Predictive Value of Serum Inflammatory Factors and FT3 for Stroke-Associated Pneumonia in Patients With Acute Ischemic Stroke.

OBJECTIVE: The ability of serum inflammatory factors and free triiodothyronine (FT3) in predicting the occurrence of stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) was assessed in this study. METHODS: A retrospective analysis was conducted on 285 consecutive patients with AIS initially diagnosed and admitted to our hospital from January to December 2022. Patients were categorized into SAP and non-SAP groups based on the presence of SAP. Both groups were compared in terms of baseline characteristics, including National Institute of Health Stroke Scale (NIHSS) score, SAP risk assessment (A2DS2), TOAST classification. Independent risk factors for SAP were identified using multivariate logistic regression analysis, and the predictive value of inflammatory markers was evaluated through ROC curves. RESULTS: Among 285 patients with AIS, 40 (14.03%) were found to have developed SAP. Higher NIHSS and A2DS2 scores, elevated serum IL-1ß, IL-8, and IL-33 levels, increased age, atrial fibrillation, swallowing difficulties, and a higher proportion of patients with low FT3 levels were observed in the SAP group compared with the non-SAP group (all P<0.05). Significant risk factors for SAP in patients with AIS were identified through multivariate logistic regression analysis, including age, swallowing difficulties, NIHSS, A2DS2 , IL-1ß , IL-8 , IL-33, and FT3 (P<0.05). The highest predictive values were observed for A2DS2, FT3, and IL-8 with AUC values of 0.854, 0.844, and 0.823, respectively. CONCLUSION: SAP can be highly predicted by A2DS2, FT3, and IL-8, enabling the early identification of patients with high-risk SAP and facilitating timely intervention and treatment.

Comprehensive profiles of sulfur cycling microbial communities along a mangrove sediment depth.

The sulfur (S) cycle is an important biogeochemical cycle with profound implications for both cellular- and ecosystem-level processes by diverse microorganisms. Mangrove sediments are a hotspot of biogeochemical cycling, especially for the S cycle with high concentrations of S compounds. Previous studies have mainly focused on some specific inorganic S cycling processes without paying specific attention to the overall S-cycling communities and processes as well as organic S metabolism. In this study, we comprehensively analyzed the distribution, ecological network and assembly mechanisms of S cycling microbial communities and their changes with sediment depths using metagenome sequencing data. The results showed that the abundance of gene families involved in sulfur oxidation, assimilatory sulfate reduction, and dimethylsulfoniopropionate (DMSP) cleavage and demethylation decreased with sediment depths, while those involved in S reduction and dimethyl sulfide (DMS) transformation showed an opposite trend. Specifically, glpE, responsible for converting S2O32- to SO32-, showed the highest abundance in the surface sediment and decreased with sediment depths; in contrast, high abundances of dmsA, responsible for converting dimethyl sulfoxide (DMSO) to DMS, were identified and increased with sediment depths. We identified Pseudomonas and Streptomyces as the main S-cycling microorganisms, while Thermococcus could play an import role in microbial network connections in the S-cycling microbial community. Our statistical analysis showed that both taxonomical and functional compositions were generally shaped by stochastic processes, while the functional composition of organic S metabolism showed a transition from stochastic to deterministic processes. This study provides a novel perspective of diversity distribution of S-cycling functions and taxa as well as their potential assembly mechanisms, which has important implications for maintaining mangrove ecosystem functions.

Prediction of epidermal growth factor receptor mutation subtypes in non-small cell lung cancer from hematoxylin and eosin-stained slides using deep learning.

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype are critical for the treatment of non-small cell lung cancer (NSCLC) patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for weakly supervised prediction of EGFR mutations in NSCLC from hematoxylin and eosin (H&E)-stained histopathology whole-slide images (WSIs). The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from WSIs. A convolutional neural network (CNN) employing a contrastive learning paradigm was implemented to extract patch-level morphological features. These features were aggregated using a vision-transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen datasets. In internal validation with a cohort from (USTC)(n=172), the model achieved patient-level areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 93.0%, and specificities of 83.3% and 92.3%, for surgical resection and biopsy specimens in EGFR mutation subtype prediction, respectively. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University (AMU) and the First Affiliated Hospital of Wannan Medical College (WMC) (n=193) yielded patient-level AUCs of 0.849 and 0.871, sensitivities of 75.7% and 72.1%, and specificities of 90.5% and 90.3% for surgical and biopsy specimens, respectively. Further validation with The Cancer Genome Atlas (TCGA) dataset (n=81) showed an AUC of 0.861, sensitivity of 84.6%, and specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, non-invasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.

A switch in the pathway of TRPC3-mediated calcium influx into brain pericytes contributes to capillary spasms after subarachnoid hemorrhage.

Calcium influx and subsequent elevation of the intracellular calcium concentration ([Ca2+]i) induce contractions of brain pericytes and capillary spasms following subarachnoid hemorrhage. This calcium influx is exerted through cation channels. However, the specific calcium influx pathways in brain pericytes after subarachnoid hemorrhage remain unknown. Transient receptor potential canonical 3 (TRPC3) is the most abundant cation channel potentially involved in calcium influx into brain pericytes and is involved in calcium influx into other cell types either via store-operated calcium entry (SOCE) or receptor-operated calcium entry (ROCE). Therefore, we hypothesized that TRPC3 is associated with [Ca2+]i elevation in brain pericytes, potentially mediating brain pericyte contraction and capillary spasms after subarachnoid hemorrhage. In this study, we isolated rat brain pericytes and demonstrated increased TRPC3 expression and its currents in brain pericytes after subarachnoid hemorrhage. Calcium imaging of brain pericytes revealed that changes in TRPC3 expression mediated a switch from SOCE-dominant to ROCE-dominant calcium influx after subarachnoid hemorrhage, resulting in significantly higher [Ca2+]i levels after SAH. TRPC3 activity in brain pericytes also contributed to capillary spasms and reduction in cerebral blood flow in an in vivo rat model of subarachnoid hemorrhage. Therefore, we suggest that the switch in TRPC3-mediated calcium influx pathways plays a crucial role in the [Ca2+]i elevation in brain pericytes after subarachnoid hemorrhage, ultimately leading to capillary spasms and a reduction in cerebral blood flow.

Effect of mean platelet volume and platelet count on the prognosis of branch atheromatous disease.

OBJECTIVE: The purpose of this study was to investigate the predictive value of mean platelet volume (MPV) and platelet count (PC) in branch atheromatous disease (BAD). METHODS: This retrospective study included 216 patients with BAD-stroke within 48 h of symptom onset. These patients were divided into good and poor prognosis groups according to their 3-month modified Rankin scale scores after discharge. Multiple logistic regression analysis was used to evaluate independent predictors of poor prognosis in BAD-stroke patients. Receiver-operating characteristic (ROC) analysis was used to estimate the predictive value of MPV and PC on BAD-stroke. RESULTS: Our research showed that a higher MPV (aOR, 2.926; 95% CI, 2.040-4.196; p < .001) and PC (aOR, 1.013; 95% CI, 1.005-1.020; p = .001) were independently associated with poor prognosis after adjustment for confounders. The ROC analysis of MPV for predicting poor prognosis showed that the sensitivity and specificity were 74% and 84.9%, respectively, and that the AUC was .843 (95% CI, .776-.909, p < .001). The optimal cut-off value was 12.35. The incidence of early neurological deterioration (END) was 24.5% (53 of 163), and 66% of patients in the poor prognosis group had END (33 of 50). Multiple logistic regression analyses showed that elevated MPV and PC were associated with the occurrence of END (p < .05). CONCLUSION: Our results suggested that an elevated MPV and PC may be important in predicting a worse outcome in BAD-stroke patients. Our study also demonstrated an independent association of MPV and PC with END, which is presumably the main reason for the poor prognosis.

