An extensive introduction is provided for the non-expert regarding enantioselective, amine catalyzed, aldol reactions (Sections 1-3). There, a broad perspective is provided regarding: methodology limitations, mechanism, in-water versus on-water definitions and their theoretical basis, small-and large-scale physical-mechanical aspects, solid versus liquid starting material considerations, reproducibility, ball milling, diketone substrates, etc. The thematic emphasis then turns to practical outcomes for the reaction of nineteen aliphatic, cyclic, and aromatic ketones with 4-nitrobenzaldehyde and benzaldehyde prior to 2021. In doing so, 172 catalysts are highlighted. The ketone substrates are listed in the Table of Contents (Section 4) and their structures are shown in Figure 1. Each ketone is summarized by a: (i) schematic, (ii) text summary, (iii) catalyst Figures, and (iv) tabular reaction/ product data. Individual ketone summaries, at times, represent the distillation of over six hundred and fifty research articles, and the data refinement and its tabular reconstitution is not reproducible using a chemical database search with filters. In the review's broadest use, the Figure 1 ketone structures serve as templates to extrapolate to hypothetical substrates holding more functionality, but of related steric and electronic similarity. This pseudo matching permits a rapid answer to, "Does this methodology suit the ketone substrate at hand or not?" In a positive outcome, the best reaction conditions (stoichiometry, catalyst structure and loading, solvent, time, etc.) to affect aldol product formation (yield, dr, and ee) are delineated. A second envisioned use, allows the directed construction of diketone substrates (after viewing the tabularized data of Section 4) capable of undergoing regioselective mono-aldol product formation. This ketone regioselectivity tactic, reacting one carbonyl moiety while the other remains unreacted, avoids ketone protection-deprotection, and is demonstrated for the advantageous synthesis of an Alzheimer drug candidate.

Background The association between atrial fibrillation (Afib) and sinus and AV nodal dysfunction has previously been reported. However, no data are available regarding the association between Afib and bundle branch block (BBB). Methods Patient data were obtained from the Nationwide Inpatient Sample (NIS) database between years 2009 and 2015. Patients with a diagnosis of Afib and BBB were identified using validated International Classification of Diseases, 9th revision, and Clinical Modification (ICD‐9‐CM) codes. Statistical analysis using the chi‐square test and multivariate linear regression analysis were performed to determine the association between Afib and BBB. Results The total number of patients with BBB was 3,116,204 (1.5%). Patients with BBB had a mean age of 73.5 ± 13.5 years, 53.6% were males, 39.1% belonged to the age group ≥80 years, and 72.9% were Caucasians. The prevalence of Afib was higher in the BBB group, as compared to the non‐BBB group (29% vs 11.8%, p value<.001). This association remained significant in multivariate regression analysis with an odds ratio of 1.25 (CI: 1.24‐1.25, P < .001). Among the subtypes of BBB, Afib was comparatively more associated with RBBB (1.32, CI 1.31‐1.33, p value<.0001) than LBBB (1.17, CI 1.16‐1.18, p value<.0001). The mean cost was higher among Afib with BBB, compared with Afib patients without BBB ($15 795 vs $14 391, p value<.0001). There was no significant difference in the mean length of stay (5.6 vs 5.9 days, p value<.0001) or inpatient mortality (4.9% vs 4.8%). Conclusion This study demonstrates that prevalence of Afib is higher in patients with BBB than without BBB. Cost are higher for Afib patients with BBB, compared to those without BBB, with no significant increase in mortality or length of stay.

Background Studies have shown that the incidence of atrial fibrillation (AF) in cancer is most likely due to the presence of inflammatory markers. The purpose of our study is to determine the association of AF with different cancer subtypes and its impact on in‐hospital outcomes. Methods Data were obtained from the National Inpatient Sample database between 2005 and 2015. Patients with various cancers and AF were studied. ICD‐9‐CM codes were utilized to verify variables. Patients were divided into three age groups: Group 1 (age < 65 years), Group 2 (age 65‐80 years), and Group 3 (age > 80 years). Statistical analysis was performed using Pearson chi‐square and binary logistic regression analysis to determine the association of individual cancers with AF. Results The prevalence of AF was 14.6% among total study patients (n = 46 030 380). After adjusting for confounding variables through multivariate regression analysis, AF showed significant association in Group 1 with lung cancer (odds ratio, OR = 1.92), multiple myeloma (OR = 1.59), non‐Hodgkin lymphoma (OR = 1.55), respiratory cancer (OR = 1.55), prostate cancer (OR = 1.20), leukemia (OR = 1.12), and Hodgkin's lymphoma (OR = 1.03). In Group 2, the association of AF with multiple myeloma (1.21), lung cancer (OR = 1.15), Hodgkin lymphoma (OR = 1.15), non‐Hodgkin lymphoma (OR = 1.12), respiratory cancer (OR = 1.08), prostate cancer (OR = 1.06), leukemia (OR = 1.14), and colon cancer (OR = 1.01) were significant. In Group 3, AF showed significant association with non‐Hodgkin lymphoma (OR = 1.06), prostate (OR = 1.03), leukemia (OR = 1.03), Hodgkin's lymphoma (OR = 1.02), multiple myeloma (OR = 1.01), colon cancer (OR = 1.01), and breast cancer (OR = 1.01). The highest mortality was found in lung cancer in age <80 and prostate cancer in age >80. Conclusion In patients age <80 years, AF has significant association with lung cancer and multiple myeloma, whereas in patients age >80 years, it has significant association with non‐Hodgkin lymphoma and prostate cancer. In patients age <80 years, increased mortality was seen in AF with lung cancer and in patients age >80 years, increased mortality was seen in those with AF and prostate cancer. Twitter Abstract In age <80, lung cancer and multiple myeloma have a strong association with AF while thyroid and pancreatic cancers have no association with AF at any age. In age greater than 80, NHL and prostate cancer have a significant association with AF.