Modelling lung infection with Klebsiella pneumoniae after murine traumatic brain injury.

Pneumonia is a common comorbidity in patients with severe traumatic brain injury (TBI), and is associated with increased morbidity and mortality. In this study, we established a model of intratracheal Klebsiella pneumoniae administration in young adult male and female mice, at 4 days following an experimental TBI, to investigate how K. pneumoniae infection influences acute post-TBI outcomes. A dose-response curve determined the optimal dose of K. pneumoniae for inoculation (1 x 10^6 colony forming units), and administration at 4 days post-TBI resulted in transient body weight loss and sickness behaviors (hypoactivity and acute dyspnea). K. pneumoniae infection led to an increase in pro-inflammatory cytokines in serum and bronchoalveolar lavage fluid at 24 h post-infection, in both TBI and sham (uninjured) mice. By 7 days, when myeloperoxidase + neutrophil numbers had returned to baseline in all groups, lung histopathology was observed with an increase in airspace size in TBI + K. pneumoniae mice compared to TBI + vehicle mice. In the brain, increased neuroinflammatory gene expression was observed acutely in response to TBI, with an exacerbated increase in Ccl2 and Hmox1 in TBI + K. pneumoniae mice compared to either TBI or K. pneumoniae alone. However, the presence of neuroinflammatory immune cells in the injured brain, and the extent of damage to cortical and hippocampal brain tissue, was comparable between K. pneumoniae and vehicle-treated mice by 7 days. Examination of the fecal microbiome across a time course did not reveal any pronounced effects of either injury or K. pneumoniae on bacterial diversity or abundance. Together, these findings demonstrate that K. pneumoniae lung infection after TBI induces an acute and transient inflammatory response, primarily localized to the lungs with some systemic effects. However, this infection had minimal impact on secondary injury processes in the brain following TBI. Future studies are needed to evaluate the potential longer-term consequences of this dual-hit insult.

PfbZIP85 Transcription Factor Mediates ω-3 Fatty Acid-Enriched Oil Biosynthesis by Down-Regulating PfLPAT1B Gene Expression in Plant Tissues.

The basic leucine zipper (bZIP) transcription factor (TF) family is one of the biggest TF families identified so far in the plant kingdom, functioning in diverse biological processes including plant growth and development, signal transduction, and stress responses. For Perilla frutescens, a novel oilseed crop abundant in polyunsaturated fatty acids (PUFAs) (especially α-linolenic acid, ALA), the identification and biological functions of bZIP members remain limited. In this study, 101 PfbZIPs were identified in the perilla genome and classified into eleven distinct groups (Groups A, B, C, D, E, F, G, H, I, S, and UC) based on their phylogenetic relationships and gene structures. These PfbZIP genes were distributed unevenly across 18 chromosomes, with 83 pairs of them being segmental duplication genes. Moreover, 78 and 148 pairs of orthologous bZIP genes were detected between perilla and Arabidopsis or sesame, respectively. PfbZIP members belonging to the same subgroup exhibited highly conserved gene structures and functional domains, although significant differences were detected between groups. RNA-seq and RT-qPCR analysis revealed differential expressions of 101 PfbZIP genes during perilla seed development, with several PfbZIPs exhibiting significant correlations with the key oil-related genes. Y1H and GUS activity assays evidenced that PfbZIP85 downregulated the expression of the PfLPAT1B gene by physical interaction with the promoter. PfLPAT1B encodes a lysophosphatidate acyltransferase (LPAT), one of the key enzymes for triacylglycerol (TAG) assembly. Heterogeneous expression of PfbZIP85 significantly reduced the levels of TAG and UFAs (mainly C18:1 and C18:2) but enhanced C18:3 accumulation in both seeds and non-seed tissues in the transgenic tobacco lines. Furthermore, these transgenic tobacco plants showed no significantly adverse phenotype for other agronomic traits such as plant growth, thousand seed weight, and seed germination rate. Collectively, these findings offer valuable perspectives for understanding the functions of PfbZIPs in perilla, particularly in lipid metabolism, showing PfbZIP85 as a suitable target in plant genetic improvement for high-value vegetable oil production.

Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

BACKGROUND: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS: Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

Impact of Metal Salt Oxidants and Preparation Technology on Efficacy of Bacterial Cellulose/Polypyrrole Flexible Conductive Fiber Membranes.

In this study, we investigated the preparation and characterization of flexible conductive fiber membranes (BC/PPy) using different metal salt oxidants on bacterial cellulose (BC) and pyrrole (Py) in the in situ polymerization and co-blended methods, respectively. The effects of these oxidants, namely, ferric chloride hexahydrate (FeCl3·6H2O) and silver nitrate (AgNO3), on the structural characterization, conductivity, resistance value and thermal stability of the resulting materials were assessed by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and X-ray photoelectron spectroscopy (XPS). A comparative study revealed that the BC/PPy conductive fiber membrane prepared using FeCl3·6H2O as the oxidant had a resistance value of 12 Ω, while the BC/PPy conductive fiber membrane prepared using AgNO3 as the oxidant had an electrical resistance value of 130 Ω. The conductivity of the same molar ratio of BC/PPy prepared using FeCl3·6H2O as an oxidant was 10 times higher than that of the BC/PPy prepared using AgNO3 as an oxidant. Meanwhile, the resistance values of the conductive fiber membranes prepared from BC and PPy by the co-blended method were much higher than the BC/PPy prepared by in situ polymerization. SEM and XPS analyses revealed that when FeCl3·6H2O was used as the oxidant, the Fe-doped polypyrrole conductive particles could form uniform and dense conductive layers on the BC nanofiber surfaces. These two metal salt oxidants demonstrated differences in the binding sites between PPy and BC.

Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma.

Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.

Association between liver enzymes and type 2 diabetes: a real-world study.

Aim: This study aimed to examine the association of liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl-transferase (GGT), with type 2 diabetes (T2D) risk, particularly their dose-response relationship. Methods: This cross-sectional study enrolled participants aged >20 years old who underwent physical examination at our local hospital from November 2022 to May 2023. A generalized additive model (GAM) was fit to assess the dose-response relationship between liver enzymes and T2D risk. Furthermore, data from the UK Biobank (n=217,533) and National Health and Nutrition Examination Survey (NHANES 2011-2018; n= 15,528) were analyzed to evaluate whether the dose-response relationship between liver enzymes and T2D differed by population differences. Results: A total of 14,100 participants were included (1,155 individuals with T2D and 12,945 individuals without diabetes) in the analysis. GAM revealed a non-linear relationship between liver enzymes and T2D risk (P non-linear < 0.001). Specifically, T2D risk increased with increasing ALT and GGT levels (range, <50 IU/L) and then plateaued when ALT and GGT levels were >50 IU/L. Elevated AST within a certain range (range, <35 IU/L) decreased the risk of T2D, whereas mildly elevated AST (>35 IU/L) became a risk factor for T2D. The UK Biobank and NHANES data analysis also showed a similar non-linear pattern between liver enzymes and T2D incidence. Conclusion: Liver enzymes were non-linearly associated with T2D risk in different populations, including China, the UK, and the US. Elevated ALT and GGT levels, within a certain range, could increase T2D risk. More attention should be given to liver enzyme levels for early lifestyle intervention and early T2D prevention. Further studies are necessary to explore the mechanism of the non-linear association between liver enzymes and T2D risk.

Clinical Evidence of the Relationship Between Alanine Aminotransferase and Diabetic Kidney Disease.

Aim: Multiple studies have investigated the association between alanine aminotransferase (ALT) and diabetes mellitus (DM); however, only a few studies have specifically examined the relationship between ALT and diabetic kidney disease (DKD). This study aimed to investigate the relationship between ALT and DKD using clinical data. Methods: A cross-sectional study was conducted on 668 individuals that included non-DM (N=281), DM without DKD (N=160), and DKD (N=227) patients. A generalized additive model (GAM) was used to examine the dose-response relationship between ALT and DKD risk. We also analyzed the data from the US National Health and Nutrition Examination Survey (NHANES) 2015-2018 using the same statistical methods; 4481, 1110, and 671 individuals were included in the non-DM, DM without DKD, and DKD groups, respectively. Results: The changes in ALT activity among the non-DM, DM without DKD, and DKD groups showed a similar pattern in both our clinical data and the NHANES dataset. ALT activity increases with the onset of DM, whereas ALT activity decreases when DM progresses to DKD. The GAM revealed a nonlinear U-shaped relationship between ALT and DKD risk in the two datasets, and the lowest range of ALT was 40-50 IU/L. Both lower (<40 IU/L) and higher (>50 IU/L) ALT activity were found to be positively associated with DKD risk. Conclusion: A U-shaped nonlinear association between ALT and DKD was found in our clinical data and NHANES data. DKD risk was increased by both lower or higher ALT activity. To confirm the causality of nonlinear relationship, larger prospective studies or Mendelian randomization analysis are required.

