Abstract: In this study, to evaluate physiological and microbial responses under heat stress conditions, blood biochemical parameters, rumen fermentation characteristics, and microbial composition in Holstein dairy cows during the dry period and early lactation stages under summer conditions in Korea. Fourteen cows were observed during the hot summer month (from the first to the third week of August), with the temperature-humidity index (THI) recorded in the barns during the dry and early lactation periods being 80.80 and 81.66, respectively. Blood and rumen fluid samples were collected to evaluate physiological responses and changes in blood parameters, rumen fermentation, and microbial composition. Blood analysis revealed significant variations between the two stages. Early-lactating cows exhibited lower glucose, blood urea nitrogen, and cholesterol levels but higher ketone and aspartate aminotransferase levels, indicating increased energy demands and protein metabolism. A complete blood count showed reduced red blood cell count, hematocrit, and hemoglobin levels during the early lactation period, whereas white blood cell counts increased. The levels of heat shock proteins (HSPs), such as HSP27, HSP70, and HSP90, also differed significantly. Rumen fermentation analysis revealed lower ammonia nitrogen concentrations but significantly higher propionate and total vol-atile fatty acid concentrations during the early lactation period, indicating adaptive changes in rumen function. Microbial analysis revealed significant differences in bacterial diversity and composition. Early-lactation cows exhibited relatively high abundances of Bacteroidota and Prevotella, whereas the dry period was dominated by Clostridia and Eubacteriales. Network analysis highlighted shifts in microbial interactions, with specific keystone species identified at each stage. These findings suggest distinct physiological and microbiological adaptations in response to HS, with early lactation characterized by heightened metabolic demands and significant shifts in rumen bacterial communities. Such insights could inform tailored management strategies to mitigate the impact of HS on dairy cows during their critical production stages.

Abstract: The removal of thorium from contaminated water sources is crucial for environmental protection and nuclear waste management. Herein, we present a dual-strategy design of a thiophene-integrated porphyrin covalent organic framework (TAPP-BTD-COF) that combines rigid macrocyclic scaffolds with flexible thiophene linkages, incorporating complementary N and S donor sites. This tailored COF achieves efficient and selective capture of Th(IV) from acidic aqueous solutions. By leveraging the topological arrangement of the porphyrin core to modulate the conformation of thiophene-based connectors, a coordination environment with N–S synergistic sites is created, which significantly enhances Th(IV) selectivity over competing ions. At pH 4.5, the synthesized TAPP-BTD-COF exhibits a high adsorption capacity of 437.18 mg g-1 and reaches equilibrium within 20 minutes. It demonstrates exceptional selectivity for Th(IV), with a separation factor exceeding 2.6×10³ relative to common interfering ions, and retains over 90% adsorption capacity after three consecutive cycles. Mechanistic studies confirm that the high performance originates from N–Th / S–Th dual-dentate coordination. This work provides a strategic design of functional COFs for thorium recovery and represents a highly efficient adsorbent system for Th(IV) removal from aqueous streams.

Abstract: Background and Objectives: We aimed to analyze the screening accuracy of the STOP and STOP-Bang questionnaires within three distinct populations from the Mediterranean region: Croatia, Greece, and Türkiye. Additionally, we aimed to optimize previously suggested and to establish population-specific cut-off points for body mass index (BMI) and neck circumference (NC) in the questionnaires to enhance their screening accuracy. Materials and Methods: A total of 9,102 patients who underwent polysomnography or polygraphy to evaluate suspected OSA were enrolled from: Split Sleep Medicine Centre (Croatia), Ege University Faculty of Medicine (Türkiye), and Thessaloniki G Papaniko-laou Hospital Aristotle University (Greece). Patients completed the STOP and STOP-Bang questionnaires before sleep assessments. Sensitivity, specificity, and area under the curve (AUC) were calculated to assess the screening properties. Additionally, optimized cut-offs for age, NC, and BMI were determined. Results: The highest AUC values were observed using the STOP-Bang≥5 method, with AUC values of 0.712 for detecting any OSA (AHI≥5/h), 0.684 for moderate or severe OSA (AHI≥15/h), and 0.663 for severe OSA (AHI≥30/h). For individual centers, the STOP-Bang≥5 method performed best in Split, while the STOP≥2+NC method yielded the highest AUCs in Izmir and Thessaloniki for moderate and severe OSA. Optimized cut-off values for age, NC, and BMI improved sensitivity and specificity across all cen-ters. Conclusions: This study highlights the necessity of population-specific considerations in the screening of OSA. Significant differences in demographics, anthropometrics, symptoms, and comorbidities across populations could impact the questionnaire's screening accuracy. Adjusting age, NC, and BMI cut-off points optimizes the STOP-Bang questionnaire.