LncRNA TUG1 and its Molecular Mechanisms in Human Cancer.

As lncRNAs have increasingly been investigated, they are no longer simply defined as RNAs with no transcription capability. Studies have identified significant associations between the abnormal expression of lncRNAs and human diseases, particularly the mechanisms by which lncRNAs play a part in cancers, which are of considerable attention to researchers. As a result of the complex spatial structure, the mechanisms of interaction of lncRNAs in cancer cells are also complicated and diversified. Among a series of lncRNAs, TUG1, which is now considered to be a very high-value lncRNA, has recently been identified to express abnormally in some malignancies, leading to different alterations in cancer cells proliferation, migration, invasion, apoptosis, and drug resistance, and hence promoting or inhibiting cancer progression. Current studies have implicitly indicated that TUG1 can be used as a therapeutic target for human cancers. However, the biological functions of TUG1 have been studied for a short period of time, and the complete molecular mechanism still needs to be clarified. Accordingly, this review focuses on the principal molecular mechanisms of TUG1 in human cancers and the specific mechanisms of action in different cancer development processes based on existing studies.

Hypoxia-induced SENP3 promotes chemosensitivity and mitochondrial fission via deSUMOylation of Drp1.

OBJECTIVE: The study aimed to investigate the effect of the SUMOylation status of Drp1 on mitochondrial fission in CDDP-treated HNSCC cells cultured under hypoxic conditions. MATERIALS AND METHODS: The effect of hypoxia on the chemosensitivity of HNCC cells was evaluated by flow cytometry and CCK-8 assays. The biological function of SUMO-specific peptidase 3 (SENP3) was evaluated by loss-of-function assays both in vitro and in vivo. SENP3-regulated deSUMOylation of Drp1 were performed with co-IP assays. RESULTS: SENP3 expression correlated with chemosensitivity in clinical HNSCC samples subjected to hypoxic conditions. Hypoxia-induced ROS increased HIF-1α/SENP3 expression and mitochondrial fission in CDDP-treated HNSCC cells, and these effects were reversed by NAC treatment. SENP3 knockdown reversed hypoxia-induced mitochondrial fission and inhibited HNSCC cell apoptosis, which decreased CDDP sensitivity. Furthermore, hypoxia-induced SENP3 deconjugated SUMO2 from Drp1. CONCLUSION: Our findings revealed that hypoxia-induced SENP3 facilitates CDDP sensitivity and mitochondrial fission via deSUMOylation of Drp1.

Integrative analyses on the ciliates Colpoda illuminate the life history evolution of soil microorganisms.

Microorganisms play a central role in sustaining soil ecosystems and agriculture, and these functions are usually associated with their complex life history. Yet, the regulation and evolution of life history have remained enigmatic and poorly understood, especially in protozoa, the third most abundant group of organisms in the soil. Here, we explore the life history of a cosmopolitan species-Colpoda steinii. Our analysis has yielded a high-quality macronuclear genome for C. steinii, with size of 155 Mbp and 37,123 protein-coding genes, as well as mean intron length of ~93 bp, longer than most other studied ciliates. Notably, we identify two possible whole-genome duplication events in C. steinii, which may account for its genome being about twice the size of C. inflata's, another co-existing species. We further resolve the gene expression profiles in diverse life stages of C. steinii, which are also corroborated in C. inflata. During the resting cyst stage, genes associated with cell death and vacuole formation are upregulated, and translation-related genes are downregulated. While the translation-related genes are upregulated during the excystment of resting cysts. Reproductive cysts exhibit a significant reduction in cell adhesion. We also demonstrate that most genes expressed in specific life stages are under strong purifying selection. This study offers a deeper understanding of the life history evolution that underpins the extraordinary success and ecological functions of microorganisms in soil ecosystems.IMPORTANCEColpoda species, as a prominent group among the most widely distributed and abundant soil microorganisms, play a crucial role in sustaining soil ecosystems and promoting plant growth. This investigation reveals their exceptional macronuclear genomic features, including significantly large genome size, long introns, and numerous gene duplications. The gene expression profiles and the specific biological functions associated with the transitions between various life stages are also elucidated. The vast majority of genes linked to life stage transitions are subject to strong purifying selection, as inferred from multiple natural strains newly isolated and deeply sequenced. This substantiates the enduring and conservative nature of Colpoda's life history, which has persisted throughout the extensive evolutionary history of these highly successful protozoa in soil. These findings shed light on the evolutionary dynamics of microbial eukaryotes in the ever-fluctuating soil environments. This integrative research represents a significant advancement in understanding the life histories of these understudied single-celled eukaryotes.

