Currently, hepatitis B virus (HBV) core antibody (anti-HBc antibody) and HBV core-related antigen (HBcrAg) are widely used as serum markers for diagnosis based on the HBV core region. While anti-HBc is very significant for clinical diagnosis, the clinical significance of quantitative assay of anti-HBc antibody has been reevaluated with improvements in diagnostic performance, including its association with clinical stage and prediction of carcinogenesis and reactivation. In addition, concerning the new HBcrAg, a high-sensitivity assay method has recently been established, and its diagnostic significance, including the prediction of reactivation, is being reevaluated. On the other hand, the quantitative level of anti-HBc antibody expressed in different units among assay systems, complicating the interpretation of the results. However, it is difficult to standardize assay systems as they vary in advantages, and caution is needed in interpreting the assay results. In this review, the clinical usefulness and remaining problems of anti-HBc antibody and HBcrAg were summarized.

In the present study, an epoxy-modified silica nano-composite coating was deposited on an aluminum substrate and ACSR conductor. For this purpose, super-hydrophobic modified silica nanoparticles based on the TEOS and MTES precursors were prepared. Then, the modified silica nanoparticles were added to the epoxy resin solution. The coating deposition on an aluminum substrate and ACSR conductors was performed by a spraying method. The structure, morphology, and chemical analysis of the nanoparticle’s surface were studied with X-ray diffractometer (XRD), Transmission electron microscopy (TEM), and Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) methods. The water contact angles on the prepared samples were measured by a water contact angle analyzer method. Icing tests were performed at a constant operating temperature of -14 °C. Also, the self-cleaning behavior of the samples was evaluated with an iron oxide powder. The results indicated that the epoxy-modified silica nano-composite coating showed hydrophobic (CA, 141°), anti-icing, and self-cleaning properties.

Crocin, a glycoside carotenoid showing several health benefits, is mainly obtained from saffron (Crocus sativus L.), whose quality and content of phytochemicals can be strongly affected by environmental conditions. Therefore, in this work, the crocin content and the in vitro antioxidant activity of saffron extracts obtained from three different varieties (Greek, Sicilian, and Iranian saffron) were assessed. Crocin content in saffron extracts was quantified by ultra-performance liquid chromatography coupled with mass spectrometry. The antioxidant activity of saffron extracts was evaluated using the oxygen radical absorbance capacity (ORAC) assay and nitric oxide (NO) radical scavenging test. The Maillard reaction was used to assess the anti-glycation activity. Although the Sicilian and Iranian saffron extracts contained higher amounts of crocin compared to the Greek one, ORAC values and % NO inhibition were higher for the Greek variety that showed a total phenolic content about two-fold greater than that of the other two extracts. Sicilian and Greek saffron had similar anti-glycation activities, while the Iranian saffron was less effective. These results suggest that the antioxidant activity of saffron extracts could be ascribed to their naturally occurring complex mixture of phytochemicals, deserving further investigations as supplement to prevent pathological conditions induced by radical species.

ntroduction: Respiratory tract Infections are the greatest cause of death worldwide. Since its outbreak in 2019, the coronavirus pandemic (COVID-19) has been a challenge to humans in terms of treatment and prevention of this respiratory viral infection. The re-emergence of respiratory viral infections like influenza, pose a serious hazard to global public health because they occur seasonally. Creating broad-spectrum antiviral compounds for rapid action in the case of an outbreak crisis, as well as bioweapon defences, is critical. Human galectin-3 is found in high concentrations in activated T lymphocytes, epithelial and endothelial cells, and fibroblasts. ProLectin -I and ProLectin -M are both galectin antagonists. Blocking viral entry with a complex polysaccharide component "ProLectin-M" has downstream effects on viral reproduction enabling it to be used as a therapeutic tool and a safe alternative to conventional anti-viral medicines. Methods: In total, 3 cell based assays have been conducted to evaluate antiviral efficacy of ProLectin–I and ProLectin-M against SARS-CoV-2 virus, Influenza-A (H1N1), and human respiratory syncytial virus (hRSV) strain A2. ProLectin-M and ProLectin-I were evaluated for their anti-viral potency using SARS-CoV2 virus strain by viral RNA Extraction and qRT-PCR assays. The plaque assays were carried out to determine the antiviral activity of the test products (ProLectin-I and ProLectin-M) against ‘Influenza-A (H1N1)-A/PR/8/34 (TC adapted)’ and ‘Human respiratory syncytial virus (hRSV) strain A2’ in a cell based in vitro setting. As eference standard, "Oseltamivir Phosphate" and "Remdesivir" were used to determine antiviral activity of test products against H1N1 and hRSV strains respectively.Results: The IC_{50 of} ProLectin-M and ProLectin-I against SARS-CoV-2 virus was found to be 6248 ng/ml (6.2µg/ml) and 4207 ng/ml (4.2μg/ml) respectively. The CC₅₀ value was similar in both the test products i.e. >100 µg/ml. Against Influenza A (H1N1) strain, ProLectin-M exhibited 95 % reduction in the viral load at the test concentration of 21.8 µg/mL with an IC₅₀ value of 5.4 µg/mL whereas, ProLectin-I did not show any cytotoxicity till 250 µg/mL concentration. Against 'human respiratory syncytial virus (hRSV) strain A2, ProLectin-M exhibited 65 % reduction in the viral load at the test concentration of 43.75 µg/mL with an IC₅₀ value of 27.41 µg/mL whereas, ProLectin- I did not show any cytotoxicity till 250 µg/mL concentration. Conclusion: Both ProLectin-I and ProLectin-M have been reported to exhibit broad-spectrum antiviral activity in ‘in-vitro’ setting. ProLectin-M reducing influenza-A virus by 95% and hRSC strain A2 by 65%. To better understand broad spectrum antiviral activity of ProLectin-I and ProLectin-M, further pre-clinical research is warranted.

Leishmaniasis is a group of neglected infectious, non-contagious diseases caused by parasites of the Leishmania genus which affects millions of people worldwide. The drugs available for its treatment, especially in cases of visceral leishmaniasis, are old, outdated, have serious side effects and in many cases are ineffective due to the emergence of resistant strains. In this context, the search for new candidates for more effective and safer drugs which can become new therapeutic alternatives is a constant need. In this work, 10 new chalcones were synthesized in high yields through Claisen-Schmidt condensation and were evaluated in vitro against Leishmania infantum promastigotes and amastigotes. CP04 and CP06 compounds were the most promising, respectively showing IC50 values = 13.64 ± 0.25 and 11.19 ± 0.22 against promastigotes, and IC50 = 18.92 ± 0.05 and 22.42 ± 0.05 against amastigotes. The compounds did not show cytotoxicity in erythrocytes, showing selectivity indexes greater than 74 and 90. Molecular docking studies carried out under the sterol 14-alpha demethylase (CYP-51) (PDB: 3L4D) and trypanothione reductase (PDB: 5EBK) enzymes from L. infantum evidenced the great affinity of the CP04 molecule for these targets, showing Moldock score values of -94.0758 and -50.5692 KJ/mol-1, with these values being much lower than the co-crystallized ligand energies. Molecular dynamics simulations demonstrated great stability of CP04 in its targets, thus indicating a real interaction possibility. Our findings confirm the potential that chalcones have as candidates for anti-leishmania drugs, and suggest that the CP04 compound may become a promising drug candidate to design and develop novel chalcone-based analogs for leishmaniosis therapy.

Lung cancer accounts for the majority of cancer cases. In recent years, checkpoint inhibitor immunotherapy (ICI) has emerged as a new treatment. A better understanding of the tumor microenvironment, or TMJ, or the immune system surrounding the tumor is needed. Cytokines are small proteins that carry messages between cells and are known to play an important role in the body's response to inflammation and infection. Cytokines are important for immunity in lung cancer. It promotes tumor growth (oncogenic cytokines) or inhibits tumor growth (anti-tumor cytokines) by controlling signaling pathways for growth, proliferation, metastasis, and apoptosis. The immune system relies heavily on cytokines. They can also be produced in the laboratory for therapeutic use. Cytokine therapy helps the immune system stop the growth or kill cancer cells. Interleukins and interferons are two types of cytokines used to treat cancer. This article begins by addressing the role of the tumor microenvironment (TMJ) and its components in lung cancer. This review also highlights the functions of various cytokines such as IL, TGF and TNF.

Seaweeds, notably cochayuyo (Durvillaea incurvata), are recognized for their rich macro- and micronutrient content, along with their inhibitory effects on the α-glucosidase enzyme. The present study aims to evaluate the effectiveness of this inhibition in actual starchy food products under in vitro gastrointestinal conditions. The study utilized freeze-dried cochayuyo, extracted using hot pressurized liquid extraction with 50% ethanol at 120 C and 1500 psi. The inhibition mechanism of α-glucosidase was determined, and the polyphenol composition of the extract was analyzed using Ultra-High-Performance Liquid Chromatography. The study further evaluated the extract’s impact on starch digestibility, total phenolic content, and antioxidant capacity in noodles as representative starchy food under gastrointestinal conditions. The results indicate that the α-glucosidase inhibition mechanism is mixed. Phenolic compounds, primarily tetraphloroethol, could contribute to this anti-enzymatic activity. The extract was observed to decrease starch digestibility, indicated by a lower rate constant (0.0158 vs. 0.0261 min-1) and digested starch at an infinite time (77.4 vs. 80.5 g/100 g). A significant increase (~1200 vs. ~390 µmol TROLOX/100 g) in antioxidant activity was also noted during digestion when the extract was used. Thus, the study suggests that the cochayuyo extract can reduce starch digestion and enhance antioxidant capacity under gastrointestinal conditions.

(1) Background: Colon cancer is one of the leading causes of cancer morbidity and mortality globally. It is a multi-step process that involves genetic and epigenetic modifications leading to histological and morphological changes. Several complementary therapeutic options have been analyzed, shedding light on plant-based medication as a potential treatment for colon cancer. Flavonoids such as quercetin are known to have anti-cancer and anti-inflammatory properties. This in vitro study examines quercetin's anti-inflammatory, anti-apoptotic, anti-angiogenesis effects and antioxidant properties in colon cancer cells. (2) Methods: The antioxidant capacity of quercetin-treated cells was investigated using biochemical assays, and angiogenesis and cytokine levels were assessed using enzyme-linked immunosorbent assay (ELISA). The epigenetic modulation and differential expression of aging, apoptotic, and proliferation genes, and Histone deacetylases (HDACs) were also investigated. (3) Results: In this study, the quercetin-treated group significantly reduced the antioxidant enzymes, cytokines, and VEGF levels, altering the expression of epigenetic factors. Quercetin also induced significant senescence in colon cancer cells. Moreover, a considerable increase was observed in the apoptotic and hTERT genes. In contrast, a decrease in p53, proliferation genes, and HDACs was observed, providing a basis for the clinical use of quercetin in cancer treatment. (4) Conclusion: In vitro studies showed that quercetin treatment efficiently induces senescence and apoptosis in colon cancer cells. We also found that quercetin effectively modulated the expression of p53, Wnt1, CTNNB1, and HDACs, indicating that it could be used to treat colon cancer.