Abstract: This paper develops a robust numerical scheme based on a frame collocation method for solving multi-term fractional ordinary differential equations (FODEs) whose solutions exhibit multiple singularities at the origin. To adaptively capture the singular behavior, we construct a hybrid basis function frame by combining shifted fractional Legendre polynomials. An efficient computational formula for the Caputo fractional derivative is derived, which transforms the original problem into a nonlinear algebraic system at the collocation points. The resulting system matrix is severely ill-conditioned due to the redundancy of frame, to mitigate this issue, we employ truncated singular value decomposition (TSVD) regularization, thereby enabling stable and high-precision solutions. Extensive numerical experiments on several benchmark problems, including the fractional Bagley–Torvik equation, linear multi-term FODEs, and nonlinear cases, demonstrate that the proposed method achieves exponential convergence rates. Notably, when the singular exponent of the solution matches a tunable parameter $\delta$ in the basis functions, superconvergence is observed, significantly outperforming standard spectral methods. Compared with traditional spectral approaches, the proposed frame collocation framework retains spectral accuracy while exhibiting superior capability in handling complex singular structures, providing a powerful and reliable tool for high-precision simulations of multi-term fractional differential equations.

Abstract: Background/Objectives: Clear cell renal cell carcinoma (ccRCC), the predominant malignant subtype of kidney cancer, is the leading cause of death among renal cell carcinoma patients. Although a subset of ccRCC patients benefit from select immune checkpoint inhibitors (ICIs), prognosis remains poor. While PD-1 and PD-L1 have been extensively studied, the prevalence and distribution of other immune checkpoints (ICs) and their relationship with epithelial-to-mesenchymal transition (EMT) remain poorly characterized. Here, we investigated the interplay between twenty ICs and EMT markers and assessed their combined prognostic relevance in ccRCC patients. Methods: Transcriptomic profiling and integrated bioinformatic analyses were performed, including differential expression, correlation analyses, survival analyses, forest plot analyses, ROC curve evaluation, and OncoPrint visualisation, complemented by analysis of single-cell RNA sequencing data, immunohistochemistry, and multiplex secretory IC (LegendPlex) assays. Results: Transcriptomic profiling of over 500 ccRCC tumors versus normal kidney tissue revealed dysregulation of ICs, particularly LAG3 and NT5E. Notably, expression of ICs, including LAG3 and NT5E, was associated with poor overall survival in 415 ccRCC patients. ICs that synergised with EMT phenotype provided improved prognostic discrimination compared to individual ICs. Correlation analyses, single-cell RNA sequencing and immunohistochemistry demonstrated that EMT-associated tumor cells exhibit coordinated expression of LAG3 and NT5E. Receiver operating characteristic analysis highlighted the potential clinical utility of LAG3 and NT5E. Conclusions: Collectively, this study defines an EMT-IC axis in ccRCC and demonstrates its relevance to tumor biology and patient outcomes, highlighting LAG3 and NT5E as potential prognostic markers and therapeutic targets.

