Aims: To investigate systemic risk factors contributing to the formation of sialolithiasis. Methods: A retrospective big-data cohort study was conducted using data from Clalit HMO in Israel. Sialolithiasis patients were identified based on ICD-10 codes and matched with controls. Univariate and multivariable logistic regression analyses were performed to assess the relationship between systemic conditions and sialolithiasis. P-value < 0.05 was considered significant. Results: 5100 sialolithiasis cases were collected. The statistical analysis revealed that hypertension (OR = 1.14, 1.08–1.24, p < 0.001), dyslipidemia (OR = 1.33, 1.27–1.41, p < 0.001), nephrolithiasis (OR = 1.55, 1.42–1.63, p < 0.001) and cholelithiasis (OR = 1.22, 1.16–1.27, p < 0.001) were significantly correlated with sialolithiasis. Conclusions: Systemic diseases, particularly dyslipidemia, play a role in the development of sialolithiasis. Further research is needed to explore the underlying mechanisms linking these conditions and to develop targeted strategies for the prevention of sialolithiasis. Clinical relevance: This study highlights the potential interplay between systemic conditions and sialolithiasis. Recognizing these associations can inform clinical practice in understanding the pathogenesis of the disease, risk assessment, early diagnosis, and preventive measures.

Background and Objectives: Maintenance of a firm and long-term stable osseointegration is the primary goal of implant dentistry. Time is used to define implant failure characteristics. Early implant failure (EIF) occurs up to one year after loading. Recent studies indicated an association between proton pump inhibitors (PPI) therapy and failure of osseointegration. The present study assessed whether the use of PPIs is a risk factor to EIF. Materials and methods: A retrospective cohort study including 687 patients and 2971 dental implants. The study group (PPIs users) comprised 17.3% (119) individuals and 18.7% (555) implants. The remaining cohort (82.7% (568) individuals and 81.3% (2416) implants) served as control. The information was taken from the patients’ files. The following information was collected: age, gender, physical status, systemic diseases, HbA1C values before and after implant-supported prosthesis delivery in cases of diabetes mellitus, smoking, implant location, number of implants per individual, bone augmentation, implant brand, length and width, and EIF. EIF was defined as implant removal within a period of up to 12 months from loading. Results: EIF in PPIs vs. non-PPIs users was 19.3% vs. 14.3% (p = 0.16) at patient level and 5.4% vs. 3.5% at implant level (p = 0.03). Univariate analysis yielded factors significantly associated with PPIs use, including older age, physical status of the American Society of Anesthesiology (ASA) 3, hypertension, hyperlipidemia, diabetes mellitus, osteoporosis, cardiovascular accident (CVA), location (anterior mandible), shorter and narrower implants, and higher number of implants per individual. Multivariate analysis yielded statistically significant OR of 1.91; p = 0.01 for EIF following PPIs use and 2.3; p < 0.001 for location in anterior mandible. Conclusions: Patients and their healthcare providers are advised to carefully consider the potential risks of taking PPIs prior to dental implant surgery. Further research is needed to confirm these risks and elucidate systemic and local factors that may be involved in such outcomes.

Background: Anticoagulants (AC) are among the most often prescribed drugs in the world. Data regarding ACs’ effect on the osseointegration of dental implants is lacking. Purpose: The aim of the present retrospective cohort study was to evaluate the effect of anticoagulants (AC) on early implant failure (EIF). The null hypothesis was that the use of AC increases the incidence of EIF. Materials and Methods: The research included 687 patients who underwent 2971 dental implant placements in the department of oral and maxillofacial surgery in Rabin medical center, Beilinson hospital, by specialists in oral and maxillofacial surgery. The study group comprised 173 (25.2%) patients and 708 (23.8%) implants using AC. The rest of the cohort served as a control. A structured form was used to collect data at patient and implant level. EIF was defined as implant failure within a period of up to 12 months from loading. EIF was the primary outcome parameter. A logistic regression model was used to predict EIF. Results: Implants placed in individuals ≥ 80 (odds ratio (OR) = 0.34, p = 0.05), and ASA 2/3 vs. ASA 1 individuals (OR = 0.30, p = 0.02/OR = 0.33, p = 0.03, respectively) had decreased odds of EIF, and implants in those using anticoagulants (OR = 2.64, p = 0.01) had increased odds of EIF. At the patient level, the odds of EIF in ASA 3 (OR = 0.53, p = 0.02) and IHD (OR = 0.40, p = 0.02) individuals decreased. In AF/VF (OR = 2.95, p = 0.01) individuals, EIF odds increased. Conclusions: Within the limitations of the present study, the use of AC is significantly associated with an increased likelihood of EIF: the OR was 2.64. Future research is required to validate and examine the prospective impact of AC on the osseointegration phenomena.

