Parvovirus B19 infection typically exhibits a biphasic clinical course with varied symptoms. While facial or extremity edemas are common in adult cases, localized eyelid swelling, referred to as Hoagland's sign, had not been recognized as a potential clinical feature. Here, we present the case of a 72-year-old woman with parvovirus B19 infection and Hoagland's sign, characterized by drooping of the swollen upper eyelid and narrowing of the ocular aperture. The patient showed symptoms of fever, myalgia, and neutrophilia with atypical lymphocytes. The diagnosis was confirmed through elevated parvovirus B19 IgM antibody levels. This is the first reported case of parvovirus B19 infection presenting with Hoagland's sign. Parvovirus B19 infection can present with the condition called “infectious mononucleosis-like syndrome" (IML), which mimics classical manifestations of infectious mononucleosis (IM), including Hoagland's sign. Our case suggested that Hoagland's sign may be one of the characteristic symptoms of the second phase in the biphasic course of parvovirus B19 infection. It is important to consider the possibility of parvovirus B19 infection, especially in elderly patients presenting with Hoagland's sign. Parvovirus B19 infection presents with highly variable symptoms along with its biphasic clinical course . Here, wA 72-year-old woman presented with fever and eyelids swelling. Two weeks before the visit, she had a fever of up to 38 °C that lasted until the visit and generalized myalgia that lasted for a few days. A week before the visit, her eyelids began to swell, and, on the next day, the eyelid edema was so severe that she could hardly keep her eyes open. It was then partially resolved spontaneously. She had been in daily contact with her grandchildren; however, they did not suffer from any disease with fever, skin rash, or lymphadenopathy. Physical examination was unremarkable except for bilateral upper eyelids edema with redness, mild pain, and heat.On the laboratory tests Table , the whiThe diagnosis of acute parvovirus B19 was confirmed due to elevated parvovirus B19 IgM antibody at a titer of 6.92 IU/mL  ,4, whichOur case suggests that Hoagland's sign may present as a second-phase symptom of acute parvovirus B19 infection. It is often difficult to consider the possibility of parvovirus B19 infection unless the characteristic second-phase symptoms appear, as the symptoms of the initial phase are nonspecific. However, even in the second phase, only 67.9% of cases present with both rash and arthralgia , was obsIn our diagnostic process, we first recalled IM and the cluster of IML by Hoagland's sign, which led to the diagnosis (the pivot and cluster strategy ). Since Hoagland's sign may be one of the characteristic symptoms of parvovirus B19 infection, in the second phase of its biphasic course. When considering parvovirus infection, sick contact, biphasic course, and edema, including Hoagland's sign, in addition to rash and arthralgia, should be examined. Conversely, in patients with Hoagland's sign, the possibility of acute parvovirus B19 infection should be considered.


While there was no difference in CTAs between T1DM and T2DM, CSAs were higher in T2DM (p = 0.0173). A significant positive association between CTAs and disease duration  and between CSAs and disease duration , age , and body mass index (BMI)  was revealed. After multiple regression analysis, duration of disease remained significant for CTA, CSA, and CAs . For CSA, BMI and the use of statins were the other important confounding variables (p = 0.0105 and p = 0.0763). Thus, this study demonstrated that both T1DM and T2DM patients had a higher number of all types of aberrations than controls, which increases with the prolonged disease duration. Higher BMI was associated with a higher frequency of CSA. The use of statins might be beneficial for reducing chromosome damage, but further investigations are needed to confirm this association.It is well-established that patients with diabetes mellitus (DM) have a higher incidence of several types of cancer. The precise mechanisms of this association are still unknown, but obesity and chronic inflammation-induced reactive oxygen species (ROS) are thought to be the main risk factors. ROS may produce different DNA damage, which could eventually lead to cancer. The main objective of this study was to evaluate the relation of chromosome aberrations (CA) with disease status, demographics, and clinical parameters in 33 subjects with type 1 DM (T1DM), 22 subjects with type 2 DM (T2DM), and 21 controls. CAs were analyzed in cultured peripheral blood lymphocytes and subdivided into chromatid (CTA)- and chromosome (CSA)-type aberrations. Compared with controls, higher levels of CTAs and CSAs were observed in T1DM ( Diabetes mellitus (DM) is a metabolic disorder characterized by impaired insulin secretion and/or insulin action resulting in hyperglycemia . The mosThe progression of diabetes complications is associated with several pathogenetic pathways. One of the most recognized is the overproduction of reactive oxygen species (ROS) and the consequent increment of oxidative stress, which causes oxidative damage to different cellular molecules such as carbohydrates, lipids, and DNA . HyperglEpidemiological studies report that patients with diabetes have a higher incidence of several types of cancer, such as hepatic, pancreatic, endometrial, colorectal, bladder, and breast cancers ,7. ExcesThe first studies on chromosome damage in patients with diabetes were published more than four decades ago ,16. AlthThus, our study aims to analyze chromosome aberrations (CA) in peripheral blood lymphocytes from patients with T1DM and T2DM and control subjects. Specific types of chromosome aberrations  were recorded separately, enabling us to evaluate their relation with disease status and different demographic and clinical parameters using univariate and multivariate models.A cross-sectional study was conducted at Vilnius University Hospital Santaros Klinikos (VUHSK) Endocrinology Center from August 2019 to June 2022. Subjects with T1DM, T2DM, and controls without DM, aged 18 years and older, were included in the study except for those with malignant, severe uncontrolled systemic diseases, mental illnesses, pregnancy, or breastfeeding. A total of 33 T1DM, 22 T2DM, and 21 controls were enrolled. Diagnosis of diabetes was established according to medical records or, for subjects with newly diagnosed diabetes, according to World Health Organization diagnostic criteria , where X is the number of chromosome aberrations per 100 cells and Y is the transformed variable. This transformation was shown to be effective in stabilizing dispersion , where X is the number of chromosome aberrations per 100 cells, and Y is the transformed variable. The results of the D’Agostino–Pearson test indicated that there is no significant difference from the normal distribution for all three transformed variables—CTAt, CSAt, and CAt . We used stepwise multiple regression to select the best subset of independent variables. The best model for the transformed frequency of CSA, showing the highest R-square, goodness of fit, and coefficient of multiple correlation values, included variables such as BMI, duration of disease, and use of statins , showing a positive relationship between the number of aberrations and years that have passed since the diagnosis. No other variables had a statistically significant influence on CTAt. The whole model explained 11.1% of the total variability in CTAt values. For CSAt, BMI was the next important confounding variable, showing a significant positive relation between increased body mass and transformed frequency of chromosome-type aberrations (p = 0.0105). In addition, the use of statins showed a negative relation with borderline significance (p = 0.0763). The whole model explained as much as 29.5% of the total variability in CSAt values. Finally, the whole model for CAt showed similar tendencies as a model for CSAt, but with lower statistical significance and lower percentage (25.8%) of total variability explained.Multiple regression was introduced as the final step of the analysis. Since the mean values of chromosome aberrations are not normally distributed , the fre statins . For comt and duration of disease was still significant after accounting for the influence of age , while the partial correlation between CSAt and age became non-significant  after accounting for the influence of duration of disease.Interestingly, the stepwise multiple regression procedure excluded the age of the subjects from the model despite the positive correlation found in univariate analysis. As mentioned above, age was also correlated with other variables, particularly with duration of disease. We performed partial regression analysis using the transformed frequency of CSAs and found that the partial correlation between CSAThe main goal of our study was to compare different types of chromosome aberrations in groups of patients with T1DM, T2DM, and control subjects without diabetes.The total frequency of chromosome aberrations in lymphocytes of control subjects in this study (1.83/100 cells) is in good agreement with our historical control data (form 1.68 to 2.11 per 100 cells) ,27.Both T1DM and T2DM had a higher number of all types of aberrations than controls. These results align with other studies of T2DM subjects ,29,30, i2 compared with normal-weight men [In our study, T2DM patients had a higher number of CAs and CSAs than T1DM patients. To the best of our knowledge, there is only one study comparing CAs between T1DM and T2DM  where thight men . Howeverp = 0.0763), there are some literature data that may support a protective effect of statins against DNA damage and prompt further investigations in this field. For example, long-term therapy with simvastatin lowered levels of DNA damage in the lymphocytes of dyslipidemic T2DM patients, as measured using the Comet assay [Drosophila melanogaster, induced by doxorubicin, an antineoplastic drug capable of generating reactive oxygen species.Some authors revealed increased CAs or MN levels in patients with diabetic neuropathy ,34 or neet assay . A negatet assay . Additioet assay . In addiet assay , and simet assay  were ablOne previous study using FISH technology reported unusually high numbers of stable chromosome aberrations (translocations and inversions) in patients with diabetes  and linkIn conclusion, we found that both T1DM and T2DM patients had a higher number of all types of aberrations than controls, which increases with the prolonged disease duration. A higher body mass index was associated with a higher frequency of chromosome-type aberrations. The use of statins might be beneficial for reducing chromosome damage, but further investigations are needed to establish this association.


