DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Structural disruption in subjective cognitive decline and mild cognitive impairment.

Subjective cognitive decline (SCD) marks the initial stage in Alzheimer's disease continuum. Nonetheless, current research findings regarding brain structural changes in the SCD are inconsistent. In this study, 37 SCD patients, 28 mild cognitive impairment (MCI) patients, and 42 healthy controls (HC) were recruited to investigate structural alterations. Morphological and microstructural differences among the three groups were analyzed based on T1- and diffusion-weighted images, correlating them with neuropsychological assessments. Additionally, classification analysis was performed by using support vector machines (SVM) categorize participants into three groups based on MRI features. Both SCD and MCI showed decreased volume in left inferior parietal lobe (IPL) compared to HC, while SCD showed altered morphologies in the right inferior temporal gyrus (ITG), right insula and right amygdala, and microstructures in fiber tracts of the right ITG, lateral occipital cortex (LOC) and insula relative to MCI. Moreover, the volume in the left IPL, right LOC, right amygdala and diffusivity value in fiber tracts of right LOC were significantly correlated with cognitive functions across all subjects. The classification models achieved an accuracy of > 0.7 (AUC = 0.8) in distinguishing the three groups. Our findings suggest that SCD and MCI share similar atrophy in the IPL but show more differences in morphological and microstructural features of cortical-subcortical areas.

Feasibility of Metrological Traceability Implementation Using the Joint Committee on Traceability in Laboratory Medicine Database Entries Including the Fulfillment of "Fit-for-Purpose" Maximum Allowable Measurement Uncertainty.

BACKGROUND: In previous publications, the Task Force on Reference Measurement System Implementation proposed a procedural approach combining a critical review of entries available in the Joint Committee on Traceability in Laboratory Medicine (JCTLM) database with a comparison of this information against analytical performance specifications for measurement uncertainty (MU) and applied it to a group of 13 measurands. CONTENT: Here we applied this approach to 17 additional measurands, of which measurements are frequently requested. The aims of the study were (a) to describe the main characteristics for implementing traceability and the potential to fulfill the maximum allowable MU (MAU) at the clinical sample level of certified reference materials and reference measurement procedures listed in the JCTLM database; (b) to discuss limitations and obstacles, if any, to the achievement of the required quality of laboratory measurements; and (c) to provide a gap analysis by highlighting what is still missing in the database. Results were integrated with those obtained in the previous study, therefore offering an overview of where we are and what is still missing in the practical application of the metrological traceability concept to 30 common biochemical tests employed in laboratory medicine. SUMMARY: Our analysis shows that for 28 out of 30 measurands, conditions exist to correctly implement metrological traceability to the International System of units and fulfill at least the MAU of the minimum quality level derived according to internationally recommended models. For 2 measurands (serum albumin and chloride), further improvements in MU of higher-order references would be necessary.

The role of OR5, which is highly expressed in the winged grain aphid Sitobion miscanthi, in specific recognition of EBF.

Winged parthenogenetic aphids are mainly responsible for migration and dispersal. Aphid alarm pheromone (E)-ß-Farnesene (EBF) has dual effects on repelling and stimulating wing differentiation in aphids. Previous studies have shown that the odorant coreceptor SmisOrco is involved in the perception of EBF by S. miscanthi; however, its EBF-specific odorant receptor (OR) and the difference between winged and wingless aphids remain unclear. In this study, the Xenopus oocyte expression system and RNAi technology were used to detect the transmission of EBF signals, and it was found that the olfactory receptor SmisOR5 is an EBF-specific OR in S. miscanthi and is specifically highly expressed in the antennae of winged aphids. Furthermore, when OR5 was silenced with dsRNA, the repellent effect of EBF was weakened, and aphids showed more active aimless movements. Therefore, as a specific OR for EBF, the high expression level of SmisOR5 in winged aphids suggests a molecular basis for its high sensitivity to EBF. This study advances our understanding of the molecular mechanisms of aphid EBF perception and provides novel ideas for effective management and prevention of the migration of winged aphids.

Novel C. elegans models of Lewy body disease reveal pathological protein interactions and widespread miRNA dysregulation.