Background: It is assumed that the prevalence of hepatitis D in HBsAg-positive individuals reaches 4.5-13% in the world, and on average about 3% in Europe. Data from several European countries, including Slovakia, are missing or are from an older period. Methods: We analyzed all available data on hepatitis D from Slovakia, including reports from the Slovak Public Health Authority and the results of one prospective study, and three smaller surveys. The determination of anti-HDV IgG and IgM antibodies and/or HDV RNA was used to detect hepatitis D. Results: In the years 2005-2022, no confirmed case of acute or chronic HDV infection was reported in Slovakia. The presented survey includes a total of 343 patients, of which 126 were asymptomatic HBsAg carriers, 33 acute hepatitis B, and 184 chronic hepatitis B cases. In a recent prospective study of 206 HBsAg-positive patients who were completely serologically and virologically examined for hepatitis B and D, only 1 anti-HDV IgG positive and no anti-HDV IgM or HDV RNA positive cases were detected. In other smaller surveys, 2 anti-HDV IgG positive patients were found without the possibility of HDV RNA confirmation. In total, only 3 of 329 HBsAg-positive patients (0.91%) tested positive for anti-HDV IgG antibodies and none of 220 tested positive for HDV RNA. Conclusion: The available data show that Slovakia is one of the countries with a very low prevalence of HDV infection reaching less than 1% in HBsAg-positive patients. Routine testing for hepatitis D is lacking in Slovakia, and therefore it is necessary to implement testing of all HBsAg-positive individuals according to international recommendations.

Background: Diabetic patients are more likely to experience morbidity and mortality as a result of microvascular complications such as retinopathy, neuropathy, nephropathy, and stroke. There are many synthetic anti-diabetic agents available which are expensive and have undesirable pathological effects. Thus, it is essential to look for cost-effective, natural, and safe antidiabetic agents. The aim of this study was to screen phytoconstituent and evaluate the in-vitro and in-silico α-amylase inhibitory activity of ethanolic extract of Adhatoda vasica leaves. Method: The extraction of Adhatoda vasica leaves was performed with ethanol through Soxhlet extraction process. Different concentrations (0.1 mg/ml to 1 mg/ml) of ethanolic extract, Acarbose, and Sitagliptin, were prepared and all concentrations were evaluated for α-amylase inhibitory activity through the spectrophotometric method. Molecular docking (AutodockVina 1.2.0) and toxicity profiling (SToPToX web server) studies were performed. Results: The plant extract showed highest inhibition of α-amylase (56.763±0.0035) at a concentration of 1 mg/ml. This inhibitory activity was supported by the in-silico study. Vasicoline (C5) and quercetin (C9), active constitute of plant Adhatoda vasica, showed the best binding energy of 8.3 and 8.0 Kcal/mol respectively with α-amylase enzyme (PDBID: 4W93). Toxicity study revealed the safety profile of plant extract. Conclusion: It was concluded that Adhatoda vasica leaves possess some bioactive compounds which are responsible for controlling blood glucose levels and its identification, purification, and isolation may lead to the development of newer therapeutic agent with lesser side effects.

Background: Epileptic patients frequently encounter cognitive impairment. Functions that are mostly affected involve memory, attention and executive function; however this is mainly dependant on the location of the epileptic activity. The aim of the study is to assess cognitive functions in MRI-negative epilepsy patients by means of neurophysiological and neuropsychological measures. Methods: The patients were enrolled from the outpatient Epilepsy/Clinical Neurophysiology clinic over a time period of 6 months. The study sample comprised 20 MRI negative epilepsy patients [mean age ± standard deviation (SD), 30.3 ± 12.56 years; age range 16-60 years; average disease duration, 13.95 years) and 10 age-matched controls (mean age ± SD, 24.22 ± 15.39 years), that were also education-matched (p>0.05) Patients with epileptogenic lesions were excluded from the study. Informed consent was obtained from all subjects involved in the study. Auditory ERPs and the cognitive screening tool EpiTrack, were administered to all subjects. Results: Latencies of P300 and slow waves were prolonged in patients compared to controls (p<0.05). ASM load and the patients’ performance in the EpiTrack maze subtest were the most significant predictors of P300 latency. A decline in memory, attention, and speed of information processing has been observed in patients with cryptogenic epilepsy compared to age-matched controls, as reflected by P300 latency and EpiTrack scores.

Accurate data on susceptibility rates against measles in general population of Greece are scarce. Many studies estimate the vaccination coverage but none has calculated the nationwide immunity rate, involving all age groups, against measles virus. The purpose of our study was to determine the immunity status to measles, especially after the latest outbreak in 2017-2018. In total, 3,972 leftover blood samples were collected from a nationwide laboratory network using a geograph-ically stratified sampling strategy and were tested for the presence of measles specific IgG anti-bodies. The overall crude seroprevalence was calculated 89.6% and the adjusted 89.8% (95% CI: 88.8% - 90.8%). There was no statistically significant difference in seropositivity among sexes (p=0.783). Higher immunity rates and antibody titer were found in older age groups ≥ 41 years old (94.9%, 95% CI: 93.7% - 95.9%, and 730.0 IU/l) in comparison to younger individuals 1-40 years old (83.4%, 95% CI: 81.6% - 85.7%, and 616.5 IU/l). ). Comparing the seroprevalence among Nomenclature of Territorial Units for Statistics (NUTS 2), a statistically significant difference was estimated among them (<0.001). The two regions where higher measles incidence was observed during the 2017-2018 outbreak, Eastern Macedonia and Thrace and Western Greece, were among the four regions with the lower seropositivity (84.6%, 95% CI: 79.9% -89.4% and 85.9%, 95% CI: 81.4% - 90.4%, respectively). Our study showed a measles immunity gap that affects younger age groups and makes a new measles outbreak likely. Enforcement of vaccination campaigns and ad-dressing vaccine hesitancy could bridge it and achieve the required target for herd immunity.

The Attentional Networks Test for Interactions and Vigilance–executive and arousal components (ANTI-Vea) is a computerized task of 32 min duration in the standard format. The task simultaneously assesses the main effects and interactions of the three attentional networks (i.e., phasic alertness, orienting, and executive control) and two dissociated components of vigilance with reasonable reliability (executive and arousal vigilance). We present this free and publicly accessible resource (ANTI-Vea-UGR; https://anti-vea.ugr.es/) developed to easily run, collect, and analyze data with the ANTI-Vea (or its subtasks measuring some attentional and/or vigilance components embedded in the ANTI-Vea). Available in six different languages, the platform allows for adaptation of stimuli timing and procedure to facilitate data collection from different populations (e.g., clinical patients, children). Collected data can be freely downloaded and easily analyzed with the provided scripts and tools, including a Shiny app. We discuss previous evidence supporting that attention and vigilance components can be assessed in typical lab conditions as well as online and outside the laboratory. We hope this tutorial will help researchers interested in measuring attention and vigilance with a tool useful to collect data from large sample sizes and easy to use in applied contexts.

Background: Pteropodine (PT) is a component of some plants with potentially useful pharmaco-logical activities for humans. This compound has biomedical properties related to the modulation of the immune system, nervous system, and inflammatory processes. This study addresses the an-ti-inflammatory and antioxidant capacity of pteropodin in a murine model of arthritis and induced edema of the mouse ear. Methods: To evaluate the anti-inflammatory activity, we used the reversed passive Arthus reaction (RPAR), which includes the rat paw edema test, the rat pleurisy test, and a mouse ear edema model. The antioxidant effect was evaluated by determining the myeloperoxidase enzyme activity. Results: PT showed an anti-inflammatory effect in the different specific and non-specific tests. We found a 52 to 74% of inhibitory effect of PT in the rat paw edema test, 52% of reduction of the pleural exudate volume, and low neutrophil count (28%), when compared to the control group. We also found a 81% of inflammatory inhibition of the ear edema, and a significant myeloperoxidase enzyme inhibition by the three doses of PT. Conclusions: PT exerted a potent an-ti-inflammatory effect in the acute inflammation model in rodents.

Background and Aim. Recently made analysis for anti-HBcor prevalence within Kazakhstan blood donors population concluded 17.2% rate. Considering these data are among the highest of infectivity results worldwide, an objective of this study was to define an association of positive Hepatitis B serology markers with risk and behavior factors. Methods. The blood donors’ samples were tested for anti-HBcore total (IgG/IgM) and anti-HBs, by CLIA on the Architect i2000SR platform (Abbott). Surrogate alanine transferase (ALT) markers for all blood donors were tested by kinetic method on the Biosystems A25 analyzer. A questionnaire was developed for socio-demographic characteristics, donors’ nutrition habits, cholesterol levels and history of smoking, alcohol consumption. Informed consent was obtained from all study participants. Statistics was calculated using the R software program (version 4.1.1, USA, 2021). Results. A group of 5709 blood donors participated in the study. Participants with positive anti-HBcore scores were on average older (41.8 vs 34.4 years, p<0.001), Kazakh (88.7% vs 83.0%, p<0.001), married (74.0% vs 55.6%, p<0.001), had a secondary education (70.1% vs. 59.4%, p=0.03), smoked (27.9% vs. 24.3%, p=0.05), had a longer smoking history (13.6±9.5 years vs. 9.8±8.5 years, p<0.001), and various hypercholestesterolaemia (6.2% vs 3.9%, p=0.02). Predominantly their main meal was dinner (17.0% vs 14.2%, p=0.03). Analysis for associations of socio-demographics characteristics, risk factors, nutrition with anti-HBs, showed that those who have secondary education level (70.1%) were more likely to be anti-HBs-positive (p=0.03). No other significant correlations for anti-HBs were observed. Conclusions. The risk factors and behavior analysis highlighted the blood donors’ overall health status. Positive Hepatitis B markers associated with statistically significant characteristics from socio-demography, nutrition habits or risk factors could support further pathogenesis studies looking for HBV treatment guides fulfilling the existing gaps.

Rapid developing antimicrobial resistance due to broad antibiotic utilisation in healthcare and food industries and the non-availability of novel antibiotics represents one of the most critical public health issues worldwide. The current advances in nanotechnology allow new materials to address drug-resistant bacterial infections in specific, focused and biologically safe ways. The unique physicochemical properties, biocompatibility, and wide range of adaptability of nanomaterials that exhibit photothermal capability can be employed to develop the next generation of photothermally induced controllable hyperthermia as antibacterial nanoplatforms. Here, we review the current state-of-the-art in different functional classes of photothermal antibacterial nanomaterials and the strategies to optimise antimicrobial efficiency. The recent achievements and trends in developing photothermally active nanostructures, including plasmonic metals, semiconductor, carbon-based, and organic photothermal polymers, and antibacterial mechanisms of action, including anti-multidrug resistant bacteria and biofilms removal, will be discussed. Insights into mechanisms of the photothermal effect and various factors influencing photothermal antimicrobial performance, emphasising the structure–performance relationship, are discussed. We will examine the photothermal agents’ functionalisation for specific bacteria, effects of near-infrared light irradiation spectrum, or active photothermal materials for multimodal synergistic-based therapies to minimise side effects and maintain low cost. Most relevant applications are presented, such as anti-biofilm formation, biofilm penetration or ablation, and nanomaterial-based infected wound therapy. Practical antibacterial applications employing the photothermal antimicrobial agents, alone or in synergistic combination with other nanomaterials, are considered. Existing challenges and limitations in photothermal antimicrobial therapy and future perspectives are presented from the structure, function, safety, and clinical potential points of view.

