Iron-driven self-assembly of dopamine into dumbbell-shaped nanozyme for visual and rapid detection of norfloxacin on a smartphone-assisted platform.

Antibiotics in aquatic environments raise health concerns. Therefore, the rapid, on-site, and accurate detection of antibiotic residues is crucial for protecting the environment and human health. Herein, a dumbbell-shaped iron (Fe3+)-dopamine coordination nanozyme (Fe-DCzyme) was developed via an iron-driven self-assembly strategy. It exhibited excellent peroxidase-like activity, which can be quenched by adding l-cysteine to prevent Fe3+/Fe2+ electron transfer but restored by adding norfloxacin. Given the 'On-Off-On' effect of peroxidase-like activity, Fe-DCzyme was used as a colourimetric sensor for norfloxacin detection, and showed a wide linear range from 0.05 to 6.00 µM (R2 = 0.9950) and LOD of 27.0 nM. A portable smartphone-assisted detection platform using Fe-DCzyme was also designed to convert norfloxacin-induced color changes into RGB values as well as to realise the rapid, on-site and quantitative detection of norfloxacin. A good linear relation (0.10-6.00 µM) and high sensitivity (LOD = 79.3 nM) were achieved for the smartphone-assisted Fe-DCzyme detection platform. Its application was verified using norfloxacin spiking methods with satisfactory recoveries (92.66%-119.65%). Therefore, the portable smartphone-assisted Fe-DCzyme detection platform with low cost and easy operation can be used for the rapid, on-site and visual quantitative detection of antibiotic residues in water samples.

KCNQ1 rs2237895 gene polymorphism increases susceptibility to type 2 diabetes mellitus in Asian populations.

BACKGROUND: The association of single nucleotide polymorphism of KCNQ1 gene rs2237895 with type 2 diabetes mellitus (T2DM) is currently controversial. It is unknown whether this association can be gene realized across different populations. AIM: To determine the association of KCNQ1 rs2237895 with T2DM and provide reliable evidence for genetic susceptibility to T2DM. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library, Medline, Baidu Academic, China National Knowledge Infrastructure, China Biomedical Liter-ature Database, and Wanfang to investigate the association between KCNQ1 gene rs2237895 and the risk of T2DM up to January 12, 2022. Review Manager 5.4 was used to analyze the association of the KCNQ1 gene rs2237895 polymorphism with T2DM and to evaluate the publication bias of the selected literature. RESULTS: Twelve case-control studies (including 11273 cases and 11654 controls) met our inclusion criteria. In the full population, allelic model [odds ratio (OR): 1.19; 95% confidence interval (95%CI): 1.09-1.29; P < 0.0001], recessive model (OR: 1.20; 95%CI: 1.11-1.29; P < 0.0001), dominant model (OR: 1.27. 95%CI: 1.14-1.42; P < 0.0001), and codominant model (OR: 1.36; 95%CI: 1.15-1.60; P = 0.0003) (OR: 1.22; 95%CI: 1.10-1.36; P = 0.0002) indicated that the KCNQ1 gene rs2237895 polymorphism was significantly correlated with susceptibility to T2DM. In stratified analysis, this association was confirmed in Asian populations: allelic model (OR: 1.25; 95%CI: 1.13-1.37; P < 0.0001), recessive model (OR: 1.29; 95%CI: 1.11-1.49; P = 0.0007), dominant model (OR: 1.35; 95%CI: 1.20-1.52; P < 0.0001), codominant model (OR: 1.49; 95%CI: 1.22-1.81; P < 0.0001) (OR: 1.26; 95%CI: 1.16-1.36; P < 0.0001). In non-Asian populations, this association was not significant: Allelic model (OR: 1.06, 95%CI: 0.98-1.14; P = 0.12), recessive model (OR: 1.04; 95%CI: 0.75-1.42; P = 0.83), dominant model (OR: 1.06; 95%CI: 0.98-1.15; P = 0.15), codominant model (OR: 1.08; 95%CI: 0.82-1.42; P = 0.60. OR: 1.15; 95%CI: 0.95-1.39; P = 0.14). CONCLUSION: KCNQ1 gene rs2237895 was significantly associated with susceptibility to T2DM in an Asian population. Carriers of the C allele had a higher risk of T2DM. This association was not significant in non-Asian populations.

HMGB1 prefers to interact with structural RNAs and regulates rRNA methylation modification and translation in HeLa cells.

