.9401, 95% CI: [0.882, 0.978]) with well-calibrated uncertainty (ECE = 0.026) and reliable coverage (97.7% at the nominal 95% level). The PC-GPR-M variant reduces DLVO monotonicity violations to 1.5% across the input domain, demonstrating effective soft constraint enforcement. Operationally, the proposed framework translates predictive uncertainty into actionable injection scheduling guidance, identifying high-risk regions at salinity >30,000 ppm and jamming ratio >0.04. These results provide an uncertainty-aware baseline for future PIML research in subsurface carbon storage.

Oleoylethanolamide enhances regulatory T Cell function to accelerate plaque regression in atherosclerosis via PPARα activation

Isopentane Disproportionation in Lewis Acidic Chloroaluminate Ionic Liquid

Revisiting fatty acid-mediated antibody purification from plasma with insights into selectivity and protein integrity

Therapeutic antibodies play an essential role in modern biopharmaceuticals, with polyclonal antibodies (pAbs) remaining indispensable for applications such as toxin and virus neutralization. However, pAb purification is complicated by serum-derived contaminants. Selective impurity precipitation using caprylic acid (C8) or sodium caprylate (NaC8) provides an effective strategy for obtaining high-purity antibody preparations and serves as a low-cost, scalable non-chromatographic alternative. However, the influence of fatty acid chain length and ionic form on differential precipitation remains poorly understood. Here, we systematically evaluated free fatty acids with varying chain length (C8–C10) and their corresponding sodium salts for pAb purification. Using model proteins, free fatty acids exhibited greater selectivity than their salt forms, and precipitation efficiency decreased with increasing chain length (C8 > C9 > C10). Importantly, C9 at 2% (v/v) provided a favorable balance between impurity removal and γ-globulin retention, achieving effective depletion of albumin while minimizing antibody loss relative to conventional C8 precipitation. Multi-spectroscopic analyses confirmed that γ-globulin maintained its native structure following fatty acid–based precipitation. When applied to the fractionation of IgG from hyperimmunized equine plasma, C9 achieved impurity reduction and IgG homogeneity comparable to conventional C8 treatment while preserving antigen-binding avidity. Collectively, these findings identify C9 as a selective and function-preserving precipitant with potential as an efficient and scalable pretreatment step in polyclonal antibody purification workflows.

Dysregulated HELLS expression alters cellular processes and serves as a potential prognostic marker in acute myeloid leukemia

Redefining ·CO ₃ ^– Formation Chemistry: Zundel-like Switches Drive Carbonate-·OH Interfacial Reactivity

Development and validation of a machine learning model to detect psychiatric symptoms in Huntington’s disease using speech analysis

Huntington’s disease (HD) causes progressive disability through motor, psychiatric, and cognitive symptoms. Machine learning speech analysis can detect motor and cognitive symptoms of HD, but not yet psychiatric symptoms. This study investigated whether speech analyses can detect the presence of psychiatric symptoms in HD. Audio recordings of six narrative tasks (cookie-theft picture description, red-riding hood storytelling, most recent 24 hours recalling, happy, sad, or angry storytelling) were prospectively collected from subsequent genetically confirmed HD participants from the BIOHD and REPAIR CAPIT-HD-Beta cohorts at the Hospital Henri-Mondor, Créteil. Speech therapists blindly annotated speech samples to allow extraction of three types of features: linguistic, LASER, and acoustic features. Psychiatric symptoms in participants were detected using the Problem Behaviors Assessment Short version (PBA-s). Machine learning classifier models were trained on 80% of the 89 participants before being tested on the remaining 20% of individuals. F1-scores were calculated and compared to chance. Linguistic features detected obsessive/compulsive behavior (OCB) with all but joy task, and best with the cookie task (F1-score: 0.67, confidence interval [0.47–0.86] (p ≦ 0.001)). They also best detected depression with the red-riding hood (F1 score 0.66, [0.45–0.87], p ≦ 0.001), apathy with the joy task (0.60, [0.39–0.81], p ≦ 0.001), but not irritability. LASER features best detected OCB (0.65, [0.45–0.84], p ≦ 0.001), depression (0.60, [0.40, 0.80], p ≦ 0.01) and apathy (0.61, [0.37, 0.86], p ≦ 0.001) from the red-riding hood task, but not irritability. Acoustic features best detected depression (0.63, [0.46, 0.80], p ≦ 0.001) and OCB (0.60, [0.43, 0.77], p ≦ 0.001) but not apathy nor irritability. This study showed that speech analyses can detect obsessive/compulsive behaviors, depression, and apathy in HD participants but not irritability. Linguistic and LASER features provided the most consistent detections, but acoustic features also detected depression and OCB, highlighting their complementary role for psychiatric characterization in HD.

