Objective: This meta-analysis with a systematic review was undertaken to assess the association between APOE allelic genotypes and the risk of Alzheimer’s disease (AD) in the Italian population. Methods: The Web of Science, PubMed, and Scopus databases were searched until 15 November 2023. The odds ratio (OR) with a 95% confidence interval (CI) was calculated using fixed and random effect models, depending on the I² statistic value. The systematic review and meta-analysis were conducted in agreement with the PRISMA guideline and registered with PROSPERO (CRD42023492580). Results: Our meta-analysis based on 15 studies revealed a higher risk of AD among Italian individuals carrying the APOE ε4 allele (OR = 3.60, 95% CI [2.90–4.47], p < 0.0001). The association of AD genotype APOE ε2ε4 (OR = 1.36, 95% CI [0.76–2.41], p = 0.29) was not statistically significant, while APOE ε3ε4 (OR = 3.43, 95% CI [2.95–3.99], p < 0.0001) has a high risk of AD development; the risk is more notably in the APOE ε4ε4 genotype (OR = 7.08, 95% CI [4.22–11.86], p < 0.0001). The APOE ε2 allele has a protective effect (APOE ε2 (OR = 0.47, 95% CI [0.29–0.74], p = 0.0013)), and similar results were achieved by APOE ε3 (OR = 0.49, 95% CI [0.37–0.65], p < 0.0001). Subgroup analysis of three areas of Italy (southern, northern, and center) revealed that that APOE ε4 allele was a risk factor with a higher OR in northern Italy (OR 4.22; 95% CI [3.46–5.16], p < 0.0001) compared to southern and center Italy (OR 3.02; 95% CI [2.28–4.01], p < 0.0001 and OR 3.97; 95% CI [1.37–11.56], p < 0.0001, respectively). As well, APOE ε4ε4 genotype carriers had a significantly higher OR in northern Italy (OR 9.69; 95% CI [4.94–18.99], p < 0.0001) compared to in southern and center Italy (OR 4.38; 95% CI [1.54–12.47], p < 0.0001 and OR 3.59; 95% CI [0.87–14.86], p < 0.0001, respectively). Conclusions: This systematic review with a meta-analysis of the Italian population on APOE alleles, genotyping, and AD incidence, highlights that individuals harboring APOE ε4 have a higher risk of developing AD compared to those with other alleles. It also supports the protective effect of the APOE ε2 allele against the progress of AD. The qualitative analysis on the complex genetic interactions influencing Alzheimer risk emphasizes the need for further research on genetic and environmental factors for effective prevention strategies.

Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed to evaluate the performance of the MySLR machine learning platform, which implements the Latent Dirichlet Allocation (LDA) algorithm in the identification and screening of papers present in the literature that focus on mutations of the apolipoprotein E (ApoE) gene in Italian Alzheimer’s Disease patients. Results: MySLR excludes duplicates and creates topics. MySLR was applied to analyze a set of 164 scientific publications. After duplicate removal, the results allowed us to identify 92 papers divided into two relevant topics characterizing the investigated research area. Topic 1 contains 70 papers, and topic 2 contains the remaining 22. Despite the current limitations, the available evidence suggests that articles containing studies on Italian Alzheimer’s Disease (AD) patients were 65.22% (n = 60). Furthermore, the presence of papers about mutations, including single nucleotide polymorphisms (SNPs) ApoE gene, the primary genetic risk factor of AD, for the Italian population was 5.4% (n = 5). Conclusion: The results show that the machine learning platform helped to identify case-control studies on ApoE gene mutations, including SNPs, but not only conducted in Italy.

Kefiran is a heteropolysaccharide that is considered a postbiotic and is obtained by kefir grains fermented in cow’s milk, while little is known about the donkey milk (DM) variety. Postbiotics are recognised as having important human health benefits that are very similar to probiotics but without the negative effects associated with their ingestion. Donkey is a monogastric animal, as are humans, and when used as an alternative food for infants who suffer from cow milk protein allergies, DM could therefore display more biocompatibility. In this study, the DM kefiran was extracted by ultrasound from kefir grains cultured in donkey milk and fully characterized for its structural and physicochemical properties by Fourier-transform infrared spectroscopy (FT-IR), High-Performance Liquid Chromatography- Refractive Index (HPLC-RI), Scanning electron microscope (SEM), Differential Scanning Calorimeters (DSC) and rheological analyses. In addition, tests were conducted on keratinocytes cell lines and human red blood cells to assess the nontoxicity and haemolysis degree of the polymer. The extraction yield of the DM kefiran was 6.5 ± 0.15%. The FT-IR analysis confirmed the structure of the polysaccharide by showing that the stretching of the C-O-C and C-O bonds in the ring, which formed two bands at 1157 and 1071 cm⁻¹, respectively, and the anomeric band at 896 cm⁻¹ indicates the β configuration and vibrational modes of glucose and galactose. Results were confirmed by HPLC-RI analysis indicating that the ratio glucose/galactose was 1:0.87. Furthermore, the SEM analysis showed a porous and homogeneous structure. The rheological analysis confirmed the pseudoplastic nature of the polymer, while the DSC analysis highlighted excellent thermal resistance (324 °C). Finally, DM kefiran was revealed to have biologically acceptable toxicity, showing a haemolytic activity of less than 2% when using fresh human red blood cells and showing no cytotoxicity on human keratinocytes. Therefore, kefiran obtained by DM shows an excellent biocompatibility, establishing it as a promising polymer for bioengineering human tissue for regenerative applications.

