Hypercholesterolemia-associated oxidative stress increases the formation of oxidized low-density lipoprotein (oxLDL), which can affect endothelial cell function and potentially contribute to renal dysfunction, as reflected by changes in urinary protein excretion. This study aimed to investigate the impact of exogenous oxLDL on urinary excretion of albumin and nephrin. LDL was isolated from a patient with familial hypercholesterolemia (FH) undergoing lipoprotein apheresis (LA) and was oxidized in vitro with Cu (II) ions. Biochemical markers of LDL oxidation, such as TBARS, conjugated dienes, and free ε-amino groups, were measured. Wistar rats were treated with a single intraperitoneal injection of PBS, LDL, or oxLDL (4 mg of protein/kg b.w.). Urine was collected one day before and two days after the injection. We measured blood lipid profiles, urinary protein excretion (specifically albumin and nephrin), and markers of systemic oxidative stress (8-OHdG and 8-iso-PGF2α). The results showed that injection of oxLDL increased urinary albumin excretion by approximately 28% (310 ± 27 μg/24 h vs. 396 ± 26 μg/24 h, p = 0.0003) but had no effect on nephrin excretion. Neither PBS nor LDL had any effect on urinary albumin or nephrin excretion. Additionally, oxLDL did not affect systemic oxidative stress. In conclusion, hypercholesterolemia may adversely affect renal function through oxidatively modified LDL, which interferes with the renal handling of albumin and leads to the development of albuminuria. Full article

In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO₂ emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO₂-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO₂ emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO₂ emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO₂ emissions from coal, the three-year average change in CO₂ emissions, GDP, CO₂ emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO₂ emissions changes, contributing to the global efforts to address climate change. Full article

Expansion damage in medium–low reactivity dolomite limestone poses significant challenges in construction and engineering projects. This study investigates the potential of fly ash in inhibiting expansion damage in such limestone formations based on RILEM AAR-5 method. Through a series of laboratory experiments, various proportions of fly ash instead of cement, respectively, were prepared and subjected to varying alkali content conditions immersion tests to simulate expansion conditions. The expansion rates and extents were monitored and compared between pure limestone samples and those mixed with different proportions of fly ash. Additionally, scanning electron microscopy (SEM) analysis was employed to investigate the microstructure of the dolomite limestone–fly ash mixtures to understand the inhibition mechanisms. Results indicate that fly ash demonstrates promising inhibitory effects on expansion damage in medium–low reactivity dolomite limestone across the addition of 40% fly ash and alkali content of 0.70%. The reaction products are calcite, brucite, and a mixture of Mg-Si-Al phases and the reaction area is within 100 μm from the boundary when the cement alkali content is 1.50% without any fly ash. However, no reaction products were found at the boundary after adding 40% fly ash when lowering the cement alkali content to 0.70%. This research contributes to a better understanding of the interaction between fly ash and dolomite limestone in inhibiting expansion damage, providing valuable insights for engineering applications. Full article

Baseflow is the part of streamflow that is mainly replenished by groundwater. The protection of the biological environment and the growth of its water resources greatly depend on the spatial and temporal evolution of baseflow. Therefore, the Baizhiao (BZA) and Shaduan (SD) catchments of the Jiaojiang River Basin (JRB) in the Zhejiang province of China were selected as study areas. The ABCD model and Eckhardt method were used to calculate baseflow and baseflow index (BFI). The temporal and spatial evolution patterns of baseflow were analyzed through statistical analysis and the Mann–Kendall test. The results showed that the ABCD model performs well in simulating overall hydrological processes on the monthly streamflow at BAZ and SD stations with NSE (Nash–Sutcliffe Efficiency) values of 0.82 and 0.83 and Pbias (Percentage Bias) values of 9.2% and 8.61%, respectively. The spatial–temporal distribution of the BFI indicates the higher baseflow contribution in upstream areas compared to downstream areas at both stations. The baseflow and BFI had significant upward trends at the BZA and SD stations in the dry season, while their trends were not uniform during the wet period. These findings are essential guidance for water resource management in the JRB regions. Full article