Lewy body diseases (LBD) comprise a group of complex neurodegenerative conditions originating from accumulation of misfolded alpha-synuclein (α-syn) in the form of Lewy bodies. LBD pathologies are characterized by α-syn deposition in association with other proteins such as Amyloid ß (Aß), Tau, and TAR-DNA-binding protein. To investigate the complex interactions of these proteins, we constructed 2 novel transgenic overexpressing (OE) C. elegans strains (α-synA53T;Taupro-agg (OE) and α-synA53T;Aß1-42;Taupro-agg (OE)) and compared them with previously established Parkinson's, Alzheimer's, and Lewy Body Dementia disease models. The LBD models presented here demonstrate impairments including uncoordinated movement, egg-laying deficits, altered serotonergic and cholinergic signaling, memory and posture deficits, as well as dopaminergic neuron damage and loss. Expression levels of total and prone to aggregation α-syn protein were increased in α-synA53T;Aß1-42 but decreased in α-synA53T;Taupro-agg animals when compared to α-synA53T animals suggesting protein interactions. These alterations were also observed at the mRNA level suggesting a pre-transcriptional mechanism. miRNA-seq revealed that cel-miR-1018 was upregulated in LBD models α-synA53T, α-synA53T;Aß1-42, and α-synA53T;Taupro-agg compared with WT. cel-miR-58c was upregulated in α-synA53T;Taupro-agg but downregulated in α-synA53T and α-synA53T;Aß1-42 compared with WT. cel-miR-41-3p and cel-miR-355-5p were significantly downregulated in 3 LBD models. Our results obtained in a model organism provide evidence of interactions between different pathological proteins and alterations in specific miRNAs that may further exacerbate or ameliorate LBD pathology.

Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.

With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we still face a series of challenges, including repetitive fragments, inverted repeats, low sequencing coverage, and the limitations of sequencing technologies. These challenges lead to unknown gaps in small genomes, hindering complete genome assembly. Although there are many existing assembly software options, they do not fully utilize the potential of artificial intelligence technologies, resulting in limited improvement in gap filling. Here, we propose a novel method, DLGapCloser, based on deep learning, aimed at assisting traditional tools in further filling gaps in small genomes. Firstly, we created four datasets based on the original genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla. To further extract effective information from the gene sequences, we also added homologous genomes to enrich the datasets. Secondly, we proposed the DGCNet model, which effectively extracts features and learns context from sequences flanking gaps. Addressing issues with early pruning and high memory usage in the Beam Search algorithm, we developed a new prediction algorithm, Wave-Beam Search. This algorithm alternates between expansion and contraction phases, enhancing efficiency and accuracy. Experimental results showed that the Wave-Beam Search algorithm improved the gap-filling performance of assembly tools by 7.35%, 28.57%, 42.85%, and 8.33% on the original results. Finally, we established new gap-filling standards and created and implemented a novel evaluation method. Validation on the genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla showed that DLGapCloser increased the number of filled gaps by 8.05%, 15.3%, 1.4%, and 7% compared to traditional assembly tools.

Laryngeal leiomyoma: A case report and review of literature.

BACKGROUND: Leiomyomas (LMs) are mesenchymal tumors that arise from smooth muscle cells. LMs most commonly arise in organs with an abundance of smooth muscle such as the uterus and gastrointestinal tract. Conversely, LMs are rarely detected in the head and neck region. In this study, we report a rare case of laryngeal LM (LLM) and summarized the clinical characteristics of reported LLMs to help clinicians better understand this rare disease and improve its diagnosis, treatment, and postoperative course. CASE SUMMARY: A 49-year-old man was admitted to our ENT outpatient clinic with a chief complaint of pharynx discomfort for 2 months. Laryngoscopy performed under topical anesthesia revealed a solitary, pink mass at the tubercle of epiglottis. Surgery via laryngeal endoscopy was performed under general anesthesia, and the lesion was excised easily. Positive immunohistochemical staining for desmin and smooth-muscle actin indicated a smooth muscle origin and the diagnosis was laryngeal leiomyoma. After surgery, the patient's condition was stable, and he was discharged 2 d after surgery. During the 1-year postoperative period, the patient's condition remained stable without evidence of recurrence. CONCLUSION: Surgical resection is the preferred treatment for LLMs, its early diagnosis and differential diagnosis have important clinical significance.