Dietary novel alkaline protease from Bacillus licheniformis improves broiler meat nutritional value and modulates intestinal microbiota and metabolites.

BACKGROUND: Different types of exogenous protease supplements have a positive impact on animal performance, but their effects on the nutritional value of meat and the gut microbial community of broilers have not been extensively studied. The objective of this investigation was to determine the impact of supplementation with a novel alkaline protease derived from Bacillus licheniformis (at doses of 0, 100, 200, 300, and 400 g/t) on the fatty acid and amino acid profiles, inosine monophosphate (IMP) levels, total volatile basic nitrogen (TVB-N) content found within the breast muscle, as well as the impact on the cecal microbiota and metabolites. RESULTS: Supplementation with 200-400 g/t of the novel protease resulted in a significant elevation in the concentration of essential amino acids (P < 0.001), flavor amino acids (P < 0.001), and total protein (P = 0.013) within the breast muscle. Results derived from the 16S rRNA sequencing and untargeted metabolomics analysis of the cecal content revealed that the novel protease reshaped the cecal microbial and metabolite profiles. In particular, it led to increased relative abundances of Bacteroides, Lactobacillus, Alistipes, and Eubacterium, while simultaneously causing a reduction in the metabolites of D-lactic acid and malonic acid. Moreover, correlation analyses unveiled significant relationships between distinct microbes and metabolites with the contents of IMP, fatty acids, and amino acids in the broiler's breast muscle. CONCLUSION: In summary, the novel protease regulated the intestinal microbial community and metabolism, thereby inducing changes in the compositions of fatty acids and amino acids profiles, as well as IMP levels in broiler meat. These alterations significantly contributed to the enhancement of the nutritional value and flavor of the meat.

PD-1 Blockade Induces Reactivation of Nonproductive T-Cell Responses Characterized by NF-κB Signaling in Patients with Pancreatic Cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) trials have evaluated CTLA-4 and/or PD-(L)1 blockade in patients with advanced disease in which bulky tumor burden and limited time to develop antitumor T cells may have contributed to poor clinical efficacy. Here, we evaluated peripheral blood and tumor T cells from patients with PDAC receiving neoadjuvant chemoradiation plus anti-PD-1 (pembrolizumab) versus chemoradiation alone. We analyzed whether PD-1 blockade successfully reactivated T cells in the blood and/or tumor to determine whether lack of clinical benefit could be explained by lack of reactivated T cells versus other factors. EXPERIMENTAL DESIGN: We used single-cell transcriptional profiling and TCR clonotype tracking to identify TCR clonotypes from blood that match clonotypes in the tumor. RESULTS: PD-1 blockade increases the flux of TCR clonotypes entering cell cycle and induces an IFNγ signature like that seen in patients with other GI malignancies who respond to PD-1 blockade. However, these reactivated T cells have a robust signature of NF-κB signaling not seen in cases of PD-1 antibody response. Among paired samples between blood and tumor, several of the newly cycling clonotypes matched activated T-cell clonotypes observed in the tumor. CONCLUSIONS: Cytotoxic T cells in the blood of patients with PDAC remain sensitive to reinvigoration by PD-1 blockade, and some have tumor-recognizing potential. Although these T cells proliferate and have a signature of IFN exposure, they also upregulate NF-κB signaling, which potentially counteracts the beneficial effects of anti-PD-1 reinvigoration and marks these T cells as non-productive contributors to antitumor immunity. See related commentary by Lander and DeNardo, p. 474.