A thermodynamic constitutive damage model for plain concrete, and other quasi-brittle aging materials, under creep relaxation is developed. The model accounts for the anisotropic damage induced through a second-order tensor damage variable. The aging viscoelasticity of the material is considered through the theory of solidification for aging solidifying materials. The material is considered a viscoelastic-damageable material. The Helmholtz free energy, utilized in the formulation, is treated based on the representation theorem of coupled damage strain tensors and Volterra integral equations. The model can analyze time-dependent damage (tertiary creep) under constant loading and can account for damage due to cyclic creep. Theoretical case studies are considered to illustrate the applicability of the model. The determination of the functions and constants, representing the material behavior, as well as any experimental companion is proposed for further research. Full article

Background and Objective: Excellent generalizability is the precondition for the widespread practical implementation of machine learning models. In our previous study, we developed the schizophrenia classification model (SZ classifier) to identify potential schizophrenia patients in the Japanese population. The SZ classifier has exhibited impressive performance during internal validation. However, ensuring the robustness and generalizability of the SZ classifier requires external validation across independent sample sets. In this study, we aimed to present an external validation of the SZ classifier using outpatient data. Methods: The SZ classifier was trained by using online survey data, which incorporate demographic, health-related, and social comorbidity features. External validation was conducted using an outpatient sample set which is independent from the sample set during the model development phase. The model performance was assessed based on the sensitivity and misclassification rates for schizophrenia, bipolar disorder, and major depression patients. Results: The SZ classifier demonstrated a sensitivity of 0.75 when applied to schizophrenia patients. The misclassification rates were 59% and 55% for bipolar disorder and major depression patients, respectively. Conclusions: The SZ classifier currently encounters challenges in accurately determining the presence or absence of schizophrenia at the individual level. Prior to widespread practical implementation, enhancements are necessary to bolster the accuracy and diminish the misclassification rates. Despite the current limitations of the model, such as poor specificity for certain psychiatric disorders, there is potential for improvement if including multiple types of psychiatric disorders during model development. Full article

Over 120 small-molecule kinase inhibitors (SMKIs) have been approved worldwide for treating various diseases, with nearly 70 FDA approvals specifically for cancer treatment, focusing on targets like the epidermal growth factor receptor (EGFR) family. Kinase-targeted strategies encompass monoclonal antibodies and their derivatives, such as nanobodies and peptides, along with innovative approaches like the use of kinase degraders and protein kinase interaction inhibitors, which have recently demonstrated clinical progress and potential in overcoming resistance. Nevertheless, kinase-targeted strategies encounter significant hurdles, including drug resistance, which greatly impacts the clinical benefits for cancer patients, as well as concerning toxicity when combined with immunotherapy, which restricts the full utilization of current treatment modalities. Despite these challenges, the development of kinase inhibitors remains highly promising. The extensively studied tyrosine kinase family has 70% of its targets in various stages of development, while 30% of the kinase family remains inadequately explored. Computational technologies play a vital role in accelerating the development of novel kinase inhibitors and repurposing existing drugs. Recent FDA-approved SMKIs underscore the importance of blood–brain barrier permeability for long-term patient benefits. This review provides a comprehensive summary of recent FDA-approved SMKIs based on their mechanisms of action and targets. We summarize the latest developments in potential new targets and explore emerging kinase inhibition strategies from a clinical perspective. Lastly, we outline current obstacles and future prospects in kinase inhibition. Full article

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R² value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland. Full article

The rapid growth of the digital economy has heightened concerns over its environmental impacts, particularly in terms of carbon dioxide emissions. In contrast to previous studies that focus on the positive effects of digital technology on reducing carbon emissions, this paper provides a detailed analysis of the various factors that influence digital economy carbon emissions and their interrelationships, using the system dynamics method to simulate and predict China’s future digital economy carbon emission baseline from 2016 to 2046. Four different scenarios were established by adjusting parameters for the percentage of the digital economy, e-waste growth rate, and data center power consumption. The simulation results indicate the following: (1) The baseline scenario shows China’s digital economy carbon emissions peaking at 1.9045 billion tons in 2041 after an initial increase and subsequent decrease. (2) Single-policy simulations indicate that changing the digital economy’s scale and e-waste growth independently leads to peak emissions of 1.9205 billion tons and 1.5525 billion tons, respectively. Adjusting data center power consumption has a greater impact, increasing the peak emissions to 2.1675 billion tons, a 13.82% rise from the baseline. (3) Under the comprehensive regulation scenario, emissions peak in 2040 at 2.0813 billion tons, considering the interactions between the digital economy, data center power, and e-waste. Based on the findings, we recommend fostering innovation in the digital industry, enhancing the e-waste treatment process, strategically developing digital infrastructure, and exploring effective carbon reduction strategies for the digital economy aimed at supporting China in achieving its dual-carbon goals. Full article