Efforts have been made to identify factors influencing clinical response in patients with atopic dermatitis (AD) treated with dupilumab. A retrospective single-center observational study was carried out by analyzing data from 492 patients aged 12 years and older with moderate-to-severe AD. The study aimed to identify baseline demographic and clinical factors that could predict the achievement of a mild level of disease, i.e., an Eczema Area and Severity Index (EASI) ≤ 7, within 4 weeks from dupilumab initiation. Classic, generalized lichenoid and inflammatory phenotypes compared with a nummular eczema phenotype  and a baseline EASI ≤ 24 and between 24–29, compared with a baseline EASI ≥ 29 , were found to be predictive factors of early response to dupilumab, highlighting the importance of early biological treatment of AD. Atopic dermatitis (AD) is a multifactorial, chronic inflammatory skin disorder that predominantly affects infants and children 20%), with a 2–8% prevalence in the adult population , imparti0%, with The pathophysiology of AD is the result of a complex interplay between genetic predisposition, immunological triggers, and environmental triggers, making it a subject of significant research interest within the field of dermatology and clinical immunology .Individuals with a family history of AD, asthma, or allergic rhinitis are at an increased risk of developing the condition, highlighting the genetic component of AD susceptibility. Furthermore, mutations in genes associated with skin barrier function, such as filaggrin, keratinocyte differentiation, and cornified envelope formation, have been implicated in the pathogenesis of AD, underscoring the importance of the skin barrier in disease development ,5. DisruImmunologically, AD is characterized by dysregulated immune responses, with a predominance of T-helper 2 (Th2) cell-driven inflammation. Th2 cytokines, such as interleukin-4 (IL-4) and interleukin-13 (IL-13), play a major role in orchestrating the inflammatory cascade, contributing to the hallmark features of AD both in the acute and chronic phases, including pruritus and eczematous skin lesions ,8.The management of AD is multifaceted and often requires a combination of therapeutic approaches. Treatment options range from topical emollients, corticosteroids, and immunomodulatory agents, including topical calcineurin inhibitors, to more advanced systemic agents for patients affected by moderate-to-severe AD, who have experienced inadequate relief from topical therapies .Dupilumab is the first biologic therapy approved for moderate-to-severe AD in adults, adolescents, children, and infants. Dupilumab is a fully human IgG4 monoclonal antibody that exerts its therapeutic effects by binding to the common α-subunit of IL-4 and IL-13 receptors, playing a pivotal role in orchestrating the Th2 inflammatory cascade . It has Recently, efforts have been made to identify possible predictors of clinical response to dupilumab in terms of improvement of disease severity, symptoms, and quality of life, but so far, no conclusive data have been obtained .Dupilumab is an effective and safe treatment for patients affected by moderate-to-severe AD.Our findings demonstrate that patients affected by classic, generalized lichenoid and inflammatory phenotypes reach a mild level of disease earlier than patients with other non-classic phenotypes of AD.Patients with an EASI < 29 and particularly ≤24 respond earlier to dupilumab compared with those who have severe dermatitis with an EASI ≥ 29.Total serum IgE levels and eosinophil count values at baseline were not associated with an early clinical response.An association was found between presence of atopic blepharoconjunctivitis and facial involvement at baseline with the development, respectively, of blepharoconjunctivitis and facial redness dermatitis as adverse events.A timely administration of dupilumab, when EASI is <29, can effectively and rapidly bring the disease under control.A retrospective, single-center observational study was carried out in patients diagnosed with moderate-to-severe AD to identify predictors of an early response to dupilumab therapy, defined by the achievement of a mild disease state.The analysis encompassed clinical data from 492 patients, comprising both adults and adolescents aged over 12 years, collected between June 2018 and March 2022 at the Dermatology Unit of Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico in Milan. All patients included in the study were affected by moderate-to-severe AD and treated with dupilumab in accordance with the recommendations and reimbursement guidelines set forth by the Italian Drug Agency (AIFA) . In ItalThe treatment regimen involved an initial subcutaneous loading dose of 600 mg administered via prefilled syringe at the baseline, followed by 300 mg every other week. For patients weighing fewer than 60 kg, the loading dose was adjusted to 400 mg, followed by 200 mg once every two weeks . ThroughAll procedures adhered to the Declaration of Helsinki of 1964 (revised in 2013). The study protocol was approved by the local review board. All patients granted written informed consent to the investigators for data extraction from patient records.Clinical response was evaluated in terms of the severity and extent of dermatitis, itching, sleep disturbances, and quality of life. The severity and extent of AD were quantified utilizing the EASI score , which cThe primary objective of this investigation was to discern the patient-specific baseline characteristics exerting influence on treatment outcomes at 4 weeks (T1) as well as the subsequent assessments at 16 weeks (T4), 32 weeks (T8), and 52 weeks (T12), with specific emphasis on the early treatment response at T1.The criteria for treatment early-response were defined as achieving a mild level of disease, characterized by an EASI score of ≤7, within the first 4 weeks following the initiation of therapy . Within Patients’ demographic and clinical baseline characteristics collected are summarized in Additionally, laboratory data, particularly total serum IgE levels and peripheral blood eosinophil count, were examined in order to attain a more comprehensive understanding of the correlation between clinical parameters and treatment response. Total serum IgE levels were determined via immunofluorometric assay and expressed in kU/L.The safety profile of dupilumab was confirmed by recording and monitoring the incidence and severity of adverse events, as well as changes in clinical signs.p values < 0.05, two-sided, were considered statistically significant. All statistical analyses were performed using SAS statistical software .Data were reported as median  or counts (percentages). Fisher’s exact test was used to compare proportions. The Wilcoxon signed-rank test was used to compare within-patient variations of continuous variables. Univariate and multivariate logistic regression analyses were performed to identify predictive factors of early response to dupilumab. Variables identified in the univariate analysis as potentially relevant predictors of early clinical response were included in the multivariate analyses. The results were expressed as the OR with the 95% CI. A total of 492 patients with moderate-to-severe AD were eligible for the study; the cohort included 451 adults and 41 adolescents aged over 12 years.Analyzing the demographic and clinical characteristics of the study cohort, the majority of participants were male, constituting 56.10%, while females made up 43.90% of the sample. The median age at the time of AD diagnosis was 3 years, with an IQR spanning from 0 to 14 years. Regarding the initiation of dupilumab treatment, the median age was 33 years. The median duration of dermatitis in the cohort was approximately 25 years. This suggests that patients underwent several years of topical and systemic treatments, as indicated by 80.49% having used cyclosporine and 71.54% having taken oral corticosteroids as previous treatments.Among the participants, the most prevalent comorbidities were rhinitis and asthma, affecting 76.22% of the sample. Conjunctivitis was present in 49.80% of participants at baseline, and 48.17% had both rhinitis and asthma in conjunction with conjunctivitis. A minority, 22.15% of participants, did not exhibit any atopic comorbidities.Regarding the onset pattern of AD, the majority (76%) experienced early-onset persistent or relapsing symptoms, while 24% had a late onset in the adulthood.The classic form of AD was the most prevalent, representing 56.71% of the sample, with lichenified/exudative flexural dermatitis alone or associated with portrait dermatitis, head-and-neck dermatitis, or hand eczema. A non-classic form was represented by 43.29% of patients, encompassing generalized lichenoid and inflammatory, prurigo nodularis, nummular, and erythrodermic variants .At baseline, participants presented with a median EASI score of 26 (IQR 24–30), a median itch-NRS score of 9 (IQR 8–10), a median sleep-NRS score of 8 (IQR 5–9), and a median DLQI score of 15 (IQR 11–20).3 (IQR 250–690), and normal values were defined as <500/mm3).Furthermore, baseline blood data revealed a median total serum IgE level of 1287 kU/L (IQR 286–3906); normal total serum IgE values were defined as <100 kU/L. The median blood eosinophil count was 400 cells/mmSignificant results were discerned within the first four weeks of treatment, with 68.2% of patients achieving a mild level of disease by week 4. Cutaneous lesions, assessed using the EASI score, exhibited substantial amelioration, manifesting a median score of 6 (IQR 6–10), representing a notable improvement from the baseline median of 26. Ninety-three patients (20%) were classified as intermediate responders, as they achieved an EASI score of ≤7 by the time point T4. Additionally, forty-eight patients (10.4%) were categorized as late responders, indicating that they achieved a clinical response in terms of disease severity by either T8 or T12. Only six patients (1.3%) did not achieve disease remission by T12 and were consequently classified as non-responders or partial responders if they were individuals that exhibited clinical improvement that was not sufficient to be considered responders but, at the same time, not significant enough to fit the definition of moderate-to-severe disease, with an EASI score ranging from 7 to 16.Furthermore, at the T1 assessment, dupilumab revealed pronounced effects, particularly concerning sleep quality and pruritus. Relative to baseline, there was a median percentage reduction −55.6% in the itch-NRS and −71.4% in the sleep-NRS. At T4, sleep-NRS further decreased, reaching a reduction of −100%. These findings underscore the paramount importance of achieving a reduction in both pruritus and clinical severity of cutaneous lesions for patients with moderate-to-severe AD to attain restful sleep, a vital contributor to overall quality of life. Indeed, through the evaluation of the DLQI questionnaire score, it was ascertained that 59.7% of our cohort had reached a DLQI value of ≤10 at T1, signifying a median percentage reduction of −64.1% compared to the baseline.3) followed by a decrease at T8 (median 400 cells/mm3) and T12 (median 370 cells/mm3) in agreement with dupilumab registration and real-life studies [p < 0.001).With regard to the analysis of laboratory parameters, the eosinophil count had a slightly significant increase at T4 .Our statistical analyses yielded significant insights when investigating the development of conjunctivitis as an adverse event, taking into account patients’ prior histories of conjunctivitis. Among the 247 patients who had no conjunctivitis at baseline (T0), 25 (10.12%) subsequently developed this adverse event, while among the 245 patients who had a history of conjunctivitis, 56 (22.86%) experienced it after initiating dupilumab treatment. Notably, an association was identified between the presence of atopic conjunctivitis at T0 and the development of blepharoconjunctivitis during dupilumab treatment (p = 0.0081). Remarkably, none of the 53 patients with a clear facial complexion at baseline experienced facial redness as an adverse effect. However, among the 439 patients with facial skin involvement at T0, 52 (11.85%) developed facial redness dermatitis.Shifting our focus to the side effect of facial redness dermatitis, a significant distinction emerged when comparing patients with and without facial involvement at T0 (Discontinuation due to mild adverse events within one year of treatment occurred in 12 patients (2.4%). Causes were attributed to ineffectiveness (four patients), facial redness dermatitis (three patients), blepharoconjunctivitis (four patients), and psoriasis (three patients). Two patients discontinued treatment due to concurrent facial redness dermatitis and conjunctivitis. The suspension was temporary for six patients due to pregnancy, medically assisted reproduction, SARS-CoV-2 illness, or other unspecified reasons.Our research findings corroborate the efficacy and safety of dupilumab as a treatment modality for individuals affected by moderate-to-severe AD as proven in clinical trials ,13.The classification of patients according to their speed of response to dupilumab, reaching an EASI score ≤ 7, facilitated the investigation of potential drug response determinants. This analysis emphasized the cohort of patients who achieved clinical remission of AD and particularly all the patients that reached a mild severity of AD within a four-week period, referred to as the early responders.Notably, within the initial four weeks of treatment, a mild level of disease was obtained by 68% of patients treated with dupilumab, resulting in a transition toward a less severe disease state. At the same timepoint, a significant reduction in score pertaining to pruritus (assessed via itch-NRS), sleep disturbances , and the adverse impact on the quality of life (measured with the DLQI) was achieved. Consequently, dupilumab has demonstrated a pivotal role in the management of patients affected by severe AD, thereby aligning with the World Health Organization’s definition of health as “a state of complete physical, mental, and social well-being” .Moreover, it is noteworthy that our study has affirmed the favorable safety profile of dupilumab, thereby corroborating the evidence from clinical trials ,12,13 anRegardless of variations in total serum IgE levels and eosinophil counts at baseline, the therapeutic effectiveness of dupilumab remained consistently evident across all patients, thereby highlighting the independence of dupilumab’s efficacy from these blood parameters.Our study identified specific AD phenotypes within the patient population that served as predictive indicators of an early response to dupilumab therapy. The classic and generalized lichenoid and inflammatory phenotypes exhibited elevated odds of an early response compared to the nummular eczema phenotype. Additionally, patients presenting with a baseline EASI score of ≤24 (adolescents) and those with an EASI score ranging between 24 and 29 were more inclined to display a rapid response to dupilumab.Limitations of our study include its retrospective nature, which introduces the potential for inherent biases associated with such designs. Although the sample size is noteworthy, larger cohorts would enhance the study’s statistical power and the generalizability of its findings. However, the study’s strengths are rooted in its capacity to furnish real-world clinical practice data and its inclusion of a diverse patient population, encompassing both adults and adolescents.The scientific literature currently lacks comprehensive studies that investigate the predictive factors influencing the clinical response to dupilumab in real-world scenarios, thus rendering our study of considerable significance. Our findings suggest that the judicious and timely administration of dupilumab can efficiently and expeditiously ameliorate the disease, offering promising prospects for the personalized management of AD .


