Seeking Cells, Targeting Bacteria: A Cascade-Targeting Bacteria-Responsive Nanosystem for Combating Intracellular Bacterial Infections.

Intracellular bacteria pose a great challenge to antimicrobial therapy due to various physiological barriers at both cellular and bacterial levels, which impede drug penetration and intracellular targeting, thereby fostering antibiotic resistance and yielding suboptimal treatment outcomes. Herein, a cascade-target bacterial-responsive drug delivery nanosystem, MM@SPE NPs, comprising a macrophage membrane (MM) shell and a core of SPE NPs. SPE NPs consist of phenylboronic acid-grafted dendritic mesoporous silica nanoparticles (SP NPs) encapsulated with epigallocatechin-3-gallate (EGCG), a non-antibiotic antibacterial component, via pH-sensitive boronic ester bonds are introduced. Upon administration, MM@SPE NPs actively home in on infected macrophages due to the homologous targeting properties of the MM shell, which is subsequently disrupted during cellular endocytosis. Within the cellular environment, SPE NPs expose and spontaneously accumulate around intracellular bacteria through their bacteria-targeting phenylboronic acid groups. The acidic bacterial microenvironment further triggers the breakage of boronic ester bonds between SP NPs and EGCG, allowing the bacterial-responsive release of EGCG for localized intracellular antibacterial effects. The efficacy of MM@SPE NPs in precisely eliminating intracellular bacteria is validated in two rat models of intracellular bacterial infections. This cascade-targeting responsive system offers new solutions for treating intracellular bacterial infections while minimizing the risk of drug resistance.

Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns.

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon.

The facial expression of schizophrenic patients applied with infrared thermal facial image sequence.

BACKGROUND: Schizophrenia is a neurological disease characterized by alterations to patients' cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients. METHODS: Schizophrenia is an emotion-related disorder, and images eliciting different types of emotions were selected from the international affective picture system (IAPS) and presented to subjects during ITFI collection. ITFIs were aligned using affine registration, and the changes induced by small irregular head movements were corrected. The average temperatures from the forehead, nose, mouth, left cheek, and right cheek were calculated, and continuous temperature changes were used as features. After performing dimensionality reduction and noise removal using the component analysis method, multivariate analysis of variance and the Support Vector Machine (SVM) classification algorithm were used to identify moderately and markedly ill schizophrenia patients. RESULTS: Analysis of five facial areas indicated significant temperature changes in the forehead and nose upon exposure to various emotional stimuli and in the right cheek upon evocation of high valence low arousal (HVLA) stimuli. The most significant P-value (lower than 0.001) was obtained in the forehead area upon evocation of disgust. Finally, when the features of forehead temperature changes in response to low valence high arousal (LVHA) were reduced to 9 using dimensionality reduction and noise removal, the identification rate was as high as 94.3%. CONCLUSIONS: Our results show that features obtained in the forehead, nose, and right cheek significantly differed between moderately and markedly ill schizophrenia patients. We then chose the features that most effectively distinguish between moderately and markedly ill schizophrenia patients using the SVM. These results demonstrate that the ITFI analysis protocol proposed in this study can effectively provide reference information regarding the phase of the disease in patients with schizophrenia.

Analysis of EEG entropy during visual evocation of emotion in schizophrenia.

BACKGROUND: In this study, the international affective picture system was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency, and entropy were used to identify normal, moderately, and markedly ill schizophrenic patients. METHODS: The signals were collected and preprocessed. Then, the signals were separated according to three types of emotions and five frequency bands. Finally, the features were calculated using three different methods of entropy. For classification, the features were divided into different sections and classification using support vector machine (principal components analysis on 95%). Finally, simple regression and correlation analysis between the total scores of positive and negative syndrome scale and features were used. RESULTS: At first, we observed that to classify normal and markedly ill schizophrenic patients, the identification result was as high as 81.5%, and therefore, we further explored moderately and markedly ill schizophrenic patients. Second, the identification rate in both moderately and markedly ill schizophrenic patient was as high as 79.5%, which at the Fz point signal in high valence low arousal fragments was calculated using the ApEn methods. Finally, the total scores of positive and negative syndrome scale were used to analyze the correlation with the features that were the five frequency bands at the Fz point signal. The results show that the p value was less than .001 at the beta wave in the 15-18 Hz frequency range.

