Search Results (318)

Background/Objectives: Zastaprazan (JP-1366) is a novel potassium-competitive acid blocker (P-CAB) used for the treatment of gastroesophageal reflux disease (GERD). To date, its metabolic pathways and metabolism-related drug–drug interactions (DDIs) in humans remain incompletely elucidated. This study aimed to determine the relative contributions (f_m) of cytochrome P450 isoforms to JP-1366 elimination and assess its DDI potential. Methods/Results: In vitro metabolic studies using human liver microsomes (HLMs) revealed that JP-1366 was first metabolized to M1, which subsequently underwent further oxidation, glucuronidation, and N-dealkylation. Mono-oxidation was estimated to contribute more than 46% to the overall metabolic clearance of JP-1366. Reaction phenotyping identified CYP3A as the major enzyme (f_m = 96.1%), followed by CYP1A2 (1.49%) and CYP2C9 (2.41%). By integrating in vitro data, clinical pharmacokinetic data and clarithromycin coadministration DDI data, a physiologically based pharmacokinetic (PBPK) model was developed and validated. Simulations predicted significant DDIs with strong CYP3A inhibitor (ketoconazole), with AUC ratios of 3.80. Moderate inhibitors (fluconazole and fluvoxamine) caused mild increases (AUC ratios: 1.14–1.74). Conversely, strong and moderate CYP3A inducers, rifampicin and efavirenz, produced pronounced DDIs, with AUC ratios of 0.22 and 0.50, respectively. Furthermore, simulations predicted that although JP-1366 functions as a CYP enzyme inhibitor, it would not cause clinically meaningful changes in the plasma exposure of corresponding CYP substrate drugs; however, potential interactions with CYP3A substrates still warranted consideration. Conclusions: JP-1366 is predominantly cleared via a CYP3A-dominated metabolic pathway. The PBPK simulations suggest that JP-1366 may be a moderately sensitive CYP3A substrate and a moderate inhibitor of sensitive CYP3A substrates, while its perpetrator DDI risk toward other major CYP pathways appears limited. These findings support caution or monitoring when JP-1366 is co-administered with strong CYP3A modulators or sensitive CYP3A substrates. Full article

A derivatisation-free HPLC–MS/MS method was developed and validated for the simultaneous quantification of selenium- and sulphur-containing amino acids in soybean leaves, and applied to a 3 × 3 factorial hydroponic experiment probing selenium–sulphur metabolic interactions. The method resolves five biologically informative analytes (Cys₂, SeCys₂, MeSeCys, Met, SeMet) within 1.5 min through multiple reaction monitoring (MRM). Ultrasound-assisted extraction (UAE) of the free fraction was jointly optimised for both analyte classes by the response-surface methodology; enzymatic hydrolysis of the extraction residue recovered the protein-bound fraction on the same platform. Limits of detection ranged from 0.036 to 0.556 µg L⁻¹, intra-day relative standard deviations were below 5%, and spike recoveries fell between 92.3 and 117.4%. Free SeAA and SAA pools were negatively correlated across the nine treatments (R² = 0.83), consistent with competitive Se–S assimilation, whereas bound pools were positively correlated (R² = 0.89), reflecting proportional protein-level incorporation. A regime of 1–5 mM of sulphate with 20 µM of selenite yielded the highest bound organo-Se with near-normal growth, providing leaf-level evidence that may inform future seed-focused studies aimed at Se-enriched soy-protein ingredient development. Full article

