Peristalsis prevents ureteral dilation.

PURPOSE: The etiology of ureteral dilation in primary nonrefluxing, nonobstructing megaureters is still not well understood. Impaired ureteral peristalsis has been theorized as one of the contributing factors. However, ureteral peristalsis and its "normal" function is not well defined. In this study, using mathematical modeling techniques, we aim to better understand how ureteral peristalsis works. This is the first model to consider clinically observed, back-and-forth, cyclic wall longitudinal motion during peristalsis. We hypothesize that dysfunctional ureteral peristalsis, caused by insufficient peristaltic amplitudes (e.g., circular muscle dysfunction) and/or lack of ureteral wall longitudinal motion (e.g., longitudinal muscle dysfunction), promotes peristaltic reflux (i.e., retrograde flow of urine during an episode of peristalsis) and may result in urinary stasis, urine accumulation, and consequent dilation. METHODS: Based on lubrication theory in fluid mechanics, we developed a two-dimensional (planar) model of ureteral peristalsis. In doing so, we treated ureteral peristalsis as an infinite train of sinusoidal waves. We then analyzed antegrade and retrograde flows in the ureter under different bladder-kidney differential pressure and peristalsis conditions. RESULTS: There is a minimum peristaltic amplitude required to prevent peristaltic reflux. Ureteral wall longitudinal motion decreases this minimum required amplitude, increasing the nonrefluxing range of peristaltic amplitudes. As an example, for a normal bladder-kidney differential pressure of 5 cmH2 O, ureteral wall longitudinal motion increases nonrefluxing range of peristaltic amplitude by 65%. Additionally, ureteral wall longitudinal motion decreases refluxing volumetric flow rates. For a similar normal bladder pressure example of 5 cmH2 O, refluxing volumetric flow rate decreases by a factor of 18. Finally, elevated bladder pressure, not only increases the required peristaltic amplitude for reflux prevention but it increases maximum refluxing volumetric flow rates. For the case without wall longitudinal motion, as bladder-kidney differential pressure increases from 5 to 40 cmH2 O, minimum required peristaltic amplitude to prevent reflux increases by 40% while the maximum refluxing volumetric flow rate increases by approximately 100%. CONCLUSION: The results presented in this study show how abnormal ureteral peristalsis, caused by the absence of wall longitudinal motion and/or lack of sufficient peristaltic amplitudes, facilitates peristaltic reflux and retrograde flow. We theorize that this retrograde flow can lead to urinary stasis and urine accumulation in the ureters, resulting in ureteral dilation seen on imaging studies and elevated infection risk. Our results also show how chronically elevated bladder pressures are more susceptible to such refluxing conditions that could lead to ureteral dilation.

Generation of spatiotemporal vortices in nonlinear photonic crystals.

Spatiotemporal vortices (STOVs) are a new, to the best of our knowledge, type of structured light in which the optical phase circulates in space-time. In this work, we propose to generate STOVs via second harmonic generation in lithium niobate nonlinear photonic crystals (NPCs) with a linearly chirped Gaussian pulse as the fundamental wave. The structural function of the NPC is derived by the inverse design method. Numerical simulations of the intensity and phase profiles of the generated second harmonic waves are performed with both the amplitude-phase-modulated and the simplified binary-phase-modulated NPCs. We anticipate our study will be valuable for the experimental generation and manipulation of STOVs in NPCs.

Application of Single Extracellular Vesicle Analysis Techniques.

Extracellular vesicles (EVs) are lipid containers that are actively released by cells and contain complex molecular cargoes. These cargoes include abundant material such as genomes and proteins from cells of origin. They are involved in intercellular communication and various pathological processes, showing excellent potential for diagnosing and treating diseases. Given the significant heterogeneity of EVs in complex physiopathological processes, unveiling their composition is essential to understanding their function. Bulk detection methods have been previously used to analyze EVs, but they often mask their heterogeneity, leading to the loss of valuable information. To overcome this limitation, single extracellular vesicle (SEV) analysis techniques have been developed and advanced. These techniques allow for analyzing EVs' physical information and biometric molecules at the SEV level. This paper reviews recent advances in SEV detection methods and summarizes some clinical applications for SEV detection strategies.

