Accessory mental foramen (AMF) is a rare anatomical variation. When accessory mental foramen is present, the nerves and vessels that go through the mental foramen (MF) must follow alternative courses and special care must be taken during dental treatment planning. The purpose of this study was to evaluate the occurrence and the location of AMF in a selected Polish population using cone-beam computed tomography (CBCT). Two hundred CBCT (105 males and 95 females) examinations were evaluated for the presence of AMFs. The location and side of AMFs were reported. The mean distance between MF and AMF was also calculated. The vertical size of MF on the side with and without AMF was measured. The obtained variables were statistically analyzed. AMFs were observed in 7% of the patients. There was no statistically significant difference between the appearance of AMF and sex (p > 0.05). We found no significant difference in the vertical size of MF between individuals with and without AMFs (p < 0.05). Twenty-eight AMFs (7%) were observed from 400 sides of 200 patients. AMFs occurred more often in males (18 AMFs) than in females (10 AMFs). Twenty AMFs (71.4%) were located anteriorly, and eight (28.6%) - posteriorly. Fifteen AMFs (53.6%) were on the right side and thirteen (46.4%) - on the left.

In Wolfram syndrome (WFS), due to the loss of wolframin function, there is increased ER stress and, as a result, progressive neurodegenerative disorders, accompanied by insulin-dependent diabetes. The aim of the study was to evaluate the oral microbiome and metabolome in WFS patients compared with patients with type 1 diabetes mellitus (T1DM) and controls. The buccal and gingival samples were collected from 12 WFS patients, 29 HbA1c-matched T1DM patients (p = 0.23), and 17 healthy individuals matched by age (p = 0.09) and gender (p = 0.91). The abundance of oral microbiota components was obtained by Illumina sequencing the 16S rRNA gene, and metabolite levels were measured by gas chromatography–mass spectrometry. Streptococcus (22.2%), Veillonella (12.1%), and Haemophilus (10.8%) were the most common bacteria in the WFS patients, while comparisons between groups showed significantly higher abundance of Olsenella, Dialister, Staphylococcus, Campylobacter, and Actinomyces in the WFS group (p < 0.001). An ROC curve (AUC = 0.861) was constructed for the three metabolites that best discriminated WFS from T1DM and controls (acetic acid, benzoic acid, and lactic acid). Selected oral microorganisms and metabolites that distinguish WFS patients from T1DM patients and healthy individuals may suggest their possible role in modulating neurodegeneration and serve as potential biomarkers and indicators of future therapeutic strategies.

Purpose: The study was performed to analyze the prevalence of the conjunctival ultraviolet autofluorescence (CUVAF) area in keratoconic eyes and changes caused by UVA-irradiation as a component of accelerated corneal cross-linking (aCXL). Methods: The study group involved 20 keratoconic patients subjected to aCXL surgery in one eye. The comparative group consisted of 111 age- and sex-matched patients with healthy corneas. The images of the anterior segment in both patient groups were taken using a Coroneo camera. In the study group the photos were taken before and immediately after the surgery, and 7 and 30 days following the procedure. Results: Nasal and temporal autofluorescence area (AN+T) were significantly smaller in a keratoconic patients group compared to control group (p = 0.0001). Patients with the third stage of keratoconus had significantly higher AN+T (p = 0.0277) compared with individuals with lower stage keratoconus. No statistically significant CUVAF changes were observed after the aCXL procedure. In keratoconic patients with primary CUVAF undergoing aCXL, a temporary fast enlargement of the autofluorescence area was observed. Conclusions: The eyes undergoing the aCXL procedure showed no difference in the size of the CUVAF area but such patients should be in strict follow-up in order to reveal UV-related ocular surface diseases.

