Characterization of just one atom using synchrotron X-rays.

Since the discovery of X-rays by Roentgen in 1895, its use has been ubiquitous, from medical and environmental applications to materials sciences1-5. X-ray characterization requires a large number of atoms and reducing the material quantity is a long-standing goal. Here we show that X-rays can be used to characterize the elemental and chemical state of just one atom. Using a specialized tip as a detector, X-ray-excited currents generated from an iron and a terbium atom coordinated to organic ligands are detected. The fingerprints of a single atom, the L2,3 and M4,5 absorption edge signals for iron and terbium, respectively, are clearly observed in the X-ray absorption spectra. The chemical states of these atoms are characterized by means of near-edge X-ray absorption signals, in which X-ray-excited resonance tunnelling (X-ERT) is dominant for the iron atom. The X-ray signal can be sensed only when the tip is located directly above the atom in extreme proximity, which confirms atomically localized detection in the tunnelling regime. Our work connects synchrotron X-rays with a quantum tunnelling process and opens future X-rays experiments for simultaneous characterizations of elemental and chemical properties of materials at the ultimate single-atom limit.

Universal inverse modeling of point spread functions for SMLM localization and microscope characterization.

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single-molecule localization, aberration correction and deconvolution. Here we present universal inverse modeling of point spread functions (uiPSF), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single-molecule localization microscopy (SMLM). Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system- or sample-specific characteristics, for example, the bead size, field- and depth- dependent aberrations, and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single-molecule blinking data.

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 µm3, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.

DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage.

DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity. Here, we apply the Bloom Filter, a space-efficient probabilistic data structure, to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin-yang codec is developed.

CATAD: exploring topologically associating domains from an insight of core-attachment structure.

Identifying topologically associating domains (TADs), which are considered as the basic units of chromosome structure and function, can facilitate the exploration of the 3D-structure of chromosomes. Methods have been proposed to identify TADs by detecting the boundaries of TADs or identifying the closely interacted regions as TADs, while the possible inner structure of TADs is seldom investigated. In this study, we assume that a TAD is composed of a core and its surrounding attachments, and propose a method, named CATAD, to identify TADs based on the core-attachment structure model. In CATAD, the cores of TADs are identified based on the local density and cosine similarity, and the surrounding attachments are determined based on boundary insulation. CATAD was applied to the Hi-C data of two human cell lines and two mouse cell lines, and the results show that the boundaries of TADs identified by CATAD are significantly enriched by structural proteins, histone modifications, transcription start sites and enzymes. Furthermore, CATAD outperforms other methods in many cases, in terms of the average peak, boundary tagged ratio and fold change. In addition, CATAD is robust and rarely affected by the different resolutions of Hi-C matrices. Conclusively, identifying TADs based on the core-attachment structure is useful, which may inspire researchers to explore TADs from the angles of possible spatial structures and formation process.

Superantigen-fused T cell engagers for tumor antigen-mediated robust T cell activation and tumor cell killing.

Inadequate T cell activation has severely limited the success of T cell engager (TCE) therapy, especially in solid tumors. Enhancing T cell activity while maintaining the tumor specificity of TCEs is the key to improving their clinical efficacy. However, currently, there needs to be more effective strategies in clinical practice. Here, we design novel superantigen-fused TCEs that display robust tumor antigen-mediated T cell activation effects. These innovative drugs are not only armed with the powerful T cell activation ability of superantigens but also retain the dependence of TCEs on tumor antigens, realizing the ingenious combination of the advantages of two existing drugs. Superantigen-fused TCEs have been preliminarily proven to have good (>30-fold more potent) and specific (>25-fold more potent) antitumor activity in vitro and in vivo. Surprisingly, they can also induce the activation of T cell chemotaxis signals, which may promote T cell infiltration and further provide an additional guarantee for improving TCE efficacy in solid tumors. Overall, this proof-of-concept provides a potential strategy for improving the clinical efficacy of TCEs.

TIMP2 protects against sepsis-associated acute kidney injury by cAMP/NLRP3 axis-mediated pyroptosis.

