ACS Applied Polymer Materials最新文献

Pancreatic alpha amylase (P-AMY) is used as a biomarker of acute pancreatitis (AP) in human medicine. To our knowledge, there are no studies evaluating the usefulness of P-AMY in dogs with AP. In this study, we evaluated the diagnostic value of P-AMY, currently not verified in veterinary medicine. The AP group (n = 40) consisted of dogs with AP diagnosed using clinical signs and laboratory examinations, including abnormal canine pancreatic lipase (cPL) concentration, and compatible abdominal ultrasound examination at first presentation. Evaluation of the canine AP severity (CAPS) score was performed. The control group (n = 38) was composed of normal dogs without any abnormalities in clinical findings, blood exams or diagnostic imaging. The correlation of P-AMY with cPL was confirmed by Pearson's correlation analysis (r = 0.564, p < .001). The sensitivity and specificity for the most appropriate cut-off values of P-AMY were recorded similar to the values of DGGR. The dogs with AP and CAPS ≥11 had significantly higher serum P-AMY (p = .016) contrary to DGGR lipase and cPL. Furthermore, there was a significant difference in the median P-AMY dependent on the presence of systemic inflammatory response syndrome (p = .001). P-AMY showed similar level of diagnostic accuracy along with sensitivity and specificity compared to DGGR lipase. In addition, P-AMY showed a significant association with CAPS score, contrary to cPL and DGGR lipase. Along with other biomarkers associated with AP, P-AMY has the potential of usefulness as a supportive diagnostic and prognostic biomarker of AP in dogs.

The circulation of tumor cells through the bloodstream is a significant step in tumor metastasis. To better understand the metastatic process, circulating tumor cell (CTC) survival in the circulation must be explored. While immune interactions with CTCs in recent decades have been examined, research has yet to sufficiently explain some CTC behaviors in blood flow. Studies related to CTC mechanical responses in the bloodstream have recently been conducted to further study conditions under which CTCs might die. While experimental methods can assess the mechanical properties and death of CTCs, increasingly sophisticated computational models are being built to simulate the blood flow and CTC mechanical deformation under fluid shear stresses (FSS) in the bloodstream.Several factors contribute to the mechanical deformation and death of CTCs as they circulate. While FSS can damage CTC structure, diverse interactions between CTCs and blood components may either promote or hinder the next metastatic step-extravasation at a remote site. Overall understanding of how these factors influence the deformation and death of CTCs could serve as a basis for future experiments and simulations, enabling researchers to predict CTC death more accurately. Ultimately, these efforts can lead to improved metastasis-specific therapeutics and diagnostics specific in the future.

Hair cortisol concentration (HCC) and a questionnaire were used as indicators of chronic stress status and quality of life (QoL), respectively, in cats. To date, there has been limited research on the simultaneous application of both indicators in unwell cats. Our aim was to evaluate HCC and questionnaire data obtained from a healthy cat cohort (n = 61) and cat cohorts with either chronic kidney disease (CKD) (n = 78) or suspected feline infectious peritonitis (FIP) (n = 24). Furthermore, we also investigated the correlation between HCC and clinical pathological data. For this study, hair from the abdomen of cats was collected and analyzed for HCC using a commercial ELISA kit. Owners also completed a questionnaire, from which average-item-weighted-impact-scores (AWISs) were calculated. Cats with late-stage-CKD (median, HCC = 330.15 pg/mg, AWIS = -0.43) presented with a significantly higher HCC (p < 0.01) and a significantly lower AWIS (p < 0.01) than cats with early-stage-CKD (HCC = 183.56 pg/mg, AWIS = 1.08). Similarly, there were significant differences in both HCC (p < 0.001) and AWIS (p < 0.001) between cats with suspected FIP (HCC = 896.27 pg/mg, AWIS = -1.97) and healthy cats (HCC = 181.24 pg/mg, AWIS = 1.24). The degree of consistency between the HCC results and the questionnaire results reminds us that the severity of a chronic disease or the presence of a life-threatening disease can significantly increase stress and thus can affect the QoL of cats.