Abstract: Credit risk modelling is essential for assessing the likelihood of borrower default and supporting informed lending decisions. Despite advances in predictive algorithms, challenges remain in ensuring model transparency, reliability, and robustness to uncertain inputs. This study investigates integrating explainable AI (XAI) and uncertainty quantification (UQ) to enhance interpretability and confidence in credit risk predictions. Three modelling approaches, Logistic Regression, Random Forest, and XGBoost, were evaluated using the Home Equity (HMEQ) dataset, with performance assessed on predictive accuracy, probability calibration, interpretability, and uncertainty handling. Ensemble methods achieved superior predictive performance, exceeding 98% accuracy and yielding near-perfect AUC scores above 0.999, whereas Logistic Regression exhibited substantially lower performance. Calibration analysis revealed a discrepancy between accuracy and probabilistic reliability: Random Forest, despite high accuracy, produced less well-calibrated predictions (ECE = 0.0475), while XGBoost achieved both strong predictive performance and reliable confidence estimates (ECE = 0.0117). Entropy-based uncertainty quantification identified instances where the model’s predictions were highly uncertain, effectively highlighting challenging cases. SHAP and LIME consistently identified DELINQ, DEROG, and DEBTINC as primary drivers of default risk, aligning with established financial risk logic. By combining SHAP, LIME, and entropy-based UQ, this study proposes a unified framework that enhances interpretability, supports regulatory compliance, and increases trust in automated lending systems, emphasising the importance of reliable confidence alongside predictive accuracy.

Abstract: Background/Objectives: Drug supply disruptions represent an increasingly serious problem for health systems worldwide, with systemic antibiotics among the most frequently affected therapeutic categories. Although regulatory authorities have repeatedly signaled this risk, comparative studies analyzing patterns of antibiotic shortages across multiple countries simultaneously remain scarce. Methods: We performed a cross-sectional comparative analysis based on data from public national shortage registries in seven jurisdictions: Belgium, France, Germany, Romania, Spain, the United States (FDA), and the Kingdom of Saudi Arabia. All records corresponding to systemic antibiotics in ATC group J01 were extracted, harmonized, and analyzed, with the active substance (INN) as the unit of analysis. The association between critical drug status according to the EMA list and the multinational recurrence of shortages was assessed using chi-square tests, the Mann–Whitney U test, and multivariate logistic regression. To verify the robustness of the results, a sensitivity analysis was also performed using alternative thresholds for jurisdictions. Results: A total of 350 shortage records were mapped, corresponding to 64 unique active pharmaceutical ingredients. On average, each active substance was reported as out of stock in 3.48 jurisdictions (SD = 1.46). Macrolides (J01F) and quinolones (J01M) exhibited the widest geographic spread of shortages. Antibiotics included on the EMA’s list of critical medicines were reported as missing in multiple countries simultaneously significantly more frequently than those not included on this list (82.86% vs. 37.14%; χ² = 71.99, p < 0.001; Cramer’s V = 0.454). In the multivariate logistic regression model, EMA critical medicine status remained an independent predictor of multinational recurrence of shortages (OR = 8.29; 95% CI: 4.93–13.94; p < 0.001), while the injectable route of administration did not reach the threshold for statistical significance (OR = 0.78; p = 0.341). Sensitivity analysis confirmed that this association remains statistically significant regardless of the threshold chosen. Conclusions: Shortages of systemic antibiotics tend to occur simultaneously in multiple countries, and drugs designated as critical by the EMA are disproportionately affected. The results suggest that the identified weaknesses are not specific to a single health system but reflect structural fragilities in international antibiotic supply chains. This underscores the need for internationally coordinated strategies, both for monitoring the availability of essential antibiotics and for preventing and managing shortages.

Abstract: Aquaculture in Europe has experienced significant growth in recent decades, driven by rising demand for sustainable production and strong policy frameworks promoting environmentally responsible aquaculture practices. Biomarkers, defined as measurable biological indicators reflecting physiological, biochemical, or molecular responses to environmental and biological stressors, have greatly expanded their use in aquaculture applications. In this regard, biomarker-based approaches are increasingly applied in multiple areas of aquaculture, including health and disease monitoring, welfare as-sessment, environmental toxicology, reproductive biology, population management, and the optimization of cryopreservation protocols. This review provides a compre-hensive overview of current biomarker applications in European aquaculture, highlig-hting recent technological advances, methodological challenges, and emerging rese-arch directions. By synthesizing current knowledge and identifying future research priorities, this review aims to contribute to the development of biomarker-driven mo-nitoring strategies that enhance the resilience, efficiency, and sustainability of aqua-culture in Europe.