Objectives: To systematically synthesize existing evidence on functional biomechanical tests of the foot and ankle in physiotherapy and sports, focusing on their outcome measures, compatibility with wearable sensor technologies, and psychometric properties. Methods: We performed a systematic review (PRISMA-guided) of PubMed, Web of Science, PEDro, and SPORTDiscus from inception to December 2025. Eligible studies evaluated functional foot/ankle biomechanics in athletes, healthy adults, or adults with musculoskeletal foot/ankle conditions using wearable sensors (e.g., IMUs, wireless pressure insoles). Two reviewers independently screened, extracted data, and appraised methodological quality using the COSMIN Risk of Bias tool, applying property-specific ratings. Heterogeneity precluded meta-analysis; findings were narratively synthesized and tabulated. Results: Twenty full texts were reviewed; four studies (n = 83 participants) met the inclusion criteria. Wearable devices included foot- or trunk-mounted IMUs and wireless pressure insoles. Reported outcomes spanned temporal gait events and inner-stance phases, vertical ground reaction force (vGRF) and centre-of-pressure trajectories, running step rate/stride length, and jump counts in competition. Validity was most frequently assessed: foot-worn IMUs showed millisecond-level agreement with in-shoe pressure references for stance and inner-stance events; pressure insoles demonstrated acceptable agreement with force plates for vGRF/COP alongside fair-to-excellent test–retest reliability; foot- vs. shank-mounted IMUs provided strong agreement for running step rate and stride length; and competition-based jump detection using IMUs achieved high sensitivity. Across studies, reliability indices were inconsistently reported, measurement error (SEM/MDC) was sparse, and MCID was not reported. The COSMIN appraisal ranged from very good/adequate to inadequate, driven primarily by small sample sizes, non-gold-standard comparators, and incomplete psychometric reporting. Full article

Bongkrekic acid (BKA) is a potent respiratory toxin produced by Pseudomonas cocovenenans. This toxin is commonly found in spoiled fermented rice- and wheat-based products, snow fungus, and black fungus and can cause severe foodborne illness. The development of a rapid onsite detection method can effectively prevent food poisoning incidents and ensure food safety. In this study, a highly specific anti-BKA monoclonal antibody was prepared, the reaction conditions were optimized, and an indirect competitive chemiluminescent enzyme-linked immunoassay (ic-CLEIA) system was developed for high-throughput screening of BKA in food. The results showed that the ic-CLEIA had good linearity in the range of 7.3–106.6 pg/mL, a limit of detection of 4.7 pg/mL, a limit of quantification of 7.3 pg/mL, a half-maximal inhibition concentration of 28.2 pg/mL, a spike recovery of 86.6–94.1%, a coefficient of variation of less than 10%, and no cross-reactivity with structural analogs. There was no significant difference between the detection results obtained with ic-CLEIA and ultraperformance liquid chromatography–tandem mass spectrometry for the samples. This method provides reliable technical support for food safety monitoring, especially for grassroots laboratories and large-scale sample screening. Full article

In recent years, soil contamination by heavy metals and organic pollutants has become a serious environmental problem. Biochar is a highly carbonaceous, water-insoluble porous material made from biomass feedstock through a thermochemical conversion process, and it has been widely used in the remediation of various soil pollutants. However, previous reviews on the modification of biochar and the remediation reaction mechanism of heavy metals and organic pollutants by biochar in soil were still not sufficiently comprehensive. Based on the current research status of the remediation of heavy metals and organic pollutants by biochar in soil, this review systematically summarized biomass feedstock types, pyrolysis methods and their applicable scenarios, as well as the modification strategies of biochar, including pore structure modification, surface functional group modification, surface charge modification, and magnetic modification. It also comparatively discussed the adsorption of heavy metals by biochar mainly through electrostatic attraction, ion exchange, complexation/precipitation, cation−π interaction, and redox transformation, while the adsorption of organic pollutants via π−π/EDA interactions, electrostatic attraction, hydrogen bonding, hydrophobic partitioning, and pore filling were outlined. The review also discussed competitive effects among pollutants during biochar adsorption under co-contamination scenarios, as well as the synergistic interactions between biochar and soil microorganisms or plants. In addition, the review addressed recent progress in field-scale applications of biochar, as well as the current state of research on aging effects, ecological risks, and economic feasibility. Finally, it identifies key research directions that warrant further attention. This review highlighted the mechanistic differences between heavy metal stabilization and organic pollutant removal in soil by biochar, and provided mechanistic insight and guidance for biochar-based soil remediation. Full article