A review of tamoxifen administration regimen optimization for Cre/loxp system in mouse bone study.

Gene knockout is a technique routinely used in basic experimental research, particularly in mouse skeletal and developmental studies. Tamoxifen-induced Cre/loxp system is known for its temporal and spatial precision and commonly utilized by researchers. However, tamoxifen has been shown its side effects on affecting the phenotype of mouse bone directly. This review aimed to optimize tamoxifen administration regimens including its dosage and duration, to identify an optimal induction strategy that minimizes potential side effects while maintaining recombination efficacy. This study will help researchers in designing gene knockout experiments in bone when using tamoxifen.

A multifunctional polymeric coating with self-adsorbed, antifouling and in situ remineralization properties for caries management.

Dental caries is a biofilm-induced bacterial infectious oral disease, where the early attachment of proteins and pathogenic bacteria to tooth surfaces has been known as the main cause of biofilm formation. Typically, dental caries is commonly accompanied by mineral depletion of enamels, thus causing dental demineralization. Multifunctional materials are highly attractive candidates for treating dental caries. Herein, we successfully synthesized diblock copolymers poly(ethylene glycol)-b-poly(aspartic acid) (PEG-PAsp) and modified them with alendronate sodium (ALN) to serve as bioactive bifunctional coatings (PEG-PAsp-ALN) on teeth. The PEG segments are employed for inhibiting proteins and bacterial adhesion. In addition, due to the presence of both PAsp and ALN, a synergistically strong binding capacity could be achieved with the tooth surface, thus promoting rapid and thorough remineralization in situ, while maintaining excellent safety. The combination treatment can significantly suppress the biofilm formation, which is beneficial for alleviating the demineralization of enamels caused by bacteria, and further, facilitate remineralization in situ. This approach thus demonstrates the potential of the copolymer PEG-PAsp-ALN coating as a multifunctional protecting layer on the tooth surface for high-efficiency prevention and treatment of dental caries.

Dual crosslinking hydrogels with tunable injectability and stability for bone repair.

As an ideal biomaterial, the injectable dual crosslinking hydrogel can fill irregular shaped defects for treating bone defects. Despite advances in the fabrication of injectable dual crosslinking hydrogels, current methods pose several limitations in the gelation process, such as unadjustable injectability, materials loss after injection and the employment of nonphysiological environments. A more rational designed hydrogel should be shear-thinning and self-healing quickly after injection, which requires a reversible noncovalent or dynamic covalent interaction. Besides, the improved mechanical properties and stability can be achieved in vivo. In this present research study, inspired by a high Ca2+ concentration in the bone defect sites, a strategy combining hydrogen bonds and ion coordination to construct dual crosslinking hydrogels is developed. The prefabricated gel precursors based on hydrogen bonds of synthetic triblock poly(aspartic acid)-poly(ethylene glycol)-poly(aspartic acid) copolymers and tannic acid can be injected smoothly and recover rapidly to the initial state after injection. In addition, the hydrogel network can be crosslinked more tightly through the -COOH-Ca2+ chelation, which results in reinforced mechanical properties and stability. The developed injectable dual crosslinking hydrogels will provide a design idea for the secondary crosslinking of injectable hydrogels in vivo, especially in the bone repair sites.

Dental Materials for Oral Microbiota Dysbiosis: An Update.

The balance or dysbiosis of the microbial community is a major factor in maintaining human health or causing disease. The unique microenvironment of the oral cavity provides optimal conditions for colonization and proliferation of microbiota, regulated through complex biological signaling systems and interactions with the host. Once the oral microbiota is out of balance, microorganisms produce virulence factors and metabolites, which will cause dental caries, periodontal disease, etc. Microbial metabolism and host immune response change the local microenvironment in turn and further promote the excessive proliferation of dominant microbes in dysbiosis. As the product of interdisciplinary development of materials science, stomatology, and biomedical engineering, oral biomaterials are playing an increasingly important role in regulating the balance of the oral microbiome and treating oral diseases. In this perspective, we discuss the mechanisms underlying the pathogenesis of oral microbiota dysbiosis and introduce emerging materials focusing on oral microbiota dysbiosis in recent years, including inorganic materials, organic materials, and some biomolecules. In addition, the limitations of the current study and possible research trends are also summarized. It is hoped that this review can provide reference and enlightenment for subsequent research on effective treatment strategies for diseases related to oral microbiota dysbiosis.