Comparative study of Scleromitrion diffusum and Oldenlandia corymbosa: Microscopy, TLC, HPLC, and antioxidant activity.

Quality control of herbal medicines is crucial, especially the role of herbal drug identification. This is essential for preventing the misuse of herbs, which can affect efficacy or cause toxicity. Scleromitrion diffusum is a common herb, yet it is often mistaken for Oldenlandia corymbosa. This study analyzed the morphology, microscopy, thin-layer chromatography (TLC), and high-pressure liquid chromatography (HPLC) using two markers, asperuloside and scandoside methyl ester, to distinguish between S. diffusum and O. corymbosa with the analysis included 10 samples of S. diffusum and 10 samples of O. corymbosa collected from the Taiwan market. By quantifying the total polyphenols and flavonoids, we investigated the antioxidant activity, including the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging effect, 2,2'-azino-bis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS•+) scavenging effect, and reducing power to further elucidate the biological effects of the two herbs. The results of this study revealed notable differences in microscopy and suggested a TLC method for distinguishing between the two herbs in the market. In HPLC, the ratios of asperuloside and scandoside methyl ester differed between the two herbs. S. diffusum contained a higher asperuloside content. In contrast, O. corymbosa contained higher concentrations of scandoside methyl esters. With more total polyphenols and flavonoids in S. diffusum than those in O. corymbosa, the antioxidant activity of S. diffusum was superior to that of O. corymbosa. This study provides a comprehensive understanding for the identification and quality evaluation of S. diffusum in the market. RESEARCH HIGHLIGHTS: The study consolidates and clarifies the morphological and microscopic differences between Scleromitrion diffusum and Oldenlandia corymbosa - a common adulterant species of S. diffusum on the Taiwan markets. Using Asperuloside and Scandoside methyl ester as two chemical markers, the study proposes a TLC method for rapidly testing S. diffusum and O. corymbosa on the market. Through HPLC analysis, our results showed that S. diffusum and O. corymbosa had a clear difference in the ratio of two markers, Asperuloside and Scandoside methyl ester: Asperuloside/Scandoside methyl ester in S. diffusum is higher than that in O. corymbosa. Through phytochemicals contents, including total phenols content, flavonoids content, and antioxidant activity, including DPPH, ABTS•+ scavenging activity, and reducing power, S. diffusum showed slightly higher levels of phenols and flavonoids as well as a better antioxidant activity than O. corymbosa.

Masked hypergraph learning for weakly supervised histopathology whole slide image classification.