The electrochemical nitrate reduction reaction (NO₃RR) represents a promising strategy for wastewater remediation and sustainable ammonia (NH₃) production. However, its practical application is hindered by low selectivity and competition from the hydrogen evolution reaction (HER). Herein, a series of PtBi-Co_X (X = 4.9, 5.3, and 6.1) ternary alloy nanoplates was synthesized via a one-pot method with tunable Co content. Structural characterization indicates that Co incorporation does not significantly alter the hexagonal crystal structure of the PtBi phase. Electrochemical measurements reveal that the NO₃RR performance varies with PtBi-Co_X (X = 4.9, 5.3, 6.1), with PtBi-Co_5.3 exhibiting the optimal balance of activity and selectivity among the studied samples. At −0.5 V vs. RHE, it achieves a Faradaic efficiency (FE) of 97.75 ± 0.75% and an NH₃ yield rate of 9.33 ± 0.50 mg h⁻¹ mg_cat⁻¹ under the tested conditions. In addition, the catalyst exhibits relatively suppressed HER activity compared to samples with higher Co content, along with good stability. These findings provide useful insights into the design of PtBi-based ternary alloy catalysts for efficient nitrate reduction. Full article

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI. Full article

Alzheimer’s disease (AD) develops as a progressive dementia condition through the step-by-step breakdown of nerve cells. Neurodegeneration in this context primarily results from metal ions, including copper, iron, zinc, and aluminum, building up in the system. The aggregation of amyloid-beta (Aβ) peptides and oxidative stress generation stem from metal ion involvement acting as defining characteristics of Alzheimer’s disease pathology. We developed a comprehensive mathematical model based on 24 coupled ordinary differential equations (ODEs) to represent the interactions between metal ions, Aβ peptides, reactive oxygen species (ROS), antioxidant defenses, and tau protein phosphorylation. The mathematical model monitors how metal ion concentrations change over time and examines their competitive binding effects, which trigger a series of reactions, resulting in oxidative stress and subsequent tau protein damage. The model uses analytical and numerical mathematical methods to expose nonlinear behaviors and threshold effects while offering mechanistic insights into the course of disease development. This model functions as a quantitative framework for assessing how therapeutic interventions that target metal dyshomeostasis and oxidative stress can potentially affect outcomes. Full article

Background: Sensor-based systems and virtual reality (VR) technologies provide new opportunities for the objective, technology-driven assessment and training of visuomotor performance in applied contexts such as sport. Methods: This study examined the effects of an integrated visual training program combining stroboscopic stimulation, VR-based vergence exercises, and instrumented reaction-light tasks in adolescent handball players. Twenty-eight adolescent handball players (under-18 competitive level) completed two baseline assessments separated by six weeks, followed by a six-session training program (approximately 15 min per session) integrated into regular team practice. The intervention targeted visuomotor reaction speed, accommodative dynamics, and peripheral visual responsiveness using sensor-based and virtual reality–assisted stimuli. Results: Compared with both baseline measurements, the intervention produced selective improvements in accommodative facility (cycles per minute, cpm)—particularly near–far focusing speed—and in multiple reaction-time conditions (milliseconds, ms) involving manual and decision-based responses. Specific peripheral-field locations showed increased response scores, whereas binocular alignment, AC/A ratio, near phoria, and stereoscopic acuity remained unchanged. Conclusions: These findings indicate that technology-supported visual training protocols incorporating sensor-based reaction systems and VR stimuli were associated with measurable adaptations in dynamic visuomotor processing while preserving fundamental binocular vision parameters. Full article

This review critically evaluates current PA6 recycling technologies, with a specific focus on caprolactam-oriented chemical recycling pathways, including hydrolysis, pyrolysis, glycolysis, ammonolysis, hydrothermal treatment, ionic-liquid-assisted depolymerization, and microwave-assisted processes. Reported caprolactam yields vary significantly depending on reaction conditions and catalyst systems, ranging from below 60 wt% in conventional hydrolysis to above 90 wt% under optimized catalytic, hydrothermal, or microwave-assisted conditions. Among these approaches, microwave-assisted hydrolysis and catalytic depolymerization have emerged as particularly promising, offering substantially reduced reaction times (minutes rather than hours), improved energy efficiency, and high monomer selectivity at moderate temperatures (typically 200–350 °C). This review integrates kinetic modeling approaches, analytical methods for monitoring depolymerization, and downstream separation considerations that govern monomer purity and recyclability. Key challenges, including energy demand, feedstock contamination, scalability, and economic competitiveness, are critically discussed in relation to industrial implementation. Overall, hydrolysis-based and microwave-assisted chemical recycling routes are the most viable pathways for closed-loop recycling of PA6. Future progress will rely on integrated reaction–separation–repolymerization designs, catalyst optimization, and process intensification to enable sustainable and industrially relevant PA6 circularity. Full article