Background: C-type lectins play a crucial role as pathogen-recognition receptors for the dengue virus, which is responsible for causing both dengue fever (DF) and dengue hemorrhagic fever (DHF). DHF is a serious illness caused by the dengue virus, which exists in four different serotypes: DEN-1, DEN-2, DEN-3, and DEN-4. We conducted a genetic association study, during a significant DEN-2 outbreak in southern Taiwan, to explore how variations in the neck-region length of L-SIGN (also known as CD209L, CD299, or CLEC4M) impact the severity of dengue infection. Methods: PCR genotyping was utilized to identify polymorphisms in variable-number tandem repeats. We constructed L-SIGN variants containing either 7- or 9-tandem repeats and transfected these constructs into K562 and U937 cells, and cytokine and chemokine levels were evaluated using enzyme-linked immunosorbent assays (ELISAs) following DEN-2 virus infection. Results: The L-SIGN allele 9 was observed to correlate with a heightened risk of developing DHF. Subsequent results revealed that the 9-tandem repeat was linked to elevated viral load alongside predominant T-helper 2 (Th2) cell responses (IL-4 and IL-10) in K562 and U937 cells. Transfecting K562 cells in vitro with L-SIGN variants containing 7- and 9-tandem repeats confirmed that the 9-tandem repeat transfectants facilitated a higher dengue viral load accompanied by increased cytokine production (MCP-1, IL-6, and IL-8). Conclusion: Considering the higher prevalence of DHF and an increased frequency of the L-SIGN neck’s 9-tandem repeat in the Taiwanese population, individuals with the 9-tandem repeat may necessitate more stringent protection against mosquito bites during dengue outbreaks in Taiwan. Full article

This work proposes an approach to the formation of receptor elements for the rapid diagnosis of the state of surface waters according to two indicators: the biochemical oxygen demand (BOD) index and toxicity. Associations among microorganisms based on the bacteria P. yeei and yeast S. cerevisiae, as well as associations of the yeasts O. polymorpha and B. adeninivorans, were formed to evaluate these indicators, respectively. The use of nanocomposite electrically conductive materials based on carbon nanotubes, biocompatible natural polymers—chitosan and bovine serum albumin cross-linked with ferrocenecarboxaldehyde, neutral red, safranin, and phenosafranin—has made it possible to expand the analytical capabilities of receptor systems. Redox polymers were studied by IR spectroscopy and Raman spectroscopy, the contents of electroactive components were determined by atomic absorption spectroscopy, and electrochemical properties were studied by electrochemical impedance and cyclic voltammetry methods. Based on the proposed kinetic approach to modeling individual stages of bioelectrochemical processes, the chitosan–neutral red/CNT composite was chosen to immobilize the yeast association between O. polymorpha (k_s = 370 ± 20 L/g × s) and B. adeninivorans (320 ± 30 L/g × s), and a bovine serum albumin (BSA)–neutral composite was chosen to immobilize the association between the yeast S. cerevisiae (k_s = 130 ± 10 L/g × s) and the bacteria P. yeei red/CNT (170 ± 30 L/g × s). After optimizing the composition of the receptor systems, it was shown that the use of nanocomposite materials together with associations among microorganisms makes it possible to determine BOD with high sensitivity (with a lower limit of 0.6 mg/dm³) and detect the presence of a wide range of toxicants of both organic and inorganic origin. Both receptor elements were tested on water samples, showing a high correlation between the results of biosensor analysis of BOD and toxicity and the results of standard analytical methods. The results obtained show broad prospects for creating sensitive and portable bioelectrochemical sensors for the early warning of environmentally hazardous situations based on associations among microorganisms and nanocomposite materials. Full article