Phenotyping of single plant cells on a microfluidic cytometry platform with fluorescent, mechanical, and electrical modules.

Compared to animal cells, phenotypic characterization of single plant cells on microfluidic platforms is still rare. In this work, we collated population statistics on the morphological, biochemical, physical and electrical properties of Arabidopsis protoplasts under different external and internal conditions, using progressively improved microfluidic platforms. First, we analyzed the different effects of three phytohormones (auxin, cytokinin and gibberellin) on the primary cell wall (PCW) regeneration process using a microfluidic flow cytometry platform equipped with a single-channel fluorescence sensor. Second, we correlated the intracellular reactive oxygen species (ROS) level induced by heavy metal stress with the concurrent PCW regeneration process by using a dual-channel fluorescence sensor. Third, by integrating contraction channels, we were able to effectively discriminate variations in cell size while monitoring the intensity of intracellular ROS signaling. Fourth, by combining an electrical impedance electrode with the contraction channel, we analyzed the differences in electrical and mechanical properties of wild-type and mutant plant cells before and after primary cell wall regeneration. Overall, our work demonstrates the feasibility and sensitivity of microfluidic flow cytometry in high-throughput phenotyping of plant cells and provides a reference for assessing metabolic and physiological indicators of individual plant cells in multiple dimensions.

scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https://github.com/Masonze/scLEGA-main.

scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.

UBE2S promotes glycolysis in hepatocellular carcinoma by enhancing E3 enzyme-independent polyubiquitination of VHL.

Background/Aims: Ubiquitination is widely involved in the progression of hepatocellular carcinoma (HCC) by regulating various cellular processes. However, systematic strategies for screening core ubiquitin-related genes, clarifying their functions and mechanisms, and ultimately developing potential therapeutics for patients with HCC are still lacking. Methods: Cox and LASSO regression analyses were performed to construct a ubiquitin-related gene prediction model for HCC. Loss- and gain-of-function studies, transcriptomic and metabolomics analysis were used to explore the function and mechanism of UBE2S on HCC cell glycolysis and growth. Results: Based on 1423 ubiquitin-related genes, a four-gene signature was successfully constructed to evaluate the prognosis of patients with HCC. UBE2S was identified in this signature with the potential to predict the survival of patients with HCC. E2F2 transcriptionally upregulated UBE2S expression by directly binding to its promoter. UBE2S positively regulated glycolysis in a HIF-1α-dependent manner, thus promoting the proliferation of HCC cells. Mechanistically, UBE2S enhanced K11-linkage polyubiquitination at lysine residues 171 and 196 of VHL independent of E3 ligase, thereby indirectly stabilizing HIF-1α protein levels by mediating the degradation of VHL by the proteasome. In particular, the combination of cephalomannine, a small molecule compound that inhibits the expression of UBE2S, and PX-478, an inhibitor of HIF-1α, significantly improved the anti-tumor efficacy. Conclusions: UBE2S is identified as a key biomarker in HCC among the thousands of ubiquitin-related genes and promotes glycolysis by E3 enzyme-independent ubiquitination, thus serving as a therapeutic target for the treatment of HCC.

A Plasmonic Optoelectronic Resistive Random-Access Memory for In-Sensor Color Image Cryptography.

The optoelectronic resistive random-access memory (RRAM) with the integrated function of perception, storage and intrinsic randomness displays promising applications in the hardware level in-sensor image cryptography. In this work, 2D hexagonal boron nitride based optoelectronic RRAM is fabricated with semitransparent noble metal (Ag or Au) as top electrodes, which can simultaneous capture color image and generate physically unclonable function (PUF) key for in-sensor color image cryptography. Surface plasmons of noble metals enable the strong light absorption to realize an efficient modulation of filament growth at nanoscale. Resistive switching curves show that the optical stimuli can impede the filament aggregation and promote the filament annihilation, which originates from photothermal effects and photogenerated hot electrons in localized surface plasmon resonance of noble metals. By selecting noble metals, the optoelectronic RRAM array can respond to distinct wavelengths and mimic the biological dichromatic cone cells to perform the color perception. Due to the intrinsic and high-quality randomness, the optoelectronic RRAM can produce a PUF key in every exposure cycle, which can be applied in the reconfigurable cryptography. The findings demonstrate an effective strategy to build optoelectronic RRAM for in-sensor color image cryptography applications.

