Extracellular signal-regulated protein kinase 5 modulates the spindle assembly to coordinate the oocyte meiotic maturation.

Extracellular signal-regulated protein kinase 5 (Erk5), a member of the mitogen-activated protein kinase (MAPK) family, is ubiquitously expressed in all eukaryotic cells and is implicated in the various mitotic processes such as cell survival, proliferation, migration, and differentiation. However, the potential functional roles of Erk5 in oocyte meiosis have not been fully determined. In this study, we document that ERK5 participates in the meiotic maturation of mouse oocytes by regulating the spindle assembly to ensure the meiotic progression. We unexpectedly found that phosphorylated ERK5 was localized in the spindle pole region at metaphase I and II stages by immunostaining analysis. Inhibition of ERK5 activity using its specific inhibitor XMD8-92 dramatically reduced the incidence of first polar body extrusion. In addition, inhibition of ERK5 evoked the spindle assembly checkpoint to arrest oocytes at metaphase I stage by impairing the spindle assembly, chromosome alignment and kinetochore-microtubule attachment. Mechanically, over-strengthened microtubule stability was shown to disrupt the microtubule dynamics and thus compromise the spindle assembly in ERK5-inhibited oocytes. Conversely, overexpression of ERK5 caused decreased level of acetylated α-tubulin and spindle defects. Collectively, we conclude that ERK5 plays an important role in the oocyte meiotic maturation by regulating microtubule dynamics and spindle assembly.

Clinical features and multiomics profiles indicate coagulation and platelet dysfunction in COVID-19 viral sepsis.

Increased cases of sepsis during COVID-19 in the absence of known bacterial pathogens highlighted role of viruses as causative agents of sepsis. In this study, we investigated clinical, laboratory, proteomic, and metabolomic characteristics of viral sepsis patients (n = 45) and compared them to non-sepsis patients with COVID-19 (n = 186) to identify molecular mechanisms underlying the pathology of viral sepsis in COVID-19. We identified unique metabolomic and proteomic signatures that suggest a substantial perturbation in the coagulation, complement, and platelet activation pathways in viral sepsis. Our proteomic data indicated elevated coagulation pathway protein (fibrinogen), whereas a decrease in many of the complement proteins was observed. These alterations were associated with the functional consequences such as susceptibility to secondary bacterial infections and potentially contributing to both local and systemic disease phenotypes. Our data provide novel aspect of COVID-19 pathology that is centered around presence of sepsis phenotype in COVID-19.

Prevalence, clinical characteristics, and long-term outcomes of new diabetes diagnosis in elderly patients undergoing percutaneous coronary intervention.

Previous studies have reported associations between newly diagnosed diabetes and poor outcomes after percutaneous coronary intervention (PCI), but there is limited data focusing on elderly patients (age ≥ 65). This study aimed to analyze the prevalence and clinical implications of newly diagnosed diabetes in elderly patients who underwent PCI. From 2004 to 2021, a total of 2456 elderly patients who underwent invasive PCI at Korea University Guro Hospital were prospectively enrolled and followed up for a median of five years. The primary endpoint was five-year major adverse cardiovascular events (MACE). Cox regression was used to evaluate whether newly diagnosed diabetes impacted on long-term clinical outcomes. Newly diagnosed diabetes was presented in approximately 8.1% to 10.9% of elderly patients who underwent PCI. Those who had a new diagnosis of diabetes had a higher risk of MACE than previously known diabetes (25.28% vs. 19.15%, p = 0.039). After adjusting for significant factors, newly diagnosed diabetes remained an independent predictor of MACE (HR [hazard ratio] 1.64, 95% confidence interval [CI] 1.24-2.17, p < 0.001), cardiac death (HR 2.15, 95% CI 1.29-3.59, p = 0.003) and repeat revascularization (HR 1.52, 95% CI 1.09-2.11, p = 0.013), but not for non-fatal myocardial infarction (HR 1.66, 95% CI 0.94-2.12, p = 0.081). Newly diagnosed diabetes was associated with an increased risk of 5-year MACE compared with non-diabetes and previously diagnosed diabetes in elderly patients underwent PCI. More attention should be given to those elderly newly diagnosed diabetes population.

Dexmedetomidine improves the circulatory dysfunction of the glymphatic system induced by sevoflurane through the PI3K/AKT/ΔFosB/AQP4 pathway in young mice.

Multiple sevoflurane exposures may damage the developing brain. The neuroprotective function of dexmedetomidine has been widely confirmed in animal experiments and human studies. However, the effect of dexmedetomidine on the glymphatic system has not been clearly studied. We hypothesized that dexmedetomidine could alleviate sevoflurane-induced circulatory dysfunction of the glymphatic system in young mice. Six-day-old C57BL/6 mice were exposed to 3% sevoflurane for 2 h daily, continuously for 3 days. Intraperitoneal injection of either normal saline or dexmedetomidine was administered before every anaesthesia. Meanwhile the circulatory function of glymphatic system was detected by tracer injection at P8 and P32. On P30-P32, behavior tests including open field test, novel object recognition test, and Y-maze test were conducted. Primary astrocyte cultures were established and treated with the PI3K activator 740Y-P, dexmedetomidine, and small interfering RNA (siRNA) to silence ΔFosB. We propose for the first time that multiple exposure to sevoflurane induces circulatory dysfunction of the glymphatic system in young mice. Dexmedetomidine improves the circulatory capacity of the glymphatic system in young mice following repeated exposure to sevoflurane through the PI3K/AKT/ΔFosB/AQP4 signaling pathway, and enhances their long-term learning and working