Alström (ALMS) and Bardet-Biedl syndromes (BBS) are rare ciliopathies characterized by obesity and hyperglycemia that lead to type 2 diabetes, but also other disorders, including neurodegeneration. However, isolated clinical manifestations can be observed in carriers of heterozygous mutations in the ALMS1 and BBS genes. Recently, the influence of oral bacteria on the presence of obesity, type 2 diabetes, and neurodegenerative processes has been widely discussed. The purpose of the research project was to analyze the profile of the microbiome in the oral cavity by sequencing the 16S rRNA gene in ALMS/BBS patients and carriers of causative variants in these genes. Oral mucosal swabs were taken from 8 ALMS/BBS patients, 24 family members, 20 obese patients, and 29 healthy individuals. Streptococcus (30.7%), Haemophilus (18.9%), and Prevotella (11%) were the most common bacteria in the study group. Comparison between groups showed a higher abundance of Prevotella, Enterococcus, Eikenella, Capnocytophaga, Parvimonas, Selenomonas, and Corynobacterium, and a lower abundance of Lactobacillus in the study group compared to other groups. The specific profile of the oral microbiome found in patients with variants in the ALMS1 and BBS genes may enable the identification of the modulatory role of the oral microbiome in these disorders and point to new directions for additional therapy for these patients and heterozygous family members in the future.

Metabolomic profiling enables the identification of specific biochemical alterations in various diseases, including rare monogenic diabetes and obesity syndromes such as Alström syndrome (ALMS) and Bardet–Biedl syndrome (BBS). These disorders are characterized by early-onset obesity, insulin resistance, diabetes mellitus, retinodystrophy and other symptoms, but some features may also occur in heterozygous carriers of pathogenic or likely pathogenic variants in ALMS1 and BBS genes. The aim of the study was to compare the metabolomic profiles of saliva, gingival crevicular fluid (GCF) and serum between ALMS/BBS patients and heterozygous carriers (n = 33) in relation to participants with simple obesity (n = 20) and healthy controls (n = 30) using gas chromatography coupled to mass spectrometry (GC-MS). The study showed significant differences in the levels of metabolites in saliva, GCF and serum, comparing the ALMS+BBS group with the other groups. In the analysis of statistically significant metabolites in all matrices, seven of them (valine, 3-HBA, alanine, threonine, urea, isoleucine, and phenylalanine) were consistently significant in all sample types. Levels of aromatic amino acids/branched-chain amino acids in saliva and serum were correlated with insulin resistance and the severity of obesity. In conclusion, the study identifies metabolic indicators that distinguish individuals with monogenic diabetes and obesity syndromes or genetic predisposition alone from other groups. The multi-matrix approach using available biological materials such as saliva and GCF provides a broader view of the metabolic alterations associated with variants in ALMS1 and BBS genes. This approach may support both future mechanistic and translational studies of metabolic dysfunction in these populations.

Artificial intelligence (AI) has emerged as a promising tool in endodontic diagnostics, particularly in radiographic interpretation and treatment outcome prediction. This narrative review provides a comprehensive overview of current applications of machine learning (ML) and deep learning (DL) algorithms in endodontics, focusing on periapical lesion detection, assessment of root canal morphology, prognosis prediction, and navigation-guided procedures. A literature search was conducted in PubMed, Scopus, and Web of Science for studies published between 2021 and 2026. Following qualitative screening, 45 studies were included in the evidence synthesis. Given the narrative design of the review, no formal meta-analysis was performed. The reviewed evidence indicates that convolutional neural networks (CNNs), U-Net architectures, YOLO-based systems, and other AI models have demonstrated promising diagnostic performance across multiple imaging modalities, with the highest performance generally reported for CBCT-based applications. However, the current body of evidence remains limited by retrospective study designs, small datasets, methodological heterogeneity, and insufficient external validation. Overall, AI should be considered a valuable adjunctive decision-support tool in endodontic diagnostics rather than a replacement for clinical expertise. Further prospective multicenter studies, external validation, and standardized methodological frameworks are required to support its reliable integration into routine clinical practice.