Upregulation of POC1A in lung adenocarcinoma promotes tumour progression and predicts poor prognosis.

Lung adenocarcinoma (LUAD) is characterized by a high incidence rate and mortality. Recently, POC1 centriolar protein A (POC1A) has emerged as a potential biomarker for various cancers, contributing to cancer onset and development. However, the association between POC1A and LUAD remains unexplored. We extracted The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) data sets to analyse the differential expression of POC1A and its relationship with clinical stage. Additionally, we performed diagnostic receiver operator characteristic (ROC) curve analysis and Kaplan-Meier (KM) survival analysis to assess the diagnostic and prognostic value of POC1A in LUAD. Furthermore, we investigated the correlation between POC1A expression and immune infiltration, tumour mutation burden (TMB), immune checkpoint expression and drug sensitivity. Finally, we verified POC1A expression using real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). Cell experiments were conducted to validate the effect of POC1A expression on the proliferation, migration and invasion of lung cancer cells. POC1A exhibited overexpression in most tumour tissues, and its overexpression in LUAD was significantly correlated with late-stage presentation and poor prognosis. The high POC1A expression group showed lower levels of immune infiltration but higher levels of immune checkpoint expression and TMB. Moreover, the high POC1A expression group demonstrated sensitivity to multiple drugs. In vitro experiments confirmed that POC1A knockdown led to decreased proliferation, migration, and invasion of lung cancer cells. Our findings suggest that POC1A may contribute to tumour development by modulating the cell cycle and immune cell infiltration. It also represents a potential therapeutic target and marker for the diagnosis and prognosis of LUAD.

Shape Memory Polyurethane Composite With Fast Response to Near-Infrared Light Based on Tannic Acid-Iron and Dynamic Phenol-Carbamate Network.

Intelligent materials derived from green and renewable bio-based materials garner widespread attention recently. Herein, shape memory polyurethane composite (PUTA/Fe) with fast response to near-infrared (NIR) light is successfully prepared by introducing Fe3+ into the tannic acid-based polyurethane (PUTA) matrix through coordination between Fe3+ and tannic acid. The results show that the excellent NIR light response ability is due to the even distribution of Fe3+ filler with good photo-thermal conversion ability. With the increase of Fe3+ content, the NIR light response shape recovery rate of PUTA/Fe composite films is significantly improved, and the shape recovery time is reduced from over 60 s to 40 s. In addition, the mechanical properties of PUTA/Fe composite film are also improved. Importantly, owing to the dynamic phenol-carbamate network within the polymer matrix, the PUTA/Fe composite film can reshape its permanent shape through topological rearrangement and show its good NIR light response shape memory performance. Therefore, PUTA/Fe composites with high content of bio-based material (TA content of 15.1-19.4%) demonstrate the shape memory characteristics of fast response to NIR light; so, it will have great potential in the application of new intelligent materials including efficient and environmentally friendly smart photothermal responder.

Knowledge of Alzheimer's disease and associated factors among adults in Zhuhai, China: a cross-sectional analysis.

BACKGROUND: This study aimed to assess the public knowledge regarding Alzheimer's Disease (AD) in Zhuhai, China, focusing on identifying knowledge gaps and the influence of demographic and health factors. METHODS: A cross-sectional study was conducted in Zhuhai, China, from October to November 2022. A total of 1986 residents from 18 communities were selected employing stratified multi-stage equi-proportional sampling. Questionnaires covering general information and the Alzheimer's Disease Knowledge Scale (ADKS) were investigated face-to-face. Ordinal multiclass logistic regression was applied to assess the relationship between AD awareness and demographic and health characteristics. RESULTS: The average ADKS score was 18.5 (SD = 3.36) in Zhuhai. The lowest awareness rates were observed in the "Symptoms" and "Caregiving" subdomains of ADKS, with rates of 51.01% and 43.78%, respectively. The correct rates for the 30 ADKS questions ranged from 16.62 to 92.6%, showing a bimodal pattern with clusters around 80% and 20%. Women (OR = 1.203, 95% CI: 1.009-1.435), individuals aged 60 years or older (OR = 2.073, 95% CI: 1.467-2.932), those living in urban areas (OR = 1.361, 95% CI: 1.117-1.662), higher average monthly household income per capita (OR = 1.641, 95% CI: 1.297-2.082), and without any neurological or mental disorders (OR = 1.810, 95% CI: 1.323-2.478) were more likely to have higher levels of awareness about Alzheimer's disease. CONCLUSIONS: Adults in Zhuhai show a limited knowledge of AD, particularly in the 'Symptoms' and 'Caregiving' subdomains. Upcoming health campaigns must focus on bridging the knowledge gaps in different subdomains of AD, especially among subgroups with lower awareness, as identified in our study.

Preoperative neutrophil-to-lymphocyte ratio predicts symptomatic anastomotic leakage in elderly colon cancer patients: Multicenter propensity score-matched analysis.

BACKGROUND: The neutrophil-to-lymphocyte ratio (NLR), a composite inflammatory biomarker, is associated with the prognosis in patients with colorectal tumors. However, whether the NLR can be used as a predictor of symptomatic postoperative anastomotic leakage (AL) in elderly patients with colon cancer is unclear. AIM: To assess the role of the NLR in predicting the occurrence of symptomatic AL after surgery in elderly patients with colon cancer. METHODS: Data from elderly colon cancer patients who underwent elective radical colectomy with anastomosis at three centers between 2018 and 2022 were retrospectively analyzed. Receiver operating characteristic curve analysis was performed to determine the best predictive cutoff value for the NLR. Twenty-two covariates were matched using a 1:1 propensity score matching method, and univariate and multivariate logistic regression analyses were used to determine risk factors for the development of postoperative AL. RESULTS: Of the 577 patients included, 36 (6.2%) had symptomatic AL. The optimal cutoff value of the NLR for predicting AL was 2.66. After propensity score matching, the incidence of AL was significantly greater in the ≥ 2.66 NLR subgroup than in the < 2.66 NLR subgroup (11.5% vs 2.5%; P = 0.012). Univariate logistic regression analysis revealed statistically significant correlations between blood transfusion intraoperatively and within 2 d postoperatively, preoperative albumin concentration, preoperative prognostic nutritional index, and preoperative NLR and AL occurrence (P < 0.05); multivariate logistic regression analysis revealed that an NLR ≥ 2.66 [odds ratio (OR) = 5.51; 95% confidence interval (CI): 1.50-20.26; P = 0.010] and blood transfusion intraoperatively and within 2 d postoperatively (OR = 2.52; 95%CI: 0.88-7.25; P = 0.049) were risk factors for the occurrence of symptomatic AL. CONCLUSION: A preoperative NLR ≥ 2.66 and blood transfusion intraoperatively and within 2 d postoperatively are associated with a higher incidence of postoperative symptomatic AL in elderly patients with colon cancer. The preoperative NLR has predictive value for postoperative symptomatic AL after elective surgery in elderly patients with colon cancer.

Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.

INTRODUCTION: Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA. METHODS: We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction. We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction. Feature selection strategies (selection by filter, recursive feature elimination, and learned vector quantization) were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development. RESULTS: Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity. The best results [area under the receiver operating characteristic curve (AUROC) of 97%, sensitivity of 92%, and specificity of 95%] were obtained utilizing an ensemble of the algorithms random forest, C5.0, sparse linear discriminant analysis, and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor. The model achieved a good performance trade-off for a screening application with 6% false-positive rate (FPR) at 95% sensitivity, 35% FPR at 99% sensitivity, and 39% FPR at 100% sensitivity. CONCLUSIONS: The classification results and approach of this research can be utilized by clinicians globally, to improve the overall discovery of MMA in pediatric patients. The improved method, when adjusted to 100% precision, can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

[Evaluation and spatial-temporal variations of ecological comprehensive index along the Yellow River of Shanxi Province, China]. / 山西省沿黄地区生态综合指数评价及时空变化.

Ecological comprehensive index can quantitatively and visually analyze the temporal and spatial variations of ecological environment quality in a region. Based on the five indices of fractional vegetation coverage, leaf area index, total primary productivity, land surface temperature and wetness obtained by MODIS satellite data in 2001, 2005, 2010, 2015 and 2020, and coupled the comprehensive quality of the eco-environment (K), we analyzed the temporal and spatial variations of ecological quality along the Yellow River of Shanxi Province from 2001 to 2020 by using the principal component analysis and spatial autocorrelation method. The results showed that the mean value of K in the study area increased from 0.3354 to 0.4389 during 2001-2020. The ecological quality along the Yellow River of Shanxi Province improved overall, but with obvious temporal and spatial variations. On the large scale, it presented a pattern of "better in the south and worse in the north". There was difference between hills and mountains on the small scale. It showed a trend of continuous improvement in time, but the rate of change was different. The trend of improvement in the south was stronger than that in the north. From 2001 to 2020, the global Moran I values of K were all greater than 0.93, indicating that the ecological quality along the Yellow River of Shanxi Province had a strong spatial correlation. The types of spatial agglomeration were mainly high-high and low-low. The high-high agglomeration areas were mainly distributed in blocks in the south, while the low-low agglomeration areas were mainly concentrated in the north. The ecological quality of areas alone the Yellow River of Shanxi Province had been greatly improved during the research period, but there was still obvious spatial heterogeneity, which need to strengthen ecological protection.