The search for growth and sustainability in any organization largely depends on its ability to carry out digital transformation and agility processes. These processes are essential to adapt to an increasingly changing and competitive business environment, allowing companies to be more efficient and effective in creating an unbreakable bond with their customers. This manuscript addresses the importance of detecting and eliminating anti-patterns in organizational culture for a successful digital and agile transformation. During the early stages of the transformation process, prejudices and paradigms rooted in the organization can become an obstacle to change. The goal of this writing is to offer a practical guide for leaders and teams in organizations seeking successful cultural transformation. Effective strategies are proposed to overcome the challenges faced by organizations during this early stage. In summary, the key steps to be followed in the initial stages of digital transformation with agility are described, including the importance of establishing strong leadership, defining a clear vision and scope of the transformational process, among others. A set of recommendations will be provided to overcome recurring anti-patterns, such as lack of stakeholder participation and commitment, lack of proper planning, and confusion of roles and responsibilities.

Antigenic drift in influenza strains allows viruses to avoid being fully suppressed by seasonal vaccines. As a result, public interest has led to increased scrutiny and reevaluation of anti-influenza antiviral drugs as possible solutions. Unfortunately, many anti-influenza drugs developed around the globe suffer from a lack of sufficient clinical trials, as well as a lack of toxicity data. This is especially true of Arbidol, a popularly used drug for the prevention and treatment of influenza strains in China and Russia. Neuraminidase inhibitors, which were developed in the United States, also fall victim to inconclusive clinical trials and adverse effects. Adamantanes, while proven to be effective in treating influenza A, are encountering rapid, widespread cross-resistance. Baloxavir marboxil, a newer anti-influenza medication, shows promise in treating acute uncomplicated influenza, and may avoid the development of resistance when coadministered with other antiviral drugs. This review explores the antivirals available for influenzas treatment at this time.

Neural electrode insertion trauma impedes the recording and stimulation capabilities of numerous diagnostic and treatment avenues. Implantation leads to the activation of inflammatory markers and cell types, which is detrimental to neural tissue health and recording capabilities. Oxidative stress and inflammation at the implant site have been shown to decrease with chronic administration of antioxidant melatonin at week 16, but its effects on the acute landscape have not been studied. To assess the effect of melatonin administration in the acute phase, specifically the first week post-implantation, we utilized histological and q-PCR methods to quantify cellular and molecular indicators of inflammation and oxidative stress as well as two-photon microscopy to track the microglial responses in real-time. Histological results indicate that melatonin effectively maintained neuron density surrounding the electrode, inhibited accumulation and activation of microglia, astrocytes, and reduced oxidative tissue damage. The expression of the pro-inflammatory cytokines, TNF-α and IL-6, were significantly reduced in melatonin-treated animals. Additionally, microglia encapsulation of the implant surface was inhibited by melatonin as compared to control animals following implantation. Our results combined with previous research suggest that melatonin is a particularly suitable drug for modulating inflammatory activity around neural electrode implants both acutely and chronically, translating to more stable and reliable interfaces.

The Kalanchoe genus is composed of more than 100 species that usually thrive in tropical environments, which have been used in folkloric medicine to treat various illnesses, including dermatological conditions. With this, the present study assesses the pharmacognostical and pharmacological properties of different species of the Kalanchoe genus as elements for a potential treatment for dermatological-related conditions, from findings of existing literature and studies. It was analyzed that the Kalanchoe pinnata plant, or one of the most common species of Kalanchoe, have been observed to have distinct morphological and microscopic characteristics. Further, it was discovered that different species of Kalanchoe have anti-inflammatory, antioxidant, antibacterial, and wound healing properties, which enable the plant to be used for dermatological products that are available to the market. With this, it is recommended that further studies be conducted in other understudied species of Kalanchoe regarding their pharmacological properties, as well as the use of other structures of the Kalanchoe plant for treatment of various dermatological conditions.

Toll-like receptors (TLRs) are a class of pattern recognition receptors (PRRs) family that identify pathogen-associated molecular patterns derived from microbes and activate immune cell response. Following TLRs ligation, different adaptor and transcription molecules such as myeloid differentiation primary response gene 88 (MyD88) and nuclear factor kappa B (NF-kB) are recruited that initiate inflammatory signaling pathways. The human Toll-like receptor 10 (hTLR10) is a novel member of the PRRs family with a regulatory function of immune responses because of unique cytoplasmic domains which lead to induction of both inflammatory and anti-inflammatory properties. Recent studies have reported the association of TLR10 polymorphisms with many inflammatory diseases and human cancer. Engagement of TLR10 on the surface of the epithelium and macrophages leads to the production of proinflammatory cytokines and chemokines, while other studies have proven an anti-inflammatory role of TLR10. Accordingly, TLR10 suppresses proinflammatory cytokine production via negative regulation of MyD88 and the Akt (protein kinase B) and MAPK (mitogen-activated protein kinase) signaling pathways. This review aimed to provide answers for these conflicting findings (Inflammatory and anti-inflammatory properties of TLR10) to further identify distinct biological functions of TLR10.

Silybum marianum (L.) Gaertn is a rich source of antioxidants and anti-inflammatory flavonolignans with great potential for use in pharmaceutical and cosmetic products. Its biotechnological production using in vitro culture system has been proposed. Chitosan is a well-known elicitor that strongly affects both secondary metabolites and biomass production by plants. The effect of chitosan on S. marianum cell suspension is not known yet. In the present study, suspension cultures of S marianum were exploited for their in vitro potency to produce bioactive flavonolignans in the presence of chitosan. Established cell suspension culture was maintained on the same hormonal media supplemented with 0.5 mg/L BAP (6-benzylaminopurine) and 1.0 mg/L NAA (α-naphthalene acetic acid) under photoperiod 16/8 h (light/dark) and exposed to various treatments of chitosan (ranging from 0.5 to 50.0 mg/L). The highest biomass production was observed for cell suspension treated with 5.0 mg/L chitosan, resulting in 123.3 g/L fresh weight (FW) and 17.7 g/L dry weight (DW) productions. Chitosan treatment resulted in an overall increase in the accumulation of flavonoids, phenolic compounds and silymarin. High accumulation levels of silybin B, silydianin and silybin A were recorded by HPLC analysis. The corresponding extracts displayed interesting antioxidant and anti-inflammatory capacities. In particular, high ABTS antioxidant activity (741.5 μM Trolox C equivalent antioxidant capacity) was recorded in extracts obtained in presence of 0.5 mg/L of chitosan. On the opposite, highest inhibitions of cyclooxygenase 2 (COX-2, 30.5 %), secretory phospholipase A2 (sPLA2, 33.9 %) and 15-lipoxygenase (15-LOX-2, 31.6 %) enzymes involved in inflammation process were measured in extracts obtained in presence of 5.0 mg/L of chitosan. Taken together, these results highlight the high potential of the chitosan elicitation of the S. marianum cell suspension for enhanced production of antioxidant and anti-inflammatory silymarin-rich extracts.

The virtually complete loss of intestinal worms, known as helminths, from Western society has resulted in elimination of a range of helminth-induced morbidities. Unfortunately, that loss has also led to inflammation-associated deficiencies in immune function, ultimately contributing to widespread pandemics of allergies, autoimmunity, and neuropsychiatric disorders. Several socio-medical studies have examined the effects of intentional reworming, or self-treatment with helminths, on a variety of inflammation-related disorders. In this study, the latest results from ongoing socio-medical studies are described. The results point toward two important factors that appear to be overlooked in some if not most clinical trials. Specifically, (a) the method of preparation of the helminth can have a profound effect on its therapeutic efficacy, and (b) variation between individuals in the effective therapeutic dosage apparently covers a 10-fold range, regardless of the helminth used. These results highlight current limits in our understanding of the biology of both hosts and helminths, and suggest that information from self-treatment may be critical in moving the field forward into mainstream medicine.

Background: The Balaruc-les-Bains’ thermal mud was found to be colonized predominantly by microorganisms, with cyanobacteria constituting the primary organism in the microbial biofilm observed on the mud surface. The success of cyanobacteria in colonizing this specific ecological niche can be explained in part by their taxa-specific adaptation capacities, and also the diversity of bioactive natural products that they synthesize. This array of components has physiological and ecological properties that may be exploited for various applications.

A new iboga-vobasine-type isomeric bisindole alkaloid named voacamine A (1), along with eight known compounds, voacangine (2), voacristine (3), coronaridine (4), tabernanthine (5), iboxygaine (6), voacamine (7), voacorine (8), and conoduramine (9), were isolated from the stem bark of Voacanga africana. The structures of the compounds were determined by comprehensive spectroscopic analyses (1D- and 2D-NMR). Compounds 1, 2, 3, 4, 6, 7 and 8 were found to inhibit the motility of both the microfilariae (Mf) and adult male worms of Onchocerca ochengi, in a dose-dependent manner, but were only moderately active on the adult female worms upon biochemical assessment at 30 μM drug concentrations. The IC50 values of the isolates are 2.49-5.49 µM for microfilariae and 3.45-17.87 µM for adult males. Homology modeling was used to generate a 3D model of the the O. ochengi thioredoxin reductase target and docking simulation attempted to offer an explanation of the anti-onchocercal structure-activity relationship (SAR) of the isolated compounds. These alkaloids are new potential leads for the development of antifilirial drugs. The results of this study validate the traditional use of V. africana in the treatment of human onchocerciasis.

Fibrosis is a condition characterized by thickening or/and scarring of various tissues. Fibrosis may develop in almost all tissues and organs, and it may be one of the leading causes of morbidity and mortality. It provokes excessive scarring that excels the usual wound healing response to trauma in numerous organs. Currently, very little can be done to prevent tissue fibrosis, and it is almost impossible to reverse it. Therefore, fibrosis is frequently associated with premature aging. In turn, aging is associated with more frequent incidences of fibrosis. Anti-inflammatory and immunosuppressive drugs are among the few treatments that may be efficient in preventing fibrosis. Numerous publications suggest that cannabinoids and extracts of Cannabis sativa have potent anti-inflammatory and anti-fibrogenic properties. In this review, we describe the types and mechanisms of fibrosis in various tissues and discuss various strategies for prevention and dealing with tissue fibrosis. We further introduce cannabinoids and their potential for the prevention and treatment of fibrosis, and therefore for extending healthy lifespan.

Many species of the Asteraceae family are used in traditional Mexican medicine for possessing healing properties. Ageratina pichinchensis (Asteraceae) is a plant used for the treatment of gastric ulcers, deep wounds and for its antifungal effects. The aim of this study was to establish a cell suspension culture of A. pichinchensis, quantify the anti-inflammatory constituents 2,3-dihydrobenzofuran and 3-epilupeol, to evaluate the anti-inflammatory potential of its extracts and perform a phytochemical analysis. Cell suspension cultures were established in MS culture medium supplemented with 30 g L-1 sucrose and 1.0 g L-1 α-naphthaleneacetic acid (NAA) plus 0.1 mg L-1 6-furfurylaminopurine (KIN). The ethyl acetate extracts of cell suspension cultures analyzed by GC revealed that the maximum production of compounds The anti-inflammatory activity of these extracts exhibited significant inhibition of NO production. Furthermore, the phytochemical study of EtOAc and MeOH extracts of the biomass on day 20 led to the identification of 17 known compounds. The structures of compounds were assigned by analysis of 1D and 2D NMR data and the remainder by GC-MS. This is the first report of the production of the (-)-Artemesinol, (-)-Artemesinol glucoside, Encecalin and 3,5-diprenyl-acetophenone compounds by a cell suspension cultures of A. pichinchensis.