The tissue inhibitor of metalloproteinases 2 (TIMP2) has emerged as a promising biomarker for predicting the risk of sepsis-associated acute kidney injury (SA-AKI). However, its exact role in SA-AKI and the underlying mechanism remains unclear. In this study, we investigated the impact of kidney tubule-specific Timp2 knockout mice on kidney injury and inflammation. Our findings demonstrated that Timp2-knockout mice exhibited more severe kidney injury than wild-type mice, along with elevated levels of pyroptosis markers NOD-like receptor protein 3 (NLRP3), Caspase1, and gasdermin D (GSDMD) in the early stage of SA-AKI. Conversely, the expression of exogenous TIMP2 in TIMP2-knockout mice still protected against kidney damage and inflammation. In in vitro experiments, using recombinant TIMP2 protein, TIMP2 knockdown demonstrated that exogenous TIMP2 inhibited pyroptosis of renal tubular cells stimulated by lipopolysaccharide (LPS). Mechanistically, TIMP2 promoted the ubiquitination and autophagy-dependent degradation of NLRP3 by increasing intracellular cyclic adenosine monophosphate (cAMP), which mediated NLRP3 degradation through recruiting the E3 ligase MARCH7, attenuating downstream pyroptosis, and thus alleviating primary tubular cell damage. These results revealed the renoprotective role of extracellular TIMP2 in SA-AKI by attenuating tubular pyroptosis, and suggested that exogenous administration of TIMP2 could be a promising therapeutic intervention for SA-AKI treatment.NEW & NOTEWORTHY Tissue inhibitor of metalloproteinase 2 (TIMP-2) has been found to be the best biomarker for predicting the risk of sepsis-associated acute kidney injury (SA-AKI). However, its role and the underlying mechanism in SA-AKI remain elusive. The authors demonstrated in this study using kidney tubule-specific knockout mice model of SA-AKI and primary renal tubule cells stimulated with lipopolysaccharide (LPS) that extracellular TIMP-2 promoted NOD-like receptor protein 3 (NLRP3) ubiquitination and autophagy-dependent degradation by increasing intracellular cyclic adenosine monophosphate (cAMP), thus attenuated pyroptosis and alleviated renal damage.

Efficacy and safety of oral semaglutide vs sitagliptin in a predominantly Chinese population with type 2 diabetes uncontrolled with metformin: PIONEER 12, a double-blind, Phase IIIa, randomised trial.