Abstract: The transition to a hydrogen-based energy economy emphasizes the potential of hydrogen as a fuel for plug-in hybrid electric vehicles (PHEVs). The performance of a hydrogen engine within a PHEV depends on the choice of its operating modes, which influence both efficiency and emissions. This study proposes a method for developing engine operating lines (EOLs) on engine maps based on minimizing nitrogen oxide (NOx) emissions while considering constraints on maximum engine power. A total of 15 EOLs are proposed for configurations with both constant and variable maximum engine power. Using mathematical modeling of PHEV operation under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), the impact of EOL selection on engine characteristics, as well as on battery and generator parameters, is analyzed. For a comprehensive evaluation of EOL effectiveness, five criteria are introduced, considering fuel energy consumption, NOx emissions, wear, mechanical fatigue, and noise, vibration, and harshness (NVH). The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied to determine the weighting factors of the criteria and to rank the proposed EOLs, thereby identifying the most efficient configurations. The results show that, for the base hydrogen engine configuration, selecting appropriate operating modes alone enables NOx emissions to be reduced significantly below Euro 6 limits, without any hardware modifications or exhaust aftertreatment.

Abstract: Water is an indispensable resource for the survival of all living organisms on Earth. However, many coastal villages continue to face challenges in accessing potable water, particularly during extended droughts. This comprehensive study evaluates the implementation and performance of a solar desalination system that employs photovoltaic (PV) panels and a parabolic solar concentrator to meet clean water demand in a drought-prone area of Indonesia. The system harnesses both solar-generated electricity and thermal energy to power an advanced desalination apparatus, effectively converting seawater into safe drinking water. Over a rigorous 4 months testing period, the device maintained an average steam outlet temperature of 105.9 °C, enabling a direct single-stage evaporation and condensation desalination process. Under optimal sunlight conditions, the system produced 1,500 mL of purified water every 30 minutes, resulting in a total daily output of approximately 12 L (1,500 mL × 8 cycles over 4 hours). Laboratory analysis revealed a decrease in pH from 8.0 in raw seawater to 6.8 in treated water after post-treatment pH adjustment, meeting established safety standards for human consumption. Electrical conductivity measurements fell from 40-50 mS/cm to 480-500 µS/cm, confirming substantial salt removal. These results demonstrate the system’s capacity to generate potable water using sustainable energy sources and support circular economy principles by repurposing renewable resources for water desalination in water-scarce environments.

Abstract:

Biomineralization in plant tissues is a widespread process accompanied by carbon sequestration in biogenic minerals. This study aimed to evaluate the effect of CaCO₃ application to soil on the yield of Buglossoides arvensis (L.) I.M. Johnst., on the formation and localization of biominerals in the pericarp of fruits, as well as on the accumulation of carbon in minerals. B. arvensis seeds were sown in clay loam soil treated with CaCO₃ at concentrations of 0.0 (0Ca), 2.5 (2.5Ca), 5.0 (5Ca), 7.5 (7.5Ca), and 10.0 (10Ca) t ha⁻¹. CaCO₃ application improved B. arvensis germination, plant biomass accumulation, fruit weight and number, which consequently increased both per plant and per area yield, but did not significantly affect the rate of photosynthesis and leaf respiration. Scanning electron microscopy analysis showed no significant effect of soil liming on the localization of amorphous silica and calcium carbonate in the pericarp of B. arvensis fruits. However, with increasing soil calcium availability, the silica to carbonate ratio changed towards a decrease in silica content and an increase in carbonate content. Liming of soil from acidic to slightly alkaline increased the content of organic carbon, occluded in biosilica (phytoliths) and also increased carbon content of calcium carbonate in the pericarp of B. arvensis fruits. We concluded that B. arvensis responds to increased soil calcium and soil pH by increasing carbon sequestration in biominerals formed in fruit pericarps. Studying the relationship between biomineralization in plants and environmental conditions may be useful for assessing key biogeochemical processes of carbon sequestration.

