Potential Clinical Applications Research Articles

Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman’s correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model’s discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer–Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Malate dehydrogenase as a multi-purpose target for drug discovery.

Malate dehydrogenase (MDH) enzymes play critical roles in cellular metabolism, facilitating the reversible conversion of malate to oxaloacetate using NAD+/NADH as a cofactor. The two human isoforms of MDH have roles in the citric acid cycle and the malate-aspartate shuttle, and thus both are key enzymes in aerobic respiration as well as regenerating the pool of NAD+ used in glycolysis. This review highlights the potential of MDH as a therapeutic drug target in various diseases, including metabolic and neurological disorders, cancer, and infectious diseases. The most promising molecules for targeting MDH have been examined in the context of human malignancies, where MDH is frequently overexpressed. Recent studies have led to the identification of several antagonists, some of which are broad MDH inhibitors while others have selectivity for either of the two human MDH isoforms. Other promising compounds have been studied in the context of parasitic MDH, as inhibiting the function of the enzyme could selectively kill the parasite. Research is ongoing with these chemical scaffolds to develop more effective small-molecule drug leads that would have great potential for clinical applications.

Finite Element Simulations of Effective Stimulated Volume in Rat Brain Optoelectronic Stimulation

Abstract This study presents a finite element model (FEM) simulation of optoelectronic stimulation in the rat brain, designed to investigate the effects of this emerging neural stimulation technique. A detailed 3D model of the rat brain and an equivalent circuit representation of the organic semiconductor-based stimulation device was used to simulate the subdural application of optoelectronic stimuli to the rat brain. The model is based on realistic electrode geometries and material properties. The stimulated brain volume is approximately 38 mm3 with a maximal stimulation depth of 2.6 mm. Our results indicate that optoelectronic stimulation can achieve targeted activation of cortical neurons. Further studies will focus on optimizing device parameters, exploring long-term effects, and expand this approach for specific neural bioelectronics to fully exploit its potential in clinical applications.

Clinical-proteomic classification and precision treatment strategy of chordoma

Chordoma is a rare and heterogeneous mesenchymal malignancy, with distinct clinical and biological behaviors. Till now, its comprehensive clinical-molecular characteristics and accurate molecular classification remain obscure. In this research, we enroll 102 patients with chordoma and describe their clinical, imageological, and histopathological features. Through tandem mass tag-based proteomic analysis and nonnegative matrix factorization clustering, we classify chordoma into three molecular subtypes: bone microenvironment-dominant, mesenchymal-derived, and mesenchymal-to-epithelial transition-mediated pattern. The three subtypes exhibit discrete clinical prognosis and distinct biological attributes of osteoclastogenesis and immunogenicity, oxidative phosphorylation, and receptor tyrosine kinase activation, suggesting targeted therapeutic strategies of denosumab, S-Gboxin, and anlotinib, respectively. Notably, these approaches demonstrate positive treatment outcomes for each subtype in vitro and in vivo. Altogether, this work sheds light on the clinical-proteomic characteristics of chordoma and provides a candidate precision treatment strategy for chordoma according to molecular classification, underscoring their potential for clinical application.

Diagnosis of periprosthetic loosening of total hip and knee arthroplasty using 68Gallium-Zoledronate PET/CT.

Periprosthetic loosening is a major complication after total hip and knee arthroplasty. Early and accurate diagnosis is essential to choose the right therapeutic path and to avoid further complications. The aim of the study was to evaluate the diagnostic performance of 68Gallium-Zoledronate ([68Ga]Ga-DOTAZol) PET/CT in detecting periprosthetic loosening in total hip (THA) and total knee arthroplasty (TKA). This retrospective study included 26 patients with painful prosthesis (THA n = 17; TKA n = 16) and clinical suspicion of periprosthetic loosening, but without a confirmed diagnosis. Patients underwent [68Ga]Ga-DOTAZol PET/CT at least one year post-implantation. Diagnosis was confirmed through revision surgery or long-term clinical follow-up, with an observation period of at least 6months. The analysis included both an assessment of the prosthesis as a unit and a separate evaluation of the individual components. Statistical analysis involved calculating sensitivity, specificity and accuracy using SPSS. Overall, a sensitivity of 77.8%, a specificity of 95.8% and an accuracy of 90.9% were found for detecting periprosthetic loosening, when considering the prosthesis as a unit. Individual component analyses showed a sensitivity of 71.4% and specificity of 96.2%. The use of [68Ga]Ga-DOTAZol PET/CT in periprosthetic loosening is a remarkable diagnostic tool and a promising approach. In comparison to established radionuclide tracers, 68Gallium-Zoledronate offers notable advantages due to its availability via 68Ge/68Ga-generators, improving its potential for clinical application.