Grape foods fermented with probiotics are sources of beneficial bacteria for the GI tract and also have a high antioxidant capacity. The addition of probiotics to ferment food has always been a traditional process; therefore, probiotic dairy and non-dairy products might contribute to a daily antioxidant diet to improve consumers’ life quality and health. This research was undertaken to determine the viability of 4 wild isolates of Lactobacillus for storage at 5 and 25ºC within 90 days in simulated synthetic grape media and a standard grape marmalade formulation. Changes in active culture numbers, pH level, glucose concentration, and antioxidant properties were evaluated. Most of the isolates demonstrated higher growth in the grape marmalade than the synthetic grape marmalade, which was greater than 7 Log cfu/g within 90 days of storage at 5ºC. In addition, most of the wild isolates grew beyond the critical count of 106 cfu/g in sampling between 60 and 90 days of storage. Moreover, fermented grape marmalade with probiotics showed a strong antioxidant capacity that failed to differ significantly with the synthetic medium. The study confirmed L. paraplantarum, L. plantarum, W. paramesenteroides, and E. feacalis were ideal probiotics for fermentation process of grape marmalade.

Carcinogenesis is the process whereby a normal cell is transformed into a neoplastic cell. This action involves several steps starting with initiation and followed by promotion and progression. Driving these stages are oxidative stress and inflammation, which in turn encompasses a myriad of aberrant gene expressions, both within the transforming cell population and the cells within the surrounding lesion. Chemoprevention of cancer with bioreactive foods or their extracted/purified components occurs via normalizing these inappropriate gene activities. Various foods/agents have been shown to affect different gene expressions. In this review we discuss whereby the chemoprevention activities of the red beetroot itself may disrupt carcinogenesis and the activities of the water soluble betalains extracted from the plant.

Bufadienolides-like chemicals, which mostly composed the active ingredient of Chansu, had been widely discovered to possess anti-inflammatory, tumor-suppressing and antipain activity, but the mechanisms of action were not clearly illuminated. In this research, in order to explore the potential mechanism of bufadienolides-like chemicals on breast cancer, a serious of bioinformatics analysis, included (1) differentially expressed genes identification combined with gene set variation analysis, (2) tissue specific co-expression network construction, (3) differentially regulated sub-networks detection with disease phenome, (4) hub gene selection and it’s relation to survival probability, and (5) similar small molecule detection were performed with gene expression profiles of bufadienolides-like chemicals. Results indicated bufadienolides-like chemicals had the most same target with valproic, estradiol and etc, could disturbed the pathways in RNA splicing, apoptotic process, cell migration, extracellular matrix organization, adherens junction organization, synaptic transmission, Wnt signaling, AK-STAT signaling, BMP signaling pathway and unfolded protein response, and had the potential ability to be used as anticancer, hormones and vasoprotectives agents.

Hyperkalemia burden in non-dialysis CKD under nephrology care is undefined. We prospectively followed 2443 patients with two visits (referral and control with 12-month interval) in 46 nephrology clinics. Patients were stratified in four categories of hyperkalemia (sK≥5.0 mEq/L) by sK at visit 1 and 2: Absent (no-no), Resolving (yes-no), New Onset (no-yes), Persistent (yes-yes). We assessed competing risks of ESKD and death after visit 2. Age was 65±15 y, eGFR 35±17 mL/min/1.73 m2, proteinuria 0.40 (0.14-1.21) g/24h. In the two visits sK was 4.8±0.6 and levels ≥6 mEq/L were observed in  4%. Hyperkalemia was absent in 46%, resolving 17%, new onset 15% and persistent 22%. Renin-angiotensin-system inhibitors (RASI) were prescribed in 79% patients. During 3.6-year follow-up, 567 patients reached ESKD and 349 died. Multivariable competing risk analysis [sub-hazard ratio-sHR, 95%Confidence Interval-CI] evidenced that new onset [sHR 1.34, 95%CI 1.05-1.72] and persistent [sHR 1.27, 95%CI 1.02-1.58] hyperkalemia predicted higher ESKD risk versus absent, independently from main determinants of outcome including eGFR change. Conversely, no effect on mortality was observed. Results were confirmed by testing sK as continuous variable. Therefore, in CKD under nephrology care, mild-to-moderate hyperkalemia status is common (37%) and predicts per se higher ESKD risk but not mortality.

Neovascular age-related macular degeneration (nAMD) accounts for one of the leading causes of blindness among the aging population. The current treatment options for nAMD include intravitreal injections of anti-vascular endothelial growth factor (anti-VEGF). However, standardised frequent administration of anti-VEGF injections only improves the vision in approximately 30%-40% of nAMD patients. Current therapies targeting nAMD pose a significant risk of retinal fibrosis and geographic atrophy (GA) development in nAMD patients. A need exists to develop new therapies to treat nAMD with effective and long-term anti-angiogenic effects. Recent research on nAMD has discovered novel therapeutic targets and angiogenic signalling mechanisms involved in its pathogenesis. For example, tissue factor, human intravenous immune globulin, interferon-β signalling, cyclooxygenase-2 (COX-2) and cytochrome P450 monooxygenase lipid metabolites have been identified as key players in the development of angiogenesis in AMD disease models. Furthermore, novel therapies such as NLRP3 inflammasome inhibition, targeted intraceptor nanoparticle therapy, inhibitors of integrins and tissue factor are currently being tested at the level of clinical trials to treat nAMD. The aim of this review is to discuss the scope for alternative therapies proposed to anti-VEGFs for the treatment of nAMD.

Percutaneous coronary intervention(PCI) with stenting for the treatment of acute coronary syndrome(ACS) is the contemporary standard of care. Such treatment is followed by Dual anti-platelet therapy(DAPT) comprising of aspirin and a P2Y12 inhibitor. The efficacy of this therapy has been well established but the optimal duration of DAPT remains elusive, and has thus far attracted a prodigious deal of scientific attention. Decision regarding DAPT duration can be challenging clinically in the modern era with the evolution of newer stents, more potent antiplatelet agents and novel anticoagulant drugs in addition to an older patient population with multiple comorbidities. Major societal guidelines have emphasized comprehensive assessment of ischemic and bleeding risk, in turn recommending individualization of DAPT duration, thus encouraging "shared decision making". The following review is aimed at critically evaluating the available evidence to help make these crucial clinical decisions regarding duration of DAPT and triple therapy.

Two new dimacrolide sesquiterpene pyridine alkaloids (DMSPAs), dimacroregelines A (1) and B (2), were isolated from the stems of Tripterygium regelii. The structures of both compounds were characterized by extensive 1D and 2D NMR spectroscopic analyses, as well as HRESIMS data. Compounds 1 and 2 are two rare DMSPAs possessing unique 2-(3′-carboxybutyl)-3-furanoic acid units forming the second macrocyclic ring, representing the first example of DMSPAs bearing an extra furan ring in their second macrocyclic ring system. Compound 2 showed inhibitory effects on the proliferation of human rheumatoid arthritis synovial fibroblast cell (MH7A) at a concentration of 20 μM.

UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.

Whether the different NR-AMPs could ever be utilized as drugs not only against prokaryotic (bacteria) pathogens but eukaryotic (fungal pathogens, and parasitic protists) depends on the side effects. To get experimental experience about the option of applying EPB-produced antimicrobials to pathogens, and parasites of veterinary significance, we present here the results of an in vitro, and an accompanying in vivo study on chicken. In the in vitro study, we tested the cytotoxic potential of the cell-free conditioned culture media (CFCM) of three entomopathogenic bacterium species, - X. budapestensis, DSM16342 (EMA); X. szentirmaii DSM16338 (EMC); Photorhabdus luminescens ssp. akhurstii TT01 - on chicken tissue culture cells, namely, on the Leghorn Male Hepatoma (LMH), [92] cells, (a permanent confluent hepato-carcinoma cell line). Each CFCM proved rather cytotoxic in this test. In the in vivo study, we fed freshly hatched male broiler chickens for 42 days with XENOFOOD [39] which contained autoclaved cultures of EMA, and EMC). These bacteria were grown on standard chicken (starter and grower) [HM3] [u4] feed, and the whole culture was used as a “food supplement”. [HM5]. It had been known that these EPB species cannot grow that is, not viable) atbody temperature (above 33 C).

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

Hashimoto's thyroiditis (HT) is essentially featured by self-tissue destruction by means of the adaptive immune responses that evade immune regulation. Vitamin D carries out an immunomodulatory role that appears to promote immune tolerance. The aim of this study is to elaborate a narrative review about the relationship between vitamin D status and HT and the role of vitamin D supplementation to reduce HT risk by modulating the immune system. There is an extensive literature confirming that vitamin D levels are significantly lower in HT patients compared to healthy people. On the other hand, after the supplementation with cholecalciferol in patients with HT and vitamin D deficiency, thyroid autoantibody titer decreased significantly, However, there are no conclusive results on the effect of vitamin D supplementation on the thyroid function. The confirmation of the beneficial effects of vitamin D in the prevention and treatment of autoimmune thyroid diseases require additional randomized, double-blind, placebo-controlled trials with longer follow-up.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

High-power laser facilities necessitate predicting incremental damage to final optics to identify evolving damage trends. In this study, we propose a surface damage detection method utilizing image segmentation employing ResNet-18, and a damage area estimation network employing U-Net++. Paired sets of online and offline images of optics obtained from a large laser facility is used to train the network. The trends of varying damage could be identified by incorporating additional experimental parameters. A key advantage of the proposed method is that the network can be trained end-to-end on small samples, eliminating the need for manual labeling or feature extraction. The software developed based on these models can facilitate the daily inspection and maintenance of optics in large laser facilities.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

"Deep fake" technologies are often associated with face-swapping algorithms based on images or videos. Although much research is focused on developing these techniques, there is still room for improvement, especially in human and visual assessment. In this study, we present an innovative approach to the problem of face replacement based on one model while preserving background features. Our model uses the Adaptive Attentional Denormalization method from FaceShifter while integrating identity features from the ArcFace and BiSeNet models to support the attribute extraction process. We used Fast GAN as a generative model to optimize and accelerate the training process on smaller data sets. Our research proves the effectiveness of the proposed approach by combining innovative methods and architectures in one coherent solution.

Traditional image steganography conceals secret messages into unprocessed natural images by modifying the pixel value, causing the obtained stego different from the original image in terms of statistical distribution, thereby could be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial Intelligence Generated Content (AIGC) becoming popular in social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image style transformation neural network with steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder-decoder model, and finally generates a styled image containing the secret messages at the same time. An adversarial training technique is applied to enhance the imperceptibility of the generated art-styled stego image with from plain style-transferred images. The lack of the original cover image makes it difficult for the opponent learning a steganalyzer to identify the stego. The recommended approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of 3 bits per pixel for a color image, according to experimental results.

The increased number of buildings in urban areas limits the creation of vegetated areas, leading to the search for alternatives to create spaces to promote contact with nature. In this context, green roofs have been increasingly studied. These structures have specific microclimatic condi-tions requiring an accurate study of the most appropriate vegetation to use. This study aims to analyze the long-term viability of vegetation installed on an experimental green roof open-air lab. This analysis was done through images obtained from photographic records and later in-serted into the ImageJ program, to identify species and evaluate the area covered by vegetation. Only a few of the species that were planted in the test beds over the years have persisted to the present, while other species have spontaneously appeared. Also, surveys were used to learn about people's preferences for the vegetation on these test beds. These showed that people favor recognizable plants with plenty of vibrantly colored blossoms. It was feasible to choose the best plants for green roofs in the studied conditions as a result of the analysis, taking into account the ground cover percentage by vegetation, its persistence, and the preferences of the respondents.