Abstract:

This study evaluated the validity of the leg stiffness metric provided by the Stryd running power meter against the Morin (2005) sine-wave spring-mass model. Twenty-three highly trained trail runners (11 women) completed a 12-min uphill time trial at +12% grade and one hour of submaximal level running. Leg stiffness was calculated from contact time, flight time, running speed, and leg length using the Morin’s method, and compared with Stryd values. Agreement was assessed following the Dhahbi and Chamari Level-1 analytical framework, including intraclass correlation coefficient (ICC_2,1), Bland-Altman analysis, mean absolute percentage error (MAPE), and paired t-tests. Stryd and Morin estimates showed excellent agreement in both conditions: uphill running: ICC_2,1 = 0.96 (95%CI: 0.91–0.98), bias = −0.02 kN·m⁻¹, limits of agreement (LoA) = [−0.61, 0.58] kN·m⁻¹, MAPE = 2.5% (p = 0.803), and level running: ICC_2,1 = 0.97 (95%CI: 0.93–0.99), bias = −0.04 kN·m⁻¹, LoA = [−0.62, 0.54] kN·m⁻¹, MAPE = 2.6% (p = 0.505). The Stryd sensor provides valid leg stiffness estimates in highly trained trail runners on both level and inclined terrain. The negligible systematic bias and narrow limits of agreement support the use of Stryd for leg stiffness monitoring in field and laboratory settings.

Abstract: Lack of recombination in parthenogenetic organisms limits their ability to adapt to changing environments by natural selection. However, some obligatory parthenogens, such as rock lizards of the genus Darevskia, could survive for millions of years across multiple Pleistocene glaciations. There are different explanations for this phenomenon. Analysis of phenotypic variation may shed further light on the high adaptability of the parthenogenetic lizards. We compared the genetic and phenotypic variability of 186 individuals of the parthenogenetic Darevskia dahli and 54 individuals of its sexually reproducing paternal species, D. portschinskii, whose ranges almost coincide in Georgia (the Caucasus). The analysis showed that, despite the higher genetic variability of the individuals and metapopulations of D. portschinskii, phenotypic variability (as measured by KW dispersion and the normalized effective number of individuals per metapopulation), based on the nominal traits, was almost equal in the two species. Moreover, phenotypes of the parthenogen correlated with the distances among the localities, and with the annual rainfall level at a locality. The latter species also had more outlier phenotypes. Phenotypic plasticity may be a strategy for adaptation of parthenogenetic rock lizards, to a certain extent, compensating for the lack of genetic diversity.

Abstract: Decision tree ensembles, such as Random Forests and Gradient Boosting Machines, achieve high predictive accuracy but often suffer from limited transparency due to their structural complexity. Due to this lack, interpretability challenges arise in domains where model understanding, accountability, and trust are essential. So, many interpretability/explainability techniques have been proposed for tree-based ensembles. However, although there are enough surveys or overviews concerning interpretability/explainability in artificial intelligence or machine learning in general, there are very few surveys of overviews on interpretability/explainability for tree-based ensembles. This paper provides an overview of recent approaches to interpretability and explainability in decision tree ensembles. We present two categorizations; one based on the kind of technique/architecture used and the second based on the level of scope. The former is a unified taxonomy of acquired (or post-hoc) and inherent methods further analyzed in two more levels. The latter concerns the distinction between local (or instance-related) and global (or model-related) methods. We additionally provide a survey of the interpretability/explainability methods/techniques used in various domain applications, like healthcare, finance, law, privacy preserving. This overview clarifies the current landscape of interpretable/explainable ensemble learning, explicitly addressing emerging challenges. Ultimately, it aims to support researchers and practitioners in selecting and developing ensemble models that move beyond the traditional accuracy-interpretability trade-off, aligning predictive power with strict regulatory, operational, and domain-specific transparency requirements.