BACKGROUND: High-mobility group B1 (HMGB1) is both a DNA binding nuclear factor modulating transcription and a crucial cytokine that mediates the response to both infectious and noninfectious inflammation such as autoimmunity, cancer, trauma, and ischemia reperfusion injury. HMGB1 has been proposed to control ribosome biogenesis, similar as the other members of a class of HMGB proteins. RESULTS: Here, we report that HMGB1 selectively promotes transcription of genes involved in the regulation of transcription, osteoclast differentiation and apoptotic process. Improved RNA immunoprecipitation by UV cross-linking and deep sequencing (iRIP-seq) experiment revealed that HMGB1 selectively bound to mRNAs functioning not only in signal transduction and gene expression, but also in axon guidance, focal adhesion, and extracellular matrix organization. Importantly, HMGB1-bound reads were strongly enriched in specific structured RNAs, including the domain II of 28S rRNA, H/ACA box snoRNAs including snoRNA63 and scaRNAs. RTL-P experiment showed that overexpression of HMGB1 led to a decreased methylation modification of 28S rRNA at position Am2388, Cm2409, and Gm2411. We further showed that HMGB1 overexpression increased ribosome RNA expression levels and enhanced protein synthesis. CONCLUSION: Taken together, our results support a model in which HMGB1 binds to multiple RNA species in human cancer cells, which could at least partially contribute to HMGB1-modulated rRNA modification, protein synthesis function of ribosomes, and differential gene expression including rRNA genes. These findings provide additional mechanistic clues to HMGB1 functions in cancers and cell differentiation.

GDF1 ameliorates cognitive impairment induced by hearing loss.

Hearing loss is associated with an increased risk of Alzheimer disease (AD). However, the mechanisms of hearing loss promoting the onset of AD are poorly understood. Here we show that hearing loss aggravates cognitive impairment in both wild-type mice and mouse models of AD. Embryonic growth/differentiation factor 1 (GDF1) is downregulated in the hippocampus of deaf mice. Knockdown of GDF1 mimics the detrimental effect of hearing loss on cognition, while overexpression of GDF1 in the hippocampus attenuates the cognitive impairment induced by deafness. Strikingly, overexpression of GDF1 also attenuates cognitive impairment in APP/PS1 transgenic mice. GDF1 activates Akt, which phosphorylates asparagine endopeptidase and inhibits asparagine endopeptidase-induced synaptic degeneration and amyloid-ß production. The expression of GDF1 is downregulated by the transcription factor CCAAT-enhancer binding protein-ß. These findings indicate that hearing loss could promote AD pathological changes by inhibiting the GDF1 signaling pathway; thus, GDF1 may represent a therapeutic target for AD.

Lercanidipine's Antioxidative Effect Prevents Noise-Induced Hearing Loss.

Noise-induced hearing loss (NIHL) is a prevalent form of adult hearing impairment, characterized by oxidative damage to auditory sensory hair cells. Although certain dihydropyridines, the L-type calcium channel blockers, exhibit protective properties against such damage, the ability of third-generation dihydropryidines like lercanidipine to mitigate NIHL remains unclear.We utilized glucose oxidase (GO)-treated OC1 cell lines and cochlear explants to evaluate the protective influence of lercanidipine on hair cells. To further investigate its effectiveness, we exposed noise-stimulated mice in vivo and analyzed their hearing thresholds. Additionally, we assessed the antioxidative capabilities of lercanidipine by examining oxidation-related enzyme expression and levels of oxidative stress markers, including 3-nitrotyrosine (3NT) and 4-hydroxynonenal (4HNE). Our findings demonstrate that lercanidipine significantly reduces the adverse impacts of GO on both OC-1 cell viability (0.3 to 2.5 µM) and outer hair cell (OHC) survival in basal turn cochlear explants (7 µM). These results are associated with increased mRNA expression of antioxidant enzyme genes (HO-1, SOD1/2, and Txnrd1), along with decreased expression of oxidase genes (COX-2, iNOS). Crucially, lercanidipine administration prior to, and following, noise exposure effectively ameliorates NIHL, as evidenced by lowered hearing thresholds and preserved OHC populations in the basal turn, 14 days post-noise stimulation at 110 dB SPL. Moreover, our observations indicate that lercanidipine's antioxidative action persists even three days after simultaneous drug and noise treatments, based on 3-nitrotyrosine and 4-hydroxynonenal immunostaining in the basal turn. Based on these findings, we propose that lercanidipine has the capacity to alleviate NIHL and safeguard OHC survival in the basal turn, potentially via its antioxidative mechanism. These results suggest that lercanidipine holds promise as a clinically viable option for preventing NIHL in affected individuals.