BACKGROUND AND OBJECTIVES: Graph neural network (GNN) has been extensively used in histopathology whole slide image (WSI) analysis due to the efficiency and flexibility in modelling relationships among entities. However, most existing GNN-based WSI analysis methods only consider the pairwise correlation of patches from one single perspective (e.g. spatial affinity or embedding similarity) yet ignore the intrinsic non-pairwise relationships present in gigapixel WSI, which are likely to contribute to feature learning and downstream tasks. The objective of this study is therefore to explore the non-pairwise relationships in histopathology WSI and exploit them to guide the learning of slide-level representations for better classification performance. METHODS: In this paper, we propose a novel Masked HyperGraph Learning (MaskHGL) framework for weakly supervised histopathology WSI classification. Compared with most GNN-based WSI classification methods, MaskHGL exploits the non-pairwise correlations between patches with hypergraph and global message passing conducted by hypergraph convolution. Concretely, multi-perspective hypergraphs are first built for each WSI, then hypergraph attention is introduced into the jointed hypergraph to propagate the non-pairwise relationships and thus yield more discriminative node representation. More importantly, a masked hypergraph reconstruction module is devised to guide the hypergraph learning which can generate more powerful robustness and generalization than the method only using hypergraph modelling. Additionally, a self-attention-based node aggregator is also applied to explore the global correlation of patches in WSI and produce the slide-level representation for classification. RESULTS: The proposed method is evaluated on two public TCGA benchmark datasets and one in-house dataset. On the public TCGA-LUNG (1494 WSIs) and TCGA-EGFR (696 WSIs) test set, the area under receiver operating characteristic (ROC) curve (AUC) were 0.9752±0.0024 and 0.7421±0.0380, respectively. On the USTC-EGFR (754 WSIs) dataset, MaskHGL achieved significantly better performance with an AUC of 0.8745±0.0100, which surpassed the second-best state-of-the-art method SlideGraph+ 2.64%. CONCLUSIONS: MaskHGL shows a great improvement, brought by considering the intrinsic non-pairwise relationships within WSI, in multiple downstream WSI classification tasks. In particular, the designed masked hypergraph reconstruction module promisingly alleviates the data scarcity and greatly enhances the robustness and classification ability of our MaskHGL. Notably, it has shown great potential in cancer subtyping and fine-grained lung cancer gene mutation prediction from hematoxylin and eosin (H&E) stained WSIs.

Pt-O-Ce interaction enhanced by Al substitution to promote the acetone degradation through accelerating the breaking of CC bond in acetic acid intermediate.

The reaction rate of volatile organic compounds (VOCs) oxidation is controlled by the rate-limiting step in the total reaction process. This study proposes a novel strategy, by which the rate-limiting step of acetone oxidation is accelerated by enhanced chemical bond interaction with more electrons transfer through Al-substituted CeO2 loaded Pt (Pt/Al-CeO2). Results indicate that the rate-limiting step in the process of acetone oxidation is the decomposition of acetic acid. Al substitution enhances the Pt-O-Ce interaction that transfers more electrons from Pt/Al-CeO2 to acetic acid, promoting the breaking of its CC bond with a lower free energy barrier. Attributing to these, the reaction rate of Pt/Al-CeO2 is 13 times as high as that of Pt/CeO2 and its TOFPt value is 11 times as high as that of Pt/CeO2 at 150 °C. Moreover, the CO2 selectivity of Pt/Al-CeO2 also increases by 22 %. This work establishes the relationship between Pt-O-Ce interaction and acetone oxidation that provides novel perspectives on the development of efficient materials for VOCs oxidation.

Anti-phasic oscillatory development for speech and noise processing in cochlear implanted toddlers.

Human brain demonstrates amazing readiness for speech and language learning at birth, but the auditory development preceding such readiness remains unknown. Cochlear implanted (CI) children (n = 67; mean age 2.77 year ± 1.31 SD; 28 females) with prelingual deafness provide a unique opportunity to study this stage. Using functional near-infrared spectroscopy, it was revealed that the brain of CI children was irresponsive to sounds at CI hearing onset. With increasing CI experiences up to 32 months, the brain demonstrated function, region and hemisphere specific development. Most strikingly, the left anterior temporal lobe showed an oscillatory trajectory, changing in opposite phases for speech and noise. The study provides the first longitudinal brain imaging evidence for early auditory development preceding speech acquisition.

Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval.