Background: Critical studies have unwaveringly established the importance of peculiar single-nucleotide polymorphisms (SNPs) in apolipoproteins (Apos) genes as genetic risk factors for dyslipidemias and their related comorbidities. In this study, we employed in silico approaches to analyze mutations in Apos. Methods: A comprehensive set of computational tools was utilized. The tools for predictions derived from sequence analysis were: SIFT, PolyPhen-2, FATHMM and SNPs&GO; The tools for structure analysis were: mCSM, DynaMut2, MAESTROweb, and PremPS; for prediction of pathogenic potential were: MutPred2, and PhD-SNP; for profiling of aggregation propensity were: Camsol, and Aggrescan3D 2.0, and lastly, for residual frustration analysis, the Frustratometer was used. These approaches assess variant effects on protein structure, stability, and function. Results: We identified seventeen SNPs in total, twelve for ApoB, one for ApoC2, one for ApoC3, and three for ApoE, representing 70%, 6%, 6% and 18%, respectively. The pathogenity of ApoE, was highlighted in two SNPs the rs769452 with amino acid replacement L46P, and rs769455 with amino acid replacement R163C. The aggregation/solubility analysis revealed that the L46P leads to a decrease in ApoE aggregation. The R163C, showed a decrease in solubility in one of two tools used, resulting in destabilizing effects altering its solubility. Conclusions: The two mutations in ApoE studied with the in silico methodologies identified clinically significant genetic variants, highlighting the robustness of the integrated approach. The future direction of the research is to create a multiplex panel with the SNPs identified here in APOE and expanding to other proteins to have a panel genetic risk assessment and disease prediction in which ApoE correlates.

A critical yet frequently overlooked factor is the tumor’s metabolic profile. Diabetes and chronic moderate hyperglycemia are known risk factors for triple-negative breast cancer (TNBC) that do not respond to hormonal therapy. So, identifying novel therapeutic targets and developing more effective treatments is needed. One of the key pathways involved in the aggressive nature of TNBC is the Toll-like receptor 4 (TLR4) signaling cascade. To this end, curcumin (CUR) has shown effects consistent with modulating inflammatory stress by inhibiting TLR4/MD-2. This study evaluated CUR at concentrations observed in the bloodstream (0.025–25 ng/mL) in MDA-MB-231 TNBC cells under different glucose conditions (normal, moderate, and severe hyperglycemia) and inflammatory states (LPS-induced), using cell viability assays and molecular docking. A zinc complex (Zn–CUR) was also used. Results were validated through cell viability assays. Under severe hyperglycemia, CUR unexpectedly increased cell viability in a dose-dependent manner, while Zn–CUR had no activity across all glucose levels. In LPS-induced inflammation, CUR exhibited a biphasic, dose-dependent response, being protective at mid-level doses but cytotoxic at higher doses, whereas Zn–CUR showed more consistent effects, consistent with modulation of inflammatory stress. Molecular docking suggests that Zn–CUR binds more stably within the MD-2 hydrophobic pocket than CUR, particularly when bound to LPS, with binding energies of −8.7 and −8.3 kcal/mol, respectively. However, better in silico affinity did not always translate into improved cellular effects. These findings indicate that metabolic context significantly influences CUR’s biological activity and that forming a zinc complex offers a safer, more reliable profile. This positions Zn–CUR as a candidate warranting further investigation for TNBC, particularly in the context of hyperglycemia.

Background: Targeting Apolipoprotein E4 (ApoE4) represents a frontier in Alzheimer’s disease therapeutics. This study investigates the therapeutic potential of a nutraceutical panel (Polydatin, trans-resveratrol, luteolin, and PEA) by exploring their interaction with the ApoE4 EZ-482 cavity. Methods: Using a dual-platform docking strategy (SwissDock and Schrödinger Maestro) across three structural constructs. Results and Discussion: We identified the full-length protein (1–299) as the optimal target, showing a robust correlation between normalized docking scores (Spearman ρ = 0.79). Crucially, biophysical analysis via dynamic light scattering (DLS) revealed that the ApoE4–oxLDL complex exhibits a ζ-potential of −10.97 mV, a state prone to pathological aggregation. Luteolin and PEA effectively altered this electrostatic environment, inducing significant positive shifts to +2.15 mV and +1.05 mV, respectively. The alignment between computational rankings and experimental ζ-potential perturbations supports the predictive reliability of our model. These findings suggest that nutraceuticals can modulate the ApoE4–oxLDL biophysical profile and highlight that a full structural context is mandatory for developing effective ApoE4-targeted interventions.