Polysaccharide-Targeting Lipid Nanoparticles to Kill Gram-Negative Bacteria.

The rapid increase and spread of Gram-negative bacteria resistant to many or all existing treatments threaten a return to the preantibiotic era. The presence of bacterial polysaccharides that impede the penetration of many antimicrobials and protect them from the innate immune system contributes to resistance and pathogenicity. No currently approved antibiotics target the polysaccharide regions of microbes. Here, describe monolaurin-based niosomes, the first lipid nanoparticles that can eliminate bacterial polysaccharides from hypervirulent Klebsiella pneumoniae, are described. Their combination with polymyxin B shows no cytotoxicity in vitro and is highly effective in combating K. pneumoniae infection in vivo. Comprehensive mechanistic studies have revealed that antimicrobial activity proceeds via a multimodal mechanism. Initially, lipid nanoparticles disrupt polysaccharides, then outer and inner membranes are destabilized and destroyed by polymyxin B, resulting in synergistic cell lysis. This novel lipidic nanoparticle system shows tremendous promise as a highly effective antimicrobial treatment targeting multidrug-resistant Gram-negative pathogens.

Utilizing Ceftazidime/Avibactam Therapeutic Drug Monitoring in the Treatment of Neurosurgical Meningitis Caused by Difficult-to-Treat Resistant Pseudomonas aeruginosa and KPC-Producing Enterobacterales.

Background: Central nervous system (CNS) infections caused by Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacterales and difficult-to-treat resistant (DTR) Pseudomonas aeruginosa represent a formidable clinical challenge. Antimicrobial regimens that efficiently penetrate the cerebrospinal fluid (CSF) and achieve sufficient concentrations associated with microbiologic and clinical cure are limited. We evaluated therapy with ceftazidime-avibactam (CAZ-AVI) in order to guide precise dosing in the treatment of CNS infections. Methods: Therapeutic drug monitoring (TDM) was performed in 3 patients with health care-associated ventriculitis and meningitis (HAVM) using CAZ-AVI 2.5 g infused intravenously every 8 hours as standard and extended infusion. Simultaneous CSF and plasma samples were obtained throughout the dosing interval in each patient. Concentrations of CAZ and AVI were determined by liquid chromatography/mass spectrometry. Results: Bacterial identification revealed KPC-producing Klebsiella pneumoniae (KPC-Kp), DTR Pseudomonas aeruginosa, and KPC-producing Enterobacter cloacae (KPC-Ent.c). All isolates were resistant to carbapenems. The minimum inhibitory concentrations (MICs) of CAZ-AVI were 0.25/4, 4/4, and 0.25/4 µg/mL, respectively. CAZ and AVI concentrations were determined in CSF samples ranging from 29.0 to 15.0 µg/mL (CAZ component) and 4.20 to 0.92 µg/mL (AVI component), respectively. AVI achieved concentrations ≥1 µg/mL in 11 out of 12 CSF samples collected throughout the dosing interval. Clinical and microbiologic cure were attained in all patients. Conclusions: Postinfusion concentrations of CAZ-AVI were measured in plasma and CSF samples obtained from 3 patients with complicated CNS infections caused by antimicrobial-resistant isolates. The measured concentrations revealed that standard CAZ and AVI exposures sufficiently attained values correlating to 50% fT > MIC, which are associated with efficient bacterial killing.

Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.

Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcome the limitations of regression analysis, a deep learning-based analysis pipeline was developed. Study Design and Setting: Lumpectomy (breast-conserving surgery) and mastectomy are the two most commonly used surgical procedures for early-stage female breast cancer patients. An emulation trial was designed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data to evaluate their relative effectiveness in overall survival. The analysis pipeline consisted of a propensity score step, a weighted survival analysis step, and a bootstrap inference step. Results: A total of 65,997 subjects were enrolled in the emulated trial, with 50,704 and 15,293 in the lumpectomy and mastectomy arms, respectively. The two surgery procedures had comparable effects in terms of overall survival (survival year change = 0.08, 95% confidence interval (CI): -0.08, 0.25) for the elderly SEER-Medicare early-stage female breast cancer patients. Conclusion: This study demonstrated the power of "mining large EMR data + deep learning-based analysis," and the proposed analysis strategy and technique can be potentially broadly applicable. It provided convincing evidence of the comparative effectiveness of lumpectomy and mastectomy.