AIMS/HYPOTHESIS: The aim of this study was to assess the efficacy and safety of oral semaglutide vs sitagliptin in a predominantly Chinese population with type 2 diabetes inadequately controlled with metformin treatment. METHODS: The Peptide Innovation for Early Diabetes Treatment (PIONEER) 12 trial was a randomised, double-dummy, active-controlled, parallel-group, Phase IIIa trial conducted over 26 weeks at 90 sites across the China region (including mainland China, Taiwan and Hong Kong) and five other countries. Adults aged ≥18 years (≥20 years in Taiwan) with a diagnosis of type 2 diabetes, HbA1c between 53 and 91 mmol/mol (inclusive) and treated with a stable daily dose of metformin were eligible for inclusion. Participants were randomised (1:1:1:1) using a web-based randomisation system to either once-daily oral semaglutide (3 mg, 7 mg or 14 mg) or once-daily oral sitagliptin 100 mg. Treatment allocation was masked to both participants and investigators. Randomisation was stratified according to whether participants were from the China region or elsewhere. The primary endpoint was change in HbA1c from baseline to week 26. The confirmatory secondary endpoint was change in body weight (kg) from baseline to week 26. All randomised participants were included in the full analysis set (FAS). All participants exposed to at least one dose of trial product were included in the safety analysis (SAS). RESULTS: Of 1839 participants screened, 1441 were randomly assigned to oral semaglutide 3 mg (n=361), 7 mg (n=360), 14 mg (n=361) or sitagliptin 100 mg (n=359) and included in the FAS. A total of 1438 participants were included in the SAS. In total, 75.2% of participants were from the China region. A total of 1372 (95.2%) participants completed the trial and 130 participants prematurely discontinued treatment (8.3%, 8.6% and 15.0% for oral semaglutide 3 mg, 7 mg and 14 mg, respectively; 4.2% for sitagliptin 100 mg). Significantly greater reductions in HbA1c from baseline to week 26 were reported for all doses of oral semaglutide vs sitagliptin 100 mg. For oral semaglutide 3 mg, 7 mg and 14 mg vs sitagliptin 100 mg, the estimated treatment differences (ETDs [95% CI]) were -2 (-4, -1) mmol/mol, -8 (-9, -6) mmol/mol and -11 (-12, -9) mmol/mol, respectively. The corresponding ETDs (95% CI) in percentage points vs sitagliptin 100 mg were -0.2 (-0.3, -0.1), -0.7 (-0.8, -0.6) and -1.0 (-1.1, -0.8), respectively. Reductions in body weight were significantly greater for all doses of oral semaglutide vs sitagliptin 100 mg (ETD [95% CI] -0.9 [-1.4, -0.4] kg, -2.3 [-2.8, -1.8] kg and -3.3 [-3.8, -2.8] kg for 3 mg, 7 mg and 14 mg, respectively). In the subpopulation of participants from the China region (75.2% of trial participants), reductions in HbA1c and body weight from baseline to week 26 were similar to those seen in the overall population. The most frequent adverse events in the semaglutide treatment arms were gastrointestinal, although these were mostly transient and mild/moderate in severity. CONCLUSIONS/INTERPRETATION: Significantly greater reductions in both HbA1c and body weight over 26 weeks were seen with oral semaglutide 3 mg, 7 mg and 14 mg than with sitagliptin 100 mg in a predominantly Chinese population with type 2 diabetes inadequately controlled with metformin treatment. Oral semaglutide was generally well tolerated, with a safety profile consistent with that seen in the global PIONEER trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT04017832. FUNDING: This trial was funded by Novo Nordisk A/S, Søborg, Denmark.

Giant Expanded Porous Metallo-Hexagons.

Molecular self-assembly is a widely recognized approach for fabricating biomimetic functional nanostructures. Here, we report the synthesis of two giant hollow coronoid-like supramolecular hexagons, H1 and H2. These hexagons feature large cavities, showcasing unique inner and outer hexagons fixed by specific connectivities for enhanced stability and high metal center density. H1 exhibits properties that can be transformed through the thermodynamic conversion of the metallopolymer formed by L1 and L2. With an edge length of 6.8 nm, H2 is one of the largest hexagons reported to date. 1D and 2D NMR, TEM, ESI-MS, and TWIM-MS experiments provided conclusive evidence for the composition and structure of the assembled hexagons. This work demonstrates the feasibility of constructing giant supramolecular architectures with precise control over their size and shape, opening up new possibilities for the design and synthesis of sophisticated supramolecules and nonbiological materials.

Fibroblastic reticular cell-derived exosomes are a promising therapeutic approach for septic acute kidney injury.

Sepsis-induced acute kidney injury (S-AKI) is highly lethal, and effective drugs for treatment are scarce. Previously, we reported the robust therapeutic efficacy of fibroblastic reticular cells (FRCs) in sepsis. Here, we demonstrate the ability of FRC-derived exosomes (FRC-Exos) to improve C57BL/6 mouse kidney function following cecal ligation and puncture-induced sepsis. In vivo imaging confirmed that FRC-Exos homed to injured kidneys. RNA-Seq analysis of FRC-Exo-treated primary kidney tubular cells (PKTCs) revealed that FRC-Exos influenced PKTC fate in the presence of lipopolysaccharide (LPS). FRC-Exos promoted kinase PINK1-dependent mitophagy and inhibited NLRP3 inflammasome activation in LPS-stimulated PKTCs. To dissect the mechanism underlying the protective role of Exos in S-AKI, we examined the proteins within Exos by mass spectrometry and found that CD5L was the most upregulated protein in FRC-Exos compared to macrophage-derived Exos. Recombinant CD5L treatment in vitro attenuated kidney cell swelling and surface bubble formation after LPS stimulation. FRCs were infected with a CD5L lentivirus to increase CD5L levels in FRC-Exos, which were then modified in vitro with the kidney tubular cell targeting peptide LTH, a peptide that binds to the biomarker protein kidney injury molecule-1 expressed on injured tubule cells, to enhance binding specificity. Compared with an equivalent dose of recombinant CD5L, the modified CD5L-enriched FRC-Exos selectively bound PKTCs, promoted kinase PINK-ubiquitin ligase Parkin-mediated mitophagy, inhibiting pyroptosis and improved kidney function by hindering NLRP3 inflammasome activation, thereby improving the sepsis survival rate. Thus, strategies to modify FRC-Exos could be a new avenue in developing therapeutics against kidney injury.

