Advancing Open Science

The challenges of glioblastoma multiforme treatment are related to limitations in tumor removal surgery, its high heterogeneity and aggressiveness, development of resistance to standard therapy, the blood–brain barrier, and the side and toxic effects of the conventional antitumor agents used in clinical practice. Although new treatment strategies continue to emerge, progress remains slow and has not resulted in substantial improvements in patient survival. The main goal of research in recent years has been aimed at developing ways to deal with all these challenges. One of the ways to improve the control of glioblastomas is the introduction of effective new antitumor agents. Metal complexes represent a particularly promising class of compounds in this context. This is why the aim of this study was to assess the effects of six homo- and heterometallic coordination compounds bearing Schiff base ligands—[Zn₂(Ampy)(µ-OH)(H₂O)₂](ClO₄)₂ (ZnAmpy), [Zn₂(Dmen)(µ-OH)(H₂O)₂](ClO₄)₂ (ZnDmen), ¹_∞[{Zn₂(Ampy)(μ₃-OH)}₂(H₂O){μ-[Au(CN)₂]}](ClO₄)₃·THF·H₂O (ZnAmpyAu), [{Zn₂(Dmen)(μ-OH)}₂{μ-[Au(CN)₂]}{[Au(CN)₂]₂}](ClO₄)·H₂O (ZnDmenAu), ¹_∞[Zn(Salampy){μ-Au(CN)₂}] (ZnSalampyAu), and ¹_∞[Zn(Saldmen)(μ-Au(CN)₂}] (ZnSaldmenAu)—on the viability and proliferation of 8MGBA and U251MG human glioblastoma multiforme cells (HDmen and HAmpy are bicompartmental Schiff base ligands resulting from the condensation of 2,6-diformyl-p-cresol with N,N-dimethylethylenediamine and 2-(aminomethyl)pyridine, respectively, while HSaldmen and HSalampy are tridentate Schiff base ligands obtained via condensation of salicylaldehyde with N,N-dimethylethylenediamine and 2-(aminomethyl)pyridine, respectively). Among these compounds, ZnSaldmenAu is a new compound and is reported here for the first time. Cytotoxicity of the compounds was evaluated through analysis of cell viability, 2D/3D growth, cytopathological alterations, and induction of cell death. The results obtained by methods with different targets in cells and the associated mechanisms of action revealed that the compounds investigated show promising cytotoxic/potential antitumor activity in treated cells.

Background: Endoscopic retrograde cholangiopancreatography (ERCP) is the primary treatment option for choledocholithiasis. However, this procedure carries an inherent non-negligible risk of complications, requiring precise indications and careful patient selection. Endoscopic ultrasonography (EUS) can verify the presence of bile duct stones prior to ERCP. The current ESGE recommendations permit ERCP in high-risk patients without confirmation; however, several individuals undergo ERCP without evident advantage, indicating a necessity for enhanced stratification. Objectives: We aim to evaluate the rate of EUS-validated choledocholithiasis in patients with suspected common bile duct (CBD) stones and to determine the predictors of residual stones. A secondary objective was to create and internally validate a streamlined scoring system to enhance risk assessment in ESGE high-risk patients. Methods: We conducted a retrospective analysis of patients who had endoscopic ultrasound for suspected choledocholithiasis from January 2023 to December 2024 at a tertiary center. Multivariate logistic regression determined independent predictors of retained calculi. A simplified score was derived from model coefficients and internally validated. Results: Among 438 examined patients, 186 were included and 87 had choledocholithiasis confirmed via EUS. ERCP was conducted in 81 patients and postponed for 6 patients due to contraindications. According to the ESGE criteria, 10 patients (5.4%) were classified as low risk, 92 (49.5%) as intermediate risk, and 84 (45.2%) as high risk for choledocholithiasis. For high-risk individuals, EUS identified stones in 45 (53.5%), while 39 (46.4%) experienced spontaneous clearance. Acute pancreatitis (aOR 0.075), cholangitis (aOR 6.939), and EUS CBD diameter (aOR 1.220 per mm) were independent predictors of stones. The resultant three-component score (−2 to +4 points) demonstrated effective discrimination (AUROC 0.788). A criterion of ≥2 resulted in 85.7% sensitivity and 59.0% specificity. Conclusions: Almost fifty percent of ESGE high-risk patients were not found to have CBD stones during EUS. Integrating EUS data with a straightforward predictive score may enhance risk classification and avert superfluous ERCP procedures.

As precision agriculture places higher demands on real-time field weed detection and recognition accuracy, this paper proposes a multi-scale information-enhanced weed detection algorithm, MIE-YOLO (Multi-scale Information Enhanced), for precision agriculture. Based on the popular YOLO12 (You Only Look Once 12) model, MIE-YOLO combines edge-aware multi-scale fusion with additive gated blocks and two-stage self-distillation to boost small-object and boundary detection while staying lightweight. First, the MS-EIS (Multi-Scale-Edge Information Select) architecture is designed to effectively aggregate and select edge and texture information at different scales to enhance fine-grained feature representation. Next, the Add-CGLU (Additive-Convolutional Gated Linear Unit) pyramid network is proposed, which enhances the representational power and information transfer efficiency of multi-scale features through additive fusion and gating mechanisms. Finally, the DEC (Detail-Enhanced Convolution) detection head is introduced to enhance detail and refine the localization of small objects and fuzzy boundaries. To further improve the model’s detection accuracy and generalization performance, the DS (Double Self-Knowledge Distillation) strategy is defined to perform double self-knowledge distillation within the entire network. Experimental results on the custom Weed dataset, which contains 9257 images of eight weed categories, show that MIE-YOLO improves the F1 score by 1.9% and the mAP by 2.0%. Furthermore, it reduces computational parameters by 29.9%, FLOPs by 6.9%, and model size by 17.0%, achieving a runtime speed of 66.2 FPS. MIE-YOLO improves weed detection performance while maintaining a certain level of inference efficiency, providing an effective technical path and engineering implementation reference for intelligent field inspection and precise weed control in precision agriculture. The source code is available on GitHub.

Odour emissions from wastewater infrastructure represent a significant environmental and social challenge in urban areas. This study evaluates the odour impact of a municipal wastewater pumping station using an integrated field-based approach that combines sensory observations, chemical measurements and meteorological data. Field olfactometry and on-site gas monitoring were applied over a two-year campaign covering different operational and seasonal conditions. The results indicate that odour perception is strongly influenced by hydrogen sulphide concentration, air temperature and wind speed, with short-term high-intensity episodes playing a disproportionate role in odour nuisance. To support integrated interpretation, a Synthetic Odour Index (SOI) was developed to consolidate chemical, sensory and microclimatic information into a single numerical indicator, extending existing odour indices by explicitly integrating field-based sensory and meteorological data. The SOI showed a moderate but statistically significant association with odour intensity (r ≈ 0.3) and effectively differentiated low- and high-nuisance conditions. The proposed methodology demonstrates the value of combining field measurements with integrated data analysis for assessing and managing odour emissions from urban wastewater pumping stations and provides a practical basis for operational monitoring and odour mitigation strategies.

Our new Theoretically Dynamic Regression (TDR) modeling methodology was recently applied in three types of real data modeling cases using physically based dynamic model structures with low-order linear regression static functions. Two of the modeling cases achieved the validation set modeling goal of . However, the third case, consisting of eleven (11) type one (1) sensor glucose data sets, and thus, eleven individual models, all fail considerably short of this modeling goal and the average $r_{fit,val}$, ${\overline{r}}_{fit,val}$ = 0.68. For this case, the dynamic forms are highly complex 60 min forecast, second-order-plus-dead-time-plus-lead (SOPDTPL) structures, and the static form is a twelve (12) input first-order linear regression structure. Using these dynamic structure results, the objective is to significantly increase $r_{fit}$ for each of the eleven (11) modeling cases using the recently developed Wiener-Physically-Informed-Neural-Network (W-PINN) approach as the static modeling structure. Two W-PINN stage-two static structures are evaluated–one developed using the JMP® Pro Version 16, Artificial Neural Network (ANN) toolbox and the other developed using a novel ANN methodology coded in Python version, 3.12.3. The JMP ${\overline{r}}_{fit,val}$ = 0.74 with a maximum of 0.84. The Python ${\overline{r}}_{fit,val}$ = 0.82 with a maximum of 0.93. Incorporating bias correction, using current and past SGC residuals, the Python estimator improved the average ${\overline{r}}_{fit,val}$ from 0.82 to 0.87 with the maximum still 0.93.

This review summarizes recent applications of carbon-based materials as catalysts in the ozonation of wastewater contaminated with persistent organic pollutants. Methods available for production of commonly used inexpensive carbonaceous materials such as biochar and hydrochar are presented. Differences between production methods of active carbon and biochar or hydrochar are discussed. Interestingly, biochar, in a role of rather simple and cheap charcoal, is catalytically active and increases the rate of oxidative degradation of nonbiodegradable aqueous contaminants such as drugs or textile dyestuffs. This review documents that even the addition of biochar to the ozonized wastewater increases the rate of removal of persistent organic pollutants. Cheap bio-based carbonaceous materials such as biochar work as adsorbent of dissolved pollutants and catalysts for ozone-based degradation of organic compounds via the formation of reactive oxygen species (ROS). Low-molecular-weight degradation products produced by ozonation of pharmaceuticals and textile dyes are presented. The combination of air-based ozone generation, together with application of biochar, represents a sustainable AOP-based wastewater treatment method.

