High-Strength Polycrystalline Covalent Organic Framework with Abnormal Thermal Transport Insensitive to Grain Boundary.

Grain boundaries (GBs) in two-dimensional (2D) covalent organic frameworks (COFs) unavoidably form during the fabrication process, playing pivotal roles in the physical characteristics of COFs. Herein, molecular dynamics simulations were employed to elucidate the fracture failure and thermal transport mechanisms of polycrystalline COFs (p-COFs). The results revealed that the tilt angle of GBs significantly influences out-of-plane wrinkles and residual stress in monolayer p-COFs. The tensile strength of p-COFs can be enhanced and weakened with the tilt angle, which exhibits an inverse relationship with the defect density. The crack always originates from weaker heptagon rings during uniaxial tension. Notably, the thermal transport in p-COFs is insensitive to the GBs due to the variation of minor polymer chain length at defects, which is abnormal for other 2D crystalline materials. This study contributes insights into the impact of GBs in p-COFs and offers theoretical guidance for structural design and practical applications of advanced COFs.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study.

PURPOSE: Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging. METHODS: We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis). RESULTS: We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001). CONCLUSION: In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation.

PURPOSE: The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI. METHODS: MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility. RESULTS: In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC. CONCLUSIONS: The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

Dynamic Insights into the Growth Mechanisms of 2D Covalent Organic Frameworks on Graphene Surfaces.

Producing high-quality two-dimensional (2D) covalent organic frameworks (COFs) is crucial for industrial applications. However, this remains significantly challenging with current synthetic techniques. A deep understanding of the intermolecular interactions, reaction temperature, and oligomers is essential to facilitate the growth of highly crystalline COF films. Herein, molecular dynamics simulations were employed to explore the growth of 2D COFs from monomer assemblies on graphene. Our results showed that chain growth reactions dominated the COF surface growth and that van der Waals (vdW) interactions were important in enhancing the crystallinity through monomer preorganization. Moreover, appropriately tuning the reaction temperature improved the COF crystallinity and minimized the effects of amorphous oligomers. Additionally, the strength of the interface between the COF and the graphene substrate indicated that the adhesion force was proportional to the crystallinity of the COF. This work reveals the mechanisms for nucleation and growth of COFs on surfaces and provides theoretical guidance for fabricating high-quality 2D polymer-based crystalline nanomaterials.

Spatially and Temporally Programmable Transparency Evolutions in Hydrogels Enabled by Metal Coordination toward Transient Anticounterfeiting.

Hydrogels have emerged as promising candidates for anticounterfeiting materials, owing to their unique stimulus-responsive capabilities. To improve the security of encrypted information, efforts are devoted to constructing transient anticounterfeiting hydrogels with a dynamic information display. However, current studies to design such hydrogel materials inevitably include sophisticated chemistry, complex preparation processes, and particular experimental setups. Herein, a facile strategy is proposed to realize the transient anticounterfeiting by constructing bivalent metal (M2+)-coordination complexes in poly(acrylic acid) gels, where the cloud temperature (Tc) of the gels can be feasibly tuned by M2+ concentration. Therefore, the multi-Tc parts in the gel can be locally programmed by leveraging the spatially selective diffusion of M2+ with different concentrations. With the increase of temperature or the addition of a complexing agent, the transparency of the multi-Tc parts in the gel spontaneously evolves in natural light, enabling the transient information anticounterfeiting process. This work has provided a new strategy and mechanism to fabricate advanced anticounterfeiting hydrogel materials.

A Soft Sensor for Multirate Quality Variables Based on MC-CNN.

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.

Ultrasensitive Ionic Conductors with Tunable Resistance Switching Temperature Enabled by Phase Transformation of Polymer Cocrystals.

Phase-transformable ionic conductors (PTICs) show significant prospects for functional applications due to their reversible resistance switching property. However, the representative design principle of PTICs is utilizing the melt-crystallization transition of ionic liquids, and the resistance switching temperatures of such PTICs cannot be tuned as desired. Herein, a new strategy is proposed to design PTICs with on-demand resistance switching temperatures by using the melt-crystallization transition of polymer cocrystal phase, whose melting temperature shows a linear relationship with the polymer compositions. Owing to the melt of polymer cocrystal domains and the tunable migration of ions in the resistance switching region, the obtained PTICs display ultrahigh temperature sensitivity with a superior temperature coefficient of resistance of -8.50% °C-1 around human body temperature, as compared to various ionic conductors previously reported. Therefore, the PTICs can detect tiny temperature variation, allowing for the intelligent applications for overheating warning and heat dissipation. It is believed that this work may inspire future researches on the development of advanced soft electrical devices.

