Objective: This study investigated the relationship of anthropometric and metabolic risk factors with seminal and sex steroidal hormone parameters in a screened population of healthy males. Methods: The participants were healthy young men without chronic or congenital diseases. The body composition parameters that we investigated were measured weight, height, and waist circumference (WC), as well as bioelectrical impedance analysis. Semen samples were analyzed for semen volume, sperm concentration, sperm motility and morphology, seminal pH, and liquefaction time. Biochemistry analysis, including glucose and lipid metabolism parameters, was conducted on fasting blood samples. Testicular volume was calculated separately for each testis using ultrasonography. Results: Body mass index exhibited an inverse association with total sperm count. WC showed negative correlations with numerous seminal parameters, including sperm concentration, total sperm count, sperm morphology, and follicle-stimulating hormone levels. The basal metabolic rate was associated with seminal pH, liquefaction time, and sperm motility. WC, fat mass percentage, and triglyceride levels exhibited negative correlations with sex hormone binding globulin. The measures of glucose metabolism were associated with a greater number of seminal parameters than the measures of cholesterol metabolism. C-reactive protein levels were inversely associated with sperm concentration and total sperm count. Conclusion Anthropometric and metabolic risk factors were found to predict semen quality and alterations in sex steroidal hormone levels.

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, includingcanal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, thesensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.