Research on Indoor Environment Prediction of Pig House Based on OTDBO-TCN-GRU Algorithm.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO-TCN-GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO-TCN-GRU model, OTDBO-TCN-GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO-TCN-GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO-TCN-GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO-TCN-GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.

Case report: A case of novel homozygous LRBA variant induced by chromosomal segmental uniparental disomy - genetic and clinical insights.

Objective: The study aims to report a rare case of a novel homozygous variant in the LRBA gene, originating from uniparental disomy of paternal origin. This case contributes new clinical data to the LRBA gene variant database. Methods: The study details the case of a 2-year-old child diagnosed in May 2023 at our center with a homozygous LRBA gene variant. Detailed clinical data of the patient were collected, including whole-exome sequencing of peripheral blood mononuclear cells, with parental genetic verification. Results: The child presented with recurrent respiratory infections and chronic neutropenia, progressing to pancytopenia. Imaging showed splenomegaly and enlarged lymph nodes in the axillary and abdominal regions. Peripheral blood lymphocyte count revealed reduced B cells and NK cells. Elevated cytokine levels of IFN-α and IFN-r were observed. Whole-exome sequencing revealed a nonsense homozygous variant in the LRBA gene, specifically c.2584C>T (p.Gln862Ter). The father exhibited a heterozygous variant at this locus, while no variant was found in the mother. Sample analysis indicated characteristics of uniparental disomy. According to the guidelines of the American College of Medical Genetics and Genomics (ACMG), this variant is preliminarily classified as "Likely pathogenic". Currently, there are no reports in academic literature regarding this specific variant site. Conclusion: LRBA gene variants can lead to a rare inborn error of immunity disease. The c.2584C>T (p.Gln862Ter) variant in exon 22 of the LRBA gene is a newly identified pathogenic variant, and the homozygous variant caused by uniparental disomy is exceedingly rare. This case represents the second global report of an LRBA gene function loss due to uniparental disomy abnormalities.

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.

Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

Abnormal Innervation, Demyelination, and Degeneration of Spiral Ganglion Neurons as Well as Disruption of Heminodes are Involved in the Onset of Deafness in Cx26 Null Mice.

GJB2 gene mutations are the most common causes of autosomal recessive non-syndromic hereditary deafness. For individuals suffering from severe to profound GJB2-related deafness, cochlear implants have emerged as the sole remedy for auditory improvement. Some previous studies have highlighted the crucial role of preserving cochlear neural components in achieving favorable outcomes after cochlear implantation. Thus, we generated a conditional knockout mouse model (Cx26-CKO) in which Cx26 was completely deleted in the cochlear supporting cells driven by the Sox2 promoter. The Cx26-CKO mice showed severe hearing loss and massive loss of hair cells and Deiter's cells, which represented the extreme form of human deafness caused by GJB2 gene mutations. In addition, multiple pathological changes in the peripheral auditory nervous system were found, including abnormal innervation, demyelination, and degeneration of spiral ganglion neurons as well as disruption of heminodes in Cx26-CKO mice. These findings provide invaluable insights into the deafness mechanism and the treatment for severe deafness in Cx26-null mice.

Enhanced performance of fiber-based perovskite solar cell achieved by multi-functional high polymer doping strategy.

Toward the realization of efficient, durable, and sustainable fiber-based perovskite solar cells (fb-PSCs), a comprehensive optimization strategy focused on enhancing electron transport layer (ETL), perovskite (PVK) photovoltaic layer, and hole transport layer (HTL) is presented. A champion PCE of 10.66 % with 37.9 % relative enhancement over control has been achieved in the optimized fb-PSC. A significantly improved mechanical resilience and storage durability are also recorded. Decorating the SnO2 ETL with methylammonium lead triiodide (MAPbI3) strengthened the ETL/PVK interfacial integrity, and doping the MAPbI3 layer with the multi-functional polymer of PJ71 remarkably enhanced the PVK layer's crystallization quality, and effectively passivated the grain boundary defects. A CO2 pre-treatment of the spiro-OMeTAD HTL enhanced its hole conductivity. It is the synergetic combination of these methodologies that mutually contributed to the performance boost of the fb-PSC. The phenomenological model based on layer conductance shows that the PVK layer chiefly influences the device's anti-bending ability, followed by the ETL, and HTL the least impact. To further enhance the PCE of fb-PSCs, optimizing the interface and minimizing the stress-induced defects are essential. These measures, coupled with increasing carrier diffusion length and reducing surface recombination, are key to advancing the fb-PSC performance. An encapsulation with polyolefin elastomer substantially reduced the potential lead leakage of the device, and facilitated its eco-friendly application.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning.