Analysis of personality traits' correlation to facial width-to-height ratio (fWHR) and mandibular line angle based on 16 personality factor in Chinese college students.

Facial appearance reveals clues about personality. Studies have found that facial width-to-height ratio (fWHR) correlates with some personality traits, and mandibular morphology as a potential facial feature that might have correlation with personality traits. Therefore, a face recognition study was carried out to explore the personality traits' correlation to both fWHR and bilateral mandibular line angles. Specifically, face images of 904 college students in China were collected and measured, with the personality traits evaluated using the 16 Personality Factor Questionnaire. Analyses revealed that the average bilateral mandibular line angle of the male were significantly more extensive than that of the female, while the fWHR of the female was significantly more extensive than that of the male. We found facial features (fWHR and average bilateral mandibular line angle) were correlated with 16PF in the canonical correlation analysis and the loadings of bilateral mandibular line angles were greater than that of fWHR. The fWHR was significantly negatively correlated with the scores of sensitivity and self-reliance in male but none of the factors related to fWHR in female. The bilateral mandibular line angles were significantly negatively correlated with the scores of social boldness in male, and were significantly negatively correlated with the scores of vigilance and apprehension in female. Over all, the correlations between fWHR, average bilateral mandibular line angle and certain 16PF factors in male and female tend to be different, suggesting that such correlations might vary with gender. In the future, mandibular morphology could be selected as a potential indicator in facial perception. The limitations of this study were the participants were limited to 18-30 years of age and the mandibular morphology was not measured with anthropometry, which could be further improved in future studies.

Classifying Drosophila olfactory projection neuron boutons by quantitative analysis of electron microscopic reconstruction.

In Drosophila melanogaster, olfactory projection neurons (PNs) convey odor information from the antenna lobe to higher brain regions. Recent transcriptomic studies reveal a large diversity of transcription factors, cell-surface molecules, neurotransmitter-coding, and neuropeptide-coding genes in PNs; however, their structural diversity remains unknown. Herein, we achieved a volumetric reconstruction of 89 PN boutons under Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and quantitatively analyzed the internal presynaptic active zones (PAZs) and dense-core vesicles (DCVs). The ultrastructure-based cluster analysis reveals three morphological distinct bouton subtypes: complex boutons, unilobed boutons, and simple boutons. The complex boutons contain the most PAZs and DCVs, which suggests that they are of the highest capability of releasing neurotransmitters and neuromodulators. By labeling a subset of boutons under FIB-SEM, we found that DCVs are preferentially distributed in certain GH146-positive subtypes. Our study demonstrates that PN boutons display distinct morphology, which may determine their capacity of releasing neurotransmitters and neuromodulators.

Modification of Lipid-Based Nanoparticles: An Efficient Delivery System for Nucleic Acid-Based Immunotherapy.

Lipid-based nanoparticles (LBNPs) are biocompatible and biodegradable vesicles that are considered to be one of the most efficient drug delivery platforms. Due to the prominent advantages, such as long circulation time, slow drug release, reduced toxicity, high transfection efficiency, and endosomal escape capacity, such synthetic nanoparticles have been widely used for carrying genetic therapeutics, particularly nucleic acids that can be applied in the treatment for various diseases, including congenital diseases, cancers, virus infections, and chronic inflammations. Despite great merits and multiple successful applications, many extracellular and intracellular barriers remain and greatly impair delivery efficacy and therapeutic outcomes. As such, the current state of knowledge and pitfalls regarding the gene delivery and construction of LBNPs will be initially summarized. In order to develop a new generation of LBNPs for improved delivery profiles and therapeutic effects, the modification strategies of LBNPs will be reviewed. On the basis of these developed modifications, the performance of LBNPs as therapeutic nanoplatforms have been greatly improved and extensively applied in immunotherapies, including infectious diseases and cancers. However, the therapeutic applications of LBNPs systems are still limited due to the undesirable endosomal escape, potential aggregation, and the inefficient encapsulation of therapeutics. Herein, we will review and discuss recent advances and remaining challenges in the development of LBNPs for nucleic acid-based immunotherapy.