Cellular and Molecular Roles of Immune Cells in the Gut-Brain Axis in Migraine.

Migraine is a complex and multi-system dysfunction. The realization of its pathophysiology and diagnosis is developing rapidly. Migraine has been linked to gastrointestinal disorders such as irritable bowel syndrome and celiac disease. There is also direct and indirect evidence for a relationship between migraine and the gut-brain axis, but the exact mechanism is not yet explained. Studies have shown that this interaction appears to be influenced by a variety of factors, such as inflammatory mediators, gut microbiota, neuropeptides, and serotonin pathways. Recent studies suggest that immune cells can be the potential tertiary structure between migraine and gut-brain axis. As the hot interdisciplinary subject, the relationship between immunology and gastrointestinal tract is now gradually clear. Inflammatory signals are involved in cellular and molecular responses that link central and peripheral systems. The gastrointestinal symptoms associated with migraine and experiments associated with antibiotics have shown that the intestinal microbiota is abnormal during the attacks. In this review, we focus on the mechanism of migraine and gut-brain axis, and summarize the tertiary structure between immune cells, neural network, and gastrointestinal tract.

Segmentation of lung nodules based on a refined segmentation network.

BACKGROUND: Accurate segmentation of lung nodules is of great significance for early screening and diagnosis of lung cancer. PURPOSE: However, the heterogeneity of lung nodules and the similarities between them and other lung tissues make it difficult to accurately segment these nodules. As regards the use of deep learning to segment lung nodules, convolutional neural networks would gradually lead to errors accumulating at the network layer due to the presence of multiple upsampling and downsampling layers, resulting in poor segmentation results. METHODS: In this study, we developed a refined segmentation network (RS-Net) for lung nodule segmentation to solve this problem. Accordingly, the proposed RS-Net was first used to locate the core region of the lung nodules and to gradually refine the segmentation results of the core region. In addition, to solve the problem of misdetection of small-sized nodules owing to the imbalance of positive and negative samples, we devised an average dice-loss function computed on nodule level. By calculating the loss of each nodule sample to measure the overall loss, the network can address the misdetection problem of lung nodules with smaller diameters more efficiently. RESULTS: Our method was evaluated based on 1055 lung nodules from Lung Image Database Consortium data and a set of 120 lung nodules collected from Shanghai Chest Hospital for additional validation. The segmentation dice coefficients of RS-Net on these two datasets were 85.90% and 81.13%, respectively. The analysis of the segmentation effect of different properties and sizes of nodules indicates that RS-Net yields a stable segmentation effect. CONCLUSIONS: The results show that the segmentation strategy based on gradual refinement can considerably improve the segmentation of lung nodules.

MetaFormer Baselines for Vision.

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant role in achieving competitive performance. In this paper, we further explore the capacity of MetaFormer, again, by migrating our focus away from the token mixer design: we introduce several baseline models under MetaFormer using the most basic or common mixers, and demonstrate their gratifying performance. We summarize our observations as follows: (1) MetaFormer ensures solid lower bound of performance. By merely adopting identity mapping as the token mixer, the MetaFormer model, termed IdentityFormer, achieves [Formula: see text]80% accuracy on ImageNet-1 K. (2) MetaFormer works well with arbitrary token mixers. When specifying the token mixer as even a random matrix to mix tokens, the resulting model RandFormer yields an accuracy of [Formula: see text]81%, outperforming IdentityFormer. Rest assured of MetaFormer's results when new token mixers are adopted. (3) MetaFormer effortlessly offers state-of-the-art results. With just conventional token mixers dated back five years ago, the models instantiated from MetaFormer already beat state of the art. (a) ConvFormer outperforms ConvNeXt. Taking the common depthwise separable convolutions as the token mixer, the model termed ConvFormer, which can be regarded as pure CNNs, outperforms the strong CNN model ConvNeXt. (b) CAFormer sets new record on ImageNet-1 K. By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1 K: it achieves an accuracy of 85.5% at 224 ×224 resolution, under normal supervised training without external data or distillation. In our expedition to probe MetaFormer, we also find that a new activation, StarReLU, reduces 71% FLOPs of activation compared with commonly-used GELU yet achieves better performance. Specifically, StarReLU is a variant of Squared ReLU dedicated to alleviating distribution shift. We expect StarReLU to find great potential in MetaFormer- like models alongside other neural networks. Code and models are available at https://github.com/sail-sg/metaformer.