Characterization of a novel anti-PVRIG antibody with Fc-competent function that exerts strong antitumor effects via NK activation in preclinical models.

Poliovirus receptor-related immunoglobulin domain-containing protein, or PVRIG, is a newly discovered immune checkpoint that has emerged as a promising target for cancer immunotherapy. It is primarily expressed on activated T and natural killer (NK) cells, and once engaged with its ligand, PVRL2, it induces inhibitory signaling in T cells, thereby promoting the functional exhaustion of tumor-infiltrating lymphocytes (TILs). Here, we characterized IBI352g4a, a novel humanized anti-PVRIG antibody with Fc-competent function, explored the mechanism of its antitumor activity in preclinical models, and systemically evaluated the contribution of FcrR engagement to PVRIG blockade-induced antitumor activity. IBI352g4a binds to the extracellular domain of human PVRIG with high affinity (Kd = 0.53 nM) and specificity, and fully blocks the interaction between PVRIG and its ligand PVRL2. Unlike other immune checkpoints, IBI352g4a significantly induced NK cell activation and degranulation, but had a minimal effect on T-cell activation in in vitro functional assays. IBI352g4a induced strong antitumor effect in several preclinic models, through in vivo mechanism analysis we found that both NK and T cells contribute to the antitumor effect, but NK cells play predominant roles. Specifically, a single dose of IBI352g4a induced significant NK cell activation in TILs, but T-cell activation was observed only after the second dose. Moreover, the Fc effector function is critical for both NK cell activation and treatment efficacy in vitro and in vivo. Our study, for the first time, demonstrates that both NK activation and FcrR engagement are required for antitumor efficacy induced by PVRIG blockade.

A Hydrogel-coated Wood Membrane with Intelligent Oil Pollution Detection for Emulsion Separation.

Considering the potential threats posed by oily wastewater to the ecosystem, it is urgently in demand to develop efficient, eco-friendly, and intelligent oil/water separation materials to enhance the safety of the water environment. Herein, an intelligent hydrogel-coated wood (PPT/PPy@DW) membrane with self-healing, self-cleaning, and oil pollution detection performances is fabricated for the controllable separation of oil-in-water (O/W) emulsions and water-in-oil (W/O) emulsions. The PPT/PPy@DW is prepared by loading polypyrrole (PPy) particles on the delignified wood (DW) membranes, further modifying the hydrogel layer as an oil-repellent barrier. The layered porous structure and selective wettability endow PPT/PPy@DW with great separation performance for various O/W emulsions (≥98.69% for separation efficiency and ≈1000 L m-2 h-1 bar-1 for permeance). Notably, the oil pollution degree of PPT/PPy@DW can be monitored in real-time based on the changed voltage generated during O/W emulsion separation, and the oil-polluted PPT/PPy@DW can be self-cleaned by soaking in water to recover its separation performance. The high affinity of PPT/PPy@DW for water makes it effective in trapping water from the mixed surfactant-stabilized W/O emulsions. The prepared eco-friendly and low-cost multifunctional hydrogel wood membrane shows promising potential in on-demand oil/water separation and provides new ideas for the functional improvement of new biomass oil/water separation membrane materials.

Conjugation Linked PEDOT:PSS with Low Impedance and High Stretchability for Epidermal Electrophysiology.