Transcription and post-translational mechanisms: dual regulation of adiponectin-mediated Occludin expression in diabetes

BackgroundOccludin, a crucial component of tight junctions, has emerged as a promising biomarker for the diagnosis of acute ischemic disease, highlighting its significant potential in clinical applications. In the diabetes, Occludin serves as a downstream target gene intricately regulated by the adiponectin (APN) signaling pathway. However, the specific mechanism by which adiponectin regulates Occludin expression remains unclear.Methods and resultsEndothelial-specific Ocln knockdown reduced APN-mediated blood flow recovery after femoral artery ligation and nullified APN's protection against high-fat diet (HFD)-triggered apoptosis and angiogenesis inhibition in vivo. Mechanically, we have meticulously elucidated APN's regulatory role in Occludin expression through a comprehensive analysis spanning transcriptional and post-translational dimensions. Foxo1 has been elucidated as a crucial transcriptional regulator of Occludin that is modulated by the APN/APPL1 signaling axis, as evidenced by validation through ChIP-qPCR assays and Western blot analysis. APN hindered Occludin degradation via the ubiquitin–proteasome pathway. Mass spectrometry analysis has recently uncovered a novel phosphorylation site, Tyr467, on Occludin. This site responds to APN, playing a crucial role in inhibiting Occludin ubiquitination by APN. The anti-apoptotic and pro-angiogenic effects of APN were attenuated in vitro and in vivo following Foxo1 knockdown or expression of a non-phosphorylatable mutant, OccludinY467A. Clinically, elevated plasma concentrations of Occludin were observed in patients with diabetes. A significant negative correlation was found between Occludin levels and APN concentrations.ConclusionOur study proposes that APN modulates Occludin expression through mechanisms involving both transcriptional and post-translational interactions, thereby conferring a protective effect on endothelial integrity within diabetic vasculature.Graphical abstract

Active learning based on multi-enhanced views for classification of multiple patterns in lung ultrasound images

There are several main patterns in lung ultrasound (LUS) images, including A-lines, B-lines, consolidation and pleural effusion. LUS images of healthy lungs typically only exhibit A-lines, while other patterns may emerge or coexist in LUS images associated with different lung diseases. The accurate categorization of these primary patterns is pivotal for effective lung disease screening. However, two challenges complicate the classification task: the first is the inherent blurring of feature differences between main patterns due to ultrasound imaging properties; and the second is the potential coexistence of multiple patterns in a single case, with only the most dominant pattern being clinically annotated. To address these challenges, we propose the active learning based on multi-enhanced views (MEVAL) method to achieve more precise pattern classification in LUS. To accentuate feature differences between multiple patterns, we introduce a feature enhancement module by applying vertical linear fitting and k-means clustering. The multi-enhanced views are then employed in parallel with the original images, thus enhancing MEVAL’s awareness of feature differences between multiple patterns. To tackle the patterns coexistence issue, we propose an active learning strategy based on confidence sets and misclassified sets. This strategy enables the network to simultaneously recognize multiple patterns by selectively labeling of a small number of images. Our dataset comprises 5075 LUS images, with approximately 4% exhibiting multiple patterns. Experimental results showcase the effectiveness of the proposed method in the classification task, with accuracy of 98.72%, AUC of 0.9989, sensitivity of 98.76%, and specificity of 98.16%, which outperforms than the state-of-the-art deep learning-based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.

The emerging role of bacterial extracellular vesicles in human cancers.

Bacterial extracellular vesicles (BEVs) have emerged as pivotal mediators between bacteria and host. In addition to being crucial players in host homeostasis, they have recently been implicated in disease pathologies such as cancer. Hence, the study of BEVs represents an intriguing and rapidly evolving field with substantial translational potential. In this review, we briefly introduce the fundamentals of BEV characteristics, cargo and biogenesis. We emphatically summarize the current relationship between BEVs across various cancer types, illustrating their role in tumorigenesis, treatment responses and patient survival. We further discuss the inherent advantages of BEVs, such as stability, abundance and specific cargo profiles, that make them attractive candidates for non-invasive diagnostic and prognostic approaches. The review also explores the potential of BEVs as a strategy for cancer therapy, considering their ability to deliver therapeutic agents, modulate the tumour microenvironment (TME) and elicit immunomodulatory responses. Understanding the clinical significance of BEVs may lead to the development of better-targeted and personalized treatment strategies. This comprehensive review evaluates the current progress surrounding BEVs and poses questions to encourage further research in this emerging field to harness the benefits of BEVs for their full potential in clinical applications against cancer.