Optimizing postbiotic production through solid-state fermentation with Bacillus amyloliquefaciens J and Lactiplantibacillus plantarum SN4 enhances antibacterial, antioxidant, and anti-inflammatory activities.

Background: Postbiotics are an emerging research interest in recent years and are fairly advanced compared to prebiotics and probiotics. The composition and function of postbiotics are closely related to fermentation conditions. Methods: In this study, we developed a solid-state fermentation preparation method for postbiotics with antimicrobial, antioxidant, and anti-inflammatory activities. The antibacterial activity was improved 3.62 times compared to initial fermentation conditions by using optimization techniques such as single factor experiments, Plackett-Burman design (PBD), steepest ascent method (SAM), and central composite design (CCD) methods. The optimized conditions were carried out with an initial water content of 50% for 8 days at 37°C and fermentation strains of Bacillus amyloliquefaciens J and Lactiplantibacillus plantarum SN4 at a ratio of 1:1 with a total inoculum size of 8%. The optimized SSF medium content ratios of peptide powder, wheat bran, corn flour, and soybean meal were 4, 37.4, 30, and 28.6%, respectively. Results: Under these optimized conditions, postbiotics with a concentration of 25 mg/mL showed significant broad-spectrum antibacterial capabilities against Escherichia coli, Salmonella, and Staphylococcus aureus and strong antioxidant activity against ABTS, DPPH, and OH radicals. Moreover, the optimized postbiotics exhibited good anti-inflammatory ability for reducing nitric oxide (NO) secretion in RAW 264.7 macrophage cells in response to LPS-induced inflammation. Furthermore, the postbiotics significantly improved intestinal epithelial wound healing capabilities after mechanical injury, such as cell scratches in IPEC-J2 cells (p < 0.05). Conclusion: In brief, we developed postbiotics through optimized solid-state fermentation with potential benefits for gut health. Therefore, our findings suggested that the novel postbiotics could be used as potential functional food products for improving body health.

Dietary Fiber and Microbiota Metabolite Receptors Enhance Cognition and Alleviate Disease in the 5xFAD Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder with poorly understood etiology. AD has several similarities with other "Western lifestyle" inflammatory diseases, where the gut microbiome and immune pathways have been associated. Previously, we and others have noted the involvement of metabolite-sensing GPCRs and their ligands, short-chain fatty acids (SCFAs), in protection of numerous Western diseases in mouse models, such as Type I diabetes and hypertension. Depletion of GPR43, GPR41, or GPR109A accelerates disease, whereas high SCFA yielding diets protect in mouse models. Here, we extended the concept that metabolite-sensing receptors and SCFAs may be a more common protective mechanism against Western diseases by studying their role in AD pathogenesis in the 5xFAD mouse model. Both male and female mice were included. Depletion of GPR41 and GPR43 accelerated cognitive decline and impaired adult hippocampal neurogenesis in 5xFAD and WT mice. Lack of fiber/SCFAs accelerated a memory deficit, whereas diets supplemented with high acetate and butyrate (HAMSAB) delayed cognitive decline in 5xFAD mice. Fiber intake impacted on microglial morphology in WT mice and microglial clustering phenotype in 5xFAD mice. Lack of fiber impaired adult hippocampal neurogenesis in both W and AD mice. Finally, maternal dietary fiber intake significantly affects offspring's cognitive functions in 5xFAD mice and microglial transcriptome in both WT and 5xFAD mice, suggesting that SCFAs may exert their effect during pregnancy and lactation. Together, metabolite-sensing GPCRs and SCFAs are essential for protection against AD, and reveal a new strategy for disease prevention.Significance Statement Alzheimer's disease (AD) is one of the most common neurodegenerative diseases; currently, there is no cure for AD. In our study, short-chain fatty acids and metabolite receptors play an important role in cognitive function and pathology in AD mouse model as well as in WT mice. SCFAs also impact on microglia transcriptome, and immune cell recruitment. Out study indicates the potential of specialized diets (supplemented with high acetate and butyrate) releasing high amounts of SCFAs to protect against disease.

Optimization of Exopolysaccharide Produced by Lactobacillus plantarum R301 and Its Antioxidant and Anti-Inflammatory Activities.

In this study, the yield of exopolysaccharide (EPS) from Lactobacillus plantarum R301 was optimized using a single-factor experiment and response surface methodology (RSM). After optimization, the EPS yield was increased with a fold-change of 0.85. The significant factors affecting EPS production, as determined through a Plackett-Burman design and Central Composite Design (CCD), were MgSO4 concentration, initial pH, and inoculation size. The maximum yield was 97.85 mg/mL under the condition of 0.01% MgSO4, an initial pH 7.4, and 6.4% of the inoculation size. In addition, the EPS exhibited strong antioxidant activity, as demonstrated by its ability to scavenge DPPH, ABTS, and hydroxyl radicals. The scavenging rate was up to 100% at concentrations of 4 mg/mL, 1 mg/mL, and 2 mg/mL, respectively. Moreover, the EPS also exhibited reducing power, which was about 30% that of ascorbic acid when both tended to be stable with the increased concentration. These results suggest that L. plantarum R301 EPS possesses different antioxidant mechanisms and warrants further investigation. In addition to its antioxidant activity, the EPS also demonstrated good anti-inflammatory activity by inhibiting the inflammation induced by lipopolysaccharide (LPS) in RAW 264.7 cells, which could decrease nitric oxide (NO) production and expression of the proinflammatory cytokine Il-6. These findings suggest that L. plantarum R301 EPS could be used as a potential multifunctional food additive in the food industry.

Novel efficient capture of hexavalent chromium by polyethyleneimine/amyloid fibrils/polyvinyl alcohol aerogel beads: Functional design, applicability, and mechanisms.

The harmful effects of hexavalent chromium (Cr(VI)) on the environment and human health have aroused wide public concern. In this study, bulk spherical aerogel beads (PAP) were synthesized from polyethyleneimine (PEI), protein amyloid fibrils (AFL), and polyvinyl alcohol (PVA) through green technology and its removal of Cr(VI) from wastewater was comprehensively studied. The results showed that although the bulk PAP beads (∼ 5 mm) only had an average pore size of 16.88 nm and a BET surface area of 12 m2/g, its maximum adsorption capacity for Cr(VI) reached 121.44 mg/g (at 298 K). Cr(VI) adsorption onto PAP conformed to pseudo-second-order adsorption kinetics and was endothermic. The adsorption of Cr(VI) decreased stepwise with the increase of solution alkalinity (pH = 2: 91.97%; pH = 10: 0.04%). Importantly, PAP showed high selectivity towards Cr(VI) in mixed heavy metal solutions (Cr(VI) > Pb(II) > Ni(II) > Cu(II) > Cd(II)) and good reusability (removal efficiency > 88% after 5 cycles). PAP had excellent anti-interference ability against FA and HCO3- with the overall removal rate exceeding 87% in the presence of 5 - 25 mg/L of these ions. Cations such as Na+, Mg2+, and other heavy metal ions at high concentrations could promote the removal efficiency of Cr(VI). The removal rates of Cr(VI) and Cr(III) by PAP in a tannery wastewater were 34.4% and 59.3%, respectively. Meanwhile, the removal rates of Cr(VI) in a electroplating wastewater and a contaminated soil leachate reached 84.4∼89.7%, showing high practicability. Mechanism studies revealed that electrostatic attraction, hydrogen bonding, reduction, and complexation were the main reactions for Cr(VI) removal by PAP. In general, the study of PAP provides a new insight into using bulk monolith materials for treating Cr(VI) contaminated wastewater.

Highly efficient removal of Cr(VI) from wastewater using electronegative SA/EGCG@Ti/SA/PVDF sandwich membrane.

The use of green, non-toxic raw materials is of great significance to the sustainable development of the environment, among which epigallocatechin gallate (EGCG) is a renewable carbon source from plants. At present, there is a lack of research on the metal-polyphenol nanomaterials their use in water decontamination. In this study, a novel SA/EGCG@Ti/SA/PVDF (SESP) sandwich membrane was prepared to effectively solve the problems of difficult recovery of nanomaterials and the leaching of metal ions. The membrane was made by scraping SA on the surface of the PVDF substrate as the bottom protective layer, depositing EGCG@Ti NPs as the functional layer, then coating SA as the surface isolation layer, and finally cross-linking with anhydrous calcium chloride. Results showed that EGCG@Ti NPs dispersed well on the surface of the SA/PVDF basement membrane. SESP sandwich membrane had good hydrothermal and acid-base stability, and it can be applied to wastewater with multiple co-existing heavy metals (e.g., Cu, Pb, Cd, and Ni). The contact angle and pure water flux of the SESP sandwich membrane with a negatively charged surface were 14.0-15.6° and 171.40 L/m2 h, respectively. The pure water flux of the regenerated membrane after BSA pollution recovered to 98.68 L/m2 h, and the interception efficiency and the interception flux of Cr(VI) were 100 % and 72.92 L/m2 h at 40 min of interception, respectively. Additionally, the removal efficiency of Cr(VI) by SESP sandwich membrane was maintained above 83 % for simulated wastewater and 100 % for actual wastewater after five adsorption-desorption cycles. Cr(VI) and Cr(III) can be removed simultaneously with the negatively charged SESP sandwich membrane. EDS and XPS analysis showed that the removal of Cr(VI) was controlled by the Donnan effect, anion exchange, chelation/complexation, and reduction mechanism. In contrast, Cr(III) was mainly influenced by electrostatic attraction and chelation/complexation mechanisms. In conclusion, the newly prepared sandwich membrane has good application potential in treating Cr(VI) wastewater.

Machine learning versus regression for prediction of sporadic pancreatic cancer.

BACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed. RESULTS: The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively. CONCLUSIONS: The three models complement each other, but each has unique contributions.

Prediction of persistent chronic cough in patients with chronic cough using machine learning.

Introduction: The aim of this study was to develop and validate prediction models for risk of persistent chronic cough (PCC) in patients with chronic cough (CC). This was a retrospective cohort study. Methods: Two retrospective cohorts of patients 18-85 years of age were identified for years 2011-2016: a specialist cohort which included CC patients diagnosed by specialists, and an event cohort which comprised CC patients identified by at least three cough events. A cough event could be a cough diagnosis, dispensing of cough medication or any indication of cough in clinical notes. Model training and validation were conducted using two machine-learning approaches and 400+ features. Sensitivity analyses were also conducted. PCC was defined as a CC diagnosis or any two (specialist cohort) or three (event cohort) cough events in year 2 and again in year 3 after the index date. Results: 8581 and 52 010 patients met the eligibility criteria for the specialist and event cohorts (mean age 60.0 and 55.5 years), respectively. 38.2% and 12.4% of patients in the specialist and event cohorts, respectively, developed PCC. The utilisation-based models were mainly based on baseline healthcare utilisations associated with CC or respiratory diseases, while the diagnosis-based models incorporated traditional parameters including age, asthma, pulmonary fibrosis, obstructive pulmonary disease, gastro-oesophageal reflux, hypertension and bronchiectasis. All final models were parsimonious (five to seven predictors) and moderately accurate (area under the curve: 0.74-0.76 for utilisation-based models and 0.71 for diagnosis-based models). Conclusions: The application of our risk prediction models may be used to identify high-risk PCC patients at any stage of the clinical testing/evaluation to facilitate decision making.

In-situ synthesis of floating ZnIn2S4/cellulose foam for facile photocatalysis.

The delicate design of photocatalyst monoliths is of great significance for the practical applications of artificial photocatalysis. An in-situ synthesis to prepare ZnIn2S4/cellulose foam was developed. Cellulose is dispersed in a highly concentrated ZnCl2 aqueous solution to prepare Zn2+/cellulose foam. Zn2+ ions are pre-anchored by hydrogen bonds on cellulose and become in-situ sites for synthesizing ultra-thin ZnIn2S4 nanosheets. This synthesis method makes ZnIn2S4 nanosheets and cellulose tightly bound and prevents ZnIn2S4 nanosheets from stacking in multiple layers. As a proof of concept, the prepared ZnIn2S4/cellulose foam exhibits a favorable performance for photocatalytic reduction of Cr(VI) under visible light. By adjusting the concentration of zinc ions, the optimal ZnIn2S4/cellulose foam is capable to completely reduce Cr(VI) in 2 h and the photocatalytic activities show no decrease after 4 cycles. This work could inspire people to build floating cellulose-based photocatalysts via in-situ synthesis.

Effects of Clostridium butyricum as an Antibiotic Alternative on Growth Performance, Intestinal Morphology, Serum Biochemical Response, and Immunity of Broilers.

The current study aimed to investigate the effects of Clostridium butyiricum on growth performance, intestinal morphology, serum biochemical response, and immunity in broiler chickens. A total of 330 commercial one-day-old, mixed-sex Ross 308 broilers were randomly divided into five treatment groups with six replicates per group. The broilers were fed the basal diet (CON), the basal diet with 150 mg/kg of aureomycin (AM), the basal diet with C. butyricum at 2 × 108 CFU/kg (CBL), the basal diet with C. butyricum at 4 × 108 CFU/kg (CBM), and the basal diet with C. butyricum at 8 × 108 CFU/kg (CBH). Results showed that the final body weight (BW) (p < 0.01; p < 0.05), ADG from day 22 to 39 (p < 0.05), and ADG from day 1 to 39 (p < 0.01; p < 0.05) were improved in a linear and quadratic response with the inclusion of C. butyricum. There were no differences in feed conversion rate (FCR) among all groups (p > 0.05). Supplementation with C. butyricum quadratically reduced the crypt depth at day 21 (p < 0.01), linearly improved the villus height in the jejunum at day 39 (p < 0.001), and linearly and quadratically increased the villus height to crypt depth (V/C) ratio in the jejunum at day 21 (p < 0.01) and day 39 (p < 0.01; p < 0.001). Dietary C. butyricum affected the thymus index at day 21 and day 39 (linear, p < 0.01), and the bursa of Fabricius index at day 39 (quadratic, p < 0.05). Compared to the AM group, the serum urea contents were decreased (p < 0.05) but the IgG contents were increased in the CBL and CBH groups at day 21 (p < 0.01); in addition, serum albumin (ALB) concentrations in all the C. butyricum-supplemented groups (p < 0.01) and IgG concentrations in the CBM group were augmented at day 39 (p < 0.05). In conclusion, dietary C. butyricum could enhance growth performance by improving jejunal morphology and stimulating immunity organ development in broilers, and could be an alternative to antibiotics in poultry feeds.

Evaluation of diagnostic efficacy of NRP-1/CD304 in hematological diseases.

BACKGROUND: Previous studies had explored the diagnostic or prognostic value of NRP-1/CD304 in blastic plasmacytoid dendritic cell neoplasm (BPDCN), acute myeloid leukemia (AML), and B-cell acute lymphoblastic leukemia (B-ALL), whereas the expression and application value of NRP-1/CD304 in other common hematological diseases have not been reported. METHODS: Bone marrow samples from 297 newly diagnosed patients with various hematological diseases were collected to detect the expression of NRP-1/CD304 by flow cytometry (FCM). The diagnostic efficacy of NRP-1/ CD304-positive diseases was analyzed by receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was compared. RESULTS: In the research cohort, the total positive rate of NRP-1/CD304 was 14.81% (44/297), mainly distributed in BPDCN (100%, 6/6), B-ALL (48.61%, 35/72), and AML (4.48%, 3/67), with statistically significant differences (p < 0.01). Other diseases, such as T-cell acute lymphoblastic leukemia (T-ALL), B-cell non-Hodgkin lymphoma (B-NHL), T/NK-cell lymphoma and plasma cell neoplasms, did not express NRP-1/CD304. The AUC of NRP-1/CD304 was 0.936 (95% CI 0.898-0.973), 0.723 (95% CI 0.646-0.801), and 0.435 (95% CI 0.435) in BPDCN, B-ALL and AML, respectively. Besides, CD304 was commonly expressed in B-ALL with BCR-ABL1 gene rearrangement (p = 0.000), and CD304 expression was positively correlated with CD34 co-expression (p = 0.009) and CD10 co-expression (p = 0.007). CONCLUSIONS: NRP-1/CD304 is only expressed in BPDCN, B-ALL and AML, but not in other common hematological diseases. This indicates that NRP-1/CD304 has no obvious diagnostic and follow-up study value in hematological diseases other than BPDCN, B-ALL, and AML.