Computed tomography (CT) scans used in treatment response assessment in prostate cancer (PCa) patients are a useful tool for nutritional status evaluation. The aim of this study was to assess the nutritional status, including sarcopenia development based on CT scans, in PCa patients and its association with progression-free survival (PFS). Sixty-four PCa patients were included (group 1: 34 patients undergoing androgen deprivation therapy (ADT) with docetaxel due to newly diagnosed, hormone-sensitive, metastatic PCa and group 2: 30 patients with castration-resistant metastatic PCa continuing ADT therapy with enzalutamide or abiraterone acetate). Nutritional status was evaluated with anthropometrical parameters, Nutritional Risk Score (NRS), and CT scans at the L3 vertebrae. Survival analyses were performed. According to NRS, nutritional status was significantly related to PFS. In both groups, there was a significant reduction in muscle tissue . A significant increase in the distribution of adipose tissue  in group one was observed. Sarcopenia was diagnosed in patients but with no influence on PFS. Significant reduction in muscle mass and increase in fat mass was observed in patients treated for PCa with no impact on PFS. The NRS was related to PFS in PCa patients and associated with body composition, assessed by CT after the castration therapy. Long-term castration combined with abiraterone therapy with prednisone or enzalutamide significantly influenced muscle tissue and may lead to sarcopenia development. Prostate cancer (PCa) is one of the most frequently diagnosed neoplasms in men, diagnosed mostly after the age of 60 and with peak incidence after the age of 75 . It can This is a prospective observational study conducted in the Greater Poland Cancer Centre (GPCC) between 2016 and 2021. Qualification for oncological treatment was based on the current guidelines of the Polish Society of Clinical Oncology (PTOK) , the EurThe risk of malnutrition was assessed according to the Nutritional Risk Score (NRS) 2002 scale. In accordance with ESPEN recommendations, patients who obtained an NRS score of 3 or more were encouraged to start a nutritional intervention, i.e., dietary consultation and introduction of Oral Nutritional Support (ONS)  ,14,15. S32 + (Trus (2.2 ‘bipyridyl) ruthenium (II) -complex). The PSA was measured at each visit. The PSA Doubling Time was used for the evaluation of dynamic changes. An increase in PSA concentration must meet certain criteria to determine the biochemical progression of the disease [Total Prostate-Specific Antigen (PSA) concentration (ng/mL) was determined on a Roche cobas e801 analyzer using the ECLIA (electrochemiluminescence) method based on streptavidin-coated microparticles, biotin-labeled anti-PSA antibodies, and ruthenium-Ru (bpy) complex  disease ,17. To f disease . To find2 were recorded, and the obtained value was used to calculate the Skeletal Muscle Index (SMI) parameter according to the following formula: SMI(cm2/m2) = TM(cm2)/Height(m2). The obtained value was corrected in relation to the patient’s height, obtaining the Subcutaneous Adipose Tissue Index (SATI) with the use of the following formula: SATI (cm2/m2) = SF(cm2)/Height2(m2), and the Visceral Adipose Tissue Index (VATI) with the use of the following formula: VATI (cm2/m2) = VF(cm2)/Height2(m2). By means of changes in SMI, SATI, and VATI, changes in the scope of tissue composition of the body during therapy were visualized. Based on the obtained value of the SMI index, sarcopenia was diagnosed in study patients. For sarcopenic men, the most commonly recognized SMI value is 53 cm2/m2, and it was used as the cut-off point [2 at 43 cm2/m2 [2 and SMI < 53 cm2/m2.The CT scans were performed on a 128-row Siemens Somatom Sensation AS PLUS (Adaptive Scanning) apparatus; Ultravist 370 or Visipaque 320 contrasts were used for the study. Progression according to the international criteria for evaluating the response to treatment in solid tumors—the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) were used . The CT ff point ,21. The t-test for related and unrelated variables or k > 2 ANOVA and distribution inconsistent with the normal distribution), Friedman’s test for related variables, and Kruskal-Wallis test for unrelated variables. Contingency table analysis, including Relative Risk (RR) was performed with the Exact Fisher’s Test. The survival analysis was calculated with the log-rank test (Mantel-Cox) and presented with a hazard ratio (data were adjusted for age).For quantitative variables, descriptive statistics were used, such as arithmetic mean, range, and standard deviation. For qualitative variables, an assessment of the number of percentages was used. Depending on the data distribution, when assessing changes before and during treatment, the following test was used: normal distribution  and GraphPad Prism 5.01 (GraphPad Software).The characteristics of the study population are presented in The analysis of changes in body composition based on the measurements obtained from CT  and the SMI, SATI, and VATI are presented in n = 45) and high risk (n = 19) of malnutrition, according to NRS, had a median survival time of 36 and 23 months, respectively . In group 1, patients at low (n = 21) and high risk (n = 13) of malnutrition, according to NRS, had a median survival time of 28 and 19 months, respectively . In group 2, the results were not statistically significant; however, only 6 patients were in NRS high-risk group   .n = 16 in group 1 and n = 14 in group 2). During the treatment, the percentage of patients with sarcopenia increased in both groups but was statistically significant in group 2 only. Nevertheless, these results did not significantly translate into survival in either group 1 or group 2 , the median survival time was 35 months, and for patients with BMI <30 kg/m2, the median survival time was 30 months; however, the differences were not statistically significant (p = 0.8864). Similarly, there were no statistically significant differences in either group. No significant effect on changes in muscle mass (stable and 10% decrease) was observed  , the median survival time in group 1 was 25 months, while for patients with a stable SF content, it was 23 months; however, the differences were also not statistically significant. In the case of group 2, there was also no significant effect of changes in SF on PFS, but the median survival for patients with a 10% increase was 59 months, and for those with a stable content, the median survival was 56 months , there was no significant effect on disease PFS in individual groups of patients. The median survival time in group 1 of patients with a 10% increase was 21 months, while for patients with stable VF content, it was 26 months. In the case of group 2, the median survival for patients with a 10% increase was 56 months, and for those with a stable content, it was 38 months , kept it at a stable level , and patients who noticed weight gain ), no significant association between PFS and weight change was found  . For pat6 months . Similar8 months . When an8 months . There w8 months . When anas found .The nutritional status assessed by the NRS scale was significantly related to PFS and associated with changes in several body composition parameters , assessed by CT after castration inclusion. Castration with abiraterone therapy with prednisone or enzalutamide also significantly reduced muscle tissue. Based on CT scans, a tendency to develop sarcopenia in the studied population of patients was observed, although it was not significantly associated with PFS.2) is associated with a better prognosis in patients with various types of cancer. After the initiation of castration therapy in patients with hormone-sensitive prostate cancer (group 1), the increase in adipose tissue was dynamic and slow in the castration-resistance phase (group 2). On this basis, it can be assumed that the addition of new hormonal drugs does not significantly affect the changes in adipose tissue. Nevertheless, there was no significant association between SF, VF, SATI, VATI, and PFS. However, a trend was observed for a longer median PFS in patients with a 10% increase in SATI. Strorer et al. [Primary sarcopenia develops as a result of the aging process, and secondary sarcopenia is a result of a chronic inflammatory state. The presence of sarcopenia affects the survival prognosis in multiple health conditions that are commonplace in the elderly, including cancer. It is worth mentioning that sarcopenic patients have poorer tolerance to cancer therapies, with a greater incidence of complications, chemotherapy toxicity, and perioperative problems. Moreover, sarcopenia can exist in isolation or as a component of cancer cachexia. There is considerable overlap in the pathophysiology of cachexia and sarcopenia development. The key differentiating feature of cachexia is a hypercatabolic state and a negative protein energy imbalance, which is difficult to reverse. The cancer cachexia is described as loss of total body weight, and sarcopenia as specific loss of lean muscle mass. Finally, sarcopenic obesity also represents a poor prognosis in cancer patients . Assessmr et al.  indicater et al.  that lowIt is worth mentioning that the high incidence of sarcopenia in patients with PCa (30–80%) ,33 is obThis study has several limitations. It was not possible to calculate OS due to the low number of deaths noted in the study population. As the sarcopenia was assessed only with the use of CT scans, the proposed criteria by EWGSOP and AWGS for sarcopenia diagnosis were not fulfilled. Firm conclusions cannot be drawn as the study population number was low.The NRS is related to PFS in prostate cancer patients and is associated with body composition assessed by computed tomography after castration therapy. Long-term castration combined with abiraterone therapy with prednisone or enzalutamide significantly influenced muscle tissue and may lead to sarcopenia development.