Feasibility and safety assessment of RF double applications in very high power and short duration ablation

BackgroundThe QDOT-MICRO catheter enables very high power and short duration (vHPSD) ablation. However, low first-pass isolation rates have been reported, possibly because of shallow lesion formation, necessitating deeper lesions to improve treatment outcomes. ObjectiveThis study aimed to confirm the safety and efficacy of double radiofrequency applications of vHPSD ablation in an in vivo beating swine heart model. MethodsEighteen swine were anesthetized and underwent vHPSD ablation using the QDOT-MICRO catheter at 90 W for 4 seconds, targeting a contact force of 10 g. Radiofrequency applications were performed as single application (SA) and double applications (DAs) with 4- to 8-second rest intervals. Lesion surface area and volume were measured postablation. ResultsA total of 337 atrial lesions and 74 ventricular lesions were created. Both 4- to 6-second DA and 7- to 8-second DA produced significantly larger and deeper lesions than did SA, with atrial surface lengths averaging 9.0 mm for 4- to 6-second DA, 9.2 mm for 7- to 8-second DA, and 8.0 mm for SA. Transmurality was observed at 100% for 4- to 6-second and 7- to 8-second DAs, while it was 94% for SA (P = .002). Ventricular lesion metrics followed similar trends. Except for 1 event of tiny char formation during 4-second DA in the ventricle, neither steam pops nor char formation was observed in either the atrium or the ventricle. ConclusionIn an in vivo swine heart model, DA with 4- to 6-second and 7- to 8-second intervals create deeper and wider lesions than does SA, suggesting its potential for clinical application in areas with thicker myocardial walls. However, DA with very short intervals may still pose a risk of excessive tissue heating.

Fabrication of core-shell quantum dots microbeads with high stability, enhanced sensitivity and potential application in C-reactive protein (CRP) and procalcitonin (PCT) immunochromatography in vitro diagnostic reagents

Cardiovascular and cerebrovascular diseases represent the leading causes of mortality among middle-aged and elderly populations in China, with inflammation identified as a significant contributing factor. Consequently, the early diagnosis and management of inflammation are of paramount importance. C-reactive protein (CRP) and procalcitonin (PCT) serve as biomarkers for myocardial inflammation. In this study, we developed a quantum dot immunochromatographic strip that was capable of simultaneously detecting these two biomarkers with enhanced sensitivity and specificity. The comb amphiphilic poly (octadecyl-alt-polyethylene glycol) was synthesized through an amide reaction, which was employed to coat oil-soluble quantum dots, thereby transforming them into water-soluble quantum dots microbeads suitable for the coupling with biological macromolecules. These quantum dots microbeads were utilized as luminescent materials in the immunochromatographic strip for the concurrent detection of CRP and PCT. The assay required only 100 μL of sample and provided results within 10 min, demonstrating commendable precision, stability, sensitivity, and accuracy. Clinical evaluations confirmed that the developed CRP and PCT quantum dots immunochromatographic test strips were accurate and reliable, underscoring their significant potential for practical routine clinical application in the early accurate diagnosis of myocardial inflammation.

Three-in-One Nanozyme for Radiosensitization of Bladder Cancer.

Bladder cancer is a common malignancy of the urinary system and the development of noninvasive therapeutic methods is imperative to avoid radical cystectomy, which results in a poor quality of life for patients. In this study, ultrasmall copper-palladium nanozymes decorated with cysteamine (CPC) nanoparticles (NPs) were synthesized to enhance the efficacy of radiotherapy (RT) in treating bladder cancer. CPC NPs react with intracellular overexpressed H2O2 in the tumor microenvironment to produce large quantities of reactive oxygen species (ROS) and induce tumor cell apoptosis. Furthermore, the CPC nanozymes can generate ample oxygen within tumors by utilizing H2O2, addressing hypoxia conditions, and mitigating radioresistance. Additionally, CPC facilitates the oxidation of glutathione (GSH) into oxidized glutathione disulfide (GSSG), blocking the self-repair mechanisms of tumor cells post-treatment. Simultaneously, CPC enhances the ionization energy deposition effect on tumor cells. The results demonstrate an increased level of ROS and an elevation in oxygen content at the tumor site. Importantly, tumor growth was restrained without apparent systemic toxicity during the combined treatment. In summary, this study highlights the potential of CPC nanozyme-mediated radiotherapy as a promising avenue for the effective treatment of bladder cancer and demonstrates its potential for future clinical applications in the synergistic therapy of bladder cancer.

