A dynamic approach to support outbreak management using reinforcement learning and semi-connected SEIQR models.

BACKGROUND: Containment measures slowed the spread of COVID-19 but led to a global economic crisis. We establish a reinforcement learning (RL) algorithm that balances disease control and economic activities. METHODS: To train the RL agent, we design an RL environment with 4 semi-connected regions to represent the COVID-19 epidemic in Tokyo, Osaka, Okinawa, and Hokkaido, Japan. Every region is governed by a Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model and has a transport hub to connect with other regions. The allocation of the synthetic population and inter-regional traveling is determined by population-weighted density. The agent learns the best policy from interacting with the RL environment, which involves obtaining daily observations, performing actions on individual movement and screening, and receiving feedback from the reward function. After training, we implement the agent into RL environments describing the actual epidemic waves of the four regions to observe the agent's performance. RESULTS: For all epidemic waves covered by our study, the trained agent reduces the peak number of infectious cases and shortens the epidemics (from 165 to 35 cases and 148 to 131 days for the 5th wave). The agent is generally strict on screening but easy on movement, except for Okinawa, where the agent is easy on both actions. Action timing analyses indicate that restriction on movement is elevated when the number of exposed or infectious cases remains high or infectious cases increase rapidly, and stringency on screening is eased when the number of exposed or infectious cases drops quickly or to a regional low. For Okinawa, action on screening is tightened when the number of exposed or infectious cases increases rapidly. CONCLUSIONS: Our experiments exhibit the potential of the RL in assisting policy-making and how the semi-connected SEIQR models establish an interactive environment for imitating cross-regional human flows.

Current Management and Volar Locking Plate Fixation with Bone Cement Augmentation for Elderly Distal Radius Fractures-An Updated Narrative Review.

Distal radius fractures (DRFs) are the most common among all kinds of fractures with an increase in incidence due to the rapidly expanded size of the elderly population in the past decades. Both non-surgical and surgical treatments can be applied for this common injury. Nowadays, more and more elderly patients with DRFs undergo surgical treatments to restore pre-injury activity levels faster. However, optimal treatment for geriatric DRFs is still debated, and careful evaluation and selection of patients are warranted considering clinical and functional outcomes, and complications following surgical treatments. Furthermore, osteoporosis is a predominant factor in elderly DRFs mostly deriving from a low-energy trauma, so many treatment modalities are developed to enhance better bone healing. Among various options for bone augmentation, bone cement is one of the most widely used measures. Bone cement such as calcium phosphate theoretically improves fracture stability and healing, but whether the elderly patients with DRFs can significantly benefit from surgical fixation with bone cement augmentation (BCA) remains controversial. Hence, in the present review, the latest literature regarding current concepts of management and evidence about volar locking plate fixation (VLPF) with BCA for elderly DRFs was searched in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Web of Science; out of >1000 articles, full texts of 48 and 6 articles were then examined and analyzed separately for management and VLPF with BCA for elderly DRFs. We aim to provide the readers with updates concerning the above issues.

Predictive roles of brain-derived neurotrophic factor Val66Met polymorphism on antidepressant efficacy of different forms of prefrontal brain stimulation monotherapy: A randomized, double-blind, sham-controlled study.

BACKGROUND: Although repetitive transcranial magnetic stimulation (rTMS) and prolonged intermittent theta-burst stimulation (piTBS) can induce changes in synaptic plasticity, the influence of brain-derived neurotrophic factor (BDNF) genotypes on their antidepressant effects remain unknown. Hence, we investigated the BDNF polymorphism contribution to the antidepressant effect of different forms left-sided prefrontal stimulations in a randomized, sham-controlled study METHODS: Seventy-five patients with medication-resistant depression were randomly assigned into three monotherapy groups: piTBS, high-frequency(HF) rTMS, or sham. The acute treatment period was two weeks. 17-item Hamilton Depression Rating scale (HDRS-17) were applied at baseline, week-1, and week-2. The primary outcome was percentage changes of HDRS-17 (%HDRS-17 changes) analyzed by generalized estimating equation (GEE) model. RESULTS: The GEE analysis revealed a significant interaction between group, time, and BDNF genotypes effects on %HDRS-17 changes over time.  In patients carrying Val homozygotes, piTBS and HF-rTMS both exhibited significantly greater %HDRS reduction than sham at week-2. In Met carriers, only piTBS showed better efficacy than sham at week-2 (piTBS vs. sham, -41.1% vs.-18.9%, p=0.004). Regarding the influence of different BDNF genotypes on antidepressant efficacy in each intervention, only HF-rTMS exhibited significantly different degrees of %HDRS-17 changes between Val homozygotes and Met carriers (-68.5% vs. -26.4%, p=0.012, respectively), but piTBS delivered the consistent efficacy regardless of the BDNF polymorphism. CONCLUSIONS: This is the first study to confirm the different impacts of BDNF genotypes on the effect of different left-sided prefrontal brain stimulation. BDNF Val66Met polymorphism may play a role in the antidepressant response of piTBS and HF-rTMS. (Trial Registration Number UMIN-CTR:UMIN000020892: Registration date: Feb.4, 2016).