Diagnostic value of WB-DWI versus 18F-FDG PET/CT for the detection of multiple myeloma.

Background: Whole-body diffusion-weighted imaging (WB-DWI) is commonly used for the detection of multiple myeloma (MM). Comparative data on the efficiency of WB-DWI compared with F-18 fluoro-2-deoxy-d-glucose positron emission tomography-computed tomography (18F-FDG PET/CT) to detect MM is limited. Methods: This was a retrospective, single-center study of 22 patients with MM enrolled from January 2018 to December 2019. All patients underwent WB-DWI and 18F-FDG PET/CT. Pathological and clinical manifestations, as well as radiologic follow-up, were used for diagnosis. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of both methods were compared. The apparent diffusion coefficient (ADC) values of MM lesions and false-positive lesions were estimated. Results: A total of 214 MM bone lesions were evaluated. There was no significant difference in the accuracy of WB-DWI and PET/CT (86.92 versus 88.32%). Though WB-DWI had a higher sensitivity (99.26% versus84.56%) and PET-CT had a higher specificity (96.10% versus 64.56%), these differences were not statistically significant. There was a statistically significant difference in PPV (83.33% versus 96.64%) and NPV (98.08% versus 77.89%) of WB-DWI and PET/CT, respectively. The ADC value for MM lesions was significantly lower than that for false-positive lesions (P < 0.001). Receiver operating curve analysis showed that the AUC was 0.846, and when the cut-off value was 0.745 × 10-3 mm2/s, the sensitivity and specificity were 86.3 and 83.4%, respectively, which distinguished MM lesions from non-MM lesions. Conclusion: WB-DWI and PET-CT scans have similar overall accuracy for detecting MM lesions. The higher PPV of PET-CT and NPV of WB-DWI make them complementary imaging modalities. The ADC value for MM lesions is significantly lower than that for false-positive lesions. An ADC cutoff value of 0.745 × 10-3 mm2/s results in sensitivity and specificity of 86.3 and 83.4%, respectively.

Genome-Wide Analysis of Glycerol-3-Phosphate Acyltransferase (GPAT) Family in Perilla frutescens and Functional Characterization of PfGPAT9 Crucial for Biosynthesis of Storage Oils Rich in High-Value Lipids.

Glycerol-3-phosphate acyltransferase (GPAT) catalyzes the first step in triacylglycerol (TAG) biosynthesis. However, GPAT members and their functions remain poorly understood in Perilla frutescens, a special edible-medicinal plant with its seed oil rich in polyunsaturated fatty acids (mostly α-linolenic acid, ALA). Here, 14 PfGPATs were identified from the P. frutescens genome and classified into three distinct groups according to their phylogenetic relationships. These 14 PfGPAT genes were distributed unevenly across 11 chromosomes. PfGPAT members within the same subfamily had highly conserved gene structures and four signature functional domains, despite considerable variations detected in these conserved motifs between groups. RNA-seq and RT-qPCR combined with dynamic analysis of oil and FA profiles during seed development indicated that PfGPAT9 may play a crucial role in the biosynthesis and accumulation of seed oil and PUFAs. Ex vivo enzymatic assay using the yeast expression system evidenced that PfGPAT9 had a strong GPAT enzyme activity crucial for TAG assembly and also a high substrate preference for oleic acid (OA, C18:1) and ALA (C18:3). Heterogeneous expression of PfGPAT9 significantly increased total oil and UFA (mostly C18:1 and C18:3) levels in both the seeds and leaves of the transgenic tobacco plants. Moreover, these transgenic tobacco lines exhibited no significant negative effect on other agronomic traits, including plant growth and seed germination rate, as well as other morphological and developmental properties. Collectively, our findings provide important insights into understanding PfGPAT functions, demonstrating that PfGPAT9 is the desirable target in metabolic engineering for increasing storage oil enriched with valuable FA profiles in oilseed crops.

Intertumoral lineage diversity and immunosuppressive transcriptional programs in well-differentiated gastroenteropancreatic neuroendocrine tumors.

Neuroendocrine tumors (NETs) are rare cancers that most often arise in the gastrointestinal tract and pancreas. The fundamental mechanisms driving gastroenteropancreatic (GEP)-NET growth remain incompletely elucidated; however, the heterogeneous clinical behavior of GEP-NETs suggests that both cellular lineage dynamics and tumor microenvironment influence tumor pathophysiology. Here, we investigated the single-cell transcriptomes of tumor and immune cells from patients with gastroenteropancreatic NETs. Malignant GEP-NET cells expressed genes and regulons associated with normal, gastrointestinal endocrine cell differentiation, and fate determination stages. Tumor and lymphoid compartments sparsely expressed immunosuppressive targets commonly investigated in clinical trials, such as the programmed cell death protein-1/programmed death ligand-1 axis. However, infiltrating myeloid cell types within both primary and metastatic GEP-NETs were enriched for genes encoding other immune checkpoints, including VSIR (VISTA), HAVCR2 (TIM3), LGALS9 (Gal-9), and SIGLEC10. Our findings highlight the transcriptomic heterogeneity that distinguishes the cellular landscapes of GEP-NET anatomic subtypes and reveal potential avenues for future precision medicine therapeutics.

Temperature-Sensitive Fragrance Microcapsules with Double Capsule Walls: A Study on Preparation and Sustained Release Mechanism.

Microcapsules are small particles that can effectively protect a core material from degradation. Microcapsules with double capsule walls can improve stability and reduce breakage due to the fact that the physical and chemical properties of double-walled materials can complement each other, thus enhancing the quality and applicability of a microcapsule. Microcapsules can achieve controlled release of core materials by using a temperature-sensitive wall material. In this research, gelatin was used as the inner wall material for these double-walled microcapsules. The outer wall material was a composite material prepared by the reaction of a hydroxyl group in gum arabic with an amino group in N-isopropylacrylamide (NIPAM) in the presence of N, N'-methylene bisacrylamide (BIS), while lavender fragrance oil served as the core material. A complex coalescence method was used for the preparation of microcapsules with double capsule walls. The effects of different proportions of gum arabic to NIPAM on the core loading, microcapsule yield and thermal stability of microcapsules were studied in detail. Additionally, the stability of these fragrance microcapsules with double capsule walls in different solvents and pH values was evaluated. The sustained release properties and mechanism of cotton fabrics treated with prepared fragrance microcapsules were investigated. The results show that the microcapsules prepared with a 10:1 ratio of NIPAM to gum arabic have good temperature responsiveness. Therefore, clothing treated with microcapsules with temperature-sensitive wall materials can ensure that the human body has a fresh and pleasant smell in the case of perspiring in summer.

Distribution and Rate of Myxoid Liposarcoma Spine Metastases: Impact on Surveillance Imaging.

BACKGROUND: Myxoid liposarcoma (LPS) has a unique tendency to spread to extrapulmonary sites, including osseous sites such as the spine, and adjacent sites such as the paraspinous tissue. No clear consensus exists to guide the approach to imaging in these patients. OBJECTIVE: The aim of this study was to investigate the rate and distribution of spine metastases in patients with myxoid LPS and detection modality. METHODS: Records of all patients with myxoid LPS evaluated at our sarcoma center were retrospectively reviewed. Disease patterns and imaging modality utilization were analyzed. RESULTS: Between 2000 and 2020, 164 patients with myxoid LPS were identified. The majority (n = 148, 90%) presented with localized disease, with half (n = 82, 50%) of all patients developing metastases or recurrence during their disease course. With a median follow-up of 69.2 months, spine/paraspinous metastases developed in 38 patients (23%), of whom 35 (92%) already had synchronous, non-spine metastases. Spine disease was only visible on magnetic resonance imaging (MRI), as opposed to other imaging modalities, for over one-quarter of patients with spine metastases (n = 10). For patients with metastatic disease, spine metastases were associated with worse median overall survival (2.1 vs. 8.7 years, p < 0.001). CONCLUSION: Spine metastases occurred in nearly one-quarter of patients with myxoid LPS and represented an advanced disease state, as they primarily presented in the setting of synchronous, non-spine metastases, and were associated with worse overall survival. Routine surveillance with spine MRI in patients with localized disease likely provides no benefit but may be considered in those with known metastatic disease.

An information-motivation-behavioral skills model-based intervention for patients with epilepsy.

PURPOSE: Self-management is crucial for patients with epilepsy (PWE) as it can effectively control the condition, improve treatment outcomes, and reduce the risk of seizures. The objective of this study was to evaluate the effectiveness of health education based on the Information-Motivation-Behavioral Skills (IMB) model in improving self-management, quality of life, and medication adherence among PWE. METHOD: This study was conducted in a district in Shanghai, China, from September 2022 to March 2023. A total of 90 PWE were randomly assigned to an intervention group or a control group. Patients in the intervention group received health education based on the IMB model, whereas those in the control group received routine medical treatment only. The effectiveness of the intervention was evaluated by measuring self-management ability, quality of life, and medication adherence at three months and six months after the intervention. RESULTS: The study included a total of 90 PWE; among them, 55 patients (61.11%) were male, divided into two groups: 45 participants in the control group and 45 participants in the intervention group. After 3 and 6 months of intervention, the self-management scores of the intervention group were significantly higher than those of the control group (t = 14.274, 12.448; P < 0.05). After 6 months of the intervention, the quality-of-life scores of the intervention group were significantly higher than those of the control group (t = 2.025, P < 0.05), and the medication adherence in the intervention group was significantly higher than that in the control group (X2 = 9.003, P < 0.05). CONCLUSION: Health education based on the IMB model is a promising approach to improving self-management, quality of life, and medication adherence among PWE. It provides personalized, patient-centered interventions that consider patients' knowledge, motivation, and behavior skills, and could be a useful tool for healthcare providers in developing comprehensive care plans for individuals with epilepsy.

Effective Detergency Determination for Single Polymeric Fibers Using Confocal Microscopy.

Detergency determination for single polymeric fibers is of significant importance to screening effective detergents for laundry, but remains challenging. Herein, we demonstrate a novel and effective method to quantify the detergency for single polymeric fibers using a confocal laser scanning microscope (CLSM). It was applied to visualize the oil-removing process of single polymeric fibers and thus assess the detergency of various detergents. Four typical surfactants were selected for comparison, and a compounded detergent containing multiple components (e.g., anionic and nonionic surfactants, enzymes) was demonstrated to be the most effective and powerful soil-removing detergent because more than 50% of oil on the cotton fiber could be easily removed. Moreover, the oil removal process of three kinds of fibers (i.e., cotton, viscose, and polyester) was imaged and monitored by confocal microscopy. It was found that the percentage of the detergency of a single polyester fiber exceeded 70%, which is much higher than that of cotton and viscose fibers (~50%), which may be due to its relatively smooth surface. Compared to traditional methods, the CLSM imaging method is more feasible and effective to determine the detergency of detergents for single polymeric fibers.

CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise.

Identifying human actions from video data is an important problem in the fields of intelligent rehabilitation assessment. Motion feature extraction and pattern recognition are the two key procedures to achieve such goals. Traditional action recognition models are usually based on the geometric features manually extracted from video frames, which are however difficult to adapt to complex scenarios and cannot achieve high-precision recognition and robustness. We investigate a motion recognition model and apply it to recognize the sequence of complicated actions of a traditional Chinese exercise (ie, Baduanjin). We first developed a combined convolutional neural network (CNN) and long short-term memory (LSTM) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of Baduanjin. Moreover, this method has been compared with the traditional action recognition model based on geometric motion features in which Openpose is used to identify the joint positions in the skeletons. Its performance of high recognition accuracy has been verified on the testing video dataset, containing the video clips from 18 different practicers. The CNN-LSTM recognition model achieved 96.43% accuracy on the testing set; while those manually extracted features in the traditional action recognition model were only able to achieve 66.07% classification accuracy on the testing video dataset. The abstract image features extracted by the CNN module are more effective on improving the classification accuracy of the LSTM model. The proposed CNN-LSTM based method can be a useful tool in recognizing the complicated actions.