Background: Familial hypercholesterolemia (FH) is a common genetic disorder and, if not diagnosed and treated early, results in premature cardiovascular disease. Most individuals with FH are undiagnosed and machine learning offers a new prospect to improve FH identification. Our objective was to create a machine learning model from basic lipid profile data with better screening performance than LDL-C (low-density lipoprotein cholesterol) cutoff levels and diagnostic performance comparable to the Dutch Lipid Clinic Network criteria. Methods: The model was developed combining logistic regression, deep learning, and random forest classification and trained on a 70% split of a data set of individuals clinically suspected of having FH. Model performance, as well as that of the LDL-C cutoff and Dutch Lipid Clinic Network criteria, were assessed on the internal 30% testing data set and an external data set by comparing the area under the receiver operator characteristic (AUROC) curves. All methodologies were measured against the gold standard of FH diagnosis by mutation identification. Furthermore, the model was also tested on 2 lower prevalence data sets. Results: The machine learning model achieved an AUROC curve of 0.711 on the external data set (n=1376; FH prevalence=64%), which was superior to the LDL-C cutoff (AUROC=0.642) and comparable to the Dutch Lipid Clinic Network criteria (AUROC=0.705). The model performed even better when tested on the medium-prevalence (n=2655; FH prevalence=20%) and low-prevalence (n=1616; FH prevalence=1%) data sets, with AUROC curve values of 0.801 and 0.856, respectively. Conclusions: Despite absence of clinical information, the model better identified genetically confirmed FH in a cohort of individuals suspected of having FH than LDL-C cutoff values and was comparable to the Dutch Lipid Clinic Network criteria. The model achieved higher accuracy when tested on 2 cohorts with lower FH prevalence. The application of machine learning is, therefore, a promising tool in both the screening for, and diagnosis of, individuals with FH.

Present study evaluated the efficacy of laser activation to control intra- and post-operative pain in single-visit root treatment for mandibular molar teeth with acute irreversible pulpitis following 2% lignocaine inferior alveolar nerve block. Ninety-eight patients presenting with pain were randomly divided into two anesthetic groups. Group-I inferior alveolar nerve block plus buccal infiltration and intra-ligamentary injections, Group-II inferior alveolar nerve block followed by laser irradiation focused directly on the pulp tissue. Intra- and post-operative pain intensities were assessed on a 10-point scale.The mean intra-operative pain scores in group-I was 6.62 ± 1.6 and in group-II before and after laser irradiation pain scores was 6.94 ± 2.1 and 1.3 ± 2.04, respectively. Post-operative pain scores at 24-hrs in the laser group were significantly higher. Laser irradiation applied directly on pulp tissue for control of intra-operative pain was effective, thereby negating the need for additional local anesthesia.Clinical relevanceLaser activation was effective method to control intra-operative pain in irreversibly inflamed pulp.Laser irradiation did not cause adverse post-operative pain.

Knowledge of basic root and root canal morphology and possible variation in anatomy of root canal system is important to achieve successful non-surgical root canal treatment this is followed by negotiation, cleaning and shaping and obturation of the entire canal system in three dimensions. This case presents the diagnosis and successful non-surgical endodontic management of a two rooted mandibular first premolar.