Some people resist or recover from health challenges better than others. We studied Lithuanian clean-up workers of the Chornobyl nuclear disaster (LCWC) who worked in the harshest conditions and, despite high ionising radiation doses as well as other factors, continue ageing relatively healthily. Thus, we hypothesised that there might be individual features encoded by the genome which act protectively for better adaptiveness and health that depend on unique positive selection signatures. Whole-genome sequencing was performed for 40 LCWC and a control group composed of 25 men from the general Lithuanian population (LTU). Selective sweep analysis was performed to identify genomic regions which may be under recent positive selection and determine better adaptiveness. Twenty-two autosomal loci with the highest positive selection signature values were identified. Most important, unique loci under positive selection have been identified in the genomes of the LCWC, which may influence the survival and adaptive qualities to extreme conditions, and the disaster itself. Characterising these loci provide a better understanding of the interaction between ongoing microevolutionary processes, multifactorial traits, and diseases. Studying unique groups of disease-resistant individuals could help create new insights for better, more individualised, disease diagnostics and prevention strategies. Survival and adjustment to the ever-changing environmental conditions are essential components of adaptation and evolution in a population. Adaptation is directly dependent on the forces of natural selection. Therefore, natural selection is a crucial factor in evolution that has the power to alter the gene pool of a population . In thisMMP3, CYP3A, and AGT genes) [CAPN10 gene) [IL13, IL4, and IL1A genes) [CCR5 gene, which disrupts the activity of the encoded receptor. This genome variant prevents HIV from entering the cell, thus protecting the individual from the disease. This knowledge has been used to develop a treatment strategy [The increasing availability of large-scale genomic data has led to many genome-wide association studies being performed, including those on the adaptation of different populations. These studies allowed us to gain insights into new candidate genomic loci, and genes and their interactions regarding the processes of adaptation to different environments, such as nutrition and diseases ,3. IdentT genes) ,7,8, typ10 gene) , and astA genes) ,12. Thesstrategy .ST, Tajima D) [PNLIP and PPARD), skin pigmentation , and immune response  [ST, extended haplotype homozygosity, Tajima D) to complement existing results. Thus, we applied a new analysis method to detect positive natural selection signatures in the Lithuanian subpopulation of Lithuanian clean-up workers of Chornobyl  has been chosen instead of the more widespread name Chernobyl (transcribed from the Russian language)) nuclear power plant disaster (LCWC) using the RAiSD [Each population has unique genomic characteristics that may lead to different disease susceptibility or other adaptive properties. The Lithuanian population is genetically similar to neighbouring European populations, such as Slavs or Finno-Ugrians . Analysiajima D) . Resultsajima D) ,18,19. Pnd IL22) . These fhe RAiSD  tool. WeIn 1986 in Chornobyl, Ukraine, a nuclear disaster occurred after the failure of a nuclear power plant reactor. About 600,000 people, during the period between 1986–1990, were recruited to clean-up the consequences of the disaster. Subsequently, they experienced long-term effects of ionising radiation (IR)  . As a reMassive catastrophes, wars, and ecological disasters serve as quasi-experiments. For this reason, populations that survive become precious objects for studies of adaptation. Studying adaptiveness and survival in such conditions is especially relevant in these times of uncertainty when we live in the shadow of nuclear threat. Potentially, some part of the resistance to the toxic and traumatising environment may depend on the unique genome variation and positive selection signatures. Until now, genomic studies of LCWC were predominantly cytogenetic, identifying significant changes in clean-up workers’ genomic stability ,29. This® Puregene® Blood Kit  extraction protocol (LTU samples). DNA concentration and purity were determined with a NanoDrop® ND-1000  spectrophotometer. All procedures performed in this study adhered to the institutional and national research committee ethical standards. Written informed consent was received from all study participants.LCWC is a unique population group and resource for this study. These individuals not only experienced the consequences of the Chornobyl nuclear power plant disaster itself but also participated in its clean-up. Among the surviving LCWC, there are both diseased individuals and those who are ageing relatively healthily. They survived extreme conditions and adapted to the lifelong effects of ionising radiation (IR) and the consequences of the disaster itself . Samples were collected between 2019–2021, about 30 years after the Chornobyl nuclear disaster. Whole-genome sequencing (WGS) was performed for 40 men who, according to the questionnaire responses, experienced the harshest conditions  during clean-up work in Chornobyl. We compared their whole-genome data with the WGS dataset of 25 men from LTU [During the sample collection, LCWC were required to complete a questionnaire that allowed the collection of information regarding individuals’ experienced IR dose (according to the expedition documents), clinical data, and more. At the time of the study, the average age of LCWC was 64 years (age variation between 50–78 years). The average age of LTU group individuals was 36 years (age variation between 29–49 years). There was no possibility of assimilating the two cohorts by the average age. This may be considered a limitation of the study. However, the age difference (in one generation) does not affect natural selection significantly. Moreover, the analysis method see  is desigLithuanian clean-up workers of the Chornobyl nuclear disaster have experienced higher than usual doses of IR. The average annual dose due to natural sources of IR in Lithuania is 2.2 mSv . The ann® Nano DNA Library Prep Kit . Demultiplexing of the sequencing reads was performed with Illumina bcl2fastq (2.20). Adapters were trimmed with Skewer (version 0.2.2). Quality trimming of the reads was not performed. The quality of the .fastq files was analysed with FastQC (version 0.11.5-cegat). Sequencing quality control Q30 values were above 88.59%.Sequencing and primary quality control of raw data files (.fastq) were performed using the Illumina NovaSeq 6000  sequencing system and standard DRAGEN version 3.6.4 workflow (the Centre for Genomics and Transcriptomics (CeGaT), Germany; under the contract with Vilnius University). On average, 94.72% of the reads were mapped to the reference genome hg19. Sequencing was performed at coverage of 26.88–61.38× (an average of 36.27×) per both sample groups. An amount of 100 ng DNA was paired-end sequenced in 2 × 150 bp mode using TruSeqST, Tajima D) identify these signatures only separately. Other RAiSD tool advantages include the negligible computing memory requirements and the processing of large amounts of data in a relatively short time. RAiSD tool was designed to detect positive selection signatures when the population of interest has been exposed to selective pressures over multiple generations. Thus, further results show positive selection signatures gained before the catastrophe. However, these genomic loci under positive selection may still affect the adaptation and health of LCWC. In this study, we were not looking for causality between the impact of the catastrophe  and the effect of positive selection after the disaster. Instead, we aimed to determine the positive selection signatures already present in the genomes before the disaster and their influence on the adaptation to the calamity and health in the cohort of LCWC. Two datasets of LCWC and LTU μ values, calculated from the WGS data, were compared. Analysis was performed for 22 autosomes. The effect of a positive natural selection was considered at μ > 0. The higher the value of μ, the stronger the impact of positive selection in the analysed genomic locus. Additionally, to identify the most significant loci under positive natural selection, it was assumed that the majority of the genome was neutral, i.e., not affected by positive selection. Loci were considered significant when they reached the top 5% of the highest μ estimates. This 5% value of the highest estimates corresponds to the p-value and was used as the significance threshold [To identify genomic loci under positive selection which are unique to LCWC, RAiSD v2.9 (Raised Accuracy in Sweep Detection) software was used . The maihreshold . For furIdentified loci were analysed using the UCSC genome browser . FunctioFollowing the comparison of genomic loci under significant positive selection, 22 loci (one in each autosome) unique to the LCWC cohort were identified. In the genomes of LCWC, the autosomal loci with the highest positive selection signature values were determined for chromosomes 5 (μ = 164.2 ), 11 μ . IdentifIn total, 123 genes under positive selection in the LCWC group were identified. Analysis results of the biological function of the identified genes under the positive selection based on the PANTHER classification system  and the Biological role analysis of the identified loci shows that most of the genes (71.8%) are involved in three main groups of biological processes: cellular, metabolic, and biological regulation . The typCDKN2C, ZNF827, CENPH, CCNB1, CDK7, TAF9, RAD17, GTF2H2C, PTP4A1, WDR11, ALKBH2, UNG, ARID3B, COMMD, NEIL1, RFWD3, MACROD2, MAPK1, PPM1F, and TOP3B  [PPTC7 gene suggests the potential influence of this locus on human adaptation to toxicity.Aside from the DNA damage repair or cell cycle, we have identified more genes under positive natural selection that may affect the adaptive properties of LCWC. The ynthesis . It is aynthesis . Interesresponse , which iresponse ,48, and l toxins . In the  < 0.05) . India i < 0.05) , so it iMoreover, many of the identified genes affected by positive natural selection are associated with cancer processes, although not clear in what way. As per analysed questionnaire information, studied LCWC men exhibited fewer cancer cases than could be expected in the general population and, at the same time, experienced larger than usual doses of IR, which is known for its carcinogenic effects. Two out of 40 LCWC (5%) had malignant tumours (rectum or bronchus and lung). In the same age group of men in the Lithuanian population, the incidence of cancer was 8.2% in 2021 . The facDAO gene could be an essential protective agent of oxidative stress response mechanisms to IR as well as PPTC7—to pollution. We reveal an existing paradox in cancer morbidity among the LCWC survivors’ group, which could be partially explained by the protective activity of cancer-related genes. Our results justify our initial hypothesis, that LCWC genomes are unique and could contain genomic factors accounting for protective health-related effects and adaptiveness. However, we would like to outline that our results may have potential limitations arising from the possible differences among the sample and control groups—age, lifestyle, morbidities, etc. Therefore, our results should be interpreted more as qualitative rather than quantitative.The combination of new positive natural selection signatures and functional in silico analysis methods allowed us to identify genomic loci that are potentially important for the adaptation and survival of the LCWC. We demonstrate that DNA damage repair and cell cycle-involved coding regions are significant for human adaptation in general, regardless of population. We also show that the Characterising positive selection signature loci in cohorts such as ours might set a good precedent for studies identifying genomic loci relevant to survival, longevity, and adaptiveness, and provide a better understanding of ongoing microevolutionary processes, multifactorial traits, and diseases. These loci could be used as potential targets for gene therapy to create new disease diagnostics, prevention, and treatment strategies, thus moving towards personalised medicine.