Long-term epidermal recording of bioelectricity is of paramount importance for personal health monitoring. It requires stretchable and dry film electrodes that can be seamlessly integrated with skin. The simultaneous achievement of high conductivity and skin-like ductility of conducting materials is a prerequisite for reliable signal transduction at the dynamic interface, which is also the bottleneck of epidermal electrophysiology. Here, carbon nanotubes (CNTs) are introduced as "conjugation linkers" into a topologically plasticized conducting polymer (PEDOT:PSS). A thin-film electrode with high conductivity (≈3250 S cm-1) and high stretchability (crack-onset strain>100%) is obtained. In particular, the conjugation linker enables the high volumetric capacitance and the low film resistance, both of which synergically reduce the interfacial impedance. The capabilities of this electrode is further demonstrated in the precise recording of various electrophysiological signals.

Ionic Bonding Without Directionality Facilitates Efficient Interfacial Bridging for Perovskite Solar Cells.

Interface passivation through Lewis acid-base coordinate chemistry in perovskite solar cells (PSCs) is a universal strategy to reduce interface defects and hinder ion migration. However, the formation of coordinate covalent bonding demands strict directional alignment of coordinating atoms. Undoubtedly, this limits the selected range of the interface passivation molecules, because a successful molecular bridge between charge transport layer and perovskite bottom interface needs a well-placed molecular orientation. In this study, it is discovered that potassium ions can migrate to the hollow sites of multiple iodine ions from perovskite to form K-Ix ionic bonding, and the ionic bonds without directionality can support molecular backbone rotation to facilitate polar sites (carboxyl groups) chelating Pb at the bottom perovskite interface, finally forming a closed-loop bonding structure. The synergy of coordinate and ionic bonding significantly reduces interface defects, changes electric field distribution, and immobilizes iodine at the perovskite bottom interface, resulting in eliminating the hysteresis effect and enhancing the performance of PSCs. As a result, the corresponding devices achieve a high efficiency exceeding 24.5% (0.09 cm2), and a mini-module with 21% efficiency (12.4 cm2). These findings provide guidelines for designing molecular bridging strategies at the buried interface of PSCs.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. RESULTS: In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION: The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

Development and validation of predictive models for myopia onset and progression using extensive 15-year refractive data in children and adolescents.

BACKGROUND: Global myopia prevalence poses a substantial public health burden with vision-threatening complications, necessitating effective prevention and control strategies. Precise prediction of spherical equivalent (SE), myopia, and high myopia onset is vital for proactive clinical interventions. METHODS: We reviewed electronic medical records of pediatric and adolescent patients who underwent cycloplegic refraction measurements at the Eye & Ear, Nose, and Throat Hospital of Fudan University between January 2005 and December 2019. Patients aged 3-18 years who met the inclusion criteria were enrolled in this study. To predict the SE and onset of myopia and high myopia in a specific year, two distinct models, random forest (RF) and the gradient boosted tree algorithm (XGBoost), were trained and validated based on variables such as age at baseline, and SE at various intervals. Outputs included SE, the onset of myopia, and high myopia up to 15 years post-initial examination. Age-stratified analyses and feature importance assessments were conducted to augment the clinical significance of the models. RESULTS: The study enrolled 88,250 individuals with 408,255 refraction records. The XGBoost-based SE prediction model consistently demonstrated robust and better performance than RF over 15 years, maintaining an R2 exceeding 0.729, and a Mean Absolute Error ranging from 0.078 to 1.802 in the test set. Myopia onset prediction exhibited strong area under the curve (AUC) values between 0.845 and 0.953 over 15 years, and high myopia onset prediction showed robust AUC values (0.807-0.997 over 13 years, with the 14th year at 0.765), emphasizing the models' effectiveness across age groups and temporal dimensions on the test set. Additionally, our classification models exhibited excellent calibration, as evidenced by consistently low brier score values, all falling below 0.25. Moreover, our findings underscore the importance of commencing regular examinations at an early age to predict high myopia. CONCLUSIONS: The XGBoost predictive models exhibited high accuracy in predicting SE, onset of myopia, and high myopia among children and adolescents aged 3-18 years. Our findings emphasize the importance of early and regular examinations at a young age for predicting high myopia, thereby providing valuable insights for clinical practice.