Malnutrition poses a significant threat to global health, resulting from an inadequate intake of essential nutrients that adversely impacts vital organs and overall bodily functioning. Periodic examinations and mass screenings, incorporating both conventional and non-invasive techniques, have been employed to combat this challenge. However, these approaches suffer from critical limitations, such as the need for additional equipment, lack of comprehensive feature representation, absence of suitable health indicators, and the unavailability of smartphone implementations for precise estimations of Body Fat Percentage (BFP), Basal Metabolic Rate (BMR), and Body Mass Index (BMI) to enable efficient smart-malnutrition monitoring. To address these constraints, this study presents a groundbreaking, scalable, and robust smart malnutrition-monitoring system that leverages a single full-body image of an individual to estimate height, weight, and other crucial health parameters within a multi-modal learning framework. Our proposed methodology involves the reconstruction of a highly precise 3D point cloud, from which 512-dimensional feature embeddings are extracted using a headless-3D classification network. Concurrently, facial and body embeddings are also extracted, and through the application of learnable parameters, these features are then utilized to estimate weight accurately. Furthermore, essential health metrics, including BMR, BFP, and BMI, are computed to comprehensively analyze the subject's health, subsequently facilitating the provision of personalized nutrition plans. While being robust to a wide range of lighting conditions across multiple devices, our model achieves a low Mean Absolute Error (MAE) of ± 4.7 cm and ± 5.3 kg in estimating height and weight.

Background: Continuous Glucose Monitoring (CGM) provides a detailed representation of glucose fluctuations in individuals, offering a rich dataset for understanding glycemic control in diabetes management. This study explores the potential of Riemannian manifold-based geometric clustering to analyze and interpret CGM data for individuals with Type 1 Diabetes (T1D) and healthy controls (HC), aiming to enhance diabetes management and treatment personalization.

Methods: We utilized CGM data from publicly accessible datasets, covering both T1D individuals on insulin and HC. Data were segmented into daily intervals, from which 27 distinct glycemic features were extracted. Uniform Manifold Approximation and Projection (UMAP) was then applied to reduce dimensionality and visualize the data, with model performance validated through correlation analysis between Silhouette Score (SS) against HC cluster and HbA1c levels.

Results: UMAP effectively distinguished between T1D on daily insulin and HC groups, with data points clustering according to glycemic profiles. Moderate inverse correlations were observed between SS against HC cluster and HbA1c levels, supporting the clinical relevance of the UMAP-derived metric.

Conclusions: This study demonstrates the utility of UMAP in enhancing the analysis of CGM data for diabetes management. We revealed distinct clustering of glycemic profiles between healthy individuals and diabetics on daily insulin indicating that in most instances insulin does not restore a normal glycemic phenotype. In addition, the SS quantifies day by day the degree of this continued dysglycemia and therefore potentially offers a novel approach for personalized diabetes care.

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone modifications, chromatin conformation, and non-coding RNA. The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities. Over the last few decades, high-throughput experimental approaches have been used to identify and understand epigenetic changes, but these laboratory experimental approaches and biochemical processes are time-consuming and expensive. To overcome these challenges, machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations. In this paper we provide a narrative review of published research on AI models trained on epigenomic data to address a variety of problems such as prediction of disease markers, gene expression, enhancer-promoter interaction, and chromatin states. The purpose of this review is twofold as it is addressed to both AI experts and epigeneticists. For AI researchers, we provided a taxonomy of epigenetics research problems that can benefit from an AI-based approach. For epigeneticists, given each of the above problems we provide a list of candidate AI solutions in the literature. We have also identified several gaps in the literature, research challenges, and recommendations to address these challenges.