Two new species from China, Hymenogaster pseudoniveus and H. zunhuaensis, are described and illustrated based on morphological and molecular evidence. Hymenogaster pseudoniveus is distinguished from other species of the genus based on the color of peridium, which is snow white to white when unexcavated but then turns yellowish to earth yellow, and the basidiospores, which are ornamented with nearly longitudinally arranged ridges up to 2 μm high. H. zunhuaensis is diagnosed by its dirty white to pale yellow peridium, yellow brown to brown gleba, and the smaller (L_m × W_m = 11.7 μm × 9.8 μm) broadly ellipsoidal to subglobose basidiospores (Q = 1.1–1.3). ITS/LSU-based phylogenetic analysis supports the erection of the two new species, each placed in distinct clades with strong statistical support, suggesting that they represent two distinct species novel to science. Based on the morphological and molecular evidence, we have published two new species of Hymenogaster. A key for Hymenogaster species from China is provided. Full article

Based on the decorrelation calculation of diffusion ultrasound in time-frequency domain, this paper discusses the repeatability and potential significance of Disturbance Sensitive Zone (DSZ) in time-frequency domain. The experimental study of Barely Visible Impact Damage (BVID) on Carbon Fiber Reinforced Polymer (CFRP) is carried out. The decorrelation coefficients of time, frequency, and time-frequency domains and DSZ are calculated and compared. It has been observed that the sensitivity of the scattered wave disturbance caused by impact damage is non-uniformly distributed in both the time and frequency domains. This is evident from the non-uniform distribution of the decorrelation coefficient in time-domain and frequency-domain decorrelation calculations. Further, the decorrelation calculation in the time-frequency domain can show the distribution of the sensitivity of the scattered wave disturbance in the time domain and frequency domain. The decorrelation coefficients in time, frequency, and time-frequency domains increase monotonically with the number of impacts. In addition, in the time-frequency domain decorrelation calculation results, stable and repetitive DSZ are observed, which means that the specific frequency component of the scattered wave is extremely sensitive to the damage evolution of the impact region at a specific time. Finally, the DSZ obtained from the first 15 impacts is used to improve the decorrelation calculation in the 16-th to 20-th impact. The results show that the increment rate of the improved decorrelation coefficient is 10.22%. This study reveals that the diffusion ultrasonic decorrelation calculation improved by DSZ makes it feasible to evaluate early-stage damage caused by BVID. Full article

This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions. Full article

Dissolved gases in the aquatic environment are critical to understanding the population of aquatic organisms and the ocean. Currently, laser absorption techniques based on membrane separation technology have made great strides in dissolved gas detection. However, the prolonged water–gas separation time of permeable membranes remains a key obstacle to the efficiency of dissolved gas analysis. To mitigate these limitations, we demonstrated direct measurement of dissolved gas using the evanescent-wave absorption spectroscopy of a tapered silica micro-fiber. It enhanced the analysis efficiency of dissolved gases without water–gas separation or sample preparation. The feasibility of this sensor for direct measurement of dissolved gases was verified by taking the detection of dissolved ammonia as an example. With a sensing length of 5 mm and a consumption of ~50 µL, this sensor achieves a system response time of ~11 min and a minimum detection limit (MDL) of 0.015%. Possible strategies are discussed for further performance improvement in in-situ applications requiring fast and highly sensitive dissolved gas sensing. Full article

Cold plasma (CP) is an ionised gas containing excited molecules and ions, radicals, and free electrons, and which emits electric fields and UV radiation. CP is potently antimicrobial, and can be applied safely to biological tissue, birthing the field of plasma medicine. Reactive oxygen and nitrogen species (RONS) produced by CP affect biological processes directly or indirectly via the modification of cellular lipids, proteins, DNA, and intracellular signalling pathways. CP can be applied at lower levels for oxidative eustress to activate cell proliferation, motility, migration, and antioxidant production in normal cells, mainly potentiated by the unfolded protein response, the nuclear factor-erythroid factor 2-related factor 2 (Nrf2)-activated antioxidant response element, and the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathway, which also activates nuclear factor-kappa B (NFκB). At higher CP exposures, inactivation, apoptosis, and autophagy of malignant cells can occur via the degradation of the PI3K/Akt and mitogen-activated protein kinase (MAPK)-dependent and -independent activation of the master tumour suppressor p53, leading to caspase-mediated cell death. These opposing responses validate a hormesis approach to plasma medicine. Clinical applications of CP are becoming increasingly realised in wound healing, while clinical effectiveness in tumours is currently coming to light. This review will outline advances in plasma medicine and compare the main redox and intracellular signalling responses to CP in wound healing and cancer. Full article