Against all odds: The road to success in the development of human immune reconstitution mice.

The mouse genome has a high degree of homology with the human genome, and its physiological, biochemical, and developmental regulation mechanisms are similar to those of humans; therefore, mice are widely used as experimental animals. However, it is undeniable that interspecies differences between humans and mice can lead to experimental errors. The differences in the immune system have become an important factor limiting current immunological research. The application of immunodeficient mice provides a possible solution to these problems. By transplanting human immune cells or tissues, such as peripheral blood mononuclear cells or hematopoietic stem cells, into immunodeficient mice, a human immune system can be reconstituted in the mouse body, and the engrafted immune cells can elicit human-specific immune responses. Researchers have been actively exploring the development and differentiation conditions of host recipient animals and grafts in order to achieve better immune reconstitution. Through genetic engineering methods, immunodeficient mice can be further modified to provide a favorable developmental and differentiation microenvironment for the grafts. From initially only being able to reconstruct single T lymphocyte lineages, it is now possible to reconstruct lymphoid and myeloid cells, providing important research tools for immunology-related studies. In this review, we compare the differences in immune systems of humans and mice, describe the development history of human immune reconstitution from the perspectives of immunodeficient mice and grafts, and discuss the latest advances in enhancing the efficiency of human immune cell reconstitution, aiming to provide important references for immunological related researches.

Decomposition analysis on the equity of health examination utilization for the middle-aged and elderly people in China: based on longitudinal CHARLS data from 2011 to 2018.

BACKGROUND: This study aimed to investigate the utilization rate and equity of health examination service among the middle-aged and elderly population in China from 2011 to 2018. The contribution of various determinants to the inequity in health examination service utilization was also examined. METHODS: Data from the China Health and Retirement Longitudinal Survey (CHARLS) were analyzed to assess the health examination service utilization rate among the middle-aged and elderly population. A concentration curve and concentration index were employed to measure the equity of health examination service utilization and decomposed into its determining factors. Horizontal inequity index was applied to evaluate the trends in equity of health examination service. RESULTS: The health examination service utilization rates among the middle-aged and elderly population were 29.45%, 20.69%, 25.40%, and 32.05% in 2011, 2013, 2015, and 2018, respectively. The concentration indexes for health examination service utilization were 0.0080 (95% CI: - 0.0084, 0.0244), 0.0155 (95% CI: - 0.0054, 0.0363), 0.0095 (95% CI: - 0.0088, 0.0277), and - 0.0100 (95% CI: - 0.0254, 0.0054) from 2011 to 2018, respectively. The horizontal inequity index was positive from 2011 to 2018, evidencing a pro-rich inequity trend. Age, residence, education, region, and economic status were the major identified contributors influencing the equity of health examination service utilization. CONCLUSIONS: A pro-rich inequity existed in health examination service utilization among the middle-aged and elderly population in China. Reducing the wealth and regional gap, providing equal educational opportunities, and strengthening the capacity for chronic disease prevention and control are crucial for reducing the inequity in health examination service utilization.

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues.

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Macromers for Encapsulating Perovskite Photovoltaics and Achieving High Stability.

