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Contemporary scientific discussions are increasingly focusing on Akkermansia muciniphila due to its complex influence on intestinal physiology. This article provides a comprehensive analysis of the various effects Akkermansia muciniphila has on intestinal inflammation, while also exploring its potential associations with obesity and cardiovascular diseases. A systematic literature search was conducted using PubMed, Google Scholar, and ResearchGate with the following keywords: Akkermansia muciniphila, obesity, cardiovascular risk, and inflammatory bowel diseases. The aim of our mini-review was to examine the impact of Akkermansia bacteria on the intestines, cardiovascular system, and its relationship with obesity. Through a detailed review of current literature, the article seeks to elucidate the complex interactions of Akkermansia muciniphila within the human body, highlighting its potential contributions to health improvement and medical interventions. Research indicates that Akkermansia muciniphila positively correlates with maintaining intestinal health, modulating the cardiovascular system, and aiding in weight management. However, the number of studies available is small, and the effects of Akkermansia muciniphila on human health require further research.

Abstract To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

(1) Background: A free gingival graft (FGG) is a common technique used to reconstruct or enhance the area of keratinized mucosa, while a connective tissue graft (CTG) is utilized to boost soft tissue thickness, thereby promoting stability in interproximal marginal bone levels. Most reported complications following FGG procedure are associated with the donor site. In addition to a painful, open wound in the palate, the most frequent complications linked to FGG harvesting include excessive bleeding, postoperative bone exposure, and recurrent herpes lesions. Numerous methods for securing the donor site after a free gingival graft surgery have been documented in research publications. The main objective of this systematic review was to assess various techniques for protecting the palate after graft harvesting and their impact on patient experience, with a focus on pain management. The secondary objective was to evaluate these techniques in relation to donor site wound healing. (2) Methods: The search was performed across four databases: Medline (PubMed Central), Scopus, Web of Science, and Embase, in accordance with PRISMA guidelines and the recommendations set forth in the Cochrane Handbook for Systematic Reviews of Interventions. The initial search took place on 9 October 2023, followed by an update on 28 June 2024. The search utilized the following keywords: (“wound” OR “injury”) AND (“graft” OR “free gingival graft” OR “graft harvesting”) AND (“healing” OR “recovery”) AND “palate”. (3) Results: After conducting the follow-up search, a total of 958 papers were identified: 193 from PubMed, 314 from Scopus, 101 from Web of Science, and 350 from Embase. Ultimately, of the 49 papers that remained, 11 were excluded due to not fulfilling the inclusion criteria, leaving 38 full-text papers on free gingival grafts (FGG) for qualitative analysis. (4) Conclusions: Various methods for palatal protection after free gingival grafts (FGG) are described in the literature, stemming from biological, physical, or chemical sources. Most studies in this review examined platelet-rich fibrin and suggested that it provides no benefits for patients’ subjective experiences or wound healing outcomes. While photobiomodulation appears to support wound epithelialization, it does not influence pain perception. Alternatives such as propolis, hyaluronic acid, and medicinal plant extracts show potential for palatal protection; however, further research is needed to thoroughly evaluate their effectiveness.

Background/Objectives: To assess the impact of a vendor-agnostic deep learning model (DLM) on image quality parameters and noise reduction in dental cone-beam computed tomography (CBCT) reconstructions. Methods: This retrospective study was conducted on CBCT scans of 93 patients (41 males and 52 females, mean age 41.2 years, SD 15.8 years) from a single center using the inclusion criteria of standard radiation dose protocol images. Objective and subjective image quality was assessed in three predefined landmarks through contrast-to-noise ratio (CNR) measurements and visual assessment using a 5-point scale by three experienced readers. The inter-reader reliability and repeatability were calculated. Results: Eighty patients (30 males and 50 females; mean age 41.5 years, SD 15.94 years) were included in this study. The CNR in DLM reconstructions was significantly greater than in native reconstructions, and the mean CNR in regions of interest 1-3 (ROI1-3) in DLM images was 11.12 ± 9.29, while in the case of native reconstructions, it was 7.64 ± 4.33 (p < 0.001). The noise level in native reconstructions was significantly higher than in the DLM reconstructions, and the mean noise level in ROI1-3 in native images was 45.83 ± 25.89, while in the case of DLM reconstructions, it was 35.61 ± 24.28 (p < 0.05). Subjective image quality assessment revealed no statistically significant differences between native and DLM reconstructions. Conclusions: The use of deep learning-based image reconstruction algorithms for CBCT imaging of the oral cavity can improve image quality by enhancing the CNR and lowering the noise.