Cervical cancer remains a major public health problem. Despite the screening programs, cervical cancer is the second most commonly diagnosed cancer. Most of the cases occur in less developed countries, and it is the third leading cause of cancer death among females. In 2000–2007, the five-year relative survival rate for European women diagnosed with cervical cancer was 62%.[1] Standard treatment for locally advanced cervical cancer reveals external beam radiotherapy (EBRT) with concurrent chemotherapy followed by image-guided adaptive brachytherapy (IGABT) boost [2, 3]. In the last years, the Gynaecological (GYN) working group composed of Group Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) has developed new recommendations for the treatment of cervical cancer based on IGABT. The treatment plan includes 3-dimensional (3D) imaging based on magnetic resonance imaging (MRI) or computed tomography (CT), adaptive to the target volume and organ at risk [4–6]. The brachytherapy insertion is made under anaesthesia. At the time of the application insertion, the ultrasound is used to guide the dilatation of the uterine canal and assess the depth of the uterine cavity. In this paper, we review the data available in international literature about the use of ultrasound (US) in brachytherapy treatment of cervical cancer.

Machine learning models facilitate the search for non-linear relationships when modeling hydrological processes, but they are equally effective for automation at the data preparation stage. The tasks whose automation was analyzed consisted in estimating changes in the roughness of a mountain streambed and the extent of floods from images. The Segment Anything Model (SAM) developed this year by Meta was used for this purpose. Images from many years from the Wielka Puszcza mountain stream located in the Polish Carpathians were used as the only input data. The model was not additionally trained for the described tasks. The model can be run in several modes, but the two most appropriate were used in this study. The first one is available in the form of a web application, while the second one is available in the form of a Jupyter notebook run in the Google Colab environment. Both methods do not require specialized knowledge and can be used by virtually any hydrologist. In the roughness estimation task, the average Intersection over Union (IoU) ranges from 0.55 for grass to 0.82 for shrubs/trees. Ultimately, it was possible to estimate the roughness coefficient of the mountain streambed between 0.027 and 0.059 based solely on image data. In the task of estimation of the flood extent, when selecting appropriate images, one can expect IoU at the level of at least 0.94, which seems to be an excellent result considering that SAM is a general-purpose segmentation model. It can therefore be concluded that SAM model can be a useful tool for a hydrologist.

Body image disturbance (BID) involves negative attitudes towards shape and weight and is associated with lower levels of interoception (awareness of and attention toward internal physiological states). This association is considered a risk factor for developing eating disorders (EDs) and is linked to altered sensorimotor representations of the body (i.e., body schema). BIDs manifest across genders and are currently understudied in men. This study investigated gender-related differences in BID and its relationship to the body schema and interoception. Data were collected from 86 men and women. BID was assessed using questionnaires measuring self-objectification, state, and trait body dissatisfaction. Interoceptive sensibility was measured via the MAIA-2. The body schema was indexed via an embodied mental rotation task. Results showed that women reported higher BID than men across all scales. Gender differences in sub-components of interoceptive sensibility were found. Overall, both gender and interoceptive sensibility predicted BID. However, interoceptive sensibility exhibited its own unique association with BID beyond the influence of gender. BID, IS and gender were not significant predictors of performance in the body schema task. Therefore, while gender predicts differences in BID and interoceptive sensibility, there was no evidence of gender-related differences in body schema.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

Background: Computed tomography examinations have produced high radiation doses to patients, especially the CT brain. This study aimed to optimize the radiation dose and image quality in adult CT brain protocol. Materials and Methods: Images were acquired by Catphan 700 phantom. Radiation doses were recorded as CTDIvol and dose length product (DLP). CT brain protocols were optimized by varying parameters such as kVp, mAs, signal-to-noise ratio (SNR) level, and Clearview iterative reconstruction (IR). Image quality were also evaluataion by The AutoQA Plus software. Results: CT Number accuracy and linearity had a robust positive correlation with the linear attenuation coefficient (µ) and showed more inaccurate CT numbers when using 80 kVp. MTF showed a higher value in 100 and 120 kVp protocols, while high contrast spatial resolution showed a higher value in 80 and 100 kVp protocols. Low contrast detectability and CNR tended to increase when using high mAs, SNR and Clearview IR protocol. Noise decreased when using a high radiation dose and a high percentage of Clearview IR. CTDIvol and DLP were increased with increasing kVp, mAs, and SNR levels, while the increasing percentage of Clearview did not affect the radiation dose. Conclusion: Optimized protocols, including radiation dose and image quality, should be evaluated to preserve diagnostic capability. The recommended parameter settings include kVp set between 100-120 kVp, mAs ranging from 200-300 mAs, SNR level within the range of 0.7-1.0, and an iterative reconstruction value of 30% Clearview to 60% or higher.

Background: Assessing body image and personalized rehabilitation in lower limb amputation is essential to measure impact on an individual’s quality of life and psychosocial wellbeing. The aim of this study was to develop a Spanish version of the Amputee Body Image Scale (ABIS-E) for Spanish-speaking lower limb amputee. Methods: A cross-sectional study was conducted including amputee patients in local in Madrid, Spain. Clinical outcomes about body image (Amputee Body Image Scale, ABIS), health-related quality of life (EuroQol-5D-5L), depression and anxiety (Hospital Anxiety and Depression Scale, HADS), pain (Brief Pain Inventory-Short Form, BPI-SF) and functional capacity (Prosthesis Evaluation Questionnaire, PEQ) were collected. Results: Seventy-three participants were assessed. Excellent test-retest reliability (ICC=0.847) and good internal consistency (Cronbach's alpha=0.753) were obtained. Three factors were extracted in the factor analyses. Convergent validity with Pearson's and Spearman's correlation coefficients were calculated for the depression and anxiety questionnaires (HADS) (values between .57 and .67), functional capacity (PEQ) (values between -.35 and .71) and quality of life (EQ-5D-5L) (values between -.37 and .61). Conclusions: The ABIS-E is a reliable and valid tool for measuring body image in the Spanish population with a lower limb amputation, potentially useful in primary care to identify psychosocial problems.

Camera malfunctions or loss of storage elements in imaging devices may lead to loss of important image information or random impulse noise interference. Low rank is one of the important prior information in image optimization processing. This paper uses different low-rank constraint models of the image matrices to recover the impulse interference satellite images. Firstly, an overview of image inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F-norm is presented, and corresponding optimiza-tion iterative algorithms are provided. Then, we conducted experiments under three types of pulse interference and provided visual and numerical results. Finally, it is concluded that the WSVT_ADMM method based on the weighted nuclear norm can obtain the best image inpainting results; The UV_ADMM method based on F norm of matrix factorization has the least computa-tion time and can be used for large-scale low-rank matrix computation; The WSVT_ADMM method based on weighted nuclear norm and the TSVT_ ADMM method based on truncated nuclear norms can significantly improve the repair effect compared to the nuclear norm-based methods such as SVT, SVP, and n_ADMM.

Image encryption based on chaotic maps is an important way to ensure the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed by employing a compound chaotic map and random cyclic shift. Firstly, an improved Hybrid chaotic system is designed by coupling Logistic, ICMIC, Tent, and Chebyshev maps (HLITC). Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and Kolmogorov entropy illustrate the new hybrid chaotic map has better chaotic performance. Then the proposed HLITC chaotic system is used to develop a new chaotic image encryption scheme with the double cyclic shift. The improved HLITC chaotic system is performed to generate key sequences used in the image scrambling and diffusion stage. The spiral transformation controlled by chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments of statistical analysis, key sensitivity, and keyspace analysis are conducted. Experimental results show the proposed encryption scheme has good robustness against brute force attacks, statistical attacks, and differential attacks, and is more effective than many existing chaotic image encryption algorithms.

This work tackles the problem of image restoration, a crucial task in many fields of applied sciences, focusing on removing degradation caused by blur and noise during the acquisition process. Drawing inspiration from the multi-penalty approach based on the Uniform Penalty principle introduced in [Bortolotti et al. arXiv.math.NA/2309.14163], we develop here a new image restoration model and an iterative algorithm for its effective solution. The model incorporates pixel-wise regularization terms and establishes a rule for parameters selection, aiming to restore images through the solution of a sequence of constrained optimization problems. To achieve this, we present a modified version of the Newton Projection method, adapted to multi-penalty scenarios, and prove its convergence. Numerical experiments demonstrate the efficacy of the method in eliminating noise and blur while preserving the image edges.

Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Though many CNN based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, we propose a new network structure R-PReNet for single image deraining with good background structure maintaining. This framework fully utilizes the cyclic recursive structure of PReNet. Moreover, we introduce residual channel prior (RCP) and feature fusion modules for better deraining performance by focusing on background feature information. Compared with the previous methods, our method has significantly improvement effect on the rainstorm image with the artifacts removing and good visual detail restoring.

This study proposes a method for image matching between infrared(IR)-RGB images using a deep learning network to estimate the deformation field. We propose a deformation field generator (DFG) that estimates the deformation field of the transformation matrix to match each pixel or IR image to the RGB image. DFG is a network that receives IR and RGB images as input; the output size is two channels and has the sample resolution as the input image. By warping the IR image through a grid-sampler that warps the image according to the value of the deformation field, we can obtain a warped IR image that matches the RGB image. Additionally, to check whether the warped IR image matched the RGB image, the masking images detecting the segmentation of objects were photographed in two images. Without directly comparing IR and RGB images, we proposed mask loss that warps the IR mask image through the deformation field and grid sampler and then compares the warped IR mask image with the RGB mask image. Mask loss solves the spatial similarity comparison problem with multi-modality images, such as IR and RGB images, by comparing the mask image with the same modality image as the mask image.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, lordosis, or kyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person's diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review recent works on spinal deformity as-assessment for scoliosis diagnosis, utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022, using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

(1) Background: Colon polyps are common protrusions in the colon’s lumen with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine-learning image classification models’ performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOV8), in the detection and classification of colon polyps were evaluated. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models’ ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the `normal’ class, 0.97 - 1.00 for `polyps,’ and 0.97 - 1.00 for `resected polyps.’ While GTM exclusively classified images into these three categories, both YOLOV8 and RF3 extended their capabilities to detect normal colonic tissue, polyps, and resected polyps, with YOLOV8 and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Plant phenotype plays an important role in crop breeding and planting. Leaf phenotype is an important part of plant phenotype. In order to analyze the leaf phenotype, the target leaf is required to be segmented from the complex background image. In this paper, an automatic soybean leaf segmentation method based on object detection and interactive segmentation models is proposed. Firstly, the Libra R-CNN object detection algorithm is used to detect all soybean leaves in the image. Then, based on the idea that the target soybean leaf is located in the center of the image and the area is large, the detection bounding box of the target leaf is selected. In order not to destroy the segmentation result, the bounding box is optimized to completely enclose the whole leaf. Finally, according to the optimized bounding box, the prior channels of foreground and background are constructed using Gaussian model. The two channels together with the original image are as the input of the interactive object segmentation with inside-outside guidance model to segment the target soybean leaf. A large number of qualitative and quantitative experimental results show that the method has high segmentation accuracy and strong generalization capacity.

