Multimodal neuroimaging exploration of the mechanisms of sleep quality deterioration after SARS-CoV-2 Omicron infection.

Background: To evaluate the neurological alterations induced by Omicron infection, to compare brain changes in chronic insomnia with those in exacerbated chronic insomnia in Omicron patients, and to examine individuals without insomnia alongside those with new-onset insomnia.

Methods: In this study, a total of 135 participants were recruited between January 11 and May 4, 2023, including 26 patients with chronic insomnia without exacerbation, 24 patients with chronic insomnia with exacerbation, 40 patients with no sleep disorder, and 30 patients with new-onset insomnia after infection with Omicron (a total of 120 participants with different sleep statuses after infection), as well as 15 healthy controls who were never infected with Omicron. Neuropsychiatric data, clinical symptoms, and multimodal magnetic resonance imaging data were collected.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework.

Development and validation of a radiomics-based nomogram for predicting a major pathological response to neoadjuvant immunochemotherapy for patients with potentially resectable non-small cell lung cancer.

Introduction: The treatment response to neoadjuvant immunochemotherapy varies among patients with potentially resectable non-small cell lung cancers (NSCLC) and may have severe immune-related adverse effects. We are currently unable to accurately predict therapeutic response. We aimed to develop a radiomics-based nomogram to predict a major pathological response (MPR) of potentially resectable NSCLC to neoadjuvant immunochemotherapy using pretreatment computed tomography (CT) images and clinical characteristics.

ViSTA: A Novel Network Improving Lung Adenocarcinoma Invasiveness Prediction from Follow-Up CT Series.

To investigate the value of the deep learning method in predicting the invasiveness of early lung adenocarcinoma based on irregularly sampled follow-up computed tomography (CT) scans. In total, 351 nodules were enrolled in the study. A new deep learning network based on temporal attention, named Visual Simple Temporal Attention (ViSTA), was proposed to process irregularly sampled follow-up CT scans.

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoid false negatives. Deep learning methods based on computed tomography (CT) images may improve the noninvasive prediction of EGFR mutation status and potentially help clinicians guide biopsies by visual methods.

Persistent white matter changes in recovered COVID-19 patients at the 1-year follow-up.

There is growing evidence that severe acute respiratory syndrome coronavirus 2 can affect the CNS. However, data on white matter and cognitive sequelae at the 1-year follow-up are lacking. Therefore, we explored these characteristics in this study.

Survivors of COVID-19 exhibit altered amplitudes of low frequency fluctuation in the brain: a resting-state functional magnetic resonance imaging study at 1-year follow-up.

Although some short-term follow-up studies have found that individuals recovering from coronavirus disease 2019 (COVID-19) exhibit anxiety, depression, and altered brain microstructure, their long-term physical problems, neuropsychiatric sequelae, and changes in brain function remain unknown. This observational cohort study collected 1-year follow-up data from 22 patients who had been hospitalized with COVID-19 (8 males and 11 females, aged 54.2 ± 8.

[Trend analysis on mortality and the mortal causes among children under 5 in Sichuan province, from 2001 to 2013].

Objective: To study the mortality of children under five and the causes of death together with related trend of dynamics, from 2001 to 2013 in Sichuan province.

Methods: Using the Children Death Monitoring Network under five in Sichuan province to obtain basic data. Descriptive statistics and chi-square were used to describe the mortalities in children and infants as well as the causes of death, in both rural and urban areas of Sichuan province.

[Infant mortality and its leading causes in Sichuan province, from 2001 to 2009].

Objective: To study the trend of infant mortality and the leading cause of the deaths in Sichuan province from 2001 to 2009.

Methods: Data presented in this report was obtained from the child mortality surveillance network with target population as children under 5 years of age. Rates on infant mortality, neonatal mortality and indirect estimation of infant mortality were calculated.