Previous neuroimaging studies have suggested that cerebral changes over normal aging are not simply characterized by regional alterations, but rather by the reorganization of cortical connectivity patterns. The investigation of structural covariance networks (SCNs) using voxel-based morphometry is an advanced approach to examining the pattern of covariance in gray matter volumes among different regions of the human cortex. To date, how the organization of critical SCNs change during normal aging remains largely unknown. In this study, we used an SCN mapping approach to investigate eight large-scale networks in 240 healthy participants aged 18–89 years. These participants were subdivided into young (18–23 years), middle aged (30–58 years), and older (61–89 years) subjects. Eight seed regions were chosen from widely reported functional intrinsic connectivity networks. The voxels showing significant positive associations with these seed regions were used to describe the topological organization of an SCN. All of these networks exhibited non-linear patterns in their spatial extent that were associated with normal aging. These networks, except the primary motor network, had a distributed topology in young participants, a sharply localized topology in middle aged participants, and were relatively stable in older participants. The structural covariance derived using the primary motor cortex was limited to the ipsilateral motor regions in the young and older participants, but included contralateral homologous regions in the middle aged participants. In addition, there were significant between-group differences in the structural networks associated with language-related speech and semantics processing, executive control, and the default-mode network. Taken together, the results of this study demonstrate age-related changes in the topological organization of SCNs, and provide insights into normal aging of the human brain.

Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. We performed subgroup analysis based on age, sex, and race. We assessed the discrimination and calibration of models using the area under receiver operating characteristic (AUROC) and standardised mortality ratio (SMR) with 95% CIs. Using the development dataset (n=50 366) and predictive model building process, the XGBoost algorithm performed the best in all types of validations compared with other machine learning algorithms and clinical scores (internal validation with 5037 patients from 14 US hospitals, AUROC=0·866 [95% CI 0·851-0·880]; external validation in the US population with 20 541 patients from 169 hospitals, AUROC=0·838 [0·829-0·847]; external validation in European population with 2411 patients from one hospital, AUROC=0·833 [0·812-0·853]; temporal validation with 4311 patients from one hospital, AUROC=0·884 [0·869-0·897]). In the external validation set (US population), the median AUROCs of bias evaluations covering eight subgroups were above 0·81, and the overall SMR was 0·99 (0·96-1·03). The top ten risk predictors were the minimum Glasgow Coma Scale score, total urine output, average respiratory rate, mechanical ventilation use, best state of activity, Charlson Comorbidity Index score, geriatric nutritional risk index, code status, age, and maximum blood urea nitrogen. A simplified model containing only the top 20 features (ELDER-ICU-20) had similar predictive performance to the full model. The ELDER-ICU model reliably predicts the risk of in-hospital mortality using routinely collected clinical features. The predictions could inform clinicians about patients who are at elevated risk of deterioration. Prospective validation of this model in clinical practice and a process for continuous performance monitoring and model recalibration are needed. National Institutes of Health, National Natural Science Foundation of China, National Special Health Science Program, Health Science and Technology Plan of Zhejiang Province, Fundamental Research Funds for the Central Universities, Drug Clinical Evaluate Research of Chinese Pharmaceutical Association, and National Key R&D Program of China.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder among the elderly. It damages brain cells and nerves, and disturbs the storage and transmission of information in the brain. It is even potentially fatal without timely intervention. According to the statistics of the World Health Organization, more than 9.9 million new cases of dementia are diagnosed worldwide every year, of which about 60% to 70% are classified as AD.¹ Although scientists agree that the disease is associated with abnormalities of certain specific proteins (beta-amyloid and tau) in the brain, unfortunately the cause of AD is still unclear, and about 70% of the risks are considered hereditary. Mild cognitive impairment (MCI) is usually recognized as the prodromal stage of AD. Annually, MCI patients tend to convert to AD with a clinical conversion rate higher than 10%, while it is only 1% to 2% in healthy controls.^2,3 In amnestic MCI (aMCI), the conversion rate is even higher. Therefore, early identification of MCI is crucial for timely intervention of potential AD patients. More importantly, with the acceleration of aging, professional doctors for AD are in short supply, so fast and economical screening methods suitable for large population are increasingly needed.

3D printing (3DP) is becoming a research and development focus in nanobiomaterials as it can quickly and accurately fabricate any desired 3D tissuemodel only if itssize is appropriate. The different material powders (with different dimensional scales) and the printing strategies are the most direct factors influencing 3DP quality. With the development of nanotechnologies, 3DP is adopted more frequently for its rapidness in fabrication and precision in geometry. The fabrication in micro/nanoscale may change the performance of biomaterials and devices because it can retain more anisotropy of biomaterials compared with the traditionally rapid prototyping techniques. Thus, the biosafety issue is especially concerned by many researchers and is investigated in performance and safety of biomaterials and devices. This paper investigates the performance of 3DP in fabrication of nanobiomaterials and devices so as to partially explain how 3DP influences the performance and safety of nanobiomaterials.

Hypoxic-ischemic brain damage (HIBD) is among the leading causes of neonatal death and neurological disorders.¹ Persistent brain injury in the neonatal period has been suggested to disrupt key structural development, which results in serious consequences such as white matter abnormalities, neuronal necrosis, and intracerebral hemorrhage. Nearly 25% of survivors present neurological-related sequelae, including mental retardation, paralysis, epilepsy, and other diseases.^{2 – 4} Typical neurological symptoms of HIBD deteriorate within a few days after birth; therefore, continuous monitoring and effective evaluation of brain function in these children could help determine whether targeted intervention is necessary and allow for decisive disease diagnosis and treatment.⁵