Fractures of the talar neck and body are associated with spine fractures and scoliosis deformity, which affect cosmetic appearance and cause difficulty in ambulation. The implant design for talus surgery is thriving as a functional alternative in case of severe talar destruction, focusing on segmentation and reconstruction of the talus’s shape. However, manual segmentation of the talus is time-consuming and subjective. In this study we exploited the automatic segmentation framework to efficiently train a deep learning-based model to accurately segment the talus based on computed tomography imaging. We developed three model configurations with nnU-Net and investigated their Dice similarity coefficients (DSC) and 95% Hausdorff distances (HD95) for talus segmentation on a CT image dataset. The three configurations performed well (DSC > 0.95, HD95 < 0.6). When tested on the same samples, one of the configurations was more efficient while ensuring higher accuracy. We propose to focus on talus anatomic variations with increasing age based on this framework and apply it to clinical trials at the next stage.

To solve the problem that the common long-tailed classification method does not use the semantic features of the original label text of the image, and the difference between the classification accuracy of most classes and minority classes is large, the long-tailed image classification method based on enhanced contrast visual language trains the head class and tail class samples separately, uses text image to pre-train the information, and uses enhanced momentum contrast loss function and RandAugment enhancement to improve the learning of tail class samples. On the ImageNet-LT long-tailed dataset, the enhanced contrastive visual-language based long-tailed image classification method has improved all class accuracy, tail class accuracy, middle class accuracy, and F1 values by 3.4%, 7.6%, 3.5%, and 11.2%, respectively, compared to the BALLAD method. The difference in accuracy between the head class and tail class is reduced by 1.6% compared to the BALLAD method. The results of three comparative experiments indicate that the long-tailed image classification method based on enhanced contrastive visual-language has improved the performance of tail classes and reduced the accuracy difference between majority and minority classes.

Crop disease classification has always been a critical and persistent problem in the field of agricultural and forestry sciences, where often we do not have access to a sufficient number of samples to know the distribution of real-world samples. How to make full use of the existing data is the starting point of our thinking. To address this problem, this paper proposes a supervised image augmentation method Negative Contrast, which uses the contrast images of existing disease samples after removing disease areas as negative samples for image augmentation when samples are relatively scarce. Numerous experiments have shown that several classical models using this augmentation method have improved in disease classification of four crops, rice, wheat, corn, and soybean, with a maximum accuracy improvement of 30.8%. In addition, the comparative analysis of attentional heat map shows that the model using Negative Contrast is more accurate and intense on the area of interest of diseases, and thus reflects better generalization ability in real-world disease classification. Our dataset and codes can be found in https://www.kaggle.com/datasets/w970704112/corn-wheat-rice-soybean and https://github.com/hiter0/contrastaug .

UAV has become powerful devices for both practitioners and researchers. In recent years, there has been a substantial growth in the usage of UAV for a wide range of applications in research and engineering. UAV are mostly utilized for image capturing techniques due to their time savings, low cost, minimum field work, and high precision. The purpose of this study is to generate an accurate and reliable 3D modelling process using UAVs photogrammetric technology; hence this study allows the techniques to evaluate the practicability of modelling and mapping inaccessible tunnels using images recorded by UAVs and photogrammetry. As a result, the approach was always employed to depict the true state of the place. This research incorporates three experiments. The first experiment is to build a 3D model using only one-directional light source and evaluate the accuracy of 3D photogrammetric model and conducted at the concrete tunnel. The second one is to build a 3D model of underground sewer tunnel using different light sources one-directional and omni-directional. The last experiment was to build 3D model using an UAV system which is developed based on first and second experiment’s result, evaluate an accuracy of 3D photogrammetric model and conducted at the tunnel named in the research as a “pedestrian tunnel. There is certain limitation in this study such as Adding a payload to a drone changed its weight distribution, which affected its stability. If the payload was not properly balanced, it caused the drone to fly unevenly, making it more difficult to control.

This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (image e-book guidance) group or a control (text guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery. It was found that providing image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.

Recent advances in deep learning technology have led to increased interest in text-to-image technology, which enables computers to create images from text by simulating the human process of forming mental images. The GAN-based text-to-image technology involves the extraction of features from input text, which are combined with noise and then used as input to a GAN that generates images that are similar to the original images through competition between the generator and discriminator. Although generating images from English text is a mature area of research, text-to-image technology based on multilingualism, such as Korean, is still in its early stages of development. Webtoon is a digital comic format that allows comics to be viewed online. The creation process for webtoons is divided into story planning, content/sketching, coloring, and background drawing. Since each stage of webtoon production requires human intervention, it is both time-consuming and expensive. As a result, deep learning technologies such as automatic coloring and automatic line drawing are being used to reduce human involvement. However, there is a shortage of technology that can assist authors with story creation in webtoon production. Therefore, this study proposes a multilingual text-to-image model capable of generating webtoon images when presented with multilingual input text. The proposed model employs Multilingual BERT to extract feature vectors for multiple languages, and trains a DCGAN in conjunction with the images. The experimental results demonstrate that the model can generate images that are similar to the original images when presented with multilingual input text after training.

Deep learning technologies for skin cancer detection have been dramatically advanced based on high resolution dermoscopic images. The low-cost approach based on clinical skin images of low resolution is promising but remains technically challenging due to their undermined image quality. In this paper, we propose a coarse-to-fine efficient super resolution transformer (CF-ESRT) network to reconstruct the dermoscopy-level high resolution skin image from a low resolution clinical image. By connecting the refinement network to the original super resolution transformers and applying perceptual and gradient losses, our framework noticeably improves the finer texture details of skin lesions in the super resolution (SR) images, and is effective to elevate the perceptual quality of the SR images. Quantitative and qualitative evaluations show that our method outperforms ESRT the basis model as well as the other state-of-the-art SR models.

Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

Convolutional neural network architectures have become increasingly complex, which has improved the performance slowly on well-known benchmark datasets in the recent years. In this research, we have analyzed the true need for such complexity. We have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps and complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The investigations on the proposed architecture are evaluated on three retinal vessel segmentation publicly available datasets. The proposed G-Net light outperforms other vessel segmentation architectures by reducing the number of trainable parameters..

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algiers, Algeria. Estimates of the selected fitness function are first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion and demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm is then compared to the results of several state-of-the-art techniques, including fuzzy C-means, K-means, Otsu thresholding, local thresholding, and watershed segmentation. The results obtained using Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements demonstrate the superiority of the proposed optimized segmentation algorithm over equivalent techniques.

Problem. Image biomarker analysis, also known as radiomics, is a tool for tissue characterization and treatment prognosis that relies on routinely acquired clinical images and delineations. Due to the uncertainty in image acquisition, processing, and segmentation (delineation) protocols, radiomics often lacks reproducibility. Radiomics harmonization techniques have been proposed as a solution to reduce these sources of uncertainty and/or their influence on the prognostic model performance. A relevant question is how to estimate the protocol-induced uncertainty of a specific image biomarker, what the effect is on the model performance, and how to optimize the model given the uncertainty. In this manuscript, we show how protocol uncertainty can drastically reduce prognostic model performance. We introduce an effect-size measure η that assesses the protocol-induced uncertainty versus the measurable effect. Methods. Two non-small cell lung cancer (NSCLC) cohorts, composed of 421 and 240 patients respectively, were used for training and testing. Per patient, a Monte Carlo algorithm was used to generate three hundred synthetic contours with a surface dice tolerance measure less than 1.18 mm with respect to the original GTV. These contours were subsequently used to derive 104 radiomic features, which were ranked on their relative sensitivity to contour perturbation, expressed in the parameter η. The top four (low η) and the bottom four (high η) features were selected for two models based on Cox proportional hazards model. To investigate the influence of segmentation uncertainty on the prognostic model, we trained and tested the setup in 5000 augmented realizations (using a Monte Carlo sampling method); the log-rank test was used to assess the stratification performance and stability to segmentation uncertainty. Results. Although both low and high η setup showed significant testing set log-rank p-values (p=0.01) in the original GTV delineations (without segmentation uncertainty introduced), in the model with high uncertainty to effect ratio only around 30% of the augmented realizations resulted in model performance with p < 0.05 in the test set. In contrast, the low η setup performed with log-rank p < 0.05 in 90% of the augmented realizations. Moreover, the high η setup classification was uncertain for 50% of the subjects in the testing set (for 80% agreement rate), whereas the low η setup was uncertain only in 10% of the cases. The code and part of the data are available at https://github.com/Maastro-CDS-Imaging-Group/sure. Discussion. Estimating image biomarker model performance based only on the original GTV segmentation without considering segmentation uncertainty may be deceiving. The model might result in a significant stratification performance, but can be unstable for delineation variations, which are inherent to manual segmentation. Simulating segmentation uncertainty using the method described allows for more stable image biomarker estimation, selection, and model development. The segmentation uncertainty estimation method described here is universal and can be extended to estimate other protocol uncertainties (such as image acquisition and pre-processing).

Body-Esteem Scale is an assessment tool for adolescents and adults that evaluate three dimensions of self-evaluations of one’s body. Body-Esteem Scale has been translated and validated in some countries since America down to Europe. Lack of translation and reliability evidence in Portugal was detected. This study aimed to translate and test the validity and reliability of the Body Esteem Scale for Adolescents and Adults (BESAA) in students in the context of Portuguese higher educa-tion. A total of 173 students (60.7% are female) with a mean age of 19.7 (standard deviation = 2.2) years participated. Categorical Principal Component Analysis was used to assess the underlying dimensions of BESAA. Construct validity was evaluated through correlation with the Appearance Schemas Inventory – Revised and a three-factor model (“Appearance”, ‘‘Weight’’ and “Attribu-tion’’) was established. Confirmatory factor analysis was performed to verify the construct validity of the instrument. Items that had factor weights (λ)<.40 were removed, as well as those that were considered redundant by the modification indices estimated by the Lagrange Multipliers (LM) method (LM>11, p<.001). We observed high correlations between theoretically similar factors, and low correlations between different factors. The Portuguese BESAA showed adequate validity and reliability.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

The effect of the strain rate on damage in carbon black filled EPDM stretched during single and multiple uniaxial loading is investigated. This has been performed by analysing the stress-strain response, the evolution of damage by Digital Image Correlation (DIC), the associated dissipative heat source by InfraRed thermography (IR), and the chains network damage by swelling. The strain rates were selected to cover the transition from quasi-static to medium strain rate conditions. In single loading conditions, the increase of the strain rate yields in a preferential damage of the filler network while rubber network is preserved. Such damage is accompanied by a stress softening and an adiabatic heat source rise. Conversely, increasing the strain rate in cyclic loading conditions yields in a filler network accommodation and a high self-heating whose combined effect is proposed as a possible cause of the ability of filled EPDM to limit damage, by reducing cavities opening during loading and favoring cavities closing upon unloading.