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

The multilevel extensive diversity across the cynomolgus macaque captured by ultra-deep adaptive immune receptor repertoire sequencing.

The extent to which AIRRs differ among and within individuals remains elusive. Via ultra-deep repertoire sequencing of 22 and 25 tissues in three cynomolgus macaques, respectively, we identified 84 and 114 novel IGHV and TRBV alleles, confirming 72 (85.71%) and 100 (87.72%) of them. The heterogeneous V gene usage patterns were influenced, in turn, by genetics, isotype (for BCRs only), tissue group, and tissue. A higher proportion of intragroup shared clones in the intestinal tissues than those in other tissues suggests a close intra-intestinal adaptive immunity network. Significantly higher mutation burdens in the public clones and the inter-tissue shared IgM and IgD clones indicate that they might target the shared antigens. This study reveals the extensive heterogeneity of the AIRRs at various levels and has broad fundamental and clinical implications. The data generated here will serve as an invaluable resource for future studies on adaptive immunity in health and diseases.

LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram.

Although the remission of self-limited epilepsy with centrotemporal spikes (SeLECTS) usually occurs by adolescence, deficits in cognition and behavior are not uncommon. Several functional magnetic resonance imaging (fMRI) studies have revealed connectivity disturbances in patients with SeLECTS associated with cognitive impairment. However, the disadvantages of fMRI are expensive, time-consuming, and motion sensitive. In the current study, we used a partial directed coherence (PDC) method to analyze electroencephalogram (EEG) for exploring brain connectivity in patients with SeLECTS. This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in the controls than in the patients with SeLECTS, whereas the inflow connectivity in the MIF_L area 4 was significantly higher in the patients with SeLECTS than in the controls. Our proposed approach of combining EEG with PDC provides a convenient and useful tool for investigating functional connectivity in patients with SeLECTS. This approach is time-saving and inexpensive compared with fMRI, but it achieves similar results to fMRI.

Metallic Materials for Bone Repair.

Repair of large bone defects caused by trauma or disease poses significant clinical challenges. Extensive research has focused on metallic materials for bone repair because of their favorable mechanical properties, biocompatibility, and manufacturing processes. Traditional metallic materials, such as stainless steel and titanium alloys, are widely used in clinics. Biodegradable metallic materials, such as iron, magnesium, and zinc alloys, are promising candidates for bone repair because of their ability to degrade over time. Emerging metallic materials, such as porous tantalum and bismuth alloys, have gained attention as bone implants owing to their bone affinity and multifunctionality. However, these metallic materials encounter many practical difficulties that require urgent improvement. This article systematically reviews and analyzes the metallic materials used for bone repair, providing a comprehensive overview of their morphology, mechanical properties, biocompatibility, and in vivo implantation. Furthermore, the strategies and efforts made to address the short-comings of metallic materials are summarized. Finally, the perspectives for the development of metallic materials to guide future research and advancements in clinical practice are identified.

Liquid Metal Memory.

Storage systems are vital components of electronic devices, while significant challenges persist in achieving flexible memory due to the limitations of existing storage methodologies. Inspired by the polarization and depolarization mechanisms in the human brain, here a novel class of storage principles is proposed and achieve a fully flexible memory through introducing the oxidation and deoxidation behaviors of liquid metals. Specifically, reversible electrochemical oxidation is utilized to modulate the overall conductivity of the target liquid metals, creating a substantial 11-order resistance difference for binary data storage. To obtain the best storage performance, systematic optimizations of multiple parameters are conducted. Conceptual experiments demonstrate the memory's stability under extreme deformations (100% stretching, 180° bending, 360° twisting). Further tests reveal that the memory performs better when its unit size gets smaller, warranting superior integrability. Finally, a complete storage system achieves remarkable performance metrics, including rapid storage speed (>33 Hz), long data retention capacity (>43200 s), and stable repeatable operation (>3500 cycles). This groundbreaking method not only overcomes the inherent rigidity limitations of existing electronic storage units but also opens new possibilities for innovating neuromorphic devices, offering fundamental and practical avenues for future applications in soft robotics, wearable electronics, and bio-inspired artificial intelligence systems.