Biomimetic Nanostructure Platform for Cancer Diagnosis Based on Tumor Biomarkers.

Biomarker discovery and its clinical use have attracted considerable attention since early cancer diagnosis can significantly decrease mortality. Cancer biomarkers include a wide range of biomolecules, such as nucleic acids, proteins, metabolites, sugars, and cytogenetic substances present in human biofluids. Except for free-circulating biomarkers, tumor-extracellular vesicles (tEVs) and circulating tumor cells (CTCs) can serve as biomarkers for the diagnosis and prognosis of various cancers. Considering the potential of tumor biomarkers in clinical settings, several bioinspired detection systems based on nanotechnologies are in the spotlight for detection. However, tremendous challenges remain in detection because of massive contamination, unstable signal-to-noise ratios due to heterogeneity, nonspecific bindings, or a lack of efficient amplification. To date, many approaches are under development to improve the sensitivity and specificity of tumor biomarker isolation and detection. Particularly, the exploration of natural materials in biological frames has encouraged researchers to develop new bioinspired and biomimetic nanostructures, which can mimic the natural processes to facilitate biomarker capture and detection in clinical settings. These platforms have substantial influence in biomedical applications, owing to their capture ability, significant contrast increase, high sensitivity, and specificity. In this review, we first describe the potential of tumor biomarkers in a liquid biopsy and then provide an overview of the progress of biomimetic nanostructure platforms to isolate and detect tumor biomarkers, including in vitro and in vivo studies. Capture efficiency, scale, amplification, sensitivity, and specificity are the criteria that will be further discussed for evaluating the capability of platforms. Bioinspired and biomimetic systems appear to have a bright future to settle obstacles encountered in tumor biomarker detection, thus enhancing effective cancer diagnosis.

Radial shearing speckle pattern interference system based on a spatial light modulator.

We propose a new radial shearing speckle pattern interference system. The introduction of radial shearing and the four-step temporal phase shift are realized using a spatial light modulator to modulate the phase information of the light wavefront. The modulation mode of the spatial light modulator is controlled by programming, and thus the shearing information can be adjusted quickly and arbitrarily according to the actual measurement needs. The interference system no longer has the requirement of the defect direction in nondestructive testing and can simultaneously realize the accurate detection of defects in all directions. The basic principle of radial shearing speckle pattern interferometry and the introduction method of radial shearing are described, and the system feasibility and practicability are verified by experiments.

Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks.

(1) Purpose: To improve the capability of EfficientNet, including developing a cropping method called Random Center Cropping (RCC) to retain the original image resolution and significant features on the images' center area, reducing the downsampling scale of EfficientNet to facilitate the small resolution images of RPCam datasets, and integrating attention and Feature Fusion (FF) mechanisms with EfficientNet to obtain features containing rich semantic information. (2) Methods: We adopt the Convolutional Neural Network (CNN) to detect and classify lymph node metastasis in breast cancer. (3) Results: Experiments illustrate that our methods significantly boost performance of basic CNN architectures, where the best-performed method achieves an accuracy of 97.96% ± 0.03% and an Area Under the Curve (AUC) of 99.68% ± 0.01% on RPCam datasets, respectively. (4) Conclusions: (1) To our limited knowledge, we are the only study to explore the power of EfficientNet on Metastatic Breast Cancer (MBC) classification, and elaborate experiments are conducted to compare the performance of EfficientNet with other state-of-the-art CNN models. It might provide inspiration for researchers who are interested in image-based diagnosis using Deep Learning (DL). (2) We design a novel data augmentation method named RCC to promote the data enrichment of small resolution datasets. (3) All of our four technological improvements boost the performance of the original EfficientNet.