The widespread utilization of antibiotics in livestock has promoted the accumulation and diffusion of antibiotics and antibiotic resistance in agricultural soils and crops. Here we investigated the mechanisms of antibiotic uptake and accumulation in swine wastewater (SW)-treated radish (Raphanus sativus L.) and subsequent impacts on endophyte antibiotic resistance. Under SW treatments, exposure to 500 μg/L sulfamethazine (SMZ) and enrofloxacin (EFX) significantly affected radish biomass, with SMZ causing 63.0% increases and EFX causing 36.3% decreases relative to the untreated control. EFX uptake by radish were from 5 to 100-folds over SMZ. Passive diffusion through anion channel proteins on cell membranes was an important route for SMZ uptake, while both passive diffusion and energy-dependent processes contributed to the uptake of EFX. Bacterial community was time-dependent as a function of both antibiotics and SW, the bacterial alpha diversity in liquid solution co-treated with antibiotics and SW increased over time. The abundance of antibiotic resistance genes (ARGs) in the roots was positively correlated with ARGs in the Hoagland's solution under antibiotic-alone treatments. EFX co-exposure with SW enhanced the dissemination of ARGs from swine wastewater into plant roots, and significant correlations existed between ARGs and integrons in both Hoagland's solution and roots. These findings increased our understanding of the fate of antibiotics in crops and their subsequent impacts on antibiotic resistance of endophytic bacteria.

Immune checkpoint inhibitors have shaped the landscape of cancer treatment. However, many patients either do not respond or suffer from later progression. Numerous proteins can control immune system activity, including multiple tumor necrosis factor (TNF) superfamily (TNFSF) and TNF receptor superfamily (TNFRSF) members; these proteins play a complex role in regulating cell survival and death, cellular differentiation, and immune system activity. Notably, TNFSF/TNFRSF molecules may display either pro-tumoral or anti-tumoral activity, or even both, depending on tumor type. Therefore, TNF is a prototype of an enigmatic two-faced mediator in oncogenesis. To date, multiple anti-TNF agents have been approved and/or included in guidelines for treating autoimmune disorders and immune-related toxicities after immune checkpoint blockade for cancer. A confirmed role for the TNFSF/TNFRSF members in treating cancer has proven more elusive. In this review, we highlight the cancer-relevant TNFSF/TNFRSF family members, focusing on the death domain-containing and co-stimulation members and their signaling pathways, as well as their complicated role in the life and death of cancer cells.

Pigeon coccidiosis caused by Eimeria spp. is an important veterinary disease with a significant economic impact on the pigeon industry. Preventive measures for Eimeria columbarum in pigeons have been hampered by the lack of extensive genetic, morphological, and biological data on the oocysts. In this study, we examined the prevalence and identity of Eimeria spp. in domestic pigeons from seven cities in Guangdong Province, China. Data show that coccidiosis was prevalent in domestic pigeons in Guangdong Province, with an overall Eimeria spp. detection rate of 73.4%. Five Eimeria species were identified, including E. columbarum (73.4%), Eimeria kapotei (25.6%), Eimeria labbeana (19.6%), Eimeria duculai (19.6%), and Eimeria tropicalis (6.7%). We obtained single oocyst-derived lines of the dominant E. columbarum from fecal specimens. E. columbarum oocysts measured 20.06 ± 0.69 μm × 18.63 ± 1.03 μm, and sporocysts measured 10.29 ± 0.82 μm × 85.38 ± 0.46 μm. In infection experiment using obtained E. columbarum isolates, 60-day-old coccidia-free pigeons exhibited a prepatent period of 105 h and patent period of 9-10 days followed by severe diarrhea, depression, anorexia, and emaciation. Endogenous development of the parasite was observed mainly in the cytoplasm of epithelial cells in the duodenum, jejunum, ileum, and rectum. Two generations of meronts developed on days 3 and 4 after infection, respectively, while gamont and gamete developed on day 5 after infection. The morphological, genetic, and biological data are expected to be useful in elucidating the biological characterization of pigeon coccidiosis to develop measures against the treatment and containment of this disease.