Correction: Dual crosslinking hydrogels with tunable injectability and stability for bone repair.

Correction for 'Dual crosslinking hydrogels with tunable injectability and stability for bone repair' by Wenlin Chu et al., J. Mater. Chem. B, 2022, 10, 4386-4394, https://doi.org/10.1039/D2TB00545J.

Recent Advances in Injectable Dual Crosslinking Hydrogels for Biomedical Applications.

Injectable dual crosslinking hydrogels hold great promise to improve therapeutic efficacy in minimally invasive surgery. Compared with prefabricated hydrogels, injectable hydrogels can be implanted more accurately into deeply enclosed sites and repair irregularly shaped lesions, showing great applicable potential. Here, the current fabrication considerations of injectable dual crosslinking hydrogels are reviewed. Besides, the progress of the hydrogels used in corresponding applications and emerging challenges are discussed, with detailed emphasis in the fields of bone and cartilage regeneration, wound dressings, sensors and other less mentioned applications for their more hopeful employments in clinic. It is envisioned that the further development of the injectable dual crosslinking hydrogels will catalyze their innovation and transformation in the biomedical field.

Two-in-one strategy: a remineralizing and anti-adhesive coating against demineralized enamel.

Tooth enamel is prone to be attacked by injurious factors, leading to a de/remineralization imbalance. To repair demineralized enamel and prevent pulp inflammation caused by biofilm accumulation, measures are needed to promote remineralization and inhibit bacterial adhesion on the tooth surface. An innovative material, poly (aspartic acid)-polyethylene glycol (PASP-PEG), was designed and synthesized to construct a mineralizing and anti-adhesive surface that could be applied to repair demineralized enamel. A cytotoxicity assay revealed the low cytotoxicity of synthesized PASP-PEG. Adsorption results demonstrated that PASP-PEG possesses a high binding affinity to the hydroxyapatite (HA)/tooth surface. In vitro experiments and scanning electron microscopy (SEM) demonstrated a strong capacity of PASP-PEG to induce in situ remineralization and direct the oriented growth of apatite nanocrystals. Energy dispersive X-ray spectroscopy (EDS), X-ray diffraction analysis (XRD) and Vickers hardness tests demonstrated that minerals induced by PASP-PEG were consistent with healthy enamel in Ca/P ratio, crystal form and surface micro-hardness. Contact angle tests and bacterial adhesion experiments demonstrated that PASP-PEG yielded a strong anti-adhesive effect. In summary, PASP-PEG could achieve dual effects for enamel repair and anti-adhesion of bacteria, thereby widening its application in enamel repair.

A natural polymer based bioadhesive with self-healing behavior and improved antibacterial properties.

Bioadhesives are of great interest for tissue/wound closure to reduce surgical time, minimize treatment invasiveness, and prevent body fluid leakage. However, bacterial infections remain a major concern during wound healing and tissue bonding. Hence, the development of bioadhesives with antibacterial properties is necessary. In this study, a hydrogel bioadhesive based on silk fibroin (SF) and tannic acid (TA) was combined with silver nanoparticles (AgNPs) generated in situ during hydrogel formation to improve its antibacterial abilities. In this system, TA can be regarded as a phenolic glue molecule to spontaneously co-assemble with SF to fabricate the adhesive matrix network and also as a reductant to induce silver nitrate to form AgNPs evenly within the adhesive network. Furthermore, the influence of the amount of silver nitrate was considered. The produced hybrid hydrogel bioadhesives with different amounts of AgNPs exhibited self-healing capabilities, considerable wet tissue adhesion strengths (14.32 ± 1.85 kPa-28.80 ± 2.29 kPa) and good cytocompatibility. When the initial concentration of silver nitrate was more than 0.05 wt%, the produced STA bioadhesive showed effective antibacterial properties in vitro. These results demonstrate that these STA bioadhesives have the potential to be used in tissue/wound closure, especially when bacterial infections are a main problem.

Brain Structural Magnetic Resonance Imaging for Joint Independent Component Analysis in Schizophrenic Patients.