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI. An anchor-based WSI encoder is built to extract hierarchical region features and a prompt-based text encoder is introduced to learn fine-grained semantics from the diagnosis reports. The proposed framework is trained with a multivariate cross-modal loss function to learn semantic information from the diagnosis report at both the instance level and region level. After training, it can perform four types of retrieval tasks based on the multi-modal database to support diagnostic requirements. We conducted experiments on an in-house dataset and a public dataset to evaluate the proposed method. Extensive experiments have demonstrated the effectiveness of the proposed method and its advantages to the present histopathology retrieval methods. The code is available at https://github.com/hudingyi/FGCR.

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction.

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .

Activation of RIG-I signaling in the early stage of Paragonimus proliferus infection causes lung injury via type I immune response in rat.

Paragonimiasis is a common zoonotic parasitic disease. The retinoic acid-inducible gene I (RIG-I) signaling is very important for the host to recognize invading pathogens (especially viruses and bacteria). However, the role of RIG-I signaling in the early stages of P. proliferus infection remains unclear. Therefore, in this study, Sprague-Dawley (SD) rat models with lung damage caused by P. proliferus were established. Experimental methods including Enzyme-linked Immuno Sorbent Assay (ELISA), real-time fluorescent quantitative polymerase chain reaction (PCR), western blotting, and hematoxylin and eosin (HE) staining were used to explore the mechanisms of lung injury caused by P. proliferus. As a result, the expression of the mRNA and proteins of RIG-I signal-related key target molecules, including RIG-I, tumor necrosis factor (TNF) receptor associated factor 6 (TRAF6), interferon regulatory Factor 7 (IRF7), IPS-1, and downstream C-X-C chemokine ligand 10 (CXCL10), were significantly up-regulated immediately after infection, peaked at 3 or 7 days, and showed a downward trend on after 14 days. The levels of pro-inflammatory cytokines interleukin-1 (IL-1), interferon (IFN)-α, -ß, and -γ, which represent type 1 immune response, gradually increased and reached a peak by 14 days, which was consistent with the changes in the degree of inflammatory damage observed under HE staining of lung tissues. In conclusion, RIG-I signaling is activated in the early stage (before 14 days) of P. proliferus infection, it is inferred that the lung injury of the host may be related to the activation of RIG-I like signaling to induce type I immune response.

Nitrogen and sulfur cycling and their coupling mechanisms in eutrophic lake sediment microbiomes.

Microorganisms play important roles in the biogeochemical cycles of lake sediment. However, the integrated metabolic mechanisms governing nitrogen (N) and sulfur (S) cycling in eutrophic lakes remain poorly understood. Here, metagenomic analysis of field and bioreactor enriched sediment samples from a typical eutrophic lake were applied to elucidate the metabolic coupling of N and S cycling. Our results showed significant diverse genes involved in the pathways of dissimilatory sulfur metabolism, denitrification and dissimilatory nitrate reduction to ammonium (DNRA). The N and S associated functional genes and microbial groups generally showed significant correlation with the concentrations of NH4+, NO2- and SO42, while with relatively low effects from other environmental factors. The gene-based co-occurrence network indicated clear cooperative interactions between N and S cycling in the sediment. Additionally, our analysis identified key metabolic processes, including the coupled dissimilatory sulfur oxidation (DSO) and DNRA as well as the association of thiosulfate oxidation complex (SOX systems) with denitrification pathway. However, the enriched N removal microorganisms in the bioreactor ecosystem demonstrated an additional electron donor, incorporating both the SOX systems and DSO processes. Metagenome-assembled genomes-based ecological model indicated that carbohydrate metabolism is the key linking factor for the coupling of N and S cycling. Our findings uncover the coupling mechanisms of microbial N and S metabolism, involving both inorganic and organic respiration pathways in lake sediment. This study will enhance our understanding of coupled biogeochemical cycles in lake ecosystems.

Data-balanced transformer for accelerated ionizable lipid nanoparticles screening in mRNA delivery.

