Aquapteridospora jiangxiensis, a new aquatic hyphomycetous fungus from a freshwater habitat in China.

During an investigation of freshwater fungi in Jiangxi province, China, a new hyphomycetous fungus, Aquapteridospora jiangxiensis, was collected and isolated. Aquapteridospora jiangxiensis is characterized by its unbranched and guttulate conidiophores with multi-septa swollen at the base, polyblastic conidiogenous cells with sympodial proliferations, and denticles, and guttulate conidia with a sheath. A photo plate of the macro- and micro-morphology and a muti-loci (ITS, LSU, SSU, TEF1 and RPB2) phylogenetic tree are provided. A key to the species of Aquapteridospora is also presented in this paper.

Toxicity Analysis of Mesoporous Polydopamine on Intestinal Tissue and Microflora.

As a promising therapy, photothermal therapy (PTT) converts near-infrared (NIR) light into heat through efficient photothermal agents (PTAs), causing a rapid increase in local temperature. Considering the importance of PTAs in the clinical application of PTT, the safety of PTAs should be carefully evaluated before their widespread use. As a promising PTA, mesoporous polydopamine (MPDA) was studied for its clinical applications for tumor photothermal therapy and drug delivery. Given the important role that intestinal microflora plays in health, the impacts of MPDA on the intestine and on intestinal microflora were systematically evaluated in this study. Through biological and animal experiments, it was found that MPDA exhibited excellent biocompatibility, in vitro and in vivo. Moreover, 16S rRNA analysis demonstrated that there was no obvious difference in the composition and classification of intestinal microflora between different drug delivery groups and the control group. The results provided new evidence that MPDA was safe to use in large doses via different drug delivery means, and this lays the foundation for further clinical applications.

Delivery of Cationic Platinum Prodrugs via Reduction Sensitive Polymer for Improved Chemotherapy.

A cationic monofunctional platinum anticancer drug, phenanthriplatin (PhenPt(II)), exhibits promising anticancer effect on various cancer cell lines. Unlike the conventional platinum(II) drugs, PhenPt(II) is more likely to bind the N7 adenosine base of DNA in situ, and consequently resulting in a unique cellular response profile and unusual potency. However, since this drug is positively charged, it can easily bind to plasma protein that leads to rapid systematic clearance and deleterious toxicities, which greatly limits its in vivo application. Herein, a lipophilic phenanthriplatin (PhenPt(IV)) prodrug is synthesized. To further reduce its toxicity, a negatively charged polymer P1 with reduction responsiveness is assembled with PhenPt(IV) to form PhenPt(IV) NPs. In comparison to cisplatin, PhenPt(IV) NPs exhibit up to 30 times greater in vitro potency against various cancer cell lines. Additionally, in vivo, no obvious side effect is found on PhenPt(IV) NPs. Significant enhancement in tumor accumulation and improvement of drug efficacy in 4T1 tumor model are demonstrated. Taken together, this study provides a promising strategy for the clinical translation of phenanthriplatin.

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph.

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Preliminary Study of MR and Fluorescence Dual-mode Imaging: Combined Macrophage-Targeted and Superparamagnetic Polymeric Micelles.

Purpose: To establish small-sized superparamagnetic polymeric micelles for magnetic resonance and fluorescent dual-modal imaging, we investigated the feasibility of MR imaging (MRI) and macrophage-targeted in vitro. Methods: A new class of superparamagnetic iron oxide nanoparticles (SPIONs) and Nile red-co-loaded mPEG-Lys3-CA4-NR/SPION polymeric micelles was synthesized to label Raw264.7 cells. The physical characteristics of the polymeric micelles were assessed, the T2 relaxation rate was calculated, and the effect of labeling on the cell viability and cytotoxicity was also determined in vitro. In addition, further evaluation of the application potential of the micelles was conducted via in vitro MRI. Results: The diameter of the mPEG-Lys3-CA4-NR/SPION polymeric micelles was 33.8 ± 5.8 nm on average. Compared with the hydrophilic SPIO, mPEG-Lys3-CA4-NR/SPION micelles increased transversely (r2), leading to a notably high r2 from 1.908 µg/mL-1S-1 up to 5.032 µg/mL-1S-1, making the mPEG-Lys3-CA4-NR/SPION micelles a highly sensitive MRI T2 contrast agent, as further demonstrated by in vitro MRI. The results of Confocal Laser Scanning Microscopy (CLSM) and Prussian blue staining of Raw264.7 after incubation with micelle-containing medium indicated that the cellular uptake efficiency is high. Conclusion: We successfully synthesized dual-modal MR and fluorescence imaging mPEG-Lys3-CA4-NR/SPION polymeric micelles with an ultra-small size and high MRI sensitivity, which were effectively and quickly uptaken into Raw 264.7 cells. mPEG-Lys3-CA4-NR/SPION polymeric micelles might become a new MR lymphography contrast agent, with high effectiveness and high MRI sensitivity.