In this innovative research, we aim to reveal pyrazole-based Schiff bases as new multi-target agents. In this context, we re-synthesized three sets of pyrazole-based Schiff bases, 5a–f, 6a–f, and 7a–f, to evaluate their biological applications. The data from in vitro biological assays (including antioxidant and scavenging activities, anti-diabetes, anti-Alzheimer’s, and anti-inflammatory properties) of the pyrazole-based Schiff bases 5a–f, 6a–f, and 7a–f showed that the six pyrazole-based Schiff bases 5a, 5d, 5e, 5f, 7a, and 7f possess the highest biological properties among the compounds evaluated. The cytotoxicity against lung (A549) and colon (Caco-2) human cancer types, as well as normal lung (WI-38) cell lines, was evaluated. The data from the cytotoxicity investigation demonstrated that the three Schiff bases 5d, 5e, and 7a are active against lung (A549) cells, while the two Schiff bases 5e and 7a exhibited the highest cytotoxicity towards colon (Caco-2) cells. Additionally, the enzymatic activities against caspase-3 and Bcl-2 of the six pyrazole-based Schiff bases 5a, 5d, 5e, 5f, 7a, and 7f were evaluated. Furthermore, we assessed the in silico absorption, distribution, metabolism, and toxicity (ADMT) properties of the more potent pyrazole-based Schiff bases. After modifying the structures of the six pyrazole-based Schiff bases, we plan to further extend the studies in the future. Full article

Despite the use of advanced treatment techniques, coronary artery disease (CAD) still remains the main cause of left ventricular (LV) dysfunction and heart failure. Participation in cardiac rehabilitation (CR) programs can lead to a number of beneficial effects, but some patients do not demonstrate the expected improvement. The aim of this study is to evaluate the impact of CR on changes in exercise capacity with regard to the presence of LV dysfunction. Methods: A group of 428 patients with CAD were consecutively admitted to an outpatient comprehensive cardiac rehabilitation program comprising 24 exercise sessions of interval training on cycle ergometers, three times a week for 45 min, and a health education. The patients were compared in two subgroups, i.e., with LV systolic dysfunction (LVEF < 50%, n = 175) and LVEF ≥ 50% (n = 253). Results: In the LVEF < 50% group, the exercise capacity improved by 1 ± 0.78 MET (median 1.15 MET), and 0.86 ± 0.77 MET (median 1.08 MET) in the LVEF ≥ 50% group. Women with LVEF < 50% demonstrated a significant increase in exercise capacity by 1.2 MET, while those with LVEF ≥ 50% did not display any such increase. All men, regardless of LVEF, exhibited a similar improvement in exercise capacity greater than 1 MET. Conclusions: An outpatient eight-week cardiac rehabilitation program based on 45 min aerobic interval training sessions three times a week appears less effective for women with CAD and EF ≥ 50%. In this group, the proposed training intervention is insufficient in improving exercise capacity to an extent that could indicate a reduction in mortality risk. Full article

Prediction of the antibacterial activity of new chemical compounds is an important task, due to the growing problem of bacterial drug resistance. Generalized linear models (GLMs) were created using 85 amidrazone derivatives based on the results of antimicrobial activity tests, determined as the minimum inhibitory concentration (MIC) against Gram-positive bacteria: Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Nocardia corallina, and Mycobacterium smegmatis. For the analysis of compounds characterized by experimentally measured MIC values, we included physicochemical properties (e.g., molecular weight, number of hydrogen donors and acceptors, topological polar surface area, compound percentages of carbon, nitrogen, and oxygen, melting points, and lipophilicity) as potential predictors. The presence of R1 and R2 substituents, as well as interactions between melting temperature and R1 or R2 substituents, were also considered. The set of potential predictors also included possible biological effects (e.g., antibacterial, antituberculotic) of tested compounds calculated with the PASS (Prediction of Activity Spectra for Substances) program. Using GLMs with least absolute shrinkage and selection (LASSO), least-angle regression, and stepwise selection, statistically significant models with the optimal value of the adjusted determination coefficient and of seven fit criteria were chosen, e.g., Akaike’s information criterion. The most often selected variables were as follows: molecular weight, PASS_antieczematic, PASS_anti-inflam, squared melting temperature, PASS_antitumor, and experimental lipophilicity. Additionally, relevant to the bacterial strain, the interactions between melting temperature and R1 or R2 substituents were selected, indicating that the relationship between MIC and melting temperature depends on the type of R1 or R2 substituent. Full article

