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Investigation of simvastatin and fluvastatin permeation across cell membrane models using molecular dynamics simulations

The chief motivation for the failure of drugs in clinical trials is their low bioavailability caused by the inability to cross the cell membrane. Understanding drug–membrane interactions is crucial for improving therapeutic efficacy. In this work, molecular dynamics simulations were employed to investigate the permeation of simvastatin and fluvastatin across three lipid bilayer models representing normal and cancer cell membranes. Free energy profiles reveal that simvastatin, due to its higher lipophilicity, interacts more strongly with lipid environments, preferentially permeating cancer-like symmetric membranes. Fluvastatin, in contrast, shows less selective behavior across different membrane types, consistent with its charged nature. Statin insertion perturbs membrane structure, increasing area per lipid and decreasing bilayer thickness and lipid order. These findings highlight how membrane composition and asymmetry govern passive drug diffusion and suggest that selective membrane interactions may reflect the differential anticancer potential of statins. The results provide mechanistic insights into structure–permeability relationships and support the strategic use of realistic membrane models in drug discovery and repurposing efforts.

An investigation into enhancing sand stability and minimizing dust emissions through bacterial treatment in arid regions

Comparison between wharton’s jelly and collagen natural hydrogels for human ovarian follicle transplantation as an artificial ovary

Artificial ovary (AO) is a bioengineered approach aimed at increasing fertility potential in certain cases, especially in women and prepubertal girls with a history of cancer. Some natural and synthetic materials can be used for ovary bioengineering. In the current study, we compared two natural hydrogel composites containing alginate (Alg): Wharton’s Jelly (WJ/Alg) and Collagen (Col/Alg) as models for human AO. In total, six experimental groups were designed: WJ/Alg, Col/Alg, WJ/Alg +FSH , Col/Alg +FSH , WJ/Alg +EPO , and Col/Alg +EPO (n = 60). In each group, 40 isolated human ovarian follicles were seeded in 10 µl of the desired hydrogel and xenotransplanted into the right side of the peritoneum in ovariectomized NMRI mice for 1 week. FSH (7.5 IU) and Erythropoietin (EPO; 200 IU/kg as an angiogenic factor) were injected every other day. Histological and immunohistochemical assessments for Vimentin, CD45, and Ki67; Gene expression analysis for GDF9 , Vegf, and CD45 ; and hormonal assays for estradiol and progesterone were performed. Histological staining showed that WJ/Alg can support follicle growth. In contrast, most of follicles in Col/Alg remained at the primordial stage. Although FSH injection helped granulosa cells differentiation, especially in the WJ/Alg group (507.6 ± 134.1 vs 1444 ± 493.6), and EPO injection increased blood vessels in both groups ( p -value<0.0001), the follicles could not preserve in the experimental groups. In addition, there were no significant differences in gene expression and hormonal assays across all groups. Based on the histological analysis, the WJ/Alg-base artificial ovary provided better support for follicle development after 1 week compared to the other groups. In addition, Adjusting FSH and EPO dosage may improve follicle survival and growth in future studies.

Impact of diamino and imidazole functionalization on the photophysical properties and electronic and structural dynamics of the pyrimidine nucleobase

The photostability of DNA and RNA bases to ultraviolet radiation is essential to life, as evidenced by their ability to dissipate excess electronic energy via ultrafast internal conversion. Understanding how the functionalization of pyrimidine and purine affects electronic and structural relaxation pathways is crucial for insights into their selection as life's building blocks. It is also relevant for developing fluorescent biomarkers and photosensitizers for therapeutic applications. This study investigates how the functionalization of the C5=C6 bond in pyrimidine with heavier amino groups instead of hydrogen atoms to form 4,5-diaminopyrimidine, or with an imidazole ring to form purine, affects the photophysical properties and electronic and structural relaxation pathways observed in pyrimidine. The excited-state dynamics of 4,5-diaminopyrimidine, pyrimidine, and purine are disclosed using steady-state and time-resolved spectroscopy, supported by quantum chemical calculations. It is shown that the lowest-energy absorption band of 4,5-diaminopyrimidine is significantly red-shifted compared to that of purine and pyrimidine in both aqueous solution and acetonitrile, while the fluorescence quantum yield also increases. In acetonitrile, the initial 1ππ* state population in 4,5-diaminopyrimidine decays radiatively and nonradiatively to the ground state and can also intersystem cross to populate a long-lived triplet state. In contrast, intersystem crossing is suppressed in aqueous solution, leading to relaxation of the 1ππ* state population through fluorescence emission and internal conversion to the ground state. Our results demonstrated that both the strategic functionalization of pyrimidine and the solvent properties play important roles in tuning the optical properties and the electronic and structural relaxation pathways of the pyrimidine derivatives.