Adverse drug reaction (ADR) detection is essential for ensuring drug safety and effective pharmacovigilance. The rapid growth of users’ medication reviews posted on social media has introduced a valuable new data source for ADR detection. However, the large scale and high noise inherent in social media text pose substantial challenges to existing detection methods. Although large language models (LLMs) exhibit strong robustness to noisy and interfering information, they are often limited by issues such as stochastic outputs and hallucinations. To address these challenges, this paper proposes two generative detection frameworks based on Chain of Thought (CoT), namely LLaMA-DetectionADR for Supervised Fine-Tuning (SFT) and DetectionADRGPT for low-resource in-context learning. LLaMA-DetectionADR automatically generates CoT reasoning sequences to construct an instruction tuning dataset, which is then used to fine-tune the LLaMA3-8B model via Quantized Low-Rank Adaptation (QLoRA). In contrast, DetectionADRGPT leverages clustering algorithms to select representative unlabeled samples and enhances in-context learning by incorporating CoT reasoning paths together with their corresponding labels. Experimental results on the Twitter and CADEC social media datasets show that LLaMA-DetectionADR achieves excellent performance, with F1 scores of 92.67% and 86.13%, respectively. Meanwhile, DetectionADRGPT obtains competitive F1 scores of 87.29% and 82.80% with only a few labeled examples, approaching the performance of fully supervised advanced models. The overall results demonstrate the effectiveness and practical value of the proposed CoT-based generative frameworks for ADR detection from social media. Full article

Background/Objectives: Reaction time, hand–eye coordination, spatial orientation, and attention play a key role in handball, which is characterized by high intensity as well as high cognitive and motor demands. The level of these abilities may change during the season, potentially reflecting training adaptations and increasing physical fatigue. The aim of the study was to compare the level of psychomotor abilities in professional handball players before the start of the competition round and after the end of the league season. The study included 77 handball players playing in the Polish Handball Super League (average age: 25.6 ± 5.2 years). The players were divided according to position: pivot, center, and wing. Methods: Psychomotor abilities were assessed using the Test2Drive computer system, employing tests of simple and choice reaction time, eye–hand coordination, spatial orientation, perception and attention, and movement anticipation. Results: At the end of the season, a statistically significant reduction in reaction time was observed in the choice reaction (p = 0.001), eye–hand coordination (p = 0.002), and spatial orientation tests (p = 0.003). In terms of motor skills, an increase in time was observed in the SIRT test (p = 0.003), CHORT (p = 0.005) and HECOR (p = 0.011) tests, while the time in the PUT test was shortened for both neutral (p = 0.002) and critical (p = 0.025) stimuli. Positional analysis showed that after the season, the pivot player achieved higher effectiveness in the CHORT test than the wing player (p = 0.020). Additionally, statistically significant differences were observed for correct responses in the SPANT test (p = 0.032). In terms of correct answers in the PAMT test, the pivot player had the lowest effectiveness. Conclusions: Participation in the full season of competition coincided with significant changes in the psychomotor profile of handball players, with a simultaneous improvement in reaction speed and deterioration in movement time parameters. Full article