BACKGROUND: In past magnetic resonance imaging studies, normal participants and schizophrenia patients have usually been compared using imaging processing modes with only one parameter. A more extensive evaluation of significant differences between gray and white matter in Schizophrenic patents was necessary. METHODS: Voxel based morphometry was used to separate brain images into gray matter and white matter. Then, the images were mapped to Montreal Neurological Institute space, and DARTEL analytic template was applied for image calibration with statistical parametric mapping. Finally, joint independent component analysis was employed to analyze the gray and white matter of brain images from Schizophrenic patients and normal controls. In this study, joint independent component analysis was used to discriminate clinical differences in magnetic resonance imaging signals between Schizophrenic patients and normal controls. RESULTS: Region of interest analyses has repeatedly shown gray matter reduction in the superior temporal gyrus of Schizophrenic patients. CONCLUSION: These results strongly support previous studies regarding brain volume in schizophrenic patients. The connection networks in frontal and temporal lobes evidently did not differ between normal participants and schizophrenia patients.

A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images.

Patients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and WMV and incorporated the Wisconsin card sorting test (WCST) and the positive and negative syndrome scale (PANSS) to examine the correlation of obtained brain characteristics. We also used PANSS score to classify schizophrenic patients into acute and subacute cases, to analyze the brain structure differences. Finally, we used brain structure images and the error rate of the WCST as eigenvalues in support vector machine learning and classification. The results of this study showed that the frontal and temporal lobes of a normal brain are more apparent than those of a schizophrenia brain. The highest level of classification recognition reached 91.575%, indicating that the WCST error rate and characteristic changes in brain structure volume can be used to effectively distinguish schizophrenia and normal brains. Similarly, this result confirmed that the WCST and brain structure volume are correlated with the differences between schizophrenia and normal participants.

Automatic recognizing of vocal fold disorders from glottis images.

The laryngeal video stroboscope is an important instrument to test glottal diseases and read vocal fold images and voice quality for physician clinical diagnosis. This study is aimed to develop a medical system with functionality of automatic intelligent recognition of dynamic images. The static images of glottis opening to the largest extent and closing to the smallest extent were screened automatically using color space transformation and image preprocessing. The glottal area was also quantized. As the tongue base movements affected the position of laryngoscope and saliva would result in unclear images, this study used the gray scale adaptive entropy value to set the threshold in order to establish an elimination system. The proposed system can improve the effect of automatically captured images of glottis and achieve an accuracy rate of 96%. In addition, the glottal area and area segmentation threshold were calculated effectively. The glottis area segmentation was corrected, and the glottal area waveform pattern was drawn automatically to assist in vocal fold diagnosis. When developing the intelligent recognition system for vocal fold disorders, this study analyzed the characteristic values of four vocal fold patterns, namely, normal vocal fold, vocal fold paralysis, vocal fold polyp, and vocal fold cyst. It also used the support vector machine classifier to identify vocal fold disorders and achieved an identification accuracy rate of 98.75%. The results can serve as a very valuable reference for diagnosis.

Using image processing technology and mathematical algorithm in the automatic selection of vocal cord opening and closing images from the larynx endoscopy video.

The human larynx is an important organ for voice production and respiratory mechanisms. The vocal cord is approximated for voice production and open for breathing. The videolaryngoscope is widely used for vocal cord examination. At present, physicians usually diagnose vocal cord diseases by manually selecting the image of the vocal cord opening to the largest extent (abduction), thus maximally exposing the vocal cord lesion. On the other hand, the severity of diseases such as vocal palsy, atrophic vocal cord is largely dependent on the vocal cord closing to the smallest extent (adduction). Therefore, diseases can be assessed by the image of the vocal cord opening to the largest extent, and the seriousness of breathy voice is closely correlated to the gap between vocal cords when closing to the smallest extent. The aim of the study was to design an automatic vocal cord image selection system to improve the conventional selection process by physicians and enhance diagnosis efficiency. Also, due to the unwanted fuzzy images resulting from examination process caused by human factors as well as the non-vocal cord images, texture analysis is added in this study to measure image entropy to establish a screening and elimination system to effectively enhance the accuracy of selecting the image of the vocal cord closing to the smallest extent.