Atrial fibrillation (AF) and heart failure (HF) are highly prevalent cardiac disorders worldwide, and both are associated with poor prognosis. The incidence of AF and HF has been increasing substantially in recent years, mainly due to the progressive aging of the population. These disorders often coexist, and may have a causal relationship, with one contributing to the development or progression of the other. AF is a significant risk factor for adverse outcomes in HF patients, including mortality, hospitalization, and stroke. Although the optimal treatment for AF with HF remains unclear, catheter ablation (CA) has emerged as a promising treatment option. This review provides a comprehensive overview of the current scientific evidence regarding the efficacy of CA for managing AF in HF patients. In addition, the potential benefits and risks associated with CA are also discussed. We will also explore the factors that may influence treatment outcomes and highlight the remaining gaps in knowledge in this field. Atrial fibrillation (AF) and heart failure (HF) are both common cardiac disorders associated with poor prognosis ,2,3,4. TOver the past three decades, the prognosis of HFrEF has improved due to stepwise advancements in pharmacological/non-pharmacological treatment ,14. HoweThe current guidelines recommend catheter ablation (CA) in patients with AF and HF who remain symptomatic on medical therapy, with an emphasis on HFrEF while acknowledging the importance of patient selection ,16,17,18The optimal role of CA in managing AF patients with HFrEF is currently less clearly defined than in AF patients without HF. While current guidelines recommend CA for rhythm control in symptomatic, recurrent, and drug-resistant AF patients without HF, its use in those with HFrEF remains a topic of ongoing debate among researchers and clinicians ,19. In ATo date, a total of nine prospective randomized controlled trials (RCTs) have been reported, investigating the efficacy of CA in patients with AF and HFrEF, with some trials also including HFmrEF and HFpEF  20,21,2,26,27,28p = 0.6). However, in this study, the sinus rhythm (SR) restoration rate in the CA group was only 50%, and half of the patients undergoing CA were in AF after six months. The 10 patients who were in SR after six months had a significantly higher increase in LVEF of 10% compared to 1.5% who remained AF (p = 0.008). As the authors pointed out, the low success rate of CA would have affected the results. The ARC-HF  trial [p = 0.055) in the CA arm. The recently published AMICA  trial [p = 0.36) under a 74% maintenance of SR in patients who underwent CA. These study results differed from other previous RCTs, including results from the AATAC  trial [All nine trials assessed the effect of CA on LVEF, five of which investigated the increase in LVEF as the trial’s primary endpoint. Seven trials evaluated LVEF with transthoracic echocardiography (TTE), and the remaining two evaluated LVEF with radionuclide ventriculography. Notably, a rhythm control with CA improved LVEF compared to baseline in all studies . Six stue) trial , comparen) trial  comparedD) trial  and the D) trial . One posD) trial ,22,27 whD) trial  reportedSeven trials ,24,27,28p = 0.001, n = 294) but not at 36 months’ follow-up  and 60 months’ follow-up . In addition, the distance in both groups varied through the follow-up period . It should be noted that due to the progressive nature of HFrEF and the recurrent nature of AF over time, accurately assessing the precise effect of CA on functional capacity through 6MWD may be challenging. Nonetheless, as demonstrated in All of the nine trials examined the impact of CA on functional capacity, with eight of them focusing on the change in the six-minute walk distance (6MWD) ,26,27,28p = 0.006). In the secondary endpoints, the individual risk of all-cause death, cardiovascular death, and HF-related admission was also lower in the CA group. On the other hand, the recently published RAFT-AF  trial [p = 0.066). The combined Kaplan–Meier plots of each primary outcome in the two trials are shown in The prognostic impact of CA in patients with HFrEF was evaluated by three trials ,26,28. TData from prospective registry studies indicate that complications occur in around 4–14% of patients who undergo CA for AF, with 2–3% of these complications being potentially life-threatening ,40,41,42Given the added consideration of repeated CA procedures and the use of AADs, determining the exact recurrence rate and the follow-up period for AF recurrence presents a challenge. AF recurrence is monitored through a 12-lead electrocardiogram, 24/48 h Holter monitoring, implantable loop recorders, or implantable cardioverter defibrillator (ICD)/cardiac resynchronization therapy defibrillator (CRTD). The general SR maintenance rates after CA have been reported to range from 63 to 84% in most studies with a follow-up period of 2 to 5 years ,48,49,50Several risk factors for AF recurrence after AF ablation have been identified, including patient age, left atrium size, AF duration, renal dysfunction, and LGE in MRI ,55,56,57Although CA has demonstrated beneficial effects in several studies and should be considered a first-line treatment option for selected patients with HFrEF, its efficacy in the context of advanced or end-stage HF remains to be fully elucidated. This is primarily due to the exclusion of patients with advanced HF from these studies.p < 0.001). These findings suggest that CA might also hold promise as an effective therapeutic strategy in treating advanced HF.Recently, the CASTLE-HTx  trial  assessedAF and HFpEF are age-related, frequently coexisting, and likewise share common mechanisms and risk factors ,73,74,75This review has synthesized the available literature on the utilization of CA as a potential treatment for patients suffering from both AF and HF. While CA has emerged as a promising therapeutic option for this population, several unresolved issues persist that need to be addressed to optimize its application.One of the primary challenges associated with CA for AF in HF patients is the controversial prognostic impact of the treatment. There are several factors that may influence the effect of CA in HF patients including the duration of AF, the progression of underlying cardiac disease, and the severity of HF. These factors must be considered when evaluating the effectiveness of CA and require further investigation. Another important consideration is the need to establish patient selection criteria and predictors of AF recurrence in AF patients with HF. This will require further research to determine which patients are most likely to benefit from CA and what the best approach is to predict the risk of AF recurrence. Finally, the effect of CA in AF patients with HFpEF remains unclear, and further research is needed to understand the potential benefits and risks associated with the treatment in this population.


EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including  Medulloblastoma (MB) is the most prevalent malignant form of brain tumor among children, accounting for approximately 20% of all central nervous system (CNS) malignancies. The pathological features of MB are heterogeneous, and its emergence in the cerebellum is attributed to genetic and epigenetic alterations that disrupt critical pathways in cerebellar development . Accordi. Transcriptomic analysis can identify medulloblastoma subgroups, but it has limitations in capturing the microenvironment and impact of modifications on gene expression, as well as dealing with technical variations, noisy data, and incomplete transcriptome coverage. DNA methylation profiling is more reliable in accurately classifying medulloblastoma subgroups  datasets, including GSE85212 (N = 763), GSE130051 (N = 1390), GSE90496 (N = 390), GSE54880 (N = 276), GSE109379 (N = 128), and GSE75153 (N = 91) . All thep-value >0.05 in over 5% of samples were removed from subsequent analysis.a) We assessed the signal quality using the detectionP function from the Bioconductor minfi package. We then calculated the p-values for each CpG probe across all samples. Probes with a b) As all samples used in the current study were from the cerebellum, we used the preprocessQuantile function from the minfi package to normalize the data. We excluded CpG probes related to sex chromosomes and probes associated with single nucleotide polymorphisms (SNPs). On average, the total number of remaining probes was 420,000.c) The methylation beta values ranging between 0 and 1 were calculated using the getBeta function from the Bioconductor minfi package. Briefly, such values were obtained based on the methylated and unmethylated probe intensities using formula M/(M + U + 100) ; M and Ud) To deduce missing demographic information, including age and sex, we employed the methyAge algorithm and the predictedSex function from the Enmix  and minfWe downloaded raw data files in “idat” format for all the aforementioned GEO datasets and assessed their quality using the minfi Bioconductor package . Subsequet al. to integrate the DNA methylation dataset with gene expression data and to further generate new labels. SNF allows for the identification of similarity networks, enabling the creation of the most appropriate labels for the methylation dataset using spectral clustering. To this end, we combined 763 samples from the methylation dataset (GSE85212) with the same number of samples from the gene expression dataset (GSE85217). The data integration was performed using the following parameters: 51 nearest neighbors, sigma = 0.85, and 120 iterations. As our study focused on medulloblastoma, which is characterized by the four subgroups, we set the cluster number to four and used the result of spectral clustering as the ground truth labels. We converted the cluster numbers into subgroups by comparing the sample number from the fused dataset and actual labels. Next, we evaluated the performance of the fused network by calculating the normalized mutual information (NMI) score, ranging from 0 to 1. An NMI score of 1 indicates that the fused network leads to the same labels as the actual labels, while a score of 0 indicates the opposite.In our study, we utilized the similarity network fusion (SNF) technique  proposedFeature selection is a critical step in machine learning, as it allows for the identification of the most relevant features, resulting in decreased prediction model error rates and computational time. In this study, we utilized a random forest model (RF) to train the top 5,000 most variable CpG probes obtained from Median Absolute Deviation (MAD) through the mad function in the stats package. To this end, we grew 300 trees using the RF model and determined the importance of each probe across all subgroups using the varImp function from the caret package.To evaluate the prognostic potential of prediction biomarkers, we conducted an overall survival analysis by adapting the MethSurv webtool pipeline . We utilTo overcome the challenge posed by imbalanced sample sizes for each MB subgroup in the methylome data, we implemented a technique called synthetic minority oversampling (SMOTE)  using thWe utilized t-distributed stochastic neighbor embedding (t-SNE), a non-linear dimensionality reduction technique using the Rtsne package , to reduOur aim was to address the multiclassification challenge of accurately classifying medulloblastoma (MB) subgroups by leveraging the DNA methylation levels as a key feature. To do this, we used a diverse set of machine learning algorithms. The six algorithms employed were random forest (RF), naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and linear discriminant analysis (LDA). Furthermore, to capture the intricate nonlinear relationships, we incorporated an artificial neural network (ANN) model. Since the ensemble-based algorithms RF and XGB combine the predictions of multiple weak models to improve overall performance, we included those models in our study. On the other hand, NB operates as a probabilistic model, employing Bayes’ theorem to calculate the likelihood of class membership based on the independent features. KNN is classified as a nonparametric supervised learning algorithm, meaning that it does not make explicit assumptions about the underlying data distribution and defers computations until prediction. SVM can function either as a linear or as a nonlinear model, using a hyperplane or kernel trick to separate classes in the feature space. LDA is a linear model that projects data onto a lower-dimensional space to maximize class separation, aiding classification . The utiTo train the abovementioned machine learning prediction models, we split the data into the training and test sets with a ratio of 0.8 for machine learning models using the sample. split function from the caTools package. Furthermore, we performed cross-validation in ten random folds (k = 10) using the createFolds function from the caret package .The RF model was trained using the Random Forest package  with 300We implemented ANN models using the Keras package in R with TensorFlow 2.10 . The datTo prevent overfitting, we applied regularization techniques, including dropout , L2 regularization on the second layer (regularizer_l2 = 0.009), and early stopping after five