Extracellular Vesicles in the Treatment of Parkinson's Disease: A Review.

BACKGROUND: Parkinson's disease (PD) is one of the most common neurological disorders that can severely affect the ability to perform daily activities. The clinical presentation of PD includes motor and nonmotor symptoms. The motor symptoms generally involve movement conditions like tremors, rigidity, slowness, and impaired balance. In contrast, the nonmotor symptoms are often not apparent but can affect various organ systems, such as the urinary and gastrointestinal systems, and mental health. Gene mutations and toxic environmental factors have contributed significantly to PD; nevertheless, its cause and underlying mechanism remain unknown. Currently, treatments such as dopamine agonists, RNA molecules, and antioxidants can, to some extent, alleviate the motor symptoms triggered by PD. However, these medicines cannot effectively halt ongoing dopaminergic damage, mainly because the blood-brain barrier (BBB) lowers the efficiency of drug delivery. Recently, extracellular vesicles (EVs), a novel drug delivery platform, have been widely used in various neurological diseases, including stroke and brain tumors, because of their excellent biocompatibility, their ability to penetrate the BBB without toxicity, and their target specificity. EVs thus provide a promising therapeutic for treating PD. OBJECTIVE: This review focuses on novel therapies based on EVs in practice. Herein, we briefly introduce the biogenesis, composition, isolation, and characterization of EVs, and we discuss strategies for loading therapeutic agents onto EVs and recent applications for PD treatment. Moreover, we discuss perspectives on the direction of preclinical and clinical studies regarding novel and effective therapies. METHODS: A literature search regarding PD treatment based on extracellular vesicles was performed in PubMed (updated in June 2020). Treatment, therapy, drug delivery, extracellular vesicles, and their combinations were the search queries. Both systematic reviews and original publications were included. Searched results were selected and compared based on relevance. Articles published in the last five years were given top priority. CONCLUSION: PD is a heterogeneous disease that can be treated by using pharmacologic approaches (e.g. dopamine agonists and levodopa) and nonpharmacologic approaches (e.g. music), based on symptoms and progression level in patients. Even though current treatments have demonstrated effectiveness, clinical challenges remain because the BBB reduces the medication received and lowers the efficacy of drug delivery, which impairs the treatment's effect. Therefore, EVs, as an emerging delivery platform, are highly promising for PD treatment since they can readily cross the BBB with high therapeutic efficiency through the loading or functionalization process. However, defining a safe source of EVs, reliably purifying and isolating EVs with high yield, and improving the efficacy of therapeutic loading in EVs remain challenging in this field. Therefore, future investigations should focus on generating large-scale exosomal carriers and designing new effective drugs encapsulated in EVs for better efficacy.

Experiences and views of people with diabetes during Ramadan fasting: A qualitative meta-synthesis.

OBJECTIVES: To review and appraise the existing qualitative studies on Ramadan fasting in participants with diabetes and to integrate valuable qualitative evidence for optimizing diabetes management. METHODS: Twelve databases (PubMed, Embase, Cochrane Library, Science Direct, CINAHL, PsycINFO, JBI (Joanna Briggs institute), Web of Science, and four Chinese databases) were searched to identify qualitative studies on experiences and perspectives of Ramadan fasting in participants with diabetes. CASP (Critical Appraisal Skills Program) Qualitative Checklists were applied to appraise the included studies. A meta-synthesis approach was used to analyze the included studies. Through the strategy of inductive thematic synthesis and reciprocal interpretation, the findings and quotations of the included studies were integrated into new themes and categories. The CERQual (Confidence in the Evidence from Reviews of Qualitative Research) tool was used to grade the confidence of the new themes. RESULTS: A total of 11 qualitative studies were included, and 43 findings were isolated. Ten new themes were identified and synthesized from the findings. Finally, four new categories were integrated, including the knowledge and understanding of observing Ramadan fasting, well-being and challenges, self-efficacy, and needs and expectations of participants with diabetes during Ramadan. CONCLUSIONS: Insulin-dependent individuals call for special concern during Ramadan fasting. Ramadan-focused education needs to be developed and generalized, and existing guidelines should be improved to optimize the management of diabetes. Professional HCPs contribute to weigh the health risks and mental satisfaction for their patients, partly, to balance health and religion. Participants' psychological construction is another concern for religious scholars and psychologists.