Abstract: Trace water is one of the most critical matrix contaminants in ultra-high purity (UHP) process gases, like argon (Ar) and nitrogen (N₂), and many others. Even trace amounts can severely degrade the quality of many products reliant on these gases. Despite its importance to advanced technology sectors, notably semiconductor manufacturing, it has proven quite difficult to realize preparative or analytical trace water metrology over the full amount fraction range needed or in the broad spectrum of industrially relevant matrix gases. Within the EU-funded PROMETH2O project consortium, this challenge has been addressed through the development or significant improvement of traceable measurement methods and standards spanning 5 nmol mol⁻¹ to 5 µmol mol⁻¹, tailored for use in UHP process gas production, such as Ar, N2 and hydrogen (H2). The measurement ranges were extended and the uncertainties were improved, while being consistent with current best practice at primary humidity standards laboratories. These capabilities were validated in applications relevant to process instrumentation and the gas industry. A distributed metrological infrastructure at various European National Metrology Institutes and partner sites now provides SI-traceable trace water measurements in various UHP, strongly supporting and extending the calibration capabilities for the gas and semiconductor industries and the associated stakeholders.

Abstract: To improve the efficiency of AD for tetracycline-containing wastewater, this study systematically investigated the effects of ZVI and PAC on pollutant removal, VFAs metabolism, biogas production, and sludge physicochemical characteristics. RSM was employed to optimize the co-dosing conditions of ZVI and PAC. The results demonstrated that ZVI and PAC could significantly facilitate COD degradation and TC removal, with the TC removal rate exceeding 90%. The optimal dosages for COD removal were 1000 mg/L ZVI and 2000 mg/L PAC with removal efficiency of 61.03% and 56.9%, respectively. Both additives effectively accelerated the catabolism of typical VFAs, thereby mitigating the accumulation of intermediate metabolites that may cause AD system instability. In terms of biogas production, 1000 mg/L ZVI and 2000 mg/L PAC enhanced methane yield by 55.9% and 35.0% compared to the control group, with ZVI exhibiting a more prominent enhancement effect. Mechanistic analysis revealed that ZVI and PAC reinforced the AD process through multiple synergistic pathways: enhancing the electrical conductivity of the AD system, facilitating DIET between functional microorganisms, and stimulating the secretion of EPS by anaerobic microbes. RSM optimization yielded the optimal co-dosing parameters: 1000 mg/L ZVI and 1200 mg/L PAC. Under these conditions, the methane yield was increased by 71.18% relative to the control group, and the model validation accuracy reached 97.94%. This study provides a viable technical strategy and theoretical basis for enhancing the efficiency of anaerobic treatment of tetracycline-containing wastewater.

Abstract: Rare diseases pose a growing challenge for health systems due to the combination of high clinical need, limited evidence, and elevated costs. This article analyzes the intersection between translational medicine and pharmacoeconomics, identifying a structural gap between the clinical value of innovations and traditional economic evaluation frameworks. Drawing on a narrative review of the literature—in which 240 scientific articles were identified and the 30 most methodologically and thematically relevant were selected through systematic screening by title, abstract, and full-text relevance—and a comparative analysis of frameworks in Europe, the United States, and Latin America, we propose the concept of translational pharmacoeconomics as an integrative framework that explicitly incorporates clinical, economic, and social dimensions of value into health decision-making. This approach has direct implications for the design of more equitable, adaptive, and sustainable health policies, particularly in contexts of evidence scarcity and unmet clinical need.