The World Health Organization recommends adjusting salt intake as a part of the nine global targets to reduce premature mortality from non-communicable chronic diseases as a priority and the most cost-effective intervention. In 2006, the main aim of the Croatian Action on Salt and Health was to decrease salt intake by 16% because of its critical intake and consequences on human health. We have organized educative activities to increase awareness on salt harmfulness, define food categories of prime interest, collaborate with industries and determine salt intake (24 h urine sodium excretion). It was determined that the proportion of salt in ready-to-eat baked bread should not exceed 1.4%. In the period 2014–2022, salt in semi-white bread was reduced by 14%, 22% in bakery and 25% in the largest meat industry. Awareness of the harmfulness of salt on health increased from 65.3% in 2008 to 96.9% in 2023 and salt intake was reduced by 15.9–1.8 g/day (22.8% men, 11.7% women). In the last 18 years, a significant decrease in salt intake was achieved in Croatia, awareness of its harmfulness increased, collaboration with the food industry was established and regulatory documents were launched. However, salt intake is still very high, underlying the need for continuation of efforts and even stronger activities. Full article

Cornus mas L. is a rich source of vitamin C and polyphenols. Due to their health-benefit properties, C. mas L. extracts have been used in, e.g., dermatology and cosmetology, and as a food supplement. Peroxisome proliferator–activated receptor gamma (PPARγ) and its co-activator (PGC-1α) are now suspected to be the main target of active substances from C. mass extracts, especially polyphenols. Moreover, the PPARγ pathway is involved in the development of different diseases, such as type 2 diabetes mellitus (DM2), cancers, skin irritation, and inflammation. Therefore, the aim of the present study was to evaluate the PPARγ pathway activation by the most popular water and ethanol extracts from specific C. mas L. cultivars in an in vitro model of the human normal fibroblast (BJ) cell line. We analyzed the content of biologically active compounds in the extracts using the UPLC-DAD-MS technique and revealed the presence of many polyphenols, including gallic, quinic, protocatechuic, chlorogenic, and ellagic acids as well as iridoids, with loganic acid being the predominant component. In addition, the extracts contained cyanidin 3-O-galactoside, pelargonidin 3-O-glucoside, and quercetin 3-glucuronide. The water–ethanol dark red extract (DRE) showed the strongest antioxidant activity. Cytotoxicity was assessed in a normal skin cell line, and positive effects of all the extracts with concentrations ranging from 10 to 1000 µg/mL on the cells were shown. Our data show that the studied extracts activate the PPARγ/PGC-1α molecular pathway in BJ cells and, through this mechanism, initiate antioxidant response. Moreover, the activation of this molecular pathway may increase insulin sensitivity in DM2 and reduce skin irritation. Full article

In this paper, the gas–liquid two-phase flow within a Tesla valve under zero-gravity conditions is numerically studied. Based on the VOF model and the inlet two-phase separation method, the forward and reverse flow patterns and pressure drop changes in a Tesla valve at different inlet velocities were analyzed. At an inlet velocity of 0.1–0.2 m/s, the flow pattern was slug flow, the bubbles were evenly distributed in different positions in the Tesla valve, and the velocity difference between the main pipe and the arc branch pipe was small. When the inlet velocity was 0.4 m/s, the main flow pattern was annular flow, and there was a phenomenon of gas–liquid phase separation through different flow channels, which was related to centrifugal force. At an inlet velocity of 0.6–0.8 m/s, bubbly flow and slug flow coexisted, which was related to the uneven velocity. In the study range, the difference in the forward and reverse pressure drops of two-phase flow was smaller than that of single-phase flow, and the two-phase diodicity decreased first and then increased with the change in inlet velocity, reaching minimum values of 0.78 at 0.2 m/s and 1.44 at 0.8 m/s. Full article