Glaucoma screening in Kazakhstan

Exploring the association between smartphone-based place visitation data and neighborhood-level coronary heart disease in the United States

Coronary Heart Disease (CHD) is the leading cause of death in the United States, affecting over 20.5 million adults. Previous studies link health behaviors – such as dietary behavior, physical activity, smoking, and alcohol consumption – to CHD risk. These studies typically use surveys and interviews, which, despite their benefits, are resource-intensive and limited by small sample sizes. Using large-scale national level anonymized smartphone-based location data, our study examines whether health behaviors that are proxy measured by place visitation are associated with CHD prevalence across US census tracts. This study utilized data from multiple sources, including demographic and socioeconomic characteristics, health outcomes, and smartphone-based place visitation data. Health behavior measures were derived from aggregated smartphone location data at the census tract level, focusing on categories such as food retails, drinking places, and physical activity locations. Three sets of regression analyses were conducted: one using only demographic variables, the second including socioeconomic variables, and another incorporating the derived health behavior measures. Linear and spatial regression analyses were employed to assess the relationship between neighborhood-level CHD prevalence and these behaviors. Findings indicate a significant association between health behaviors that are proxy measured by place visitation data and the prevalence of CHD at the neighborhood level. The models incorporating these behaviors demonstrated improved fitness and highlighted specific behavioral factors such as increased visits to physical activity facilities and healthy food retail associated with lower CHD rates. Conversely, higher visits to less healthy food retail were associated with increased CHD rates. Smartphone-based visitation data offers a novel method to assess health behaviors at a large scale, providing valuable insights for targeting CHD interventions more effectively at the neighborhood level. This approach could enhance our understanding and management of CHD, informing public health strategies and interventions to mitigate this major health challenge.

Spin blockades to hot multi-exciton relaxation in nanocrystals: Case of CdTe

In a recent three pulse “spectator exciton” experiment on quantum confined CdSe nanocrystals, spin orientation conflicts between hot and cold electrons in the same doubly excited nanocrystal were shown to block the former from relaxing to the band edge. This blockage was ultimately overcome by phonon-assisted flipping of the hot carrier’s spin, allowing it to pair with its band edge counterpart within tens of picoseconds. To test the generality of this novel phenomenon, the same approach is used here on nanodots of another II–VI semiconductor, CdTe. Despite difficulties arising from slower electron cooling rates and faster bi-exciton recombination times relative to CdSe, the results demonstrate similar spin blockades to hot bi-exciton cooling in CdTe nanocrystals. This study confirms the general occurrence of spin blockade phenomena in II–VI quantum dots, underscoring the potential of this mechanism for assisting hot electron harvesting. It similarly highlights the inherent limitations of using band edge bleaching signals as ensemble exciton counters when multi-excitons are involved.

Spatial structure and influencing factors of agricultural civilization heritage in the Yellow River Basin

Dosimetry study of 3D-printed noncoplanar template-assisted CT-guided 125I seed implantation for the treatment of recurrent and metastatic tumors in the head and neck

Objective Recurrent and metastatic tumors of the head and neck pose significant treatment challenges due to their proximity to critical structures and prior radiation exposure. This study aimed to evaluate the consistency between preoperative and postoperative dosimetric parameters in CT-guided 3 D-printed noncoplanar template (3DPNCT)-assisted radioactive iodine-125 seed implantation (RISI). Methods Twenty-six patients with recurrent or metastatic head and neck cancer were retrospectively analyzed. Gross tumor volume (GTV) coverage and dosimetric parameters such as D90 (dose covering 90% of the GTV), conformity index (CI), and homogeneity index (HI) were compared before and after implantation. The Shapiro-Wilk test was used to assess data normality. Results There were no significant differences between pre- and postoperative D90, V100, V150, or CI values (P > 0.05). Bland–Altman analysis showed high agreement for key metrics. Conclusions 3DPNCT-assisted RISI demonstrated accurate dose delivery and high reproducibility. This approach may enhance local control while minimizing radiation to organs at risk in complex head and neck anatomies. These results suggest that this technique has promising clinical applicability in complex head and neck cases; however, further validation through larger prospective studies is warranted to confirm long-term efficacy and safety.

