Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models

Broadly neutralizing antibodies for HIV prevention: a comprehensive review and future perspectives

SUMMARYThe human immunodeficiency virus (HIV) epidemic remains a formidable global health concern, with 39 million people living with the virus and 1.3 million new infections reported in 2022. Despite anti-retroviral therapy's effectiveness in pre-exposure prophylaxis, its global adoption is limited. Broadly neutralizing antibodies (bNAbs) offer an alternative strategy for HIV prevention through passive immunization. Historically, passive immunization has been efficacious in the treatment of various diseases ranging from oncology to infectious diseases. Early clinical trials suggest bNAbs are safe, tolerable, and capable of reducing HIV RNA levels. Although challenges such as bNAb resistance have been noted in phase I trials, ongoing research aims to assess the additive or synergistic benefits of combining multiple bNAbs. Researchers are exploring bispecific and trispecific antibodies, and fragment crystallizable region modifications to augment antibody efficacy and half-life. Moreover, the potential of other antibody isotypes like IgG3 and IgA is under investigation. While promising, the application of bNAbs faces economic and logistical barriers. High manufacturing costs, particularly in resource-limited settings, and logistical challenges like cold-chain requirements pose obstacles. Preliminary studies suggest cost-effectiveness, although this is contingent on various factors like efficacy and distribution. Technological advancements and strategic partnerships may mitigate some challenges, but issues like molecular aggregation remain. The World Health Organization has provided preferred product characteristics for bNAbs, focusing on optimizing their efficacy, safety, and accessibility. The integration of bNAbs in HIV prophylaxis necessitates a multi-faceted approach, considering economic, logistical, and scientific variables. This review comprehensively covers the historical context, current advancements, and future avenues of bNAbs in HIV prevention.

Value function assessment to different RL algorithms for heparin treatment policy of patients with sepsis in ICU

Heparin is a critical aspect of managing sepsis after abdominal surgery, which can improve microcirculation, protect organ function, and reduce mortality. However, there is no clinical evidence to support decision-making for heparin dosage. This paper proposes a model called SOFA-MDP, which utilizes SOFA scores as states of MDP, to investigate clinic policies. Different algorithms provide different value functions, making it challenging to determine which value function is more reliable. Due to ethical restrictions, we cannot test all policies on patients. To address this issue, we proposed two value function assessment methods: action similarity rate and relative gain. We experimented with heparin treatment policies for sepsis patients after abdominal surgery using MIMIC-IV. In the experiments, TD(0) shows the most reliable performance. Using the action similarity rate and relative gain to assess AI policy from TD(0), the agreement rates between AI policy and "good" physician's actual treatment are 64.6% and 73.2%, while the agreement rates between AI policy and "bad" physician's actual treatment are 44.1% and 35.8%, the gaps are 20.5% and 37.4%, respectively. External validation using action similarity rate and relative gain based on eICU resulted in agreement rates of 61.5% and 69.1% with the "good" physician's treatment, and 45.2% and 38.3% with the "bad" physician's treatment, with gaps of 16.3% and 30.8%, respectively. In conclusion, the model provides instructive support for clinical decisions, and the evaluation methods accurately distinguish reliable and unreasonable outcomes.