Enhancing Breast Cancer Detection through Progressive Convolutional Neural Networks with Adaptive Kernel Pooling

Breast cancer is one of the leading causes of mortality among women worldwide, making early detection a crucial component in reducing fatality rates. Traditional diagnostic methods often rely on manual interpretation of mammograms or histopathological images, which can be time-consuming and prone to human error. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated significant potential in automating and enhancing breast cancer detection. Breast cancer detection remains a critical challenge in medical diagnostics, where early identification can significantly improve patient outcomes. Leveraging advanced deep learning techniques, this research proposes a novel approach by integrating Progressive Convolutional Neural Networks (PCNNs) with Adaptive Kernel Pooling (AKP) for enhanced image classification. The model architecture dynamically adjusts convolutional filter sizes across layers to capture both coarse and fine-grained features, while AKP adapts pooling operations based on spatial dimensions, optimizing feature retention. Through rigorous experimentation on a large breast cancer histopathological dataset, the proposed method achieved an impressive accuracy of 98%, demonstrating its potential in real-world clinical applications. The high accuracy indicates the model's robustness in differentiating between malignant and benign tissues. This approach not only enhances detection precision but also addresses the challenge of feature loss during pooling, a common issue in traditional Convolutional Neural Networks (CNNs). Additionally, this study contributes to the growing field of medical imaging by offering an adaptable and scalable architecture that could be employed across various medical diagnostics beyond breast cancer detection. The proposed PCNN-AKP framework sets a new benchmark for automated cancer detection models, providing a pathway for future innovations in the use of deep learning for healthcare diagnostics.

Evaluation of the clinical application value of artificial intelligence in diagnosing head and neck aneurysms

ObjectiveTo evaluate the performance of a semi-automated artificial intelligence (AI) software program (CerebralDoc® system) in aneurysm detection and morphological measurement.MethodsIn this study, 354 cases of computed tomographic angiography (CTA) were retrospectively collected in our hospital. Among them, 280 cases were diagnosed with aneurysms by either digital subtraction angiography (DSA) and CTA (DSA group, n = 102), or CTA-only (non-DSA group, n = 178). The presence or absence of aneurysms, as well as their location and related morphological features determined by AI were evaluated using DSA and radiologist findings. Besides, post-processing image quality from AI and radiologists were also rated and compared.ResultsIn the DSA group, AI achieved a sensitivity of 88.24% and an accuracy of 81.97%, whereas radiologists achieved a sensitivity of 95.10% and an accuracy of 84.43%, using DSA results as the gold standard. The AI in the non-DSA group achieved 81.46% sensitivity and 76.29% accuracy, as per the radiologists’ findings. The comparison of position consistency results showed better performance under loose criteria than strict criteria. In terms of morphological characteristics, both the DSA and the non-DSA groups agreed well with the diagnostic results for neck width and maximum diameter, demonstrating excellent ICC reliability exceeding 0.80. The AI-generated images exhibited superior quality compared to the standard software for post-processing, while also demonstrating a significantly reduced processing time.ConclusionsThe AI-based aneurysm detection rate demonstrates a commendable performance, while the extracted morphological parameters exhibit a remarkable consistency with those assessed by radiologists, thereby showcasing significant potential for clinical application.

A rapid visualization method for detecting rotavirus A by combining nuclear acid sequence-based amplification with the CRISPR-Cas12a assay.