patients. The model was optimized using the categorical cross-entropy loss, stochastic gradient descent (SGD) optimizer, 200 epochs, batch size of 16, learning rate of 0.03, decay of 0.00006, momentum of 0.05, and Nesterov momentum.To optimize the computational training time, we utilized the mclapply function from the parallel package to run the machine learning models in parallel on available CPUs. The training was performed on an Ubuntu machine equipped with an Intel Core i5-6200U processor and 16 GB RAM.In our study, we evaluated the performance of each classification model using standard metrics, which included accuracy, sensitivity, specificity, precision, F1-score, and area under the curve (AUC) as described by similar studies . BrieflyHere, true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) indicate whether the model predicted correctly or incorrectly. We also computed the AUC from the pROC package . The AUCTo plot the training and testing results of a classifier, we designed a custom R script. Initially, the dataset was partitioned into a training set and a test set. Subsequently, principal component analysis (PCA) was conducted on the training and testing sets separately using the preProcess function from the caret package, enabling the extraction of two primary components that captured the most significant variability in the data. Following this, the training and test sets were transformed using the derived PCA outcomes. A grid structure was then constructed, encompassing values pertaining to the two principal components. Utilizing the trained classifier, labels are predicted for the grid set. Moreover, a color mapping scheme was employed to associate colors with the predicted and actual subgroups, enhancing the interpretability of the resulting plot.p-value <0.05.To investigate the molecular function of the predicted CpG biomarkers and their relevance to the MB subgroups, we performed gene enrichment analysis. To annotate the CpGs with the genes, we utilized the minfi and IlluminaHumanMethylation450kanno.ilmn12.hg19 packages. The resulting genes were used as the input for the gprofiler2 package  to identTo interpret the contribution of each identified biomarker to the MB subgroup prediction, we used the Shapley value, which is a local interpretation method in IML (Interpretable Machine Learning). Since the machine learning models employed in this study cannot directly elucidate the relationship between CpG probes and their target class, we employed the Shapley value to provide human-understandable explanations of the models’ results. The Shapley value is computed as the average marginal contribution of a CpG probe or gene beta value across all possible coalitions. For a single prediction of each MB subgroup, it randomly changed the value of each beta value from zero to the actual value of the sample and calculated the prediction for all patterns of changes due to the addition of each CpG. We used the iml package  to calcuIn this study, we utilized the igraph package  to perfo(i) Collection of data from Gene Expression Omnibus (GEO), followed by pre-processing and processing steps;(ii) Implementation of similarity network fusion (SNF) to establish new class labels by integrating DNA methylation and gene expression data;(iii) Median Absolute Deviation (MAD) analysis was applied to select informative prediction biomarkers, followed by random forest (RF) analysis for feature selection. Furthermore, survival analysis was performed based on the prediction biomarkers.(iv) Construction of AI-based prediction models following Synthetic Minority Oversampling Technique (SMOTE) application;(v) Evaluation of the models using multiple parameters, including accuracy, sensitivity, precision, AUC, and F1-score;(vi) Gene Ontology (GO) analysis was used to functionally annotate the selected genes.In this study, we used a combination of data integration and AI-based techniques to effectively classify subgroups of medulloblastoma. The methodology used in this study is presented in We further conducted gene network analysis and interpreted the classifier decision by utilizing Shapley values. The subsequent sections provide detailed results from each of the steps mentioned above.In this study, using similarity network fusion (SNF), we identified four distinct clusters in both the gene expression and methylation datasets . We thenThe t-SNE visualization revealed  only a mIn our study, we employed six robust machine-learning algorithms, namely, SVM, KNN, NB, RF, XGB, and LDA, along with an artificial neural network, to predict medulloblastoma subgroups based on DNA methylation samples using 399 predictive biomarkers. As a result of the fusion process, a subset of samples (n = 16) had their labels switched . These nThe overall performance of the classifiers based on the validation set (GSE90496) is presented in Since the focus of our study was the classification of MB subgroups, we evaluated the performance of each model, considering the different MB subgroups, across multiple validation datasets. Notably, all tested classifiers exhibited exceptional performance on the GSE90496 validation set, exceeding 0.92 in accuracy, precision, sensitivity, F1-Score, specificity, and AUC . We spec. Thus, the classifiers successfully captured the underlying variability and discriminating features among the different MB subgroups.Furthermore, we visualized the ability of the classifiers based on the training and test sets, as shown in Across the different validation sets, our models consistently displayed higher performance. For example, on the GSE130051 dataset, the NB model emerged as a top-performing classifier with accuracy exceeding 0.96, while other models achieved accuracy ranging from 0.91 to 0.95 . The ANNIn summary, our analysis revealed slight variability in the performance of different prediction models across a diverse range of validation sets, with an average accuracy exceeding 0.96.p-value <0.05). The top biomarkers with the lowest p values included CBFA2T3, PRDM16, TRIM65, KIAA0182, SEMA4F, OR6N1, RPTOR, KIAA0415, SAG, and TTC15  in which the selected genes were enriched  on the model performance, we computed the Shapley values for the trained neural network model. Each gene with its corresponding beta values and their contribution in terms of Shapley values on the ANN model across different subgroups are shown in ZIC4’s hypermethylation state  has a highly positive impact on the model’s ability to predict Group 3 but has a negative effect on the WNT subgroup. At the same time, ZIC4 has a low negative impact on the model’s ability to forecast SHH and Group 4 subgroups. Additionally, we identified other genes, such as ARTN and SLC8A1, which have a positive contribution to the model’s ability to predict Group 3, with beta values equal to 0.847 and 0.327, respectively.For example, we found that CXCR4 and MEIS1 genes and lower methylation levels of NFASC had a positive impact on the ANN model’s ability to predict the Group 4 subgroup. In the WNT subgroup, ASCL2, SYNGAP1, RTNR4L, and NFASC gene hypermethylation status, as well as KIDINS220 and S100A10 gene hypomethylation, had a highly positive impact on prediction. For the SHH subgroup, we found that higher methylation levels of SLC8A1 and lower methylation levels of ROR1, CXCR4, and RTN4RL1 had a high contribution to the prediction.Furthermore, we observed that higher methylation levels of the ARTN and WNT4 for Group 3 and WNT subgroups, respectively. These genes suggest possible associations with their respective subgroups, highlighting their importance in driving molecular characteristics and prognostic outcomes. Building upon these findings, we integrated ARTN, WNT4, EP300 and ROR1 into the gene list of cluster 4, resulting in improved performance for cluster 1.We conducted network analysis using the methylation beta values of 49 genes enriched in the nervous system development process identified based on the functional enrichment analysis . The resRTN4RL1, TLX2, ARTN, WNT4, EP300, and ROR1 into the existing gene list from cluster 5. Additionally, cluster 6 was improved by using the same gene list as cluster 5. However, for cluster 3, we included SEMA4F, SLC8A1, CXCR4, SYNGAP1, NFASC, and MEIS1 in the existing list of significant genes, thereby improving its predictive power.Furthermore, by adapting a similar procedure, we intended to improve cluster 5, which initially exhibited the lowest performance. To achieve this, we incorporated the additional genes Accurate classification of molecular subgroups in medulloblastoma (MB) is vital for initiating appropriate treatment plans. In our study, we utilized a comprehensive approach integrating data and AI-based methods and utilized synthetic sample generation using SMOTE to address limited data and maintain class balance. Our developed prediction framework, MBMethPred, was designed explicitly for medulloblastoma subgroup classification using DNA methylation data. MBMethpred incorporates multiple AI models to enhance accuracy, processing speed, ease of use, and user-friendliness.et al.'s (2018) study was the classification of central nervous system tumors, rather than focusing on medulloblastoma. Furthermore, it lacked an accuracy score specifically for medulloblastoma. Similarly, Sharma et al. exclusively concentrated on the classification of Groups 3 and 4 subgroups. Additionally, both . In contrast, Compared to the molecular-based MB subgroup classification methods  Supplem, MBMethPOur study comprehensively evaluated the models’ effectiveness in classifying MB subgroups using multiple validation datasets. Although slight variations were observed in the performance of prediction models across different datasets, the overall high performance observed in our study strengthens the reliability and generalizability of the models. Thus, incorporating multiple validation sets and prediction models is essential for robust evaluation of model reliability.CBFA2T3 (. Similarly, the study by RPTOR gene, along with other identified biomarkers, in classifying non-WNT/non-SHH medulloblastomas. These findings suggest that the identified biomarkers hold the potential for predicting patient prognosis and guiding treatment decisions.Gene-specific effects on model prediction were identified using Shapley values, offering insights into the contributions of specific genes to subgroup classification. Additionally, survival analysis identified significant associations between the identified biomarkers and survival outcomes in MB patients. Moreover, the biomarkers with significant survival outcomes correlated with previously reported oncogenes. For example, the CBFA complex, which includes CBFA2T3 , is suggEP300 encodes a histone acetyltransferase protein that activates the expression of genes critical for the development and progression of medulloblastoma (CXCR4 has been suggested to be the oncogenic driver of MB (SYNGAP1 is a GTPase-activating protein that is known to cause cognitive deficits by inducing alterations in glutamatergic neurotransmission (WNT4 is a member of the Wnt signaling pathway and has been associated with the pathogenesis of WNT and SHH subgroups (Our functional enrichment analysis highlighted the association between the model performance and biological relevance. For instance, blastoma . CXCR4 her of MB . In addismission . Finallyubgroups . Thus, tFinally, network analysis considered correlations among genes enriched in nervous system development and identified distinct clusters with potential relevance to medulloblastoma. Moreover, training a separate artificial neural network model for each cluster improved the classification accuracy by gradually incorporating genes from different clusters. Thus, our integrative approach enhances the understanding of the complex molecular heterogeneity underlying medulloblastoma and provides a basis for further research.It is important to acknowledge some limitations of our study. Although we utilized gene expression profiles for data integration and further implemented SNF to define the new labels, our prediction models exclusively rely on the DNA methylation datasets. However, it is worth highlighting that the availability and accessibility of additional datasets, especially those including diverse patient populations, are currently limited, potentially impacting the generalizability of our findings. Therefore, further research in this direction is highly warranted to explore the clinical applicability of our study.In conclusion, we developed a robust classifier for medulloblastoma subgroup classification. Moreover, our functional enrichment analysis offers valuable insights into the molecular pathogenesis of medulloblastoma. Survival analysis enables the evaluation of prognostic relevance for individual biomarkers. By identifying key genes in medulloblastoma subgroup classification and their functional relevance, our study provides insights into disease stratification. While our approach has the potential to be adapted for subgroup prediction in other cancer types, it requires careful validation and adaptation to specific datasets to ensure its reliability. Despite the underlying limitations, our findings contribute to the advancement of medulloblastoma research, with the potential to improve patient outcomes.