Background and Objectives: The success rates of surgical dental implant insertions are high. However, knowledge of patients’ recovery is still lacking. “Health-related quality of life” (HRQOL) questionnaires are gaining popularity in all fields of medicine. The present survey assessed the perception of recovery after the surgical placement of dental implants. Materials and Methods: Forty individuals (26 women and 14 men; mean age, 55 ± 12 years) filled a questionnaire evaluating patients’ perception of recovery for 7 consecutive days post-surgery. Confounding factors included age, gender, oral habits, smoking, bruxism, bone quality (tactile evaluation) and quantity, implant location, number of implants, implant type, length and diameter, one-stage vs. two-stage, and the need for bone grafting. Results: The most serious difficulties were found in swelling, which became minimal after 5 days, followed by eating everyday food, ability to enjoy everyday food, maximal pain and average pain (3 days); analgesics consumption (2.5 days); limitations in daily routine, mouth opening, and speech (2 days); swallowing and sleep (1.5 days); and, within 1 day, all other measures attained minimal levels. Gender, and implant location (anterior vs. posterior) were significant predictor variables exerting their different characteristic delayed recoveries. Conclusions: (1) Patients should expect, in general, recovery within 4 days after dental implant placement; (2) women will experience a delayed recovery, (3) implants placed in the intercanine area will result in postoperative eating difficulties for nearly one week, and (4) the number of implants placed during the same appointment has no effect on post treatment recovery.

Background: Sinus augmentation has become a more predictable and successful procedure as the knowledge of its indications and complications increased. However, knowledge of risk factors leading to early implant failure (EIF) among challenging systemic and local conditions is insufficient.Objectives: The present study aims to assess risk factors for EIF following sinus augmentation in a challenging cohort.Methods: A retrospective cohort study conducted during a period of 8 years in a tertiary referral centre providing surgical and dental health care. Implant and patient variables including age, ASA (American Society of Anesthesiology) physical status classification, smoking, residual alveolar bone, type of anaesthesia and EIF were collected.Results: Cohort was comprised of 751 implants placed in 271 individuals. EIF rates at the implant and patient level were 6.3% and 12.5%, respectively. EIF was found to be higher among smokers (patient level: χ² (1) = 8.74, p = .003), ASA 2 physical classification patients (patient level: χ² (2) = 6.75, p = .03), sinuses augmented under general anaesthesia (patient level: χ² (1)=8.97, p = .003), higher bone gain (implant level: W = 12 350, p = .004), lower residual alveolar bone height (implant level: W = 13 837, p = .001) and multiple implantations (patient level: W = 3016.5, p = 0.01). However, other variables such as age, gender, collagen membrane and implant's dimensions did not reach significance.Conclusions: Within the limits of the study, we can conclude that smoking, ASA 2 physical status classification, general anaesthesia, low residual alveolar bone height and numerous implants are risk factors for EIF following sinus augmentation in challenging cohorts.

Background: Periapical cysts are the most common odontogenic cysts, often resulting in large bone defects. Guided tissue regeneration techniques support tissue healing by means of membranes and bone grafts. The present case report evaluates for the first time clinical application of a resorbable magnesium membrane in guided bone regeneration (GBR) following cystectomy. Case report: A 35-year-old male patient presented with a large periapical cystic lesion in the maxillary anterior region. Treatment involved marsupialization followed by cyst enucleation and GBR using a resorbable magnesium membrane and bovine xenograft. The magnesium membrane served as a structural support to bridge the bony discontinuity in the palatal bone. Cone-beam computed tomography (CBCT) was used for diagnosis, treatment planning, and follow-up assessments. At 16 months post-treatment, CBCT imaging revealed significant bone regeneration, with restoration of the palatal contour and cortication of the palatal wall. Clinical examination showed asymptomatic teeth with normal mobility and optimal soft tissue healing. Conclusions: This case demonstrates the potential of resorbable magnesium membranes in managing large periapical defects, offering a promising alternative to traditional GBR materials by combining mechanical strength with complete resorption, therefore eliminating the need for membrane removal surgery. However, future studies on larger patient samples should focus on confirming the long-term outcomes of this approach and investigating patient-specific factors that are important in choosing effective treatment options.