Family Resilience Factors Associated With Family Adaptation of Children With Acute Leukemia: A Cross-sectional Study.

Background: Childhood leukemia critically disrupts family life, needing support for adaptation and resilience.

Objective: Having investigated the main factors influencing the adaptation of families with children with leukemia, we provide support for clinical nurses to develop effective interventions to promote the adaptation of families with children with leukemia in future clinical practice.

Method: This cross-sectional study surveyed 197 parents of children (≤14 years old) with acute leukemia from 4 hospitals in Changsha, China.

COVID-19 Vaccine Willingness and Related Factors Among Health Care Workers in 3 Southeast Asian Jurisdictions.

Importance: COVID-19 vaccine hesitancy is widespread and may lead to refusal or delay of vaccination, eventually reducing the overall vaccination coverage rate and vaccine effectiveness. Willingness to receive COVID-19 vaccination among health care workers (HCWs) is diverse across different jurisdictions.

Objective: To assess the COVID-19 vaccine willingness among HCWs in 3 Southeast Asian jurisdictions in the context of pandemic severity and vaccination policy.

Effectiveness of a mindfulness-based psychoeducation group programme for early-stage schizophrenia: An 18-month randomised controlled trial.

Current psychosocial interventions in schizophrenia are evidenced to improve patients' illness-related knowledge, mental status and relapse rate, but substantive benefits to patients, such as their functioning and insight into the illness, remain uncertain. This multi-centre randomised clinical trial aimed to examine the effects of mindfulness-based psycho-education group intervention for adult patients with early-stage schizophrenia over an 18-month follow-up. The controlled trial was conducted with a repeated-measure, three-arm design at two psychiatric outpatient clinics in Jilin (China) and Hong Kong.

Correction: Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia.

[This corrects the article DOI: 10.1371/journal.pone.

Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia.

It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer's disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease.

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers.

Background: The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population.