Background: In traditional karate, performance effectiveness is determined, among other factors, by the speed of stimulus processing and the precision of the motor response. Body composition may indirectly modulate these abilities; however, data on karate athletes are limited. Methods: The study included 27 men—active members of the Polish national team in traditional karate (18–30 years; training experience ≥ 5 years; black belt). Body composition was assessed using segmental bioelectrical impedance analysis (InBody 770), and psychomotor abilities were measured with the TEST2DRIVE system: SIRT (simple reaction), CHORT (choice reaction), HECTOR (simple reaction), and SPANT (spatial anticipation). Results: The psychomotor profile showed the longest reaction times in CHORT and the shortest in SIRT. Associations with body composition were selective: in SIRT, the median simple reaction time demonstrated a moderate positive relationship with lean-mass-related parameters, with no associations for motor time. No significant correlations with body composition were found in CHORT or HECTOR. In SPANT, significant associations concerned motor time only, which was positively related to selected indices of adiposity and fat distribution, whereas choice reaction time and accuracy were independent of body composition. Conclusion: In traditional karate athletes, body composition is not an unambiguous predictor of psychomotor performance, and its relevance depends on task characteristics. The findings suggest that potential effects of somatic parameters are expressed mainly in selected execution components; therefore, assessments of competitive readiness should combine body composition monitoring with tests that differentiate the reaction phase from the motor phase. Full article

Advanced oxidation technology utilizing ozone as the oxidant shows great potential for the efficient purification of wastewater. However, the efficiency of ozone decomposition remains a significant bottleneck limiting the performance of ozone-based advanced oxidation processes. Catalytic ozone decomposition technology is a highly effective approach to enhancing ozone utilization efficiency; nevertheless, the competing adsorption of water molecules results in low catalytic reaction efficiency and catalyst deactivation. In this study, NiFe layered double hydroxide (LDH) was prepared successfully through the hydrothermal method. In situ DRIFTS with isotope labeling revealed that ozone combines with surface H atoms to produce H₂O, whereas the oxidation of high-valence metals destroys H₂O, producing H atoms that return to the surface of NiFe LDH. The unique structure of NiFe LDH allows water to participate in the surface H atom cycle process, and the charge exchange between Ni and Fe atoms accelerates the recovery of surface H atoms, which avoids the deactivation of the active site caused by competitive adsorption of water molecules, achieving a catalytic ozone decomposition efficiency of 99% for 80 h and 59.0% for simulated wastewater containing polyacrylamide as a model pollutant. This work presents a fresh insight into surface H cycling of LDH materials to improve the wet resistance of the catalysts. Full article

Glycidyl azide polymer (GAP)-based polyurethane, a kind of energetic thermoplastic elastomer (ETPE), is a promising binder for advanced solid propellants, but its thermal decomposition involves overlapping competitive reactions that conventional single-step kinetic models cannot characterize accurately, limiting its engineering applications. To address this limitation, a constrained asymmetric Gaussian deconvolution strategy with fixed peak area ratios and shape constraints was developed in this work. This strategy was applied to resolve overlapping reaction rate curves converted from derivative thermogravimetric data of GAP-based ETPEs with 50 wt% GAP content at four heating rates of 5, 10, 15 and 20 K·min⁻¹. The complex decomposition process was successfully split into five stages, assigned to azide cleavage, polyether backbone scission, carbamate cleavage, hydrocarbon product degradation and residue decomposition, with a goodness of fit of R² > 0.998. Apparent activation energies of the five stages were determined through cross-validation by the Friedman and Flynn–Wall–Ozawa methods without prior assumption of reaction mechanisms, following the order of residue decomposition (181.4 ± 1.0 kJ·mol⁻¹) > hydrocarbon product degradation (159.9 ± 1.0 kJ·mol⁻¹) ≈ azide cleavage (156.5 ± 0.6 kJ·mol⁻¹) > backbone scission (135.1 ± 0.7 kJ·mol⁻¹) > carbamate cleavage (111.9 ± 1.1 kJ·mol⁻¹). Pre-exponential factors with lnA₀ values ranging from 22.2 to 34.0 were derived via the kinetic compensation effect. Finally, generalized master plots were employed to compare with classic solid-state reaction models for mechanistic insight, and the Šesták–Berggren model fit three major stages excellently (R² > 0.996) by accounting for synergistic nucleation-growth and phase boundary mechanisms, enabling high-precision kinetic equations. It should be noted that the constrained deconvolution method proposed in this work has general applicability for kinetic analysis of GAP-based ETPEs with different formulations and other complex energetic polymer systems, while the obtained kinetic parameters are composition-specific and only applicable to the corresponding ETPE formulation studied herein. Full article