Circular RNA circVAPA modulates macrophage pyroptosis in sepsis-induced acute lung injury through targeting miR-212-3p/Sirt1/Nrf2/NLRP3 axis.

Sepsis-induced acute lung injury (ALI) is an inflammatory condition involving the pyroptosis of macrophages. This study investigated the role of circular RNA hsa_circ_0006990 (circVAPA) in regulating macrophage pyroptosis in ALI and the underlying mechanisms. The expression pattern of circVAPA was examined in the mouse model of ALI and in the LPS-treated RAW264.7 macrophage cell line. Lung tissue damage was evaluated by haematoxylin and eosin staining, immunohistochemistry and a myeloperoxidase activity assay. The molecular mechanisms were investigated by luciferase reporter assay, western blot, RT-qPCR and ELISA. circVAPA was down-regulated in the lung tissues of ALI mice and LPS-induced RAW264.7 cells. circVAPA over-expression alleviated lung tissue injury and dampened LPS-induced pyroptosis and Th17-associated inflammatory responses. miR-212-3p was identified as a target of circVAPA, and miR-212-3p negatively regulated the expression of Sirt1. Sirt1 knockdown largely abolished the effect of circVAPA over-expression on pyroptosis. CircVAPA/miR-212-3p/Sirt1 axis also regulates Nrf2 and NLRP3 expression upon LPS challenge. By targeting miR-212-3p, circVAPA over-expression negatively regulates the expression of Sirt1 and pyroptosis-related factors (Nrf2 and NLRP3), which alleviates the inflammatory damages in sepsis-induced ALI.

Early identification of postoperative remission for thyrotropin-secreting adenomas.

OBJECTIVE: Thyrotropin-secreting adenoma (TSHoma) is a rare type of pituitary adenoma, occurring in one per million people. Little is known about TSHoma. We summarized the demographic, clinical and hormonal characteristics of TSHoma based on a single-centre experience. Moreover, we explored the predictive value of postoperative thyroid function for long-term remission. DESIGN, PATIENTS AND MEASUREMENTS: We retrospectively analysed 63 patients who were diagnosed as TSHoma and surgically treated at our hospital from January 2015 to June 2021. The preoperative clinical characteristics were analysed and compared between remission and nonremission groups. Thyroid function was measured at 1 day, 1 month, 3 months, 6 months, 12 months and over 12 months after surgery to determine whether they could predict long-term remission. RESULTS: The male to female ratio for TSHoma was 1.25. The mean age at diagnosis was 45 ± 12 years. Clinical presentation was varied, presenting with hyperthyroidism (68.25%), space-occupying effect (15.87%), amenorrhea (7.14% of female patients) and nonsymptoms (22.22%). 88.14% of patients achieved postoperative endocrinological remission. Larger tumour size and tumour invasion into cavernous sinus and suprasellar with chiasmal compression were strong predictors of lower rates of endocrinological remission. Postoperative thyroid function at 3 months was a viable diagnostic predictor for postoperative remission, especially for FT4 level with a 20.65 pmol/L cutoff. CONCLUSIONS: Tumour size and extent are major prognostic factors for remission. Postoperative thyroid function at 3 months could be used as a clinical prediction tool for long-term endocrinological remission.

3D drift correction for super-resolution imaging with a single laser light.

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging of nanoscale structures within biological samples. However, prolonged acquisition introduces a drift between the sample and the imaging system, resulting in artifacts in the reconstructed super-resolution image. Here, we present a novel, to our knowledge, 3D drift correction method that utilizes both the reflected and scattered light from the sample. Our method employs the reflected light of a near-infrared (NIR) laser for focus stabilization while synchronously capturing speckle images to estimate the lateral drift. This approach combines high-precision active compensation in the axial direction with lateral post-processing compensation, achieving the abilities of 3D drift correction with a single laser light. Compared to the popular localization events-based cross correlation method, our approach is much more robust, especially for datasets with sparse localization points.