Hydrogels of alginate and whey proteins, in the forms of beads, microparticles, microcapsules, and nanocapsules, generally provide better encapsulation efficiency and release properties for antioxidants with respect to the hydrogel of alginate alone. The main challenges for future studies are to extend knowledge on the interactions among three components, namely alginate, whey proteins, and the encapsulated bioactive compounds, and to investigate the stability of these structures under food processing conditions. This knowledge will represent the rationale basis for the development of structures that can be tailored to specific food applications.Encapsulation of antioxidants in hydrogels, i.e., three-dimensional networks that retain a significant fraction of water, is a strategy to increase their stability and bioaccessibility. In fact, low oxygen diffusivity in the viscous gelled phase decreases the rate of oxidation. Moreover, some hydrocolloids such as alginate and whey proteins provide a pH-dependent dissolution mechanism, allowing the retention of encapsulated compounds in the gastric environment and their release in the intestine, where they can be absorbed. This paper reviews the information on alginate-whey protein interactions and on the strategies to use binary mixtures of these polymers for antioxidant encapsulation. Results showed that alginate and whey proteins strongly interact, forming hydrogels that can be modulated by alginate molecular mass, mannuronic acid: guluronic acid ratio, pH, Ca In Laminaria sp., Durvillaea sp., and Sargassum sp. Alginate is a linear anionic polysaccharide chain composed of 1,4-glycosidic bond-linked α-L-guluronic acid (G) residues and β-Dmannuronic acid (M), occurring as MM-, GG-, and MG-GM-block structures [+ or Ca2+ are the most common in food applications. The H+-type alginate gels can be formed when the pH of alginate solutions is below the pKa of the uronic acid residues, which are 3.65 and 3.38 for G and M, respectively, in 0.1 M NaCl [2+ gelation can be performed in a wide pH range, but the resulting gels have different properties. In particular, the Ca2+ alginate gel obtained at pH 3.8  has stronger chain interactions and hence a denser and interconnected microstructure than Ca2+ alginate gels obtained at a pH of 5.0 and 6.8 [Alginate is one of the most common food hydrocolloids. It is extracted primarily from brown algae such as ructures . The molructures . Alginat1 M NaCl . Convers and 6.8 .2+ alginate gels, in simulated saliva, an increase in Young’s modulus is observed due to partial exchange of Ca2+ with Na+. In simulated gastric fluid (SGF), the Young’s modulus increases further and alginate gels shrink due to the protonation of free carboxylic groups and decreased repulsive forces between polymer chains. Conversely, in simulated intestinal fluid (SIF), the Young’s modulus decreases since alginate gels swell and break, due to the ion exchange between Ca2+ in gels and Na+ in digestive juice and increased repulsive forces between alginate monomers at high pH [Due to the pH sensitivity of alginate, this polymer is used for controlled release of entrapped molecules in the intestine. The kinetic of release of encapsulated compounds is studied under in vitro digestion conditions. The mechanical properties of the hydrogels, such as the elastic modulus (Young’s modulus), have also been investigated at different stages of in vitro digestion, since they are closely associated to the release properties. For Ca high pH . ConsistHowever, for hydrophilic antioxidants, the encapsulation efficiency in alginate hydrogel is critical. In fact, the alginate gel matrix is porous, which can lead to spontaneous diffusion and loss of hydrophilic antioxidants. Moreover, shrinkage of alginate microcapsules in SGF causes the gel syneresis along with the expulsion of water-soluble materials from the gel networks . In addi2+-driven gelation of β-lactoglobulin or WPI can also be achieved through two consecutive steps: the preparation of a heat-denatured protein suspension, and chilling and addition of CaCl2 which enables cross-linking of proteins and thus promotes gelation. This latter process is called cold gelation because protein denaturation occurs before gelation and is preferred for encapsulation of heat-sensitive compounds [To this aim, β-lactoglobulin is a biopolymer that can extend alginate applications. It is the main component of whey, a byproduct of cheese-making, and it is used as purified protein or is present as the main protein in whey protein isolate  or whey protein concentrate  . β-lactoompounds .2+ or heat treatment [Beside intrinsic encapsulation properties and gelling ability, WPI can act as a surfactant, because of its amphiphilic nature. Hence, WPI rapidly adsorbs to the emulsion interface where it self-aggregates and forms continuous and homogeneous membranes around the oil droplets through intermolecular β-sheets interactions. Then, gelation of these structures can be promoted by both Careatment .To extend the properties that alginate and WPI/β-lactoglobulin display when used as single hydrocolloids, binary combinations of these polymers have been designed. This review analyzes the strategies to combine alginate and WPI/β-lactoglobulin with the aims to summarize the current knowledge regarding the nature of interaction between these polymers and to discuss the advantages of using both polymers for encapsulation of hydrophilic and lipophilic antioxidants. This will lead to identify future research needs to optimize food applications of antioxidants encapsulated in these hydrogels.The literature study was performed using two databases (Scopus and Web of Science). The search included the following keywords: “alginate” and “whey”. Papers reporting the development of an encapsulation technology for antioxidants were selected, while those related to the encapsulation of microorganisms, protein, and enzymes were not included in the focus of this review.2+ addition to promote gelation. As a result, at pH values of 3.0, 5.0 or 7.0, the amount of protein retained in the hydrogel was found to increase from 11.6% at pH 7.0, to 19.1% at pH 5, to 58.6% at pH 3.0. Moreover, the release of protein in phosphate buffer occurred relatively slowly at pH 3.0, while it was rapid at pH 5.0 and 7.0. This trend was related to the strength of the electrostatic interactions between the protein and alginate molecules [The analysis of the ζ-potential of polymer solutions was applied to predict the interactions between alginate and WPI  or alginolecules . The pH-olecules . The binolecules . The exoIn a further study, using hetero-nuclear single quantum coherence NMR spectroscopy, two different alginate binding sites were identified for monomeric β-lactoglobulin (isoform A) at pH 2.65; in contrast, only one site was observed at pH 4.0, where β-lactoglobulin occurs as a dimer, confirming that the alginate–protein network strongly depends on pH . The stoBesides the occurrence of charge interactions between alginate and β-lactoglobulin, hydrophobic interactions can also be established. Raman spectroscopy analysis revealed that the interactions between WPI and alginate cause conformational changes in WPI; in particular, the ordered structures of α-helixes and β-sheets of WPI were strengthened at pH 4.0 and 5.0 ,31. In a2+, and transaminase. This information has stimulated the development of binary matrices made of these polymers. Whey protein/β-lactoglobuin and alginate mixtures were studied for the production of edible coating and film. Interestingly, the β-lactoglobulin binding properties were maintained in the dry film and after film re-dissolution, which would allow the development of new carriers for food bioactive compounds [Research so far performed has proven that strong interactions occur between alginate and WPI/β-lactoglobulin, which can be modulated by M:G ratio, alginate molecular mass, pH, Caompounds . The preompounds  but decrompounds ,33. A vaBeads or spheres are hydrogel matrices containing a dispersed bioactive compound, having a spherical shape and diameters in the range of millimeters. Depending on their polarity, the bioactive compounds dispersed in the hydrogel matrix of the beads can be directly solubilized in water and entrapped in the gel network or dissolved in lipid droplets, which in turns are entrapped in the gel network . For hydIn the following paragraphs, the strategies to encapsulate hydrophilic and lipophilic antioxidants in alginate-WPI/β-lactoglobulin hydrogels are described, which can lead to optimized delivery of these compounds in the food matrices.2+ [2 for external gelation [Taraxacum officinale) leaf extract, which is composed of hydroxycinnamic acids, among which chlorogenic, caftaric, chicoric, and caffeic acids were identified [2 solution  radical was lower than the recovery of antioxidants, namely 61% in plain alginate and 81% in alginate added with WPI. FT-IR results confirmed the occurrence of interactions between dandelion polyphenols and the employed carriers, which may explain the decrease in antioxidant activity of the encapsulated compounds compared to the free extract, as the encapsulated bioactive compounds are involved in binding with the carriers. A slight decrease in bioactivity of phenolics encapsulated in alginate had already been observed and attributed to their interactions with the carrier [Considering small hydrophilic antioxidant compounds, encapsulation in alginate hydrogel is challenging due to the porous nature of the network and the possible loss of these compounds for spontaneous diffusion and during gel shrinkage. Hence, the design of the network by combining alginate with another hydrocolloid such as WPI or β-lactoglobulin is crucial. In fact, at the low pH of SGF, carboxyl groups of alginate are protonized and hence the electrostatic