Image fusion is a useful tool for producing a high-resolution multispectral image to be used for land use and land cover mapping. In this study, we use nine pansharpening algorithms namely Color Normalized (CN), Gram-Schmidt (GS), Hyperspherical Color Space (HCS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Principal Component Analysis (PCA), Resolution Merge (RM), Stationary Wavelet Transform (SWT), and Wavelet Resolution Merge (WRM) to fusion Worldview-3 multispectral Bands and panchromatic band. In spectral and spatial fidelity, several image quality metrics are used to evaluate the performance of pansharpening algorithms. The SWT and PCA algorithms showed better results compared to other pansharpening algorithms while GS and CN algorithms showed the worst results for the original image fusion. The effect of fusion on each band was separately investigated and according to the calculations, we found that the CoastalBlue band and the Blue band showed the best result and the NIR-1 band and NIR-2 band show the worst result for the original image fusion. In the end, we conclude that the choice of fusion method depends on the requirement of remote sensing application.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a cuckoo optimization algorithm. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary cuckoo optimization algorithm. The results procured after simulation by the cuckoo optimization algorithm are compared with Bacterial foraging algorithm and ant colony optimization based image enhancement and this approach is found to be improved.

Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the colour space transform (i.e., YUV, YIQ, or YCbCr). The resultant image was then applied to the algorithm for fruit segmentation. After it is applied Morphological Operations which is enhanced image then execute Blob counting method which identify the object and count the number of it. Accuracy of this algorithm used in this thesis is 82.21% for images that have been scanned.

Diabetes mellitus (DM) is a metabolic disorder characterized by the deficiency of insulin production and is one of the concerning health problems worldwide, especially in developing countries. Considering the systemic toxicity associated with current antidiabetic medications and their limited efficacy, there is an urgent need to discover new plant-based natural antidiabetics. Since time immemorial, medicinal plants have played an important role in the management of health disorders. In recent years, herbal medicine has experienced exponential growth in popularity worldwide, owing to its natural origin and fewer side effects, appealing to both developing and developed nations. Ecuador has a rich cultural history in ethnobotany that plays a crucial role in their people's lives. Several Ecuadorian medicinal plants have hypoglycemic or an-ti-hyperglycemic properties that are rich with bioactive phytochemicals. These medicinal plants were extensively used in traditional medicine preparation in Ecuador for many decades to treat diabetes because of their cost-effectiveness, available traditional knowledge, and low toxicity. Many Ecuadorian medicinal plants are reported to have antidiabetic properties that reduce blood sugar levels, and oxidative stress, regulate intestinal function, improve insulin resistance, inhibit α-amylase and α-glucosidase, lower gluconeogenic enzymes, stimulate glucose uptake mechanisms, and play an important role in glucose and lipid metabolism, yet there is a substantial lack of integrated approach between the existing ethnomedicinal practice and pharmacological research. Therefore, this review aims to discuss and explore the traditional medicinal plants used in Ecuador for treating DM and their bioactive phytochemicals, which are mainly responsible for their anti-diabetic properties.

The development of new and effective antimicrobial compounds is an urgent need with the emergence of resistant bacteria. Natural plant flavonoids are known to be effective molecules but their activity and selectivity have to be increased. Based on previous aurone potency, we designed new aurone derivatives bearing acetamido and amino groups at the position 5 of the A ring and managing various monosubstitutions at the B ring. A series of 31 new aurone derivatives were first evaluated for their antimicrobial activity with five derivatives being the most active (compounds 10, 12, 15, 16, and 20). The evaluation of their cytotoxicity on human cells and of their therapeutic index (TI) showed that compounds 10 and 20 have the highest TI. Finally, screening against a large panel of pathogens confirmed that compounds 10 and 20 possess a large spectrum antimicrobial activity, including on bioweapon BSL3 strains, with MIC values as low as 0.78 µM. These results demonstrate that 5-acetamidoaurones are far more active and safest compared with 5-aminoaurones, and that benzyloxy and isopropyl substitutions at the B ring are the most promising strategy in the exploration of new antimicrobial aurones.

Endocrine-disrupting chemicals (EDCs) may impact the development of Prostate Cancer (PCa) by altering the steroid metabolism. Although their exact mechanism of action in controlling tumor growth is not known, EDCs may inhibit steroidogenic enzymes such as CYP17A1 or CYP19A1 involved in the production of androgens or estrogens. High levels of circulating androgens are linked to PCa in men and Polycystic Ovary Syndrome (PCOS) in women. Essential Oils or their metabolites like lavender oil and tea tree oil have been reported to act as potential EDCs and contribute towards sex steroid imbalance in case of prepubertal gynecomastia in boys and premature thelarche in girls due to the exposure to lavender-based fragrances. We screened a range of EO components to determine their effects on CYP17A1 and CYP19A1. Computational docking was performed to predict the binding of Essential oils with CYP17A1 and CYP19A1, and functional assays were done using the radiolabeled substrates or Liquid Chromatography high‑resolution Mass Spectrometry and cell viability assays were carried out in LNCaP cells. Many of the tested compounds bind close to the active site of CYP17A1, and (+)-Cedrol had the best binding with CYP17A1 and CYP19A1. Eucalyptol, Dihydro-β-Ionone & (-)-α-pinene showed 20% to 40% inhibition of dehydroepiandrosterone production; and some compounds also effected CYP19A1. Extensive use of these Essential oils in various beauty and hygiene products is common, but only a limited knowledge about their potential detrimental side effects exists. Our results suggest that prolonged exposure to some of these essential oils may result in steroid imbalances. On the other hand, due to their effect on lowering androgen output, ability to bind at the active site of steroidogenic cytochrome P450s, these compounds may provide design ideas for the novel compounds against hyperandrogenic disorders such as PCa and PCOS.

Inflammation is a natural protective process through which the immune system responds to injury, infection, or irritation. However, hyperinflammation or long-term inflammatory response can cause various inflammatory diseases. Although idebenone was initially developed for the treatment of cognitive impairment and dementia, it is currently used to treat various diseases. However, its anti-inflammatory effects and regulatory functions in inflammatory diseases is yet to be elucidated. Therefore, this study aimed to investigate the anti-inflammatory effects of idebenone in cecal ligation puncture-induced sepsis and lipopolysaccharide-induced systemic inflammation. Murine models of cecal ligation puncture-induced sepsis and lipopolysaccharide-induced systemic inflammation were generated, followed by treatment with various concentrations of idebenone. Additionally, lipopolysaccharide-stimulated macrophages were treated with idebenone to elucidate its anti-inflammatory effects at the cellular level. Idebenone treatment significantly improved survival rate, protected against tissue damage, and decreased the expression of inflammatory enzymes and cytokines in mice models of sepsis and systemic inflammation. Additionally, idebenone treatment suppressed inflammatory responses in macrophages, inhibited the NF-κB signaling pathway, reduced reactive oxygen species and lipid peroxidation, and normalized the activities of antioxidant enzyme. Idebenone possesses potential therapeutic application as a novel anti-inflammatory agent in systemic inflammatory diseases and sepsis.

Diatomite (DME) consists of natural ordered porous structures that are excellent candidates as microcontainers. However, to the best of our knowledge, the application of DME loaded with corrosion inhibitors in anti-corrosion coatings has not hitherto been reported. In this study, DME has been used as a source of microcontainers to load benzotriazole (BTA) inhibitor by an immersion method. In order to prevent unwanted early release of the small molecule BTA inhibitor from the DME microcontainers, inspired by the adhesion characteristics of catechol and amine in mussel adhesion protein, polydopamine (PDA) was coated on their surfaces to generate BTA@DME/PDA microcapsules. Because of the depolymerization characteristics of PDA at low pH, the synthesize microcapsules showed a controllable release function. The SEM morphology, chemical structure, and loading capacity of the synthesized BTA@DME/PDA microcapsules were characterized. The corrosion resistances of epoxy coatings with different contents of BTA@DME/PDA microcapsules were compared by saline immersion and electrochemical impedance tests. A scratched coating with 5wt.% BTA@DME/PDA microcapsules showed the best anti-corrosion effect.

The differentiation between Neuromyelitis optica spectrum disorder (NMOSD) and cervical spondylotic myelopathy (CSM) is necessary, because both diseases have similar symptoms, such as motor weakness and sensory disturbances. The challenge arises when NMOSD presents with short-segment spinal cord lesions, which can mimic the clinical presentation of CSM. This case report presents an NMOSD misdiagnosed as CSM. A 66-year-old woman presented with neck pain, left arm pain, and motor weakness. She was initially diagnosed with CSM based on MRI findings that revealed the increased signal intensity of the spinal cord at the C3 to 4 levels and underwent surgery. Three years later, she had sudden left upper extremity motor weakness and mild gait disturbance. Physical exam showed left-side weakness and altered reflexes, with longitudinally extensive transverse myelitis (LETM) findings on cervical MRI. The CSF study revealed a 2+ AQP4-Ab finding. Finally, NMO was diagnosed. Steroid pulsed therapy and immunosuppression therapy led to improved motor function and gait after 20 days. We present a case report of a NMO patient who initially exhibited clinical and radiological features CSM but was later diagnosed with NMO. This case underscores the diagnostic challenge in distinguishing NMO from CSM, especially in elderly patients who commonly experience spinal degeneration leading to cord compression and associated cord signal changes on MRI. The misinterpretation or confusion with transverse myelitis can occur as a result. This case highlights the importance of considering NMO as a potential differential diagnosis in elderly patients with spinal degeneration, emphasizing the significance of precise and timely diagnosis for guiding optimal treatment decisions.

The current study aims to produce a material that has a dual effect of healing and anti-inflammatory. For this target k-carrageenan/polyacrylamide hydrogel film loaded with cetrimide (k-CAR/PAAm/CI) was performed by manual casting technique. Definite concentrations of k-CAR and AAm was heated at 80°C for 2 hours, CI and glycerol were added. The solution was cast without using initiator and cross-linker. The reaction of the sulfonic acid group -SO3H of k-CAR with -CONH2 group of PAAm lead to the formation of a sulfonamide (-SO2NH-) linkage. The characteristics of the produced films were investigated by FTIR, TGA, contact angle, and mechanical properties. Improvement in the thermal stability was performed in k-CAR/PAAm/CI2 film containing 1.5% CI compared to the film has 0.5% CI (k-CAR/PAAm/CI1). The contact angle measurement proved the films are hydrophobic that enhanced by increasing CI content. The tensile strength and elongation percent values are considered adequate for materials used in wound care. k-CAR/PAAm/CI2 (1.5% CI) film showed superior antimicrobial activity against P. aeruginosa, moderate activity against S. aureus, and low activity against E. coli. The film k-CAR/PAAm/CI2 was effectively inhibiting the heat-induced hemolysis and may offer a promising approach for the development of effective wound dressings.

Cellular aging has drawn the attention of researchers, scientists, and biotech businesses for the treatment of a number of medical conditions. Cellular aging is primarily defined by consistent cessation of proliferative development in response to internal and external stressors, including DNA damage, telomere shortening, mitochondrial dysfunction, and regulation of the senescence-associated secretory phenotype (SASP). Disturbances involving these factors may contribute to age-related disease development. Therefore, the current review aims to explore anti-aging factors targeting DNA damage response and SASP regulation and their detailed signaling networks. In addition, it provides an opportunity for researchers to identify not only anti-aging targets, but also anti-aging therapeutic strategies based on identified and non-identified targets.