Introduction. Rotavirus A is the most common pathogen causing diarrhoea in children less than 5 years, leading to severe complications such as dehydration, electrolyte imbalances, acidosis, myocarditis, convulsions, pneumonia, and other life-threatening conditions.Gap statement. There is an urgent need for a rapid and efficient nucleic acid detection strategy to enable early diagnosis and treatment, preventing rotavirus transmission and associated complications.Aim. This article aimed to develop a nuclear acid sequence-based amplification (NASBA)-Cas12a system for detecting rotavirus A using fluorescence intensity or lateral flow strips.Methodology. The NASBA technology was combined with the clustered regularly interspaced short palindromic repeats-Cas12a system to establish a NASBA-Cas12a system for detecting rotavirus A.Results. The NASBA-Cas12a system could detect rotavirus A at 37 ℃ within 70 min and had no cross-reactivity with other viruses, achieving a limit of detection of 1.2 copies μl-1. This system demonstrated a sensitivity of 100%, specificity of 90%, positive predictive value of 97.22% and negative predictive value of 100%. The kappa value was 0.933, indicating that the NASBA-Cas12a system was highly consistent with reverse transcription-PCR.Conclusion. The NASBA-Cas12a system exhibited high sensitivity and specificity for detecting rotavirus A, showing great potential for clinical application.

Engineering and physicochemical characterization of a novel, stable, symmetric bispecific antibody with dual target-binding using a common light chain.

Bispecific antibodies (BsAbs) have emerged as a major class of antibody therapeutics owing to their substantial potential in disease treatment. While several BsAbs have been successfully approved in recent years, ongoing development efforts continue to focus on optimizing various BsAbs tailored to particular antigens and action mechanisms, aiming to achieve favorable physicochemical properties. BsAbs generally encounter challenges due to their unfavorable physicochemical characteristics and poor manufacturing efficiencies, highlighting the need for optimization to achieve reliable productivity and developability. Herein, we describe the development of a novel symmetric BsAb, REGULGENT™ (N-term/C-term), comprising two Fab domains, using a common light chain. The heavy chain fragment encoded two antigen-binding determinants in one chain. The design and production of REGULGENT™ (N-term/C-term) are simple owing to the use of the same light chain, which does not induce heavy and light chain mispairing, frequently observed with the asymmetric BsAb format. REGULGENT™ (N-term/C-term) exhibited high expression and low aggregation characteristics during cell culture and stress treatment under low pH conditions. Differential scanning calorimetric data indicated that REGULGENT™ molecules had high conformational stability, similar to that of stabilized monoclonal antibodies. Surface plasmon resonance data showed that REGULGENT™ (N-term/C-term) could bind to two antigens simultaneously and exhibited a high affinity for two antigens. In summary, the symmetric BsAb format of REGULGENT™ confers its desirable IgG-like physicochemical properties, thus making it an excellent candidate for commercial development. The findings demonstrate a novel BsAb with substantial development potential for clinical applications.

A nomogram model for predicting frailty in middle-aged and elderly patients with colorectal cancer:A cross-sectional study

PurposeFrailty is a syndrome caused by multiple factors and can lead to serious consequences for middle-aged and elderly patients with colorectal cancer. However, few studies have comprehensively explored predictors of frailty and built predictive models. Therefore, our aim is to develop and evaluate a predictive model for frailty in middle-aged and elderly patients with colorectal cancer. MethodsFrom July 2023 to February 2024, a total of 502 middle-aged and elderly patients with colorectal cancer participated in this survey. Patients were randomly divided into training and validation groups in a 7:3 ratio. Univariate and multiple logistic regression analysis were employed to identify potential predictors of frailty in these patients. A nomogram was constructed based on the predictive factors, and the model underwent internal validation. ResultsIn the training cohort, logistic regression revealed that self-perceived health, chronic pain, loneliness, depression, and health-promoting lifestyle were independent predictors of frailty. The Areas Under the Curve (AUC) of the training and validation groups were 0.845 and 0.851, respectively. The calibration curve of the nomogram demonstrated good consistency between predicted and actual probabilities. Decision curve analysis revealed good clinical benefit. ConclusionsThis study established a predictive model with satisfactory predictive ability, providing empirical evidence for the early detection and intervention of frailty in middle-aged and elderly patients with colorectal cancer. The nomogram model has significant potential for clinical application, as it can be integrated into routine oncology practice to identify high-risk patients early, allowing for timely and individualized interventions to improve patient outcomes.

Preparation of cysteine-functionalized graphene quantum dots – Zinc phthalocyanines supramolecular hybrid system and their sono-photochemical studies

Current PDT agents often suffer from low singlet oxygen quantum yields, photobleaching, and poor biocompatibility. To address these issues, we propose novel PDT agents that combine the synthesized phthalocyanines with cysteine-functionalized graphene quantum dots (cys-GQDs) for the first time. This combination aims to enhance singlet oxygen production and improve solubility in biological media. In this way, new zinc phthalocyanines with halogen substituents were synthesized for potential use in photodynamic therapy (PDT). Specifically, 2-Bromo-4-methylphenol zinc (II) phthalocyanine (2a) and 2-chloro-4-methylphenol zinc(II) phthalocyanine (2b) and their graphene quantum dots derivatives were synthesized and characterized. The photochemical, sonochemical, and sono-photochemical properties of these compounds were analyzed, focusing on their efficiency in singlet oxygen production. Our studies of the 2a@cys-GQDs and 2b@cys-GQDs conjugates demonstrated higher singlet oxygen yields, suggesting their enhanced potential for clinical applications.

