The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review.

Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence".

First detection of kdr L1014F allele in Anopheles ziemanni and Anopheles pharoensis in Cameroon and distribution of the allele in members of the Anopheles gambiae complex.

Background: Knockdown resistance (kdr) is one of the primary resistance mechanisms present in anopheline species. Although this mutation is largely spread across the Anopheles gambiae s.l.

Analyzing the Interactions between Environmental Parameters and Cardiovascular Diseases Using Random Forest and SHAP Algorithms.

Background: Cardiovascular diseases (CVD) remain the predominant global cause of mortality, with both low and high temperatures increasing CVD-related mortalities. Climate change impacts human health directly through temperature fluctuations and indirectly via factors like disease vectors. Elevated and reduced temperatures have been linked to increases in CVD-related hospitalizations and mortality, with various studies worldwide confirming the significant health implications of temperature variations and air pollution on cardiovascular outcomes.

Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.

Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated.