Diagnostic and Prognostic Value of Peripheral Neutrophil CD64 Index in Elderly Patients with Community-Acquired Pneumonia.

Community-acquired pneumonia (CAP) is a leading cause of hospitalization and mortality in the elderly. The peripheral blood neutrophil CD64 (nCD64) index is increasingly recognized for its association with poor pneumonia prognosis. A comprehensive investigation involving 128 elderly patients diagnosed with CAP, including 96 with non-severe CAP and 32 with severe CAP, from January 2020 to January 2021 was performed. The nCD64 index, CD4+, CD8+, C-reactive protein (CRP), white blood cell (WBC) count, procalcitonin (PCT), neutrophil (NEUT), and B lymphocyte count were determined using flow cytometry. Our findings reveal that patients with severe CAP exhibited significantly higher levels of nCD64 index, NEUT, WBC, CRP, and PCT. Intriguingly, lower CRP, nCD64 index, CURB-65 score, and PCT were associated with a higher survival rate. Notably, the nCD64 index demonstrated remarkable predictive efficiency for 28-d survival in CAP patients [area under the curve (AUC) = 0.907], surpassing other markers and even showing enhanced predictive power when combined with the CURB-65 score (AUC = 0.905). Furthermore, a negative association was observed between the nCD64 index and both CD4+, CD4+/CD8+ ratios, and B lymphocytes, highlighting its potential role in immune dysregulation. These findings underscore the critical importance of the nCD64 index in the early diagnosis, risk stratification, and prognostic evaluation of infections and immune responses in elderly CAP patients.

Longitudinal trajectories of disability among Chinese adults: the role of cardiometabolic multimorbidity.

BACKGROUND: Cardiometabolic multimorbidity (CM) has been found to be associated with higher mortality and functional limitations. However, few studies have investigated the longitudinal association between CM and disability in the Chinese population and whether these associations vary by smoking status. METHODS: The study included 16,754 participants from four waves (2011, 2013, 2015, and 2018) of China Health and Retirement Longitudinal Study (CHARLS) (mean age: 59, female: 51%). CM was assesed at baseline and defined as having two or more of diabetes, stroke, or heart disease. Disability was repeatedly measured by summing the number of impaired activities of daily living (ADL) and instrumental activities of daily living (IADL) during the 7-year follow-up. Linear mixed-effects model was used to determine the association of CM and trajectories of disability and to assess the modification effect of smoking status in these associations. RESULTS: Participants with CM at baseline had a faster progression of disability compared to those without CM (CM: ß = 0.13, 95% CI: 0.05 to 0.21). Current smokers with CM developed disability faster than their counterparts (Pinteraction for smoking=0.011). In addition, there was a significant association between CM and the annual change of disability in current smokers (ß = 0.34, 95% CI: 0.17 to 0.50) while no such association was observed in current non-smokers (ß = 0.08, 95% CI: -0.02 to 0.17). CONCLUSION: CM was associated with more a rapid disability progression. Notably, being current smokers may amplify the adverse effects of CM on disability progression.

Risk factors for social isolation in older adults: A systematic review and meta-analysis.

OBJECTIVES: To explore the risk factors of social isolation in older adults by meta-analysis. METHODS: We examined articles that had been published on the China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service platform, VIP Periodical Chinese Journal Service platform, Medical Literature database, PubMed, Embase database, Chinese BioMedical Literature database (CBM), and the Cochrane Library. RevMan 5.3 software was used for meta-analysis. RESULTS: A total of 13 pieces of literature comprising 47,288 patients were included, including five in Chinese and eight in English. According to our review, the common risk factors reported were age, sex, education level, marital status, self-rated health status, depression, social support, cognitive function, and basic diseases. Gender (female), education level (lower education level), marital status (divorced), self-rated health status (poor), depression, lack of social support, inadequate cognitive function, and underlying disorders were the key risk factors for social isolation in older adults. CONCLUSIONS: To reduce social isolation in older adults, paying more attention to the aging population with poor marital statuses, low education levels, poor health statuses, depression, poor cognitive function, and other underlying diseases is necessary. It is essential to encourage patients and their family members to participate in social activities and build a multi-social support system to reduce social isolation in older adults.

Sex-related differences in the hypertriglyceridemic-waist phenotype in association with hyperuricemia: a longitudinal cohort study.

BACKGROUND: There is limited longitudinal evidence supporting the association between the hypertriglyceridemic-waist (HTGW) phenotype and hyperuricemia. This study aimed to examine the longitudinal relationship between hyperuricemia and the HTGW phenotype among males and females. METHODS: A total of 5562 hyperuricemia-free participants aged 45 or over from the China Health and Retirement Longitudinal Study (mean age: 59.0) were followed for 4 years. The HTGW phenotype was defined as having elevated triglyceride levels and enlarged waist circumference (cutoffs for males: 2.0 mmol/L and 90 cm; females: 1.5 mmol/L and 85 cm). Hyperuricemia was determined by uric acid cutoffs (males: 7 mg/dl; females: 6 mg/dl. Multivariate logistic regression models were used to assess the association between the HTGW phenotype and hyperuricemia. The joint effect of the HTGW phenotype and sex on hyperuricemia was quantified, and the multiplicative interaction was assessed. RESULTS: During the four-year follow-up, 549 (9.9%) incident hyperuricemia cases were ascertained. Compared with those with normal levels of triglycerides and waist circumference, participants with the HTGW phenotype had the highest risk of hyperuricemia (OR: 2.67; 95% CI: 1.95 to 3.66), followed by an OR of 1.96 (95% CI: 1.40 to 2.74) for only higher triglyceride levels and 1.39 (95% CI: 1.03 to 1.86) for only greater waist circumference. The association between HTGW and hyperuricemia was more prominent among females (OR = 2.36; 95% CI: 1.77 to 3.15) than males (OR = 1.29; 95% CI: 0.82 to 2.04), with evidence of a multiplicative interaction (P = 0.006). CONCLUSIONS: Middle-aged and older females with the HTGW phenotype may at the highest risk of hyperuricemia. Future hyperuricemia prevention interventions should be primarily targeted for females with the HTGW phenotype.

Enhanced Bioremediation of Aged Polycyclic Aromatic Hydrocarbons in Soil Using Immobilized Microbial Consortia Combined with Strengthening Remediation Strategies.

Microbial biodegradation is considered as one of the most effective strategies for the remediation of soil contaminated with polycyclic aromatic hydrocarbons (PAHs). To improve the degradation efficiency of PAHs, PAH-degrading consortia combined with strengthening remediation strategies was used in this study. The PAH biodegrading performance of seven bacterial consortia constructed by different ratios of Mycobacterium gilvum MI, Mycobacterium sp. ZL7 and Rhodococcus rhodochrous Q3 was evaluated in an aqueous system containing phenanthrene, pyrene, benzo[a]pyrene and benzo[b]fluoranthene. Bacterial consortium H6 (Q3:ZL7:MI = 1:2:2) performed a high degrading efficiency of 59% in 8 days. The H6 was subsequently screened to explore its potential ability and performance to degrade aged PAHs in soils from a coking plant and the effects of strengthening strategies on the aged PAH degradation, including the addition of glucose or sodium dodecyl benzene sulfonate (SDBS) individually or as a mixture along immobilization of the inoculant on biochar. The highest degradation efficiencies, which were 15% and 60% for low-molecular-weight (LMW) PAHs and high-molecular-weight (HMW) PAHs, respectively, were observed in the treatment using immobilized microbial consortium H6 combined with the addition of glucose and SDBS after 24 days incubation. This study provides new insights and guidance for future remediation of aged PAH contaminated soils.

Interspecific competition predicts the potential impact of little fire ant Wasmannia auropunctata (Roger) (Hymenoptera: Formicidae) invasion on resident ants in southern China.

The little fire ant Wasmannia auropunctata (Roger) is a major invasive species that seriously threatens the biodiversity of invaded areas. W. auropunctata was first reported in Chinese mainland in 2022 and its impact on native species is still unknown. To evaluate the impact of W. auropunctata invasion on the ant communities in southern China, a series of interspecific competition experiments were conducted in this study. The individual aggression index and group aggression experiments showed the advantage of W. auropunctata in competition with 5 resident ants under equal worker numbers. When encountering Anoplolepis gracilipes, Camponotus nicobarensis, Tetramorium bicarinatum, Polyrhachis dives, and Solenopsis invicta, W. auropunctata gradually gained a competitive advantage with an increase in its number of workers. In the group aggression experiments with equal worker numbers, there was a negative correlation between the body length and mortality rate of resident ants. The results of the foraging behavior experiments showed that W. auropunctata was able to dominate food resources under competition with Carebara diversa, which also displayed weak competition in the group aggression bioassay. In addition, the abilities to recruit workers and retrieve food were inhibited under competition with S. invicta and T. bicarinatum. The results of the nesting behavior experiments showed that in the 24-h bout of space resource competition, W. auropunctata was dominant over C. diversa, S. invicta, and T. bicarinatum. The results of this study show that W. auropunctata has certain advantages in competition for food and space resources over resident ants in southern China, and some resident ant species may be replaced in the future.

Association between subjective cognitive complaints, balance impairment and disability among middle-aged and older adults: Evidence from a population-based cohort study.

AIM: To quantify the association between subjective cognitive complaints and balance impairment in relation to the occurrence of disability. METHODS: In total, 6885 adults aged ≥45 who participated in the China Health and Retirement Longitudinal Study (CHARLS) were followed for 7 years. Subjective cognitive complaints were evaluated by self-reported memory problems. Balance impairment was tested by side-by-side stand, semi-tandem stand and full tandem stand. Disability was measured by activities of daily living (ADL) and instrumental activities of daily living (IADL). Multivariate logistic regression models were applied to test the joint effect between baseline subjective cognitive complaints and balance impairment on disability. The multiplicative interaction was examined. RESULTS: A joint effect of experiencing both subjective cognitive complaints and balance impairment was identified, showing a 1.63-fold higher risk of ADL and IADL disability than those experienced by neither of the two (odds ratio = 1.63, 95% confidence interval: 1.36-1.95). There was evidence of multiplicative interaction (P = 0.004). CONCLUSIONS: Among middle-aged and older people, the coexistence of subjective cognitive complaints and balance impairment may lead to a higher disability risk, which is much higher than the simple sum of the two individual effects. Future interventions are required to target these symptoms simultaneously to reduce the risks of disability. Geriatr Gerontol Int 2022; 22: 1025-1031.

The association between healthy aging index and trajectories of disability: a population-based cohort study.

BACKGROUND: Healthy aging index (HAI) could predict adverse health consequences including mortality and disability independent of age and comorbidity. We investigated the role of HAI on trajectories of disability throughout later life based on a nationally representative sample. METHODS: We examined 1733 participants aged over 60 years from the China Health and Retirement Longitudinal Study (CHARLS) followed for 7 years/4 waves repeatedly. Systolic blood pressure [SBP], cognitive function, cystatin C, peak expiratory flow [PEF], and fasting glucose were categorized using tertile or clinical reference range, and scored as 0 (healthiest), 1 (less healthy) and 2 (least healthy) respectively to further generate HAI summary scores (range 0-10). Disability was defined as the sum of impaired activities of daily living (ADL) and instrumental activities of daily living (IADL). We used linear mixed-effects model to study the association between HAI and trajectories of disability. RESULTS: A total of 10.5% of participants represented in the healthiest group and 22.5% ended up as the least healthy. After adjusting for all potential confounders, disability progression was significantly faster (ß = 0.27, 95% CI 0.11-0.42) in the least healthy group when comparing with the healthiest. CONCLUSION: Our findings suggest that HAI is associated with disability progression among adults aged over 60 years old. It might be beneficial for future interventions to specifically target older adults with high HAI scores as a means of reducing disability.

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning.

Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a limited sample size of brain networks is demanding. Currently, there are two common trends in neuroimaging data collection that could be exploited to gain more information: 1) multimodal data, and 2) longitudinal data. It has been shown that these two types of data provide complementary information. Nonetheless, it is challenging to learn brain network representations that can simultaneously capture network properties from multimodal as well as longitudinal datasets. Here we propose a general fusion framework for multi-source learning of brain networks - multimodal brain network fusion with longitudinal coupling (MMLC). In our framework, three layers of information are considered, including cross-sectional similarity, multimodal coupling, and longitudinal consistency. Specifically, we jointly factorize multimodal networks and construct a rotation-based constraint to couple network variance across time. We also adopt the consensus factorization as the group consistent pattern. Using two publicly available brain imaging datasets, we demonstrate that MMLC may better predict psychometric scores than some other state-of-the-art brain network representation learning algorithms. Additionally, the discovered significant brain regions are synergistic with previous literature. Our new approach may boost statistical power and sheds new light on neuroimaging network biomarkers for future psychometric prediction research by integrating longitudinal and multimodal neuroimaging data.

Graph routing between capsules.

Routing methods in capsule networks often learn a hierarchical relationship for capsules in successive layers, but the intra-relation between capsules in the same layer is less studied, while this intra-relation is a key factor for the semantic understanding in text data. Therefore, in this paper, we introduce a new capsule network with graph routing to learn both relationships, where capsules in each layer are treated as the nodes of a graph. We investigate strategies to yield adjacency and degree matrix with three different distances from a layer of capsules, and propose the graph routing mechanism between those capsules. We validate our approach on five text classification datasets, and our findings suggest that the approach combining bottom-up routing and top-down attention performs the best. Such an approach demonstrates generalization capability across datasets. Compared to the state-of-the-art routing methods, the improvements in accuracy in the five datasets we used were 0.82, 0.39, 0.07, 1.01, and 0.02, respectively.

FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media.

Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of fake news, that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received a lot of attention. Despite several existing computational solutions on the detection of fake news, the lack of comprehensive and community-driven fake news data sets has become one of major roadblocks. Not only existing data sets are scarce, they do not contain a myriad of features often required in the study such as news content, social context, and spatiotemporal information. Therefore, in this article, to facilitate fake news-related research, we present a fake news data repository FakeNewsNet, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information. We present a comprehensive description of the FakeNewsNet, demonstrate an exploratory analysis of two data sets from different perspectives, and discuss the benefits of the FakeNewsNet for potential applications on fake news study on social media.

Biological removal of pharmaceuticals by Navicula sp. and biotransformation of bezafibrate.

Pharmaceutically active compounds are of great concern due to their detection frequency in the environment and the unexpected risks. In this study, the simultaneous removal of mixed pharmaceuticals by microalgae was explored using a typical freshwater diatom Navicula sp. Results showed that Navicula sp. could efficiently remove atenolol, carbamazepine, ibuprofen and naproxen with the efficiencies of >90% after 21 d of exposure. As compared to the removal efficiencies of each pharmaceutical in the individual pharmaceutical treatments, the degradation of sulfamethoxazole, bezafibrate, and naproxen was improved in the mixed treatment, whereas the removal efficiencies of carbamazepine and atenolol decreased. Additionally, the presence of hydrophobic pharmaceuticals (i.e., ibuprofen and naproxen) accelerated the degradation of carbamazepine and sulfamethoxazole and inhibited the removal of atenolol in the mixture with the combination of six pharmaceuticals, while the addition of other pharmaceuticals show no significant effect on the removal of ibuprofen and naproxen. The bioaccumulation of pharmaceuticals in Navicula sp. increased as their log KOW values decreased. Four bezafibrate metabolites were identified and the degradation pathways of bezafibrate in diatom were proposed. It is the first report on the metabolism of BEZ in diatom, and further studies on the environmental risk of the metabolites should be investigated.

Multimodal Fusion of Brain Networks with Longitudinal Couplings.

In recent years, brain network analysis has attracted considerable interests in the field of neuroimaging analysis. It plays a vital role in understanding biologically fundamental mechanisms of human brains. As the upward trend of multi-source in neuroimaging data collection, effective learning from the different types of data sources, e.g. multimodal and longitudinal data, is much in demand. In this paper, we propose a general coupling framework, the multimodal neuroimaging network fusion with longitudinal couplings (MMLC), to learn the latent representations of brain networks. Specifically, we jointly factorize multimodal networks, assuming a linear relationship to couple network variance across time. Experimental results on two large datasets demonstrate the effectiveness of the proposed framework. The new approach integrates information from longitudinal, multimodal neuroimaging data and boosts statistical power to predict psychometric evaluation measures.

A novel aminoclay-curcumin hybrid for enhanced chemotherapy.

Curcumin (Cur) has been demonstrated as an efficacious anti-tumor agent. However, its therapeutic applications are largely limited by its extremely low aqueous solubility, low stability and poor bioavailability. In this work, a simple one-pot synthesis was developed to fabricate a novel aminoclay-curcumin (AC-Cur) hybrid via the in situ loading of Cur over AC. The resultant hybrid was amenable to exfoliation and readily dispersible in aqueous media. Compared with free Cur, the AC-Cur hybrid displayed significantly enhanced solubility of Cur in both acidic and neutral environments, as well as enhanced stability of Cur in both neutral and alkaline environments. More importantly, this hybrid achieved a much better therapeutic effect than free Cur against various tumor cell lines, which was mainly attributed to its significantly improved solubility, stability and cellular uptake. Our results suggest that such an AC-Cur hybrid with high bioavailability can potentially be applied in nanomedicine for the treatment of malignant tumours.

Colloidal Stability of Silk Fibroin Nanoparticles Coated with Cationic Polymer for Effective Drug Delivery.

Generally, silk fibroin nanoparticles (SFNPs) are great candidates to deliver drugs or other bioactive substances in vivo. However, their further applications are largely limited by the low colloidal stability of SFNPs, as they tend to aggregate in biological media. To address this issue, SFNP composite materials with a core-shell structure (CS-SFNPs) were fabricated by coating SFNPs with four different selected cationic polymers, glycol chitosan, N,N,N-trimethyl chitosan, polyethylenimine, and PEGylated polyethylenimine, through electrostatic interaction. According to the DLS and NTA results, compared with the bare SFNPs, the CS-SFNPs showed much higher colloidal stability in biological media. When treated with human cervical carcinoma (HeLa) cells, the CS-SFNPs were efficiently internalized and accumulated in lysosome; and when loaded with an anticancer drug, DOX, the CS-SFNPs also showed higher cytotoxicity against HeLa cells. Our results suggest that the fabricated CS-SFNPs with desirable colloidal stability in biological media have the potential to be employed as drug carriers for the anticancer drug delivery system.

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches.

Elucidating the role of free polycationic chains in polycation gene carriers by free chains of polyethylenimine or N,N,N-trimethyl chitosan plus a certain polyplex.

Polycations as gene carriers have attracted considerable attention over the past decade. Generally, polyplexes between polycations and deoxyribonucleic acid (DNA) are formed at low N/P ratios (the ratios of the numbers of nitrogen atoms in a polycation to the numbers of phosphorus atoms in DNA), but high transfection efficiency can only be obtained at much higher N/P ratios. Thus, many polycationic chains are still free in solution. In this study, we investigated the detailed functions of free polyethylenimine chains (PEI-F) and free N,N,N-trimethyl chitosan chains (TMC-F) using the same polyplex, ie, TMC polyplex (TMC-P), which has high stability in Dulbecco's Modified Eagle's Medium (DMEM). Meanwhile, PEI polyplex (PEI-P)/PEI-F was also evaluated rather than PEI-P/TMC-F because the stability of PEI-P is low in DMEM and, in the latter case, the TMC-F may replace the bound PEI chain in PEI-P to form TMC-P. The transfection results show that both TMC-F and PEI-F can significantly increase the transfection efficiency of TMC-P; however, PEI-F can upregulate the gene expression of TMC-P more efficiently than TMC-F. Further investigations on the endocytosis and intracellular trafficking show that PEI-P/PEI-F, TMC-P/PEI-F, and TMC-P/TMC-F exhibit similar cellular uptake efficiency. However, by shutting down the clathrin-mediated endocytosis or vacuolar proton pump, the transfection efficiency decreases in the order of PEI-P/PEI-F > TMC-P/PEI-F > TMC-P/TMC-F. These findings indicate that PEI-F and TMC-F may promote the transfection efficiency of the polyplex by affecting its cellular uptake pathway and intracellular trafficking.

Thixotropic silk nanofibril-based hydrogel with extracellular matrix-like structure.

Silk fibroin (SF) based materials have been widely studied and applied in bio-related areas due to their excellent structural and biological properties. Here, we present an injectable hydrogel formed by SF nanofibrils via simple fibrillation and centrifugation approach. The hydrogels with extracellular matrix-like structure not only perform the sufficient mechanical properties, but also show outstanding thixotropic character, whose storage modulus (G') can recover to 93% within 40 seconds after a large shearing strain (5000 %). More importantly, the injectable hydrogel exhibits significant biocompatibility for L929 cells cultured in hydrogel after injection, illustrated by cell viability and cytotoxicity assays. All of these results indicate that such SF nanofibril-based hydrogel has promise in application such as a cell therapy carrier.

Directed Growth of Silk Nanofibrils on Graphene and Their Hybrid Nanocomposites.

Combination of proteins with other nanomaterials offers a promising strategy to fabricate novel hybrids with original functions in biology, medicine, nanotechnology, and materials science. Under carefully selected experimental conditions, we show that graphene nanosheets are able to direct one-dimensional self-assembly of silk fibroin, forming an unprecedented type of nanohybrids. These silk/graphene hybrids combine physical properties of both constituents and form functional composites with well-ordered hierarchical structures. Due to the facile fabrication process and their tunable nanostructures, the resultant hybrids show promise in applications as diverse as tissue engineering, drug delivery, nanoelectronics, nanomedicine, biosensors, and functional composites.