Microglia maintain retinal redox homeostasis following ablation of rod photoreceptors in zebrafish

Mechanistic Characterization and Control of Branching in the Polymer of Intrinsic Microporosity PIM-1

A mathematical analysis of math anxiety dynamics using a classical SAS model: Bridging epidemic theory and pedagogical implications

Students’ cognitive function and participation in mathematical problems are adversely affected by mathematics anxiety. This research develops and evaluates a dynamic mathematical model (SAS: Susceptible-Anxious-Susceptible) to investigate the evolution and transmission of math anxiety within students over time. The model divides students into two subgroups based on presence and absence of math anxiety. Using epidemiological modeling concepts, we derive the basic reproduction number R₀, which determines the conditions for persistence (R₀  >  1) or elimination (R₀  <  1) of anxiety. Our analysis proves that the anxiety-free equilibrium is both locally and globally asymptotically stable when R₀  <  1, while the anxiety-prevailing equilibrium is stable when R₀  >  1, with the system undergoing a transcritical bifurcation at R₀  =  1. Sensitivity analysis using Partial Rank Correlation Coefficient (PRCC) reveals that the university entrance rate (π) and social transmission rate (β) positively impact R₀, while recovery rate (α) and exit rate (μ) negatively influence it. Notably, π and μ are identified as the most influential parameters. Numerical simulations demonstrate that the population of students with mathematics anxiety is highly sensitive to changes in β and μ, while remaining relatively stable with fluctuations in π and α. These findings provide a quantitative framework for developing effective interventions, suggesting that reducing social transmission of anxiety and enhancing recovery through supportive mechanisms can significantly curb math anxiety prevalence in educational settings.

Engineering Layered Nanomaterials for Cancer Theranostics: Current Progress and Future Opportunities

ABSTRACT Atomic‐level structural engineering represents a powerful paradigm for tailoring layered nanomaterials (LNs) toward advanced cancer theranostics, enabling precise control of physicochemical properties to overcome the limitations of conventional nanoplatforms. This review provides a comprehensive overview of the latest advances in engineering LNs, including layered metal oxides, layered double hydroxides, transition metal dichalcogenides, graphene, layered silicates, graphitic carbon nitride, metal carbides and nitrides, and other layered frameworks for cancer diagnosis and therapy. Five representative atomic‐level engineering strategies are discussed, including crystal phase engineering, defect engineering, heteroatom doping, interlayer spacing engineering, and crystalline‐to‐amorphous phase engineering. For each strategy, the underlying mechanisms, representative synthetic approaches, and their roles in optimizing theranostic performance, such as photothermal conversion, reactive oxygen species generation, and multimodal imaging, are critically discussed. Crucially, the advantages and inherent limitations of these engineering strategies are comparatively evaluated to provide a balanced perspective on their practical applicability. Finally, key challenges toward clinical translation, including structural stability, biosafety, and scalability, are highlighted. Future directions are proposed for developing intelligent, adaptive, and personalized LN‐based nanomedicines for precision oncology.

The DNTTIP1-PARP1 Interaction Orchestrates MiDAC Recruitment and Activity for NHEJ-Mediated Genome Stability

Correlated Atomic Vacancy Pairs Enable Electronic and Geometric Cooperation in Electrocatalysis

Astrovirus in the Brazilian Amazon: First detection of non-classical astroviruses (MLB-3) in the Americas