Abstract: Background/Objectives: The use of IgM- and IgA-enriched intravenous immunoglobulins (eIg) in septic shock remains controversial due to the lack of high-quality evidence, despite indications from observational studies and meta-analyses suggesting potential benefit. Identifying patients who may respond favorably and determining whether extended treatment could be useful are ongoing challenges. This study evaluates baseline predictors and early treatment-related trends associated with outcomes in septic shock patients receiving eIg, with particular focus on immunoglobulin levels and norepinephrine (NE) requirements. Methods: This observational analysis used data from the multicenter SORRISO registry, including 248 septic shock patients treated with eIg from 2015 to 2022. Baseline clinical and biochemical variables were recorded at the start of eIg infusion (D0). Trends in IgA, IgG, IgM, lactate, and NE dose were assessed between D0 and D4. Restricted cubic spline (RCS) models examined non-linear associations with mortality, and Kaplan–Meier analyses evaluated 28‑day survival according to parameter trends. Statistical significance was defined as p < 0.05. Results: RCS curves showed no prognostic value at D0 for INR, CRP, PCT, IgA, or IgG. Lactate (p = 0.005) and platelet count (p = 0.001) demonstrated significant associations with mortality, while IgM (p = 0.076) and NE (p = 0.059) approached significance. Of 209 evaluable patients, NE requirements decreased in 191 (NE‑DOWN) and increased in 18 (NE‑UP). Survival was 67% in the NE‑DOWN group vs. 23% in NE‑UP (p < 0.0001). No D0 variable reliably predicted NE trajectory. In conclusion, among septic shock patients treated with eIg, increasing NE requirements during the first four days were strongly associated with higher mortality. Baseline biomarkers did not predict vasopressor trends.

Abstract: To investigate the codon usage bias (CUB) and its influencing factors in the chloroplast genome of Styphnolobium japonicum f. oligophyllum, we sequenced, assembled and annotated the genome using Illumina high-throughput sequencing, and systematically analyzed 52 protein-coding sequences. The chloroplast genome is 158,837 bp with a typical quadripartite structure, containing 129 functional genes. It presents a mean GC3 content of 28.26% and a mean ENC value of 45.40, indicating weak CUB and low gene expression. Among 31 preferred codons (RSCU > 1), 29 (93.5%) end with A/U. Neutrality plot, ENC-plot and PR2-plot analyses reveal that natural selection is the primary regulator of CUB. A total of 19 optimal codons were identified. These results provide fundamental data for the genetic engineering of S. japonicum f. oligophyllum.

Abstract: Background: Psychosocial assessment is central to lung transplant evaluation. Structured tools such as the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) can be used either to support exclusionary decisions or to guide psychosocial prehabilitation by identifying modifiable targets for intervention. We examined how SIPAT functions in a program that explicitly prioritizes remediation of modifiable psychosocial risks. Methods: We conducted a retrospective observational cohort study of consecutive adult lung transplant candidates evaluated at a single center in Poland between December 2021 and November 2025. Psychosocial risk was assessed using SIPAT (locally translated), including total and domain scores, candidate categories, and binary indicators of clinically relevant alcohol, illicit substance and nicotine related risk. The primary endpoint was a pragmatic program outcome, defined as ever being listed (including transplanted) versus not listed. Analyses focused on describing psychosocial risk profiles and their relationship to the program pathway rather than on building a predictive model of listing decisions. Results: In 491 candidates (mean age 57.2 years; 40.5% women), psychosocial burden was generally low (mean total SIPAT 12.4, SD 6.8) and most patients were rated as excellent or good candidates. SIPAT total, domain scores and candidate categories were not meaningfully associated with ever being listed. Nicotine related risk was more frequent among listed candidates, consistent with a clinical strategy in which smoking histories in predominantly COPD and emphysema patients trigger intensive cessation support rather than automatic exclusion. Cluster analyses identified a smaller high-risk subgroup, and ROC analyses showed modest discrimination for alcohol and nicotine related risk, supporting SIPAT as a structured needs assessment. Conclusions: In this prehabilitation oriented program, SIPAT did not operate as a binary gatekeeping instrument for listing. Instead, it primarily served to identify modifiable psychosocial targets that trigger tailored support. These findings support using SIPAT as a structured roadmap for psychosocial prehabilitation rather than a stand-alone exclusion tool.