The use of substances to improve sports performance is a widespread phenomenon among adolescents. Several anti-doping programs have been developed, mainly based on knowledge-based evidence. The main aim of the present study was to implement an anti-doping intervention in sports high school students, based on psychological framework, such as Socio-Cognitive Theory, through the development of a Serious Game (SG), i.e., a digital learning based on the game. The experimental design included an intervention group (n=167; F=37.7%; Meanage=17.5 years) and a control group (n=112; F=42%; Meanage=17.6). Both of groups completed the same questionnaire in two different occasions (i.e., time 1 and time 2), for measuring doping intention, self-regulatory efficacy to resist social pressure for the use of substances, moral disengagement, and doping knowledge. Data were analyzed through repeated measures of Group X Time ANOVA, demonstrating some degree of efficacy of the intervention, in particular, in terms of decrease of doping intention and the strengthening of doping knowledge. Moreover, we demonstrated that the score obtained during the implementation of the Serious Game partially could represent a coherent measure of the participants’ beliefs regarding doping. These results could be considered a starting point for future research to better develop technological anti-doping interventions.

Cobalt (Co) is a transition metal and is classified as a beneficial metal for plants, but its benefits for plants remain obscure. Cobalt has been reported to negatively affect plant physiochemical processes both at higher (toxic) and lower (deficient) applied levels. High concentrations of cobalt in plant organs cause irreversible changes to the plant cells, mainly via enhanced production of reactive radicals. Moreover, Co and its compound play an essential role in humans as they are the central atoms of cobalamin, a co-enzyme precursor whose absence causes anemia. Hence, the optimum Co supply to plants is critical for customary plant metabolic workings. Henceforth, monitoring Co behavior in the soil-plant-human system is highly imperative. This review highlighted the latest literature on (i) Co in soil and plants; (ii) its mobility and phyto-availability in soil; (iii) phytouptake and translocation towards shoot tissues; (iv) toxic and deficient effects of Co on plants; (v) plant detoxification mechanisms under increased Co levels inside plants; and (v) its role in the human body. For this purpose, 1026 plant observations from literature data were analyzed related to Co biogeochemical behavior in the soil-plant system. The data analysis revealed an overall increase in 567 observations and a decrease in 381 observations. However, these general trends in plant responses vary significantly for different types of plant species and physiological attributes. Overall, the current review delineates an updated and critical representation of the biogeochemical behavior of Co in the soil-plant-human system, supported by up-to-date 9 main tables, 14 supporting tables, and 3 figures. The authors believe that the literature presented here can be of great interest to scientists, researchers, policymakers, and graduate-level students.

This study investigates the synergistic impact of fermenting kale juice with Limosilactobacillus strains on its antioxidant and anti-inflammatory properties. Kale's rich nutrient profile, especially its flavonoids, offers potential health benefits. Probiotic lactic acid bacteria are employed in kale fermentation to enhance nutrient bioavailability and generate bioactive compounds. Kale juices fermented with L. reuteri EFEL6901 or L. fermentum EFEL6800 exhibited superior microbial growth. Free sugars and amino acids were converted to alcohols and organic acids, affecting the organoleptic and health-related properties of product. In addition, fermentation increased quercetin and kaempferol content, indicating improved availability. Furthermore, the fermented juice exhibited notable antioxidant activity and suppressed NO production, revealing anti-inflammatory potential. Gene expression analysis confirmed reduced pro-inflammatory markers and elevated anti-inflammatory cytokines. This research highlights the promising potential of fermented kale juice, enriched with Limosilactobacillus strains, as a functional food with combined antioxidant and anti-inflammatory benefits.

As mitochondria are negatively charged organelles, penetrating cations are used as a part of the chimeric molecules to deliver the specific compounds into mitochondria. However, unmodified penetrating cations affect different aspects of cellular physiology as well. In this review we have attempted to summarize the data about side effects of the commonly used natural (e.gberberine) and artificial (e.g. tetraphenylphosphonium, rhodamine, methylene blue) penetrating cations on cellular physiology. For instance, it was shown that such types of molecules can (1) facilitate proton transport across membranes; (2) react with redox groups of respiratory chain; (3) induce DNA damage; (4) interfere with pleiotropic drug resistance; (5) disturb membrane integrity (6) inhibit the enzymes. Also, the products of the biodegradation of penetrating cations can be toxic. As penetrating cations accumulate in mitochondria, their toxicity is mostly due to the mitochondrial damage. Mitochondria of certain types of cancer cells appear to be especially sensitive to penetrating cations. Here we discuss the molecular mechanisms of the toxic effects and the anti-cancer activity of the penetrating cations.

Vaccines are an important tool of preventive medicine. Organized vaccination programs have saved numerous people from serious infectious diseases. Surprisingly, there is a considerable portion of the population who oppose vaccinations. In particular, the existence of anti-vaccination perceptions among travelers to countries with endemic diseases is a major public health concern. Although hesitancy towards vaccinations is not a novel phenomenon, it came back to the forefront during the fight against the COVID-19 pandemic. This review explored the etiology of anti-vaccination beliefs among travelers and drew conclusions about their impact on public health and society in general. For this purpose, a purposeful search for data on the causative factors of vaccine hesitancy and their impact on people’s health was conducted. A descriptive analysis of the findings and conclusions about possible implications in health policy and clinical practice are presented. Fear of side effects, lack of credence in the necessity of vaccines, and mistrust of medical authorities are important causative factors. Their interplay shapes hesitancy towards vaccines. However, anti-vaccination beliefs can also be an aspect of a more general unconventional stance of life. Health care professionals and organizations must be ready to tackle vaccine hesitancy by making the necessary interventions. Correcting misconceptions about vaccinations is a prerequisite for ensuring personal and public health, especially in the context of a pandemic or epidemic. Moreover, ensuring the efficacy and safety of vaccines, especially in cases of modern technology applications, is a fundamental factor in addressing people’s concerns about vaccines. For this purpose, medical authorities and organizations must provide accurate and clear information on vaccines so as to eliminate misinformation. Furthermore, clinicians should cultivate their communication skills in order to convey the appropriate messages to prospective recipients of vaccinations.

Information encryption and anti-counterfeiting play an important role in many aspects of daily life, such as in minimizing economic losses, protecting secure communication and public security, and so on. Owing to the high information capacity and ease of operation, luminescent materials for advanced information encryption and anti-counterfeiting are essential to meet the increasing demand on encryption security. Herein, we summarize two emerging luminescent materials for information encryption and anti-counterfeiting, the AIE materials and room-temperature phosphorescent materials. At last, we discuss the opportunities and anticipations of these two information encryption and anti-counterfeiting materials.

The Bcl-2 family plays a crucial role in regulating cell apoptosis, making it an attractive target for cancer therapy. In this study, a series of indole-based compounds, U1-6, were designed, synthesized, and evaluated for their anticancer activity against BCL-2-expressing cancer cell lines. The binding affinity, safety profile, cell cycle arrest, and apoptosis effects of the compounds were tested. The designed compounds exhibited potent inhibitory activity at sub-micromolar IC50 concentrations against MCF-7, MDA-MB-231, and A549 cell lines. Notably, U2 and U3 demonstrated the highest activity, particularly against MCF-7 cells. Respectively, both U2 and U3 showed potential Bcl-2 inhibition activity with IC50 values of 1.2 ± 0.02 and 11.10 ± 0.07 µM using an ELISA binding assay compared to 0.62 ± 0.01 µM for gossypol, employed as positive control. Molecular docking analysis suggested stable interactions of compound U2 at the Bcl-2 binding site through hydrogen bonding, pi-pi stacking, and hydrophobic interactions. Furthermore, U2 demonstrated significant induction of apoptosis and cell cycle arrest at G1/S phase. Importantly, U2 displayed a favorable safety profile on HDF human dermal normal fibroblast cells at 10-fold greater IC50 compared to MDA-MB-231 cells. These findings underscore the therapeutic potential of compound U2 as a Bcl-2 inhibitor and provide insights into its molecular mechanisms of action.

Eucalyptus, a therapeutic plant mentioned in the ancient Algerian pharmacopeia, specifically two species belonging to the Myrtaceae family - E. radiata and E. cinerea, were investigated in this study for their antibacterial, antioxidant, and anti-inflammatory properties. The study used aqueous extracts (AE) obtained from these plants, and the extraction yields were found to be dif-ferent. The in vitro antibacterial activity was evaluated using a disc diffusion assay against three typical bacterial strains. The results showed that E. radiata EAq was effective against all three strains, while E. cinerea EAq was only effective against E. coli. Both extracts displayed significant antioxidant activity compared to BHT. The anti-inflammatory impact was evaluated using a pro-tein (BSA) inhibition denaturation test. The E. radiata extract was found to inhibit inflammation by 75% at a concentration of 250 g/ml, significantly higher than the placebo. Ellagic acid, a pri-mary phytochemical found in the extracts, demonstrated noteworthy toxic and pharmacokinetic characteristics and a maximum binding energy of -7.53 kcal/mol for its anti-TyrRS activity in sili-co. The study suggests that the extracts and their primary phytochemicals could enhance the effi-cacy of antibiotics, antioxidants, and non-steroidal anti-inflammatory drugs (NSAIDs). As phar-maceutical engineering experts, we believe this research contributes to developing natural-based drugs with potential therapeutic benefits.

The problem of increasing life expectancy is solved with the help of many medical and social areas. It has been established that piRNAs and miRNAs can significantly modify the expression of protein-coding genes by suppressing the translation process. The aim of this work was to establish the possibility of binding piRNAs and miRNAs with mRNA of the KLOTHO and FGF23 genes, which promote health and increase life expectancy through participation in key metabolic processes. We used the MirTarget program, which determines the quantitative characteristics of complementary interactions of all piRNAs and miRNAs nucleotides with mRNA of the genes. piR-44682, piR-1940042, piR-3008660, piR-3215034, piR-6885965, piR-7980636 and one miRNA (ID00756.3p-miR) binding to the mRNA of the KLOTHO gene were found in one cluster of binding sites (BSs). piRNA-6890096 interacted with the mRNA of KLOTHO gene in a fully complementary manner using only canonical nucleotides. Among 17494 human genes, target genes interacting with five piRNAs that bind to the mRNA of KLOTHO gene were identified. mRNA of the AFF2, BCL2L11, CPT1A, DAZAP1, NDRG3, SKIDA1, WBP4, ZIC5, ZSWIM6 genes interacted with piR-3215034 and piR-6885965, which formed clusters of BSs located in 5'UTR, CDS and 3'UTR. The piR-576442, piR-1501557, piR-1845735, piR-2069834, and piR-3029987 had BSs in the mRNAs of the FGF23 gene, located only in the 3'UTR. It is proposed to use piRNAs and miRNAs as regulators of the expression of KLOTHO and FGF23 anti-aging genes.

Inflammasomes are multiprotein complexes that activate inflammatory responses by inducing pyroptosis and secretion of pro-inflammatory cytokines. Along with many previous studies on inflammatory responses and diseases induced by canonical inflammasomes, an increasing number of studies have demonstrated that non-canonical inflammasomes, such as mouse caspase-11 and human caspase-4 inflammasomes, are emerging key players in inflammatory responses and various diseases. Flavonoids are natural bioactive compounds found in plants, fruits, vegetables, and teas and have pharmacological properties in a wide range of human diseases. Many studies have successfully demonstrated that flavonoids play an anti-inflammatory role and ameliorate many inflammatory diseases by inhibiting canonical inflammasomes. Others have demonstrated the anti-inflammatory roles of flavonoids in inflammatory responses and various diseases, with a new mechanism by which flavonoids inhibit non-canonical inflammasomes. This review discusses recent studies that have investigated the anti-inflammatory roles and pharmacological properties of flavonoids in inflammatory responses and diseases induced by non-canonical inflammasomes and further provides insight into developing flavonoid-based therapeutics as potential nutraceuticals against human inflammatory diseases.