Background/Objectives: Microalgae are recognized as prolific producers of bioactive metabolites with pharmaceutical potential. This study aimed to isolate and characterize cytotoxic constituents from selected cytotoxic microalgae, collected in Hue city, Vietnam. Methods: Microalgal samples were collected from freshwater bodies, morphologically identified, and maintained in laboratory culture. Thirteen strains were successfully isolated and cultivated in BG11, Z8, and BBM media to determine optimal growth conditions. Cytotoxic effects of extracts/compounds were determined using the sulforhodamine B assay on human lung cancer (SK-LU-1) and human liver cancer (HepG2) cell lines. The methanol extract was partitioned with n-hexane and CH₂Cl₂, followed by extensive chromatographic separation and HPLC purification to afford twelve compounds, including two new and ten known compounds. The structures were elucidated by HR-ESI-MS and NMR spectra, chemical methods, and comparing compounds in the literature. Results: From the phytoplankton samples collected across six freshwater bodies in Hue city, Vietnam, thirteen microalgal strains were successfully isolated and purified under laboratory conditions. These strains were morphologically and taxonomically identified to be Microcystis aeruginosa HU05, Microcystis viridis HU13, Anabaena circinalis HU08, Aphanizomenon flos-aquae HU02, Dolichospermum smithii HU04, Calothrix braunii HU14, Nostoc muscorum HU12, Nostoc punctiforme HU11, Raphidiopsis raciborskii HU03, Lyngbya spiralis HU15, Planktothrix stagnina HU16, Phormidium subtilis HU06, and Scenedesmus quadricauda HU07. All methanol extracts of those microalgae were evaluated for cytotoxic activity. The MeOH extracts of M. viridis (HU13) and D. smithii (HU04) exhibited significant cytotoxic effects, with IC₅₀ values of 6.19 ± 0.80 and 4.89 ± 0.76 µg/mL for M. viridis, and 9.51 ± 0.84 and 8.32 ± 0.94 µg/mL for D. smithii against SK-LU-1 and HepG2 cell lines, respectively. Furthermore, chemical studies of D. smithii HU04 led to the isolation of two new compounds, smithioside A (1) and smithioside B (2) and ten known ones, 3,4,5-trimethoxyphenyl-1-O-β-D-glucopyranoside (3), 4′-hydroxy-3′-methoxyphenol-β-D-[6-O-(4″-hydroxy-3″,5″-dimethoxylbenzoate)]-glucopyranoside (4), 4′-hydroxy-2′,6′-dimethoxyphenol 1-O-β-D-(6-O-syringoyl)glucopyranoside (5), mallophenol B (6), pisoninol II (7), guaiacylglycerol (8), (E)-asarone (9), deacetylsarmentamide B (10), (E)-2-hexenyl-β-D-glucopyranoside (11), and 5,6-dihydropyridin-2(1H)-one (12). The cytotoxic activity of all isolated compounds was also evaluated against SK-LU-1 and HepG2 cancer cell lines. Compound 12 showed the strongest activity, with IC₅₀ values of 9.13 ± 0.89 µM (SK-LU-1) and 7.64 ± 0.46 µM (HepG2). Compounds 5 and 6 exhibited moderate cytotoxic activity on both human cancer cell lines with IC₅₀ values ranging from 25.99 to 51.47 µM. Conclusions: These results highlight the potential of Dolichospermum smithii HU04 as a source of bioactive compounds, particularly in anticancer applications. These findings suggest that D. smithii HU04 extracts could be developed for therapeutic purposes targeting cancer.

Beetroot juice (BRJ), a concentrated dietary source of nitrate alongside betalains and polyphenols, influences physiology through enhanced nitrate–nitrite–NO bioavailability, antioxidant activity, and interactions with oral and gut nitrate-reducing microbiota. The efficiency of these mechanisms depends on dose, timing, and preservation of oral bacteria, with antibacterial mouthwash or thiocyanate-rich foods potentially blunting NO₂⁻ generation. Acute BRJ ingestion consistently elevates circulating nitrate and nitrite, yet its impact on glucose, insulin, and lipid regulation is modest; chronic intake may reinforce nitrate-reduction capacity, improve redox balance, and shift microbial composition, though long-term metabolic outcomes remain variable. Cardiovascular adaptations appear more coherent, with acute reductions in systolic blood pressure and improved endothelial function complemented in some cases by microvascular enhancements during multi-week supplementation. Neuromuscular and cognitive effects are less uniform; BRJ does not reliably increase maximal strength or global cognition but may support electrophysiological recovery after muscle-damaging exercise and improve executive performance under fatigue. In exercise settings, dose and timing are critical, as BRJ most consistently benefits endurance performance by reducing oxygen cost, improving exercise economy, and enhancing time-trial or time-to-exhaustion outcomes, whereas effects on sprint, power, and team-sport tasks are more sensitive to contraction duration, recovery intervals, and athlete training status. Overall, available evidence supports a role for NO-mediated vascular and metabolic pathways in the physiological effects of BRJ, although marked inter-individual variability highlights the need for responder-focused dosing strategies and further mechanistic investigation integrating metabolic, microbial, and performance-related outcomes.