Multidrug resistant pulmonary tuberculosis treatment regimens and patient outcomes: an individual patient data meta-analysis of 9,153 patients.

Treatment of multidrug resistant tuberculosis (MDR-TB) is lengthy, toxic, expensive, and has generally poor outcomes. We undertook an individual patient data meta-analysis to assess the impact on outcomes of the type, number, and duration of drugs used to treat MDR-TB

Treatment Outcomes of Patients With Multidrug-Resistant and Extensively Drug-Resistant Tuberculosis According to Drug Susceptibility Testing to First- and Second-line Drugs: An Individual Patient Data Meta-analysis

The clinical validity of drug susceptibility testing (DST) for pyrazinamide, ethambutol, and second-line antituberculosis drugs is uncertain. In an individual patient data meta-analysis of 8955 patients with confirmed multidrug-resistant tuberculosis, DST results for these drugs were associated with treatment outcome

Accurate Computation of Cohesive Energies for Small to Medium-Sized Gold Clusters

High-level CCSD­(T)-F12-type procedures have been used to assess the performance of a variety of computationally less demanding methods for the calculation of cohesive energies for small to medium-sized gold clusters. For geometry optimization for small gold clusters, the PBE-PBE/cc-pVDZ-PP procedure gives structures that are in close agreement with the benchmark geometries. We have devised a CCSD­(T)-F12b-based composite protocol for the accurate calculation of cohesive energies for medium-sized gold clusters. Using these benchmark (nonspin–orbit vibrationless) cohesive energies, we find that fairly good agreement is achieved by the PBE-PBE-D3/cc-pVTZ-PP method. In conjunction with PBE-PBE/cc-pVDZ-PP zero-point vibrational energies and spin-obit corrections obtained with the PBE-PBE-2c/dhf-TZVP-2c method, we have calculated 0 K cohesive energies for Au₂–Au₂₀. Extrapolation of these cohesive energies to bulk yields an estimated value of 383.2 kJ mol^–1, which compares reasonably well with the experimental value of 368 kJ mol^–1

Accurate Computation of Cohesive Energies for Small to Medium-Sized Gold Clusters

High-level CCSD­(T)-F12-type procedures have been used to assess the performance of a variety of computationally less demanding methods for the calculation of cohesive energies for small to medium-sized gold clusters. For geometry optimization for small gold clusters, the PBE-PBE/cc-pVDZ-PP procedure gives structures that are in close agreement with the benchmark geometries. We have devised a CCSD­(T)-F12b-based composite protocol for the accurate calculation of cohesive energies for medium-sized gold clusters. Using these benchmark (nonspin–orbit vibrationless) cohesive energies, we find that fairly good agreement is achieved by the PBE-PBE-D3/cc-pVTZ-PP method. In conjunction with PBE-PBE/cc-pVDZ-PP zero-point vibrational energies and spin-obit corrections obtained with the PBE-PBE-2c/dhf-TZVP-2c method, we have calculated 0 K cohesive energies for Au₂–Au₂₀. Extrapolation of these cohesive energies to bulk yields an estimated value of 383.2 kJ mol^–1, which compares reasonably well with the experimental value of 368 kJ mol^–1

Regulating Generative AI: Ethical Considerations and Explainability Benchmarks

This study looks into the critical discussion surrounding the ethical regulation and explainability of generative artificial intelligence (AI). Amidst the rapid advancement of generative AI technologies, this paper identifies and explores the multifaceted ethical concerns that arise, highlighting the paramount importance of transparency, accountability, and fairness. Through an examination of existing regulatory frameworks and the introduction of novel benchmarks for explainability, the study advocates for a balanced approach that fosters innovation while ensuring ethical oversight. Case studies illustrate the dual potential of generative AI to benefit society and pose significant ethical challenges, underscoring the complexity of its integration into various domains. The findings emphasize the necessity for dynamic regulatory mechanisms, interdisciplinary collaboration, and ongoing research to navigate the ethical landscape of generative AI, aiming to harness its capabilities responsibly for the betterment of humanity