Aquaporin-4 IgG Antibody Detection in Neuromyelitis Optic Spectrum Disorder

Objective: To develop a sensitive and reliable diagnostic approach for optic neuromyelitis (ONM), an overexpressing cell line capable of detecting aquaporin 4 (AQP4) antibodies was established. Subsequently, immunofluorescence was employed to detect AQP4 antibodies in serum and cerebrospinal fluid (CSF) samples from patients. Additionally, the clinical utility of AQP4 antibodies in the detection of ONM patients was analyzed. Methods: An aquaporin 4 expression plasmid was constructed and transfected into cell lines. Subsequently, indirect immunofluorescence was utilized to detect anti-AQP4 antibodies in serum and cerebrospinal fluid samples. Results: The indirect immunofluorescence detection system exhibited high sensitivity in the detection of 2241 clinical samples, with a positive rate of 16.8%. The positive proportion was in line with epidemiological data, and the positive situation was consistent with clinical symptoms. Conclusion: The engineered cell line exhibits superior detection performance for identifying antibodies present in serum and cerebrospinal fluid samples. The capability to detect anti-aquaporin 4 antibodies is pivotal for improving the efficiency of screening and diagnosing neuromyelitis optica, thereby possessing considerable potential for clinical application.

Advances in Research on Semi-Synthesis, Biotransformation and Biological Activities of Novel Derivatives from Maslinic Acid

Since ancient times, humans have turned to medicinal plants for treating various ailments and curing specific diseases, as these natural plants serve as the primary source of a range of phytochemicals, including triterpenes. Maslinic acid (MA), also known as (2α,3β)-2,3-dihydroxyolean-12-en-28-oic acid, is a pentacyclic triterpene acid present in numerous plants including olive, known for its high safety profile in humans. Recent experimental data increasingly suggests that MA exhibits diverse biological properties and therapeutic effects on various organ diseases, highlighting its significant potential for clinical applications due to its diverse potential pharmacological activities that promote health and resist various diseases, such as hypoglycemic, neuroprotective, anti-tumor, anti-inflammatory, antioxidant and multiple other biological activities. However, the undesirable pharmacokinetic properties of MA, such as high lipophilicity, pose a limitation to its application and development, impacting its bioavailability. Consequently, extensive research spanning decades has focused on structurally modifying MA to overcome these limitations and enhance its pharmacokinetic and therapeutic characteristics, leading to the identification of several potential lead compounds. In this review, we focus on the progress of research in recent years on MA with interest to its chemical and enzymatic modifications as well as the relationships between the modified structures or derivatives and their biological activities.

Multi-contrast image super-resolution with deformable attention and neighborhood-based feature aggregation (DANCE): Applications in anatomic and metabolic MRI

Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissues from different perspectives and has wide clinical applications. By utilizing the auxiliary information from reference images (Refs) in the easy-to-obtain modality, multi-contrast MRI super-resolution (SR) methods can synthesize high-resolution (HR) images from their low-resolution (LR) counterparts in the hard-to-obtain modality. In this study, we systematically discussed the potential impacts caused by cross-modal misalignments between LRs and Refs and, based on this discussion, proposed a novel deep-learning-based method with Deformable Attention and Neighborhood-based feature aggregation to be Computationally Efficient (DANCE) and insensitive to misalignments. Our method has been evaluated in two public MRI datasets, i.e., IXI and FastMRI, and an in-house MR metabolic imaging dataset with amide proton transfer weighted (APTW) images. Experimental results reveal that our method consistently outperforms baselines in various scenarios, with significant superiority observed in the misaligned group of IXI dataset and the prospective study of the clinical dataset. The robustness study proves that our method is insensitive to misalignments, maintaining an average PSNR of 30.67 dB when faced with a maximum range of ±9°and ±9 pixels of rotation and translation on Refs. Given our method’s desirable comprehensive performance, good robustness, and moderate computational complexity, it possesses substantial potential for clinical applications.