Perovskite solar cells (pero-SCs) are highly unstable even under trace water. Although the blanket encapsulation (BE) strategy applied in the industry can effectively block moisture invasion, the commercial UV-curable adhesives (UVCAs) for BE still trigger power conversion efficiency deterioration, and the degradation mechanism remains unknown. For the first time, the functions of commercial UVCAs are revealed in BE-processed pero-SCs, where the small-sized monomer easily permeates to the perovskite surface, forming an insulating barrier to block charge extraction, while the high-polarity moiety can destroy perovskite lattice. To solve these problems, a macromer, named PIBA is carefully designed, by grafting two acrylate terminal groups on the highly gastight polyisobutylene and realizes an increased molecular diameter as well as avoided high-polarity groups. The PIBA macromer can stabilize on pero-SCs and then sufficiently crosslink, forming a compact and stable network under UV light without sacrificing device performance during the BE process. The resultant BE devices show negligible efficiency loss after storage at 85% relative humidity for 2000 h. More importantly, these devices can even reach ISO 20653:2013 Degrees of protection IPX7 standard when immersed in one-meter-deep water. This BE strategy shows good universality in enhancing the moisture stability of pero-SCs, irrespective of the perovskite composition or device structure.

Altered neopterin and IDO in kynurenine metabolism based on LC-MS/MS metabolomics study: Novel therapeutic checkpoints for type 2 diabetes mellitus.

BACKGROUND: This study assessed the alternations of kynurenine pathway (KP) and neopterin in type 2 diabetes mellitus (T2DM) and explored possible differential metabolites. METHODS: A fresh residual sera panel was collected from 80 healthy control (HC) individuals and 72 T2DM patients. Metabolites/ratios of interest including tryptophan (TRP), kynurenine (KYN), 5-hydroxytryptamine (5HT), kynurenic acid (KA), xanthurenic acid (XA), neopterin (NEO), KA/KYN ratio and KYN/TRP ratio were determined using a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics approach, and the difference between groups was assessed. Supervised orthogonal partial least squares-discriminant analysis and differential metabolite screening with fold change (FC) were performed to identify distinct biomarkers. The diagnostic performance of KP metabolites in T2DM was evaluated. RESULTS: Significant decreases of TRP, 5HT, KA, XA, and KA/KYN and increases of KYN/TRP and NEO in T2DM compared to HC group were observed (P < 0.05). The KP metabolites panel significantly changed between T2DM and HC groups (Q2: 0.925, P < 0.005). 5HT (FC: 0.63, P < 0.01) and NEO (FC: 3.27, P < 0.01) were proven to be distinct differential metabolites. A combined testing of fasting plasma glucose and KYN/TRP showed good value in the prediction of T2DM (AUC: 0.904, 95% CI 0.843-0.947). CONCLUSIONS: The targeted LC-MS/MS metabolomics study is a powerful tool for evaluating the status of T2DM. This study facilitated the application of KP metabolomics into future clinical practice. 5HT and NEO are promising biomarkers in T2DM. KYN/TRP was highly associated with the development of T2DM and may serve as a potential treatment target.

A hydrogel-based ratiometric fluorescent sensor relying on rhodamine B labelled AIE-featured hyperbranched poly(amido amine) for heparin detection.

The fluorescent flexible sensor for point-of-care quantification of clinical anticoagulant drug, Heparin (Hep), is still an urgent need of breakthrough. In this research, a hyperbranched poly(amido amine) (HPA) was decorated with tetraphenylethene (TPE) and Rhodamine B (RhB), constructing a ratiometric fluorescent sensor (TR-HPA) for Hep. When the sensor was exposed to Hep, the TPE units within the probe skeleton would aggregate, resulting in an increasing fluorescent emission at 483 nm. The 580 nm of fluorescence came from RhB enhance, simultaneously, due to the fluorescence resonance energy transfer. As a result, there are two good linear correlation between the fluorescence emission ratio (E483/E580) of TR-HPA and the Hep concentration over a range of 0-1.0 µM, with a low limit of detection of 3.0 nM. Furthermore, we incorporate the TR-HPA probe into a polyvinyl alcohol (PVA) hydrogel matrix to create a flexible fluorescent sensing system platform, denoted as TR-HPA/PVA. This approach offers a straightforward visual detection method by causing a fluorescence color change from pink to blue when trace amounts of Hep are present. The hydrogel-based fluorescent sensor streamlines the detection procedures for Hep in biomedical applications. It shows great potential in rapid and point-of-care human blood clotting condition monitoring, making it suitable for next-generation wearable medical devices.