Rationale and objectives: Evaluation of the diagnostic value of linearly blended (LB) and virtual monoenergetic images (VMI) reconstruction techniques with and without metal artifacts reduction (MAR) and of adaptive statistical iterative reconstructions (ASIR) in the assessment of target vessels after branched/fenestrated endovascular aortic repair (f/brEVAR) procedures. Materials and methods: CT scans of 28 patients were used in this study. Arterial phase of examination was obtained using a dual-energy fast-kVp switching scanner. CT numbers in the aorta, celiac trunk, superior mesenteric artery, and renal arteries were measured in the following reconstructions: LB, VMI 60 keV, VMI MAR 60 keV, VMI ASIR 60 % 60 keV. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated for each reconstruction. Luminal diameters (measurements at 2 levels of stent) and subjective image quality (5-point Likert scale) were assessed (2 readers, blinded to the type of reconstruction). Results: The highest mean values of CNR and SNR in vascular structures were obtained in VMI MAR 60 keV (CNR 12.526 ± 2.46, SNR 17.398 ± 2.52), lower in VMI 60 keV (CNR 11.508 ± 2.01, SNR 16.524 ± 2.07) and VMI ASIR (CNR 11.086 ± 1.78, SNR 15.928 ± 1.82), and the lowest in LB (CNR 6.808 ± 0.79, SNR 11.492 ± 0.79) reconstructions. There were no statistically significant differences in the measurements of the stent width between reconstructions (p > 0.05). The highest subjective image quality was obtained in the ASIR VMI (4.25 ± 0.44) and the lowest in the MAR VMI (1.57 ± 0.5) reconstruction. Conclusion: Despite obtaining the highest values of SNR and CNR in the MAR VMI reconstruction, the subjective diagnostic value was the lowest for this technique due to significant artifacts. The type of reconstruction did not significantly affect vessel diameter measurements (p > 0.05). Iterative reconstructions raised both objective and subjective image quality.

The purpose of this study was to evaluate whether intravoxel incoherent motion (IVIM) parameters can enhance the diagnostic performance of MRI in differentiating normal pancreatic parenchyma from solid pancreatic adenocarcinomas. This study included 113 participants: 66 patients diagnosed with pancreatic adenocarcinoma and 47 healthy volunteers. An MRI was conducted at 1.5 T MR unit, using nine b-values. Postprocessing involved analyzing both conventional monoexponential apparent diffusion coefficient (ADC) and IVIM parameters (diffusion coefficient D-pure molecular diffusion coefficient, perfusion-dependent diffusion coefficient D*-pseudodiffusion coeffitient, and perfusion fraction coefficient (f)) across four different b-value selections. Significantly higher parameters were found in the control group when using high b-values for the pure diffusion analysis and all b-values for the monoexponential analysis. Conversely, in the study group, the parameters were affected by low b-values. Most parameters could differentiate between normal and cancerous tissue, with D* showing the highest diagnostic performance (AUC 98–100%). A marked decrease in perfusion in the patients with pancreatic cancer, indicated by the significant differences in the D* medians between groups, was found. In conclusion, standard ADC maps alone may not suffice for a definitive pancreatic cancer diagnosis, and incorporating IVIM into MRI protocols is recommended, as the reduced tissue perfusion detected by the IVIM parameters is a promising marker for pancreatic adenocarcinoma.

Abdominal aortic aneurysm (AAA) is defined as a localized enlargement of the aortic cross-section where the diameter is greater than 3 cm or more than 50% larger than the diameter in a normal segment. The most important complication of AAA is rupture, which, if untreated, results in mortality rates of up to 90%. Conventional open surgical repair is associated with significant 30-day mortality. Endovascular aneurysm repair (EVAR) is a significantly less invasive procedure; it is related to a lower early mortality rate and a lower number of perioperative complications. Although EVAR is a minimally invasive technique, lifelong follow-up imaging is necessary due to possible late complications including endoleak, recurrent aneurysm formation, graft infection, migration, kinking and thrombosis. The total rate of complications after EVAR is estimated at approximately 30%, and the rate of complications that require intervention is 2–3%. Early detection and progression analysis of such situations is crucial for proper intervention.