A Benchtop Round Window Model for Studying Magnetic Nanoparticle Transport to the Inner Ear.

INTRODUCTION: The round window membrane (RWM) presents a significant barrier to the local application of therapeutics to the inner ear. We demonstrate a benchtop preclinical RWM model and evaluate superparamagnetic iron oxide nanoparticles (SPIONs) as vehicles for magnetically assisted drug delivery. METHODS: Guinea pig RWM explants were inset into a 3D-printed dual chamber benchtop device. Custom-synthesized 7-nm iron core nanoparticles were modified with different polyethylene glycol chains to yield two sizes of SPIONs (NP-PEG600 and NP-PEG3000) and applied to the benchtop model with and without a magnetic field. Histologic analysis of the RWM was performed using transmission electron microscopy (TEM) and confocal microscopy. RESULTS: Over a 4-h period, 19.5 ± 1.9% of NP-PEG3000 and 14.6 ± 1.9% of NP-PEG600 were transported across the guinea pig RWM. The overall transport increased by 1.45× to 28.4 ± 5.8% and 21.0 ± 2.0%, respectively, when a magnetic field was applied. Paraformaldehyde fixation of the RWM decreased transport significantly (NP-PEG3000: 7.6 ± 1.5%; NP-PEG600: 7.0 ± 1.6%). Confocal and electron microscopy analysis demonstrated nanoparticle localization throughout all cellular layers and layer-specific transport characteristics within RWM. CONCLUSION: The guinea pig RWM explant benchtop model allows for targeted and practical investigations of transmembrane transport in the development of nanoparticle drug delivery vehicles. The presence of a magnetic field increases SPION delivery by 45%-50% in a nanoparticle size- and cellular layer-dependent manner. LEVEL OF EVIDENCE: NA Laryngoscope, 134:3355-3362, 2024.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

Zanthoxylum-specific whole genome duplication and recent activity of transposable elements in the highly repetitive paleotetraploid Z. bungeanum genome.

Zanthoxylum bungeanum is an important spice and medicinal plant that is unique for its accumulation of abundant secondary metabolites, which create a characteristic aroma and tingling sensation in the mouth. Owing to the high proportion of repetitive sequences, high heterozygosity, and increased chromosome number of Z. bungeanum, the assembly of its chromosomal pseudomolecules is extremely challenging. Here, we present a genome sequence for Z. bungeanum, with a dramatically expanded size of 4.23 Gb, assembled into 68 chromosomes. This genome is approximately tenfold larger than that of its close relative Citrus sinensis. After the divergence of Zanthoxylum and Citrus, the lineage-specific whole-genome duplication event η-WGD approximately 26.8 million years ago (MYA) and the recent transposable element (TE) burst ~6.41 MYA account for the substantial genome expansion in Z. bungeanum. The independent Zanthoxylum-specific WGD event was followed by numerous fusion/fission events that shaped the genomic architecture. Integrative genomic and transcriptomic analyses suggested that prominent species-specific gene family expansions and changes in gene expression have shaped the biosynthesis of sanshools, terpenoids, and anthocyanins, which contribute to the special flavor and appearance of Z. bungeanum. In summary, the reference genome provides a valuable model for studying the impact of WGDs with recent TE activity on gene gain and loss and genome reconstruction and provides resources to accelerate Zanthoxylum improvement.

[Change of ACTH and cortisol in patients with obstructive sleep apnea hypopnea syndrome after the operation of uvulopalatopharyngoplasty].

OBJECTIVE: To explore the change of hypothalamus-pituitary-adrenal hormone in patients with Obstructive sleep apnea hypopnea syndrome (OSAHS) by observing the change of Corticotropin (ACTH) and cortisol after Uvulopalatopharyngoplasty (UPPP). METHOD: OSAHS patients were monitored by polysomnography (PSG). The ACTH and cortisol levels in plasma were measured by radioimmunoassay before, during and after sleep in pre-operation and six months post-operation. Their correlation were analyzed. RESULT: The cortisol concentration [ (170.4+/-56.5) microg/L, (252.2+/-62.3) microg/L, (276.9+/-70.4) microg/L, (2859.0+/-63.2) microg/L, (395.1+/-85.2) microg/L before, during and after sleep] in the before UPPP group were significantly higher than those of the after UPPP group [(133.5+/-24.8) microg/L, (99.9+/-9.2) microg/L, (103.8+/-13.2) microg/L, (146.2+/-22.5) microg/L, (199.6+/-20.9) microg/L before, during and after sleep, respectively, all P <0. 05]; but there was no difference in corticotropin(ACTH). The average blood oxygen concentration was negatively correlated with average awareness duration (r = -0.713). CONCLUSION: There are abnormal change of HPA axis in OSAHS patients, and the feedback regulation is disordered. These abnormalities are related to sleep awareness and hypoxia during sleep.