Tensor decomposed distinguishable cluster. I. Triples decomposition

We present a cost-reduced approach for the distinguishable cluster approximation to coupled cluster with singles, doubles, and iterative triples (DC-CCSDT) based on a tensor decomposition of the triples amplitudes. The triples amplitudes and residuals are processed in the singular-value-decomposition (SVD) basis. Truncation of the SVD basis according to the values of the singular values together with the density fitting (or Cholesky) factorization of the electron repulsion integrals reduces the scaling of the method to N6, and the DC approximation removes the most expensive terms of the SVD triples residuals and at the same time improves the accuracy of the method. The SVD basis vectors for the triples are obtained from the approximate CC3 triples two-electron density matrices constructed in an intermediate SVD basis of doubles amplitudes. This allows us to avoid steps that scale higher than N6 altogether. Tests against DC-CCSDT and CCSDT(Q) on a benchmark set of chemical reactions with closed-shell molecules demonstrate that the SVD-error is very small already with moderate truncation thresholds, especially so when using a CCSD(T) energy correction. Tests on alkane chains demonstrate that the SVD-error grows linearly with system size, confirming the size extensivity of SVD-DC-CCSDT within a chosen truncation threshold.

Development and validation of a competency-based ladder pathway for AI literacy enhancement among higher vocational students

Abstract The rapid integration of artificial intelligence across industries necessitates systematic AI literacy development in higher vocational education to prepare students for AI-driven professional environments. This study develops and validates a comprehensive competency-based ladder development pathway specifically designed to enhance AI literacy among vocational students. The research employs a mixed-methods approach combining theoretical framework construction, empirical investigation, and practical implementation validation. The three-tier pathway model integrates foundational cognitive, skills application, and comprehensive innovation layers to address diverse learning needs while maintaining progression standards. Through empirical investigation involving 2850 students across 15 institutions, the study identifies distinct learner profiles and competency deficits, informing personalized development strategies. The validation experiment with 420 participants demonstrates significant improvements across all competency dimensions, with overall AI literacy gains of 56.0% and sustained retention rates exceeding 85% at six-month follow-up. The innovative pedagogical approaches incorporate project-driven learning, experiential methodologies, and hybrid delivery models to optimize competency development. The comprehensive evaluation framework provides robust assessment tools that balance formative and summative approaches while maintaining alignment with industry standards. Results indicate that students in the ladder pathway intervention achieved 34.7% higher cognitive assessment scores, 42.3% superior performance on skills application tasks, and 28.9% better innovation competency outcomes compared to traditional instruction. This research contributes to the theoretical understanding of competency-based AI education while providing practical implementation guidance for enhancing workforce readiness in the artificial intelligence era.

Exploring the interplay between BMI, subjective body image perception, and health behaviors: A cross-sectional study

Background This study explored the impact of discrepancies between Body Mass Index (BMI) and Subjective Body Image Perception (SBIP) on metabolic health indicators, physical activity (PA), sedentary behavior (SB), sleep time (ST), and stress levels in Korean adults. Methods Data from 8,634 participants in the 8 th Korea National Health and Nutrition Examination Survey (KNHANES, 2019–2021) were analyzed. Participants were categorized into three groups: Group A (SBIP = BMI), Group B (SBIP < BMI), and Group C (SBIP > BMI). Chi-square tests, ANOVA, and multinomial logistic regression were used to evaluate associations among discrepancies in SBIP and BMI and health behaviors. Results Group B exhibited higher BMI levels (26.04 kg/m 2 ) and adverse metabolic indicators, including elevated fasting glucose (102.11 mg/dL) and triglycerides (161.74 mg/dL), compared to the other groups ( p  < 0.05). Group C had better High-Density Lipoprotein (HDL) cholesterol (59 mg/dL) and lower prevalence rates of hyperlipidemia (9.7%) and hypertension (5.5%) than Group B (hyperlipidemia: 11.6%; hypertension: 5.1%) and Group A (hyperlipidemia: 13.2%; hypertension: 7.6%). Moderate-to-Vigorous PA (MVPA) was significantly lower in Group C (97.88 min/week) than Group A (133.18 min/week; p  < 0.05) and Group B (169.64 min/week; p  < 0.05). SBIP discrepancies had a stronger effect on PA and SB than BMI alone, with Group C being 1.30 times more likely not to meet PA guidelines. Stress levels were significantly higher in those with lower BMI or higher SBIP (Odds Ratio [OR] = 1.93, p  < 0.01). Conclusions SBIP has a stronger influence on health behaviors, particularly PA patterns, than BMI alone. Including SBIP in health promotion strategies may improve interventions for improving PA and addressing metabolic health disparities.