Despite the widespread use of ionizable lipid nanoparticles (LNPs) in clinical applications for messenger RNA (mRNA) delivery, the mRNA drug delivery system faces an efficient challenge in the screening of LNPs. Traditional screening methods often require a substantial amount of experimental time and incur high research and development costs. To accelerate the early development stage of LNPs, we propose TransLNP, a transformer-based transfection prediction model designed to aid in the selection of LNPs for mRNA drug delivery systems. TransLNP uses two types of molecular information to perceive the relationship between structure and transfection efficiency: coarse-grained atomic sequence information and fine-grained atomic spatial relationship information. Due to the scarcity of existing LNPs experimental data, we find that pretraining the molecular model is crucial for better understanding the task of predicting LNPs properties, which is achieved through reconstructing atomic 3D coordinates and masking atom predictions. In addition, the issue of data imbalance is particularly prominent in the real-world exploration of LNPs. We introduce the BalMol block to solve this problem by smoothing the distribution of labels and molecular features. Our approach outperforms state-of-the-art works in transfection property prediction under both random and scaffold data splitting. Additionally, we establish a relationship between molecular structural similarity and transfection differences, selecting 4267 pairs of molecular transfection cliffs, which are pairs of molecules that exhibit high structural similarity but significant differences in transfection efficiency, thereby revealing the primary source of prediction errors. The code, model and data are made publicly available at https://github.com/wklix/TransLNP.

FOXO3/Rab7-Mediated Lipophagy and Its Role in Zn-Induced Lipid Metabolism in Yellow Catfish (Pelteobagrus fulvidraco).

Lipophagy is a selective autophagy that regulates lipid metabolism and reduces hepatic lipid deposition. However, the underlying mechanism has not been understood in fish. In this study, we used micronutrient zinc (Zn) as a regulator of autophagy and lipid metabolism and found that Ras-related protein 7 (rab7) was involved in Zn-induced lipophagy in hepatocytes of yellow catfish Pelteobagrus pelteobagrus. We then characterized the rab7 promoter and identified binding sites for a series of transcription factors, including Forkhead box O3 (FOXO3). Site mutation experiments showed that the -1358/-1369 bp FOXO3 binding site was responsible for Zn-induced transcriptional activation of rab7. Further studies showed that inhibition of rab7 significantly inhibited Zn-induced lipid degradation by lipophagy. Moreover, rab7 inhibitor also mitigated the Zn-induced increase of cpt1α and acadm expression. Our results suggested that Zn exerts its lipid-lowering effect partly through rab7-mediated lipophagy and FA ß-oxidation in hepatocytes. Overall, our findings provide novel insights into the FOXO3/rab7 axis in lipophagy regulation and enhance the understanding of lipid metabolism by micronutrient Zn, which may help to reduce excessive lipid accumulation in fish.

miR-26b-5p Affects the Progression of Acute Myeloid Leukemia by Regulating the USP48-Mediated Wnt/ß-Catenin Pathway.

Acute myeloid leukemia (AML) is a highly heterogeneous disease. Exploring the pathogenesis of AML is still an important topic in the treatment of AML. The expression levels of miR-26b-5p and USP48 were measured by qRT-PCR. The expression levels of related proteins were detected by Western blot. Cell proliferation and apoptosis were detected by CCK-8 and flow cytometry, respectively. Coimmunoprecipitation was used to examine the interaction between USP48 and Wnt5a. Bioinformatics analysis showed that high levels of miR-26b-5p and low levels of USP48 were associated with poor prognosis in AML. miR-26b-5p can negatively regulate the expression of USP48. Downregulation of miR-26b-5p inhibited EMT, cell viability and proliferation of AML cells and accelerated apoptosis. Furthermore, the influence of miR-26b-5p inhibition and USP48 knockdown on AML progression could be reversed by a Wnt/ß-catenin signaling pathway inhibitor. This study revealed that miR-26b-5p regulates AML progression, possibly by targeting the USP48-mediated Wnt/ß-catenin molecular axis to affect AML cell biological behavior.