Human astrovirus (HAstV) is a viral agent responsible for acute gastroenteritis (AGE), primarily affecting children and the elderly worldwide. Belonging to the Astroviridae family, HAstV is classified into eight classical serotypes (HAstV 1–8) and two other divergent non-classical clades: Melbourne (MLB 1–3) and Virginia (VA 1–6), which have been associated with gastroenteritis, central nervous system complications, and acute respiratory disease. This study aimed to investigate the frequency of classical and non-classical HAstV in fecal specimens collected from children up to 14 years of age in northern Brazil, within the Amazon region, between 2013 and 2022. A total of 560 samples, all previously tested negative for other gastroenteric viruses such as rotavirus and norovirus, were analyzed using reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR) and conventional RT-PCR. For classical HAstV, 10.7% (60/560) of the samples were positive by RT-qPCR and 2.0% (11/560) to 3.0% (17/560) by conventional RT-PCR using different primers set. Non-classical HAstV was detected in 0.2% (1/560) of the samples. Diarrhea was present in 91.7% of positive cases, vomiting in 71.7%, and fever in 53.3%. The most affected age group was children aged >5–10 years (25.0%), with no significant association between infection rate and sex. A higher number of infections occurred during the Amazon winter (11.6%), with Roraima identified as the federative unit with the highest number of cases. Fifteen samples (88.2%, 15/17) were sequenced and identified as classical HAstV, with the following genotypes detected: HAstV-1 (60.0%, 9/15), HAstV-3 (20.0%, 3/15), and HAstV-4 (20.0%, 3/15). Non-classical HAstV sequencing was performed on 12 (93.2%, 12/13) positive specimens, characterized as HAstV-1 (50%, 6/12), HAstV-3 (25%, 3/12), HAstV-4 (16.7%, 2/12), and HAstV-MLB-3 (8.3%, 1/12). A probable recombinant strain was identified, classified as HAstV-4 based on the ORF2 region and HAstV-1 based on the ORF1b region. This study provides updated epidemiological data on HAstV in the Brazilian Amazon and highlights the genetic diversity of both classical and non-classical genotypes. Notably, it reports the first detection – and the second complete genome repository – of the rare MLB-3 genotype in the Americas.

Oncogenic EGFR Mutants Differentially Alter Dimerization, Adaptor Protein Engagement, and Clathrin-Mediated Internalization

Metadynamics and Raman Spectroscopy for Glycan Structure–Spectrum Mapping

Real-world outcomes of Finerenone in patients with diabetic kidney disease in Saudi Arabia

Background Diabetic kidney disease (DKD) represents a significant microvascular complication associated with type 2 diabetes mellitus (T2DM), markedly elevating the risk of kidney failure, cardiovascular events, and premature mortality. Despite advancements in therapeutic management, such as renin–angiotensin–aldosterone system (RAAS) blockade and sodium–glucose cotransporter 2 (SGLT2) inhibitors, residual risk remains significant. Finerenone, a novel nonsteroidal and selective mineralocorticoid receptor antagonist (MRA), has demonstrated substantial cardiorenal benefits in clinical trials; however, real-world data, especially from Saudi Arabia, remain limited. Method This single-center, retrospective cohort study. All adult patients (≥18 years) who received finerenone as part of routine clinical care were eligible if they met either of the following criteria: (1) documented diabetic kidney disease (DKD) based on KDIGO-aligned clinical criteria, including diabetes mellitus with chronic kidney disease manifested by albuminuria/proteinuria and/or reduced eGFR; (2) and/or a urine protein-to-creatinine ratio (uPCR) >0.3 mg/mg. Longitudinal changes in uPCR, eGFR, and serum potassium were analyzed using linear mixed-effects models adjusted for relevant clinical covariates. Results A total of 75 patients prescribed finerenone were screened, of whom 67 met the inclusion criteria. The median age was 63 years, and 53.7% were female. Comorbidities were highly prevalent, including diabetes mellitus (89.6%), hypertension (97.0%), dyslipidemia (92.5%), heart failure (56.8%), and coronary artery disease (40.3%). A significant effect of time on uPCR was observed (F[3,193] = 3.457; P = 0.018), with a mean reduction of 0.464 mg/mg after six months (95% CI, −0.895 to −0.034; P = 0.027). The estimated eGFR slope after finerenone initiation was −1.08 mL/min/1.73 m² per month (95% CI −1.74 to −0.41; P = 0.002), corresponding to an annualized decline of approximately −12.9 mL/min/1.73 m² per year. Serum potassium increased modestly at early follow-up points (F[5,245] = 4.008; P = 0.002), rising by +0.209, + 0.256, and +0.286 mmol/L at the first three readings (P = 0.029, 0.005, and 0.006, respectively), then plateaued thereafter (P > 0.5). Conclusion Finerenone use among DKD patients in Saudi Arabia was associated with significant reductions in proteinuria, an early decline in eGFR that requires cautious interpretation, and an overall favorable safety profile. These real-world findings align with results from pivotal clinical trials and support the incorporation of finerenone into standard DKD management. Future multicenter prospective studies are warranted to confirm these outcomes and evaluate long-term cardiorenal benefits.