Large riverbed sediment flux sustained for a decade after an earthquake 

Effective spin Hamiltonians for the quantum-rotor tunneling problem in pulse EPR

We analyzed the spin-tunneling Hamiltonian of a quantum rotor coupled to an electron spin. Even under conditions where the rotor’s nuclei are magnetically inequivalent, the symmetry between the rotor’s state exchange and relabeling of nuclei holds exactly; hence, the Hamiltonian can be simplified with the help of a group theoretical approach. We demonstrated this principle on methyl-type and methane-type rotors, both in protonated and deuterated forms. We showed that the spin-tunneling problem in these cases is equivalent to solving a few spin-only problems where the tunneling interaction appears in the form of an effective spin Hamiltonian. We derived spin-operator forms of the effective Hamiltonians and discussed the application to the two-pulse electron spin echo envelope modulation experiment.

Application of construal level theory in identifying factors affecting individual decision-making in implementing flood protection measures in rural areas of Iran

Evaluating chatbots in psychiatry: Rasch-based insights into clinical knowledge and reasoning

Chatbots are increasingly being recognized as valuable tools for clinical support in psychiatry. This study systematically evaluated the clinical knowledge and reasoning of 27 leading chatbots in psychiatry. Using 160 multiple-choice questions from the Taiwan Psychiatry Licensing Examinations and Rasch analysis, we quantified performance and qualitatively assessed reasoning processes. OpenAI’s ChatGPT-o1-preview emerged as the top performer, achieving a Rasch ability score of 2.23, significantly surpassing the passing threshold (p < 0.001). While it excelled in diagnostic and therapeutic reasoning, it also demonstrated notable limitations in factual recall, niche topics, and occasional reasoning biases. Our findings indicate that while advanced chatbots hold significant potential as clinical decision-support tools, their current limitations underscore that rigorous human oversight is indispensable for patient safety. Continuous evaluation and domain-specific training are crucial for the safe integration of these technologies into clinical practice.

Just how bad will climate change get? The only way to know is to fund basic research

A machine learning assisted identification of optimum set of order parameters for study of gas hydrate nucleation in a molecular simulation

The study of crystalline materials is of scientific and technological importance. In this regard, tools such as molecular simulations are widely used to characterize their structure and study their mechanisms of formation. In this work, we develop models for identification and classification of crystal polymorphs during a molecular simulation. The models are based on the XGBoost algorithm, which is a scalable, distributed gradient-boosted decision tree model. The inputs to the model are a set of generic order parameters that have been identified from a large pool using machine learning techniques. This study focuses on gas hydrates, which are naturally occurring crystalline compounds of light gases and water. These materials have tremendous scientific and technological importance, and their formation mechanisms under natural/laboratory conditions are areas of active scientific research. The XGBoost models developed in this work are able to accurately classify gas hydrate polymorphs and also compute nucleation rates using the mean first passage time technique. The novelty of this work is to demonstrate that the use of machine learning techniques mitigates the need for considerable expertise in crystallography while identifying crystal order parameters for polymorph classification.

A novel hybrid extreme learning machine-based diagnosis model for sensor node faults in aquaculture

Resolving Acuticulata (Metridioidea: Enthemonae: Actiniaria), a clade containing many invasive species of sea anemones

Acuticulata is a globally distributed group in the actiniarian superfamily Metridioidea comprised of taxa with ecological, economic, and scientific significance. Prominent members such as Exaiptasia diaphana and Diadumene lineata serve as model organisms for studying coral symbiosis, bleaching phenomena, and ecological invasions. Despite their importance, unresolved phylogenetic relationships and outdated taxonomic frameworks hinder a full understanding of the diversity and evolution of the taxa in this clade. In this study, we employ a targeted sequence-capture approach to construct a robust phylogeny for Acuticulata, addressing long-standing questions about familial monophyly and comparing the results to results from a more conventional five-gene dataset. Specimens from previously underrepresented families and global regions, including the Falkland Islands, were included to elucidate evolutionary interrelationships and improve resolution. Our results support the monophyly of Aliciidae, Boloceroididae, Diadumenidae, Gonactiniidae, and Metridiidae. Our results reiterate the need for taxonomic revision within the family Sagartiidae, as the specimens we included from this family were recovered in four distinct clades. Based on our results, we transfer Paraiptasia from Aiptasiidae to Sagartiidae. These findings emphasize the utility of genome-scale data for resolving phylogenetic ambiguities for morphologically problematic taxa and suggest a framework for future integrative taxonomic and ecological studies within Acuticulata.