Feng Shao

This study aimed to investigate the effectiveness of phototherapy intervention on cognitive function in older adult patients with dementia. PubMed, Ovid MEDLINE, Web of Science, EMBASE, Cochrane Central Registry of Controlled Trials, PsycINFO, and Clinical Trials were searched from their inception to August 10, 2022, for randomized controlled trials involving patients with dementia who received phototherapy interventions. We used the weighted mean difference (MD) or standard weighted mean difference to generate the pooled estimates. The primary outcome was cognitive function as measured by the Mini‐Mental State Examination (MMSE) score. The secondary outcomes were the behavioral and psychological symptoms of dementia (BPSDs) and sleep. This systematic review and meta‐analysis was registered in PROSPERO (registration number: CRD42022343788). We included 12 randomized controlled trials comprising 766 patients with dementia (426 patients in the intervention group and 340 in the control group). Phototherapy interventions significantly improved MMSE scores (n = 3, MD 2.68, 95% confidence interval [CI]: 1.38–3.98, I² = 0%). There were no significant differences in the Cornell Scale for Depression in Dementia score, Cohen‐Mansfield Agitation Inventory score (MD: −3.12, 95% CI: −8.05, 1.82, I² = 0%), Neuropsychiatric Inventory score, sleep efficiency, total sleep time, and Sleep Disorders Inventory score between the groups. Our systematic review and meta‐analysis showed that phototherapy significantly improved cognitive function in patients with dementia.

The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 10¹¹ neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers.