Quorum Sensing Interference Assisted Therapy-Based Magnetic Hyperthermia Amplifier for Synergistic Biofilm Treatment.

Biofilms offer bacteria a physical and metabolic barrier, enhancing their tolerance to external stress. Consequently, these biofilms limit the effectiveness of conventional antimicrobial treatment. Recently, quorum sensing (QS) has been linked to biofilm's stress response to thermal, oxidative, and osmotic stress. Herein, a multiple synergistic therapeutic strategy that couples quorum sensing interference assisted therapy (QSIAT)-mediated enhanced thermal therapy with bacteria-triggered immunomodulation in a single nanoplatform, is presented. First, as magnetic hyperthermia amplifier, hyaluronic acid-coated ferrite (HA@MnFe2 O4 ) attenuates the stress response of biofilm by down-regulating QS-related genes, including agrA, agrC, and hld. Next, the sensitized bacteria are eliminated with magnetic heat. QS interference and heat also destruct the biofilm, and provide channels for further penetration of nanoparticles. Moreover, triggered by bacterial hyaluronidase, the wrapped hyaluronic acid (HA) decomposes into disaccharides at the site of infection and exerts healing effect. Thus, by reversing the bacterial tissue invasion mechanism for antimicrobial purpose, tissue regeneration following pathogen invasion and thermal therapy is successfully attained. RNA-sequencing demonstrates the QS-mediated stress response impairment. In vitro and in vivo experiments reveal the excellent antibiofilm and anti-inflammatory effects of HA@MnFe2 O4 . Overall, QSIAT provides a universal enhancement strategy for amplifying the bactericidal effects of conventional therapy via stress response interference.

Rare complication of rheumatoid arthritis: Charcot Neuro-osteoarthropathy.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disease.However, there are few cases of Charcot Neuro-osteoarthropathy (CN) caused by rheumatoid diseases in clinical reports. It is not easy to pay attention to the diagnosis of CN in the complications of rheumatoid disease, which greatly increases the probability of misdiagnosis and missed diagnosis. This case reported a rare complication of rheumatoid arthritis, Charcot arthritis, and the molecular mechanism and diagnosis and treatment of CN caused by RA were systematically discussed. CASE PRESENTATION: The patient, a 79-year-old woman, was hospitalized due to bilateral shoulder pain, limited activity for half a year, aggravated for 4 months to the hospital. During this period, the symptoms did not improve after treatment with acupuncture and Chinese medicine. The patient was previously diagnosed with rheumatoid arthritis for more than 3 years and intermittent irregular use of methylprednisolone and methotrexate for 2 years. She had a history of osteoporosis. PHYSICAL EXAMINATION: symmetrical malformed swelling of the finger joints of both hands; Bilateral supraspinatus and deltoid muscle atrophy, tenderness at the acromion, and attachment of the long head tendon of the biceps brachii were observed. The left Dugas test and the right Dugas test were positive.Blood test: anti-cyclic citrullinated peptide antibody (A-CCP) 33.10U/ml (normal range: 0-5RU/ml); antinuclear antibody quantification (ANA) 47.40AU/ml (normal range: Negative or < 32); anti-double stranded DNA IgG antibody quantification (dsDNA) 31.00 IU/ml (normal range: 0-100 IU/ml); D-Dimer 6.43 µg/ml (normal range: 0-0.5 mg/L); erythrocyte sedimentation rate (ESR) was 27 mm/h (normal range: < 20 mm/60 min). C-reactive protein (CRP) 39.06 mg/L(0.068-8 mg/L).MRI 3.0 T enhancement of bilateral shoulder joints, cervical spine and thoracic spine showed: 1.Large bone destruction, cartilage injury, multiple effusion, synovitis, obvious on the right side. 2.Intervertebral disc degeneration, cervical 3/4, 4/5, 5/6, 6/7 disc herniation, with cervical 3/4 obvious, posterior central herniation; CONCLUSIONS: Rheumatoid arthritis complicated with Charcot's joint is rare. Clinically, patients with rheumatoid diseases should not ignore Charcot's joint complications because of rareness. Early blood inflammatory markers, neuro electrophysiology, and imaging MRI of rheumatoid CN are of great significance for the diagnosis of this mild or early neurovascular inflammation. Early diagnosis and treatment are helpful to prevent further joint injury. The clinical diagnosis, treatment, and molecular mechanism of osteolysis in RA and peripheral sensory nerve injury remain to be further revealed.

Precision Engineering of the Co-immobilization of Enzymes for Cascade Biocatalysis.

The design and orderly layered co-immobilization of multiple enzymes on resin particles remain challenging. In this study, the SpyTag/SpyCatcher binding pair was fused to the N-terminus of an alcohol dehydrogenase (ADH) and an aldo-keto reductase (AKR), respectively. A non-canonical amino acid (ncAA), p-azido-L-phenylalanine (p-AzF), as the anchor for covalent bonding enzymes, was genetically inserted into preselected sites in the AKR and ADH. Employing the two bioorthogonal counterparts of SpyTag/SpyCatcher and azide-alkyne cycloaddition for the immobilization of AKR and ADH enabled sequential dual-enzyme coating on porous microspheres. The ordered dual-enzyme reactor was subsequently used to synthesize (S)-1-(2-chlorophenyl)ethanol asymmetrically from the corresponding prochiral ketone, enabling the in situ regeneration of NADPH. The reactor exhibited a high catalytic conversion of 74 % and good reproducibility, retaining 80 % of its initial activity after six cycles. The product had 99.9 % ee, which that was maintained in each cycle. Additionally, the double-layer immobilization method significantly increased the enzyme loading capacity, which was approximately 1.7 times greater than that of traditional single-layer immobilization. More importantly, it simultaneously enabled both the purification and immobilization of multiple enzymes on carriers, thus providing a convenient approach to facilitate cascade biocatalysis.

O-GlcNAcylation of TDP-43 suppresses proteinopathies and promotes TDP-43's mRNA splicing activity.

Pathological TDP-43 aggregation is characteristic of several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD-TDP); however, how TDP-43 aggregation and function are regulated remain poorly understood. Here, we show that O-GlcNAc transferase OGT-mediated O-GlcNAcylation of TDP-43 suppresses ALS-associated proteinopathies and promotes TDP-43's splicing function. Biochemical and cell-based assays indicate that OGT's catalytic activity suppresses TDP-43 aggregation and hyperphosphorylation, whereas abolishment of TDP-43 O-GlcNAcylation impairs its RNA splicing activity. We further show that TDP-43 mutations in the O-GlcNAcylation sites improve locomotion defects of larvae and adult flies and extend adult life spans, following TDP-43 overexpression in Drosophila motor neurons. We finally demonstrate that O-GlcNAcylation of TDP-43 promotes proper splicing of many mRNAs, including STMN2, which is required for normal axonal outgrowth and regeneration. Our findings suggest that O-GlcNAcylation might be a target for the treatment of TDP-43-linked pathogenesis.

A cross-sectional study from NHANES found a positive association between obesity with bone mineral density among postmenopausal women.

PURPOSE: Obesity has been demonstrated to improve bone mineral density (BMD), according to previous research. Nevertheless, there is a dearth of clarity regarding the optimal body mass index (BMI) and waist circumference (WC) for achieving the highest beneficial BMD in postmenopausal women. The objective of this study was to establish the correlation between obesity and BMD. METHODS: The relationship between BMI, WC, and BMD was examined by using multivariate logistic regression models, fitting smoothing curves and utilizing the latest data from the National Health and Nutrition Examination Survey (NHANES) survey conducted between 2007 and 2018. Furthermore, the analysis of saturation effects was employed to examine the association of nonlinear connections among BMI, WC, and BMD. RESULTS: The research examined information from a combination of 564 participants. A significant correlation between BMD and BMI as well as WC was observed in our findings. The enduring correlation between BMI and WC with BMD was demonstrated across subgroup analyses categorized by age and race, except among other Hispanic and other race. Furthermore, the smoothing curve fitting indicated that there existed not just a linear correlation among BMI, WC, and BMD, but also a saturation threshold in the association of these three factors. CONCLUSIONS: Based on our study, we have found a strong and positive relationship between obesity and BMD. According to the results of this research, maintaining obesity at a moderate level in postmenopausal women would result in achieving an optimal equilibrium between obesity and BMD.

The establishment of CDK9/RNA PolII/H3K4me3/DNA methylation feedback promotes HOTAIR expression by RNA elongation enhancement in cancer.

Long non-coding RNA HOX Transcript Antisense RNA (HOTAIR) is overexpressed in multiple cancers with diverse genetic profiles. Importantly, since HOTAIR heavily contributes to cancer progression by promoting tumor growth and metastasis, HOTAIR becomes a potential target for cancer therapy. However, the underlying mechanism leading to HOTAIR deregulation is largely unexplored. Here, we performed a pan-cancer analysis using more than 4,200 samples and found that intragenic exon CpG island (Ex-CGI) was hypermethylated and was positively correlated to HOTAIR expression. Also, we revealed that Ex-CGI methylation promotes HOTAIR expression through enhancing the transcription elongation process. Furthermore, we linked up the aberrant intragenic tri-methylation on H3 at lysine 4 (H3K4me3) and Ex-CGI DNA methylation in promoting transcription elongation of HOTAIR. Targeting the oncogenic CDK7-CDK9-H3K4me3 axis downregulated HOTAIR expression and inhibited cell growth in many cancers. To our knowledge, this is the first time that a positive feedback loop that involved CDK9-mediated phosphorylation of RNA Polymerase II Serine 2 (RNA PolII Ser2), H3K4me3, and intragenic DNA methylation, which induced robust transcriptional elongation and heavily contributed to the upregulation of oncogenic lncRNA in cancer has been demonstrated. Targeting the oncogenic CDK7-CDK9-H3K4me3 axis could be a novel therapy in many cancers through inhibiting the HOTAIR expression.

Putting precision and elegance in enzyme immobilisation with bio-orthogonal chemistry.

The covalent immobilisation of enzymes generally involves the use of highly reactive crosslinkers, such as glutaraldehyde, to couple enzyme molecules to each other or to carriers through, for example, the free amino groups of lysine residues, on the enzyme surface. Unfortunately, such methods suffer from a lack of precision. Random formation of covalent linkages with reactive functional groups in the enzyme leads to disruption of the three dimensional structure and accompanying activity losses. This review focuses on recent advances in the use of bio-orthogonal chemistry in conjunction with rec-DNA to affect highly precise immobilisation of enzymes. In this way, cost-effective combination of production, purification and immobilisation of an enzyme is achieved, in a single unit operation with a high degree of precision. Various bio-orthogonal techniques for putting this precision and elegance into enzyme immobilisation are elaborated. These include, for example, fusing (grafting) peptide or protein tags to the target enzyme that enable its immobilisation in cell lysate or incorporating non-standard amino acids that enable the application of bio-orthogonal chemistry.

Realtime Tracking of Passengers on the London Underground Transport by Matching Smartphone Accelerometer Footprints.

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes' movements on each line. Given the passenger's accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers' positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines.

Discrimination of Different Species of Dendrobium with an Electronic Nose Using Aggregated Conformal Predictor.

A method using electronic nose to discriminate 10 different species of dendrobium, which is a kind of precious herb with medicinal application, was developed with high efficiency and low cost. A framework named aggregated conformal prediction was applied to make predictions with accuracy and reliability for E-nose detection. This method achieved a classification accuracy close to 80% with an average improvement of 6.2% when compared with the results obtained by using traditional inductive conformal prediction. It also provided reliability assessment to show more comprehensive information for each prediction. Meanwhile, two main indicators of conformal predictor, validity and efficiency, were also compared and discussed in this work. The result shows that the approach integrating electronic nose with aggregated conformal prediction to classify the species of dendrobium with reliability and validity is promising.

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose.

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.

An experimental study of the intrinsic stability of random forest variable importance measures.

BACKGROUND: The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. RESULTS: The experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability. CONCLUSION: First, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets.

Novel mode of liquid-phase microextraction: A magnetic stirrer as the extractant phase holder.

In the present study, a novel configuration of liquid-phase microextraction was proposed, in which a magnetic stirrer with a groove was used as the extractant phase holder. It was termed as magnetic stirrer liquid-phase microextraction. In this way, the stability of the organic solvent was much improved under high stirring speed; the extraction efficiency was enhanced due to the enormously enlarged contact area between the organic solvent and aqueous phase. The extraction performance of the magnetic stirrer liquid-phase microextraction was studied using chlorobenzenes as the probe analytes. A wide linearity range (20 pg/mL to 200 ng/mL) with a satisfactory linearity coefficient (r(2) > 0.998) was obtained. Limits of detection ranged from 9.0 to 12.0 pg/mL. Good reproducibility was achieved with intra- and inter-day relative standard deviations <4.8%. The proposed magnetic stirrer liquid-phase microextraction was simple, environmentally friendly and efficient; compared to single-drop microextraction, it had obvious advantages in terms of reproducibility and extraction efficiency. It is a promising miniaturized liquid-phase technology for real applications.

Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose.

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt's method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt's method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.

[Rapid Coal Classification Based on Confidence Machine and Near Infrared Spectroscopy].

Near-infrared reflectance spectroscopy (NIRS) is a simple, convenient and safe technology which is widely used in many industries. NIRS was employed to the rapid classification of coal in this study. The new method can be a replacement of the chemical analysis which is laborious and time consuming. Confidence machine was firstly applied to NIRS in this study which was used to evaluate the risk of the analysis. The near infrared reflectance spectrum of 199 coal samples including four types of coal (50 fat coal samples, 50 coking coal samples, 49 lean coal samples and 50 meager lean coal samples) from different mines in China were collected and classifiers based on the near infrared spectra of coal samples which were established by using machine learning methods to realize the rapid classification of coal samples. Confidence machine was introduced into the analysis technology based on NIRS in this paper. Confidence machine based on support vector machine (CM-SVM) was built and applied to the classification of coal samples via NIRS. Confidence machine is a probabilistic algorithm and instead of using hyper plane (SVM) to carry out the classification, using probability (CM-SVM) turned to be more effective which had 95.45% of the samples correctly grouped. Besides that, CM-SVM also estimated the confidence and credibility for each predicted sample. By setting different confidence levels, CM-SVM can perform region prediction whose error rate was predefined by the different confidence levels, which was very important for the control of product quality when NIRS was applied to the analysis of productions. Confidence machine is designed as an on-line learning method; new samples can be added to the training set one by one to improve the efficiency of the model and is very appropriate for industry on-line analysis. On-line CM-SVM models showed that the confidence of prediction would be raised as the samples increased, which was valuable for industry on-line analysis.

Unlocking Delivery Strategies for mRNA Therapeutics.

mRNA emerged as an attractive therapy modality with the development of mRNA structure engineering techniques and delivery platforms. mRNA therapeutics, applied for vaccine therapy, protein replacement therapy, and chimeric antigen receptor (CAR) T cell-based therapy, has shown huge potential in treating a wide range of diseases, such as cancer and rare genetic diseases, with successful and exciting preclinical and clinical progress. In mRNA therapeutics, a potent delivery system is key to the success of its application for disease treatment. Herein, different types of mRNA delivery strategies, including nanoparticles produced from lipid or polymer materials, virus-based platforms, and exosome-based platforms, are mainly focused.

A Multifunctional Tissue-Engineering Hydrogel Aimed to Regulate Bacterial Ferroptosis-Like Death and Overcoming Infection Toward Bone Remodeling.

Infection is the most common complication after orthopedic surgery and can result in prolonged ailments such as chronic wounds, enlarged bone defects, and osteomyelitis. Iron, which is essential for bacterial metabolism and immune cell functions, is extremely important. Bacteria harness iron from nearby cells to promote biofilm formation, ensuring their survival. Iron deficiency within the infection microenvironment (IME) consequently hampers macrophage function, enabling further dissemination of the infection and hindering macrophage polarization to the M2 phenotype. Therefore, a novel approach is proposed to regulate macrophage polarization, aiming to restore the inflammatory immune environment. A composite hydrogel derived from natural polymers is developed to address infections and manage iron metabolism in macrophages. This IME-responsive hydrogel, named FCL-ECMH, is synthesized by encapsulating vermiculite functional core layers within a decellularized extracellular matrix hydrogel. It is noteworthy that FCL-ECMH can produce reactive oxygen species within the IME. Supplementary photothermal treatment enhances bacterial iron uptake, leading to ferroptosis-like death. This process also rejuvenates the iron-enriched macrophages around the IME, thereby enhancing their antibacterial and tissue repair functions. In vivo experiments confirmed the antibacterial and repair-promoting capabilities of FCL-ECMH, indicating its potential for clinical applications.

Low voltage electric-double-layer transistor nonenzymic erythromycin sensors based on molecularly imprinted polymers.

Erythromycin (Ery) is a commonly used antibiotic that can be found ubiquitously in water bodies. The increasing apprehension over the adverse effects of antibiotic remnants in aquatic environments necessitates the prompt advancement of erythromycin detection techniques that are both highly sensitive and compact. Here, we propose a non-enzyme Ery sensor that integrates a mesoporous SiO2-based low-voltage oxide electric-double-layer transistor (EDLT) with a molecular imprinting technique, featuring a molecularly imprinted polymers (MIP) functionalized gate electrode. The mesoporous SiO2-based oxide transistor exhibits excellent electrical characteristics, including an operating voltage of small than 1.0 V, an on/off ratio exceeding 106 and a mobility of 14.95 cm2V-1s-1. At an ultra-low operating voltage within 0.5 V, the sensor exhibits a linear response to the concentration range of 1 nM-10 µM of Ery, with a detection limit of 0.22 nM and a sensitivity of 23.3 mV dec-1. Besides, the single-spike dynamic sensing mode effectively reduces the power consumption of the detection. The proposed sensor provides a rapid and convenient approach to detect Ery in aqueous environments, with benefits such as miniaturization, high sensitivity, and simplicity.

Enhanced Bacterial Cuproptosis-Like Death via Reversal of Hypoxia Microenvironment for Biofilm Infection Treatment.

A recently emerging cell death pathway, known as copper-induced cell death, has demonstrated significant potential for treating infections. Existing research suggests that cells utilizing aerobic respiration, as opposed to those reliant on glycolysis, exhibit greater sensitivity to copper-induced death. Herein, a MnO2-loaded copper metal-organic frameworks platform is developed denoted as MCM, to enhance bacterial cuproptosis-like death via the remodeling of bacterial respiratory metabolism. The reversal of hypoxic microenvironments induced a cascade of responses, encompassing the reactivation of suppressed immune responses and the promotion of osteogenesis and angiogenesis. Initially, MCM catalyzed O2 production, alleviating hypoxia within the biofilm and inducing a transition in bacterial respiration mode from glycolysis to aerobic respiration. Subsequently, the sensitized bacteria, characterized by enhanced tricarboxylic acid cycle activity, underwent cuproptosis-like death owing to increased copper concentrations and aggregated intracellular dihydrolipoamide S-acetyltransferase (DLAT). The disruption of hypoxia also stimulated suppressed dendritic cells and macrophages, thereby strengthening their antimicrobial activity through chemotaxis and phagocytosis. Moreover, the nutritional effects of copper elements, coupled with hypoxia alleviation, synergistically facilitated the regeneration of bones and blood vessels. Overall, reshaping the infection microenvironment to enhance cuproptosis-like cell death presents a promising avenue for eradicating biofilms.

Mechanochemically Reprogrammed Interface Orchestrates Neutrophil Bactericidal Activity and Apoptosis for Preventing Implant-Associated Infection.

The onset of implant-associated infection (IAI) triggers a cascade of immune responses, which are initially dominated by neutrophils. Bacterial aggregate formation and hypoxic microenvironment, which occur shortly after implantation, may be two major risk factors that impair neutrophil function and lead to IAI. Here, the implant surface with phytic acid-Zn2+ coordinated TiO2 nanopillar arrays (PA-Zn@TiNPs) and oxygen self-supporting CaO2 nanoparticles, named as CPZTs, is mechanochemically reprogrammed. The engineered CPZTs interface integrates multiple properties to inhibit the formation of nascent biofilm, encompassing antibacterial adhesion, mechanobactericidal effect, and chemobiocidal effect. Meanwhile, continuous oxygenation fuels the neutrophils with reactive oxygen species (ROS) for efficient bacterial elimination on the implant surface and inside the neutrophils. Furthermore, this surface modulation strategy accelerates neutrophil apoptosis and promotes M2 macrophage-mediated osteogenesis both in vitro and in a rat model of IAI. In conclusion, targeting neutrophils for immunomodulation is a practical and effective strategy to prevent IAI and promote bone-implant integration.

A dibutyl phthalate sensor based on a nanofiber polyaniline coated quartz crystal monitor.

Dibutyl phthalate (DBP) is a commonly used plasticizer and additive to adhesives, printing inks and nail polishes. Because it has been found to be a powerful reproductive and developmental toxicant, a sensor to monitor DBP in some working spaces and the environment is required. In this work polyaniline nanofibers were deposited on the electrode of a quartz crystal oscillator to form a Quartz Crystal Microbalance gas sensor. The coated quartz crystal and a non-coated quartz crystal were mounted in a sealed chamber, and their frequency difference was monitored. When DBP vapor was injected into the chamber, gas adsorption decreased the frequency of the coated quartz crystal oscillator and thereby caused an increase in the frequency difference between the two crystals. The change of the frequency difference was recorded as the sensor response. The sensor was extremely sensitive to DBP and could be easily recovered by N2 purging. A low measurement limit of 20 ppb was achieved. The morphologies of the polyaniline films prepared by different approaches have been studied by SEM and BET. How the nanofiber-structure can improve the sensitivity and stability is discussed, while its selectivity and long-term stability were investigated.