Application of BERT to Enable Gene Classification Based on Clinical Evidence.

The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the classification of genetic mutations continues to be done manually nowadays. Manual classification of genetic mutations is pathologist-dependent, subjective, and time-consuming. To improve the accuracy of clinical interpretation, scientists have proposed computational-based approaches for automatic analysis of mutations with the advent of next-generation sequencing technologies. Nevertheless, some challenges, such as multiple classifications, the complexity of texts, redundant descriptions, and inconsistent interpretation, have limited the development of algorithms. To overcome these difficulties, we have adapted a deep learning method named Bidirectional Encoder Representations from Transformers (BERT) to classify genetic mutations based on text evidence from an annotated database. During the training, three challenging features such as the extreme length of texts, biased data presentation, and high repeatability were addressed. Finally, the BERT+abstract demonstrates satisfactory results with 0.80 logarithmic loss, 0.6837 recall, and 0.705 F-measure. It is feasible for BERT to classify the genomic mutation text within literature-based datasets. Consequently, BERT is a practical tool for facilitating and significantly speeding up cancer research towards tumor progression, diagnosis, and the design of more precise and effective treatments.

Nanoscale Technologies in Highly Sensitive Diagnosis of Cardiovascular Diseases.

Cardiovascular diseases (CVD) are the leading cause of death and morbidity in the world and are a major contributor to healthcare costs. Although enormous progress has been made in diagnosing CVD, there is an urgent need for more efficient early detection and the development of novel diagnostic tools. Currently, CVD diagnosis relies primarily on clinical symptoms based on molecular imaging (MOI) or biomarkers associated with CVDs. However, sensitivity, specificity, and accuracy of the assay are still challenging for early-stage CVDs. Nanomaterial platform has been identified as a promising candidate for improving the practical usage of diagnostic tools because of their unique physicochemical properties. In this review article, we introduced cardiac biomarkers and imaging techniques that are currently used for CVD diagnosis. We presented the applications of various nanotechnologies on diagnosis within cardiac immunoassays (CIAs) and molecular imaging. We also summarized and compared different cardiac immunoassays based on their sensitivities and working ranges of biomarkers.

Conjugation of Synthetic Trisaccharide of Staphylococcus aureus Type 8 Capsular Polysaccharide Elicits Antibodies Recognizing Intact Bacterium.

Staphylococcus aureus causes a wide range of life-threatening diseases. One of the powerful approaches for prevention and treatment is to develop an efficient vaccine as antibiotic resistance greatly increases. S. aureus type 8 capsular polysaccharide (CP8) has shown great potential in vaccine development. An understanding of the immunogenicity of CP8 trisaccharide repeating unit is valuable for epitope-focused vaccine design and cost-efficient vaccine production. We report the chemical synthesis of conjugation-ready CP8 trisaccharide 1 bearing an amine linker, which effectively served for immunological evaluation. The trisaccharide 1-CRM197 conjugate elicited a robust immunoglobulin G (IgG) immune response in mice. Both serum antibodies and prepared monoclonal antibodies recognized S. aureus strain, demonstrating that synthetic trisaccharide 1 can be an efficient antigen for vaccine development.