repulsions among these groups lessened, favoring matrix shrinkage. In contrast, WPI chains (isoelectric point of 5.2) are positively charged, causing repulsive forces among matrix chains. Moreover, alginate slows pepsin attach to WPI. On the contrary, the ionic environment of SIF causes relaxation of the gel, making pancreatin attack easier on WPI and consequently favoring a fast release of encapsulated compounds . As disc2+ ,37. The gelation . This prentified . The dansolution , Table 1 carrier . The rel carrier .Haematocarpus validus), which are mainly composed by epigallocatechin, gallocatechin, gallic acid, catechin, 2-coumaric acid, and rutin, were encapsulated in alginate-β-lactoglobulin microbeads at pH 5.0, achieving an encapsulation yield of 84% (Oryza sativa) polyphenols extract as bioactive compounds were produced by external gelation  extract, which are mainly represented by cyanidin 3-O-rutinoside and cyanidin 3-O-glucoside. Due to the porous nature of the alginate hydrogel, the bioactive compounds could be adsorbed to the hydrogel structure. To prevent the diffusion of the bioactive compounds, coating with different polymers such as WPI, chitosan, and gelatin was applied at pH 3.5. Interestingly, the stability of encapsulated anthocyanins during refrigerated storage was investigated and results showed that the coating process using WPI was effective in protecting these compounds from degradation, with about 80% of retention after 1 month compared to 50% retention in the microbeads without WPI. The encapsulation efficiency was not reported, but it was shown that the antioxidant activity as measured with the ORAC  assay was higher for the uncoated alginate beads than for the coated alginate beads. One reason for this behavior could be that the coating polymer could interact with anthocyanin and decrease their antioxidant activity. However, the interaction of anthocyanin with the copolymer was not so strong to markedly delete their release in SGF. Indeed, the alginate beads released 76% anthocyanins, while the beads coated with chitosan, WPI, and gelatin released 73, 71, and 70%, of anthocyanins, respectively. In the SIF, the integrity of the beads was lost upon 20 min and the remaining anthocyanins were released [Using a similar approach, polyphenols from blood fruits  water extract and then the mixture was dripped into a CaCl2 solution containing the same green tea extract (2 and green tea extract at pH 2.65. The median diameter d(0.5) was in the range 0.573–1.124 mm, with the highest sizes for the pectin- and chitosan-coated beads. WPI addition to alginate increased the encapsulation efficiency for flavanols from 48 to 60%, and coating with pectin (but not with chitosan) further increased the encapsulation efficiency to 83%. As expected, the changes in the retained antioxidant activity followed the same pattern as that of flavan-3-ols. For the plain alginate-protein beads, the release of flavan 3-ols during in vitro digestion was complete and very rapid (10 min) in SGF, regardless of the type of proteins. However, coating wet beads pectin (but not with chitosan) enabled to prolong the release of flavan-3-ols in SGF, even if after 2 h of incubation in the SGF 50% of flavan-3-ols were released. A complete release of these compounds occurred in the SIF [The basic external gelation procedure can also be modified by introducing a coating step of alginate-WPI beads with other polymers . Initial extract , Table 1 the SIF .3 was dispersed in the water phase. Sunflower oil containing Tween-80 as a surfactant and β-carotene was emulsified with the water phase. To promote internal gelation, sunflower oil containing glacial acetic acid was then added. Subsequently, the oil was removed by centrifugation and the microparticles were washed with ethanol. The microparticles obtained had average particle size of 0.3 mm, which was lower than that observed with the external gelation procedure .The internal gelation technique was used to produce microparticles of alginate and WPI . In contOlea europaea sativa) leaf extract, which mainly consists of oleuropein and hydroxytyrosol [Encapsulation efficiency for total hydroxycinnamic acids was about 80%, which was higher than that obtained by the external gelation procedure with alginate alone . Lower radical scavenging capacity as measured with the DPPH  radical was observed for internal/emulsion produced microparticles in comparison to plain alginate obtained by external/hydrophilic encapsulation, which might be due to the interaction between hydroxycinnamic acids and WPI. Indeed, hydrolysis of the microparticles and subsequent HPLC analysis coupled with FT-IR monitoring revealed that specific interactions occurred between hydroxycinnamic acids and the carrier polymers, especially in presence of WPI. Interestingly, WPI enabled β-carotene retention in the system, but the retention percentage was not reported. The high affinity of β-lactoglobulin for carotenoids, especially β-carotene had already been confirmed ,51. Resuytyrosol . In thisytyrosol ,53,54. Tytyrosol .2+, coating of β-lactoglobulin-stabilized emulsion can occur in a wide pH range, also above pH 6.0 where both polymers are negatively charged [2 for gelation (Lycopersicum eculentum), alginate and WPI were used as carriers and gelation was performed at pH 7.0, in presence of Ca2+ as described above [The encapsulation of lipophilic antioxidants in alginate-protein beads can be achieved using a carrier oil phase, which along with proteins acting as emulsifiers—forms small oil droplets that can be dispersed in the alginate hydrogel. When β-lactoglobulin is used as an emulsifier to stabilize oil droplets, coating of the droplets with alginate can be obtained from pH 3.0 to pH 6.0 at low ionic strengths, i.e., 100 mM NaCl. The alginate-coated WPI-oil droplets have better stability to flocculation. At pH 6 and 7, coating does not occur because of the strong electrostatic repulsion between the anionic alginate and anionic protein on the droplet surface ,56. Howe charged ,58. Follgelation , Table 1ed above . On the ed above . Therefoed above .2+ to encapsulate black pepper (Piper nigrum) essential oil [Alginate-β-lactoglobulin microcapsules were obtained by using transglutaminase , Table 1tial oil . The blatial oil . Similartial oil .Further studies evidenced that in the presence of 0.5% alginate, the gastric digestion of crosslinked WPI simulated in vitro is effectively reduced from 29% to 8%. The protection by alginate towards protein digestion was attributed to the charge interactions between polymers. Notably, digestion was also slowed in the intestinal phase even though the alginate protein particles were dissociating due to the pH of 7.0 of the SIF . In anot2+ addition. The average size of the primary emulsion droplets containing curcumin was 359 nm, whereas in the case of the secondary emulsion, it was increased to 841 nm. The encapsulation efficiency was 100%. In vitro digestion showed that in the gastric conditions, the emulsions remained stable, thereby not allowing the curcumin to be released from the emulsified droplets. Maximum releases of curcumin of almost 63 and 71% were attained after 2 h for the primary and secondary emulsion, respectively, and thereafter, it remained constant [A nanoencapsulation strategy based on binary all material combination WPI-alginate was proposed using a carrier oil for lipophilic antioxidants. A primary emulsion was obtained with olive oil containing curcumin and WPI, at pH 7.0 by ultrasonication. Then, the pH of the primary emulsion was adjusted to 5, added with alginate, and sonicated further. Hence, the alginate layer was formed by electrostatic interaction with WPI, without Caconstant .2. Additional layers of alginate coating were applied by repeating the procedure of alginate and CaCl2 addition up to four times. Encapsulation efficiency was not reported. This study demonstrated that the swelling and release behavior of the whey protein hydrogels can be changed easily with different layers of alginate coating. However, the release behavior under simulated digestion conditions was not investigated [Thymus vulgaris) oil with particle size of approximately 200 nm had decreased antimicrobial capacity, then free thyme oil [2  can also be applied to create carrier nanostructures. A spontaneous association of oppositely charged alginate and β-lactoglobulin can occur at low concentrations (<4.5%); this phenomenon is also called complex coacervation [Self-assembly between alginate and whey protein  is present. Alternatively, lipophilic compounds can be directly loaded into β-lactoglobulin followed by coating with alginate, without an oily phase, which leads to nanocapsules with high encapsulation efficiency and high release in SIF.Lipophilic antioxidants can be dissolved into an oil phase, emulsified by WPI/β-lactoglobulin and then coated with alginate in the presence of CaOne point to notice is that in most of the studies so far performed, no information is reported regarding relevant factors such as alginate molecular mass, M:G ratio, and pH of gelation, which would be useful to correlate with the observed encapsulation efficiency and release properties. Additionally, for both hydrophilic and lipophilic bioactive compounds, interactions with the carrier polymers can cause a decreased antioxidant activity.In this context, the main challenge for the future studies is to extend knowledge on the interactions among three components, namely alginate, WPI, and the encapsulated bioactive compound. This will represent the rationale basis for the development of structures that can be tailored to specific food applications. Moreover, knowledge on the physical and chemical stability of the structures under different conditions relevant to food processing and storage would help a better design of applications.