Flexible rotation of linear polarization conversion with a frequency selective surface.

A high-efficiency transmitted polarization converter based on a frequency selective surface (FSS) is proposed in this paper. The FSS-based polarization converter (FSS-PC) is designed based on receiving-via-transmitting (RVT) structure. The receiving and transmitting antenna structures are interconnected by the transmission line, designed in the form of metallized via holes. For any linearly polarized (LP) electromagnetic wave, our proposed FSS-PC has the capability to convert it into another LP electromagnetic wave. This converted wave will have a counterclockwise rotation angle of 2φ relative to the incident wave at 11 GHz. This is achieved by adjusting the relative azimuth φ between the polarization plane of the incident LP wave's electric field and the converter. Meanwhile, the FSS-PC can achieve exceptionally high polarization conversion above -0.30 dB at the central frequency of 11 GHz. Furthermore, as the azimuth of the incident electric field varies, this high-efficiency polarization conversion capability remains stable. The prototype has been fabricated and measured, and the measured results agree well with the simulated ones, thus confirming the effectiveness of the proposed design.

Co-existence of KMT2A::SEPTIN6 fusion and DIS3 variant in a pediatric case with acute myeloid leukemia: a case report and literature review.

The lysine(K)-specific methyltransferase 2A gene (KMT2A), previously known as mixed lineage leukemia (MLL), frequently rearranged in acute leukemia, belongs to one of the most promiscuous genes and has been found fused to more than 80 different partners. KMT2A::SEPTIN6 fusion is a relatively uncommon rearrangement observed in pediatric acute myeloid leukemia (AML) patients, some of which may harbor other mutations. We herein report a case of AML-M4-infant with KMT2A::SEPTIN6 fusion and DIS3 variant. The 8-month-old girl presented with leukocytosis, anemia and thrombocytopenia. A bone marrow smear disclosed that 64% of the total nucleated cells were blasts. Karyotype analysis showed 46,X,t(X;11)(q24;q23)[10]/46,XX[10]. Fluorescence in situ hybridization analysis suggested a possible break in the KMT2A gene. After whole transcriptome sequencing, Exon 9 of KMT2A was fused in-frame with Exon 2 of SEPTIN6. This is a typical type of chromosomal rearrangement leading to the KMT2A::SEPTIN6 fusion. Meanwhile, DIS3 variant [c.2065C>T, p.R689X, variant allele frequency (VAF): 39.8%] was identified. KMT2A::SEPTIN6 fusion has been associated with the pathogenesis of AML, whereas DIS3 variants are relatively rare genetic events in pediatric AML. Regrettably, the relatives disagreed with the combination chemotherapy, and the patient eventually died of progressive disease. In conclusion, our findings provide a foundation for a better understanding of the genotypic profile of KMT2A::SEPTIN6 associated AML, and the co-existence of KMT2A::SEPTIN6 and DIS3 variant might contribute to the disease progression and transformation of AML.

Liver transcriptomic and proteomic analyses provide new insight into the pathogenesis of liver fibrosis in mice.

BACKGROUND: Liver fibrosis (LF) is a kind of progressive liver injury reaction. The goal of this study was to achieve a more detailed understanding of the molecular changes in response to CCl4-induced LF through the identification of a differentially expressed liver transcriptomic and proteomic. RESULTS: A total of 1224 differentially expressed genes (DEGs) and 302 differentially expressed proteins (DEPs) were significantly identified at the transcriptomic and proteomic level, respectively, and 69 genes (hereafter "cor-DEGs-DEPs" genes) were detected at both levels. Pathway enrichment analysis showed that these cor-DEGs-DEPs genes were significantly enriched in 133 pathways. Importantly, among the cor-DEGs-DEPs genes, Gstm1, Gstm3, Ephx1 and Gstp1 were shown to be associated with metabolic pathways, and confirmed by RT-qPCR and parallel reaction monitoring (PRM) verification. CONCLUSIONS: Through the combined analysis of transcriptomic and proteomic data, this study provides valuable insights into the potential mechanism of the pathogenesis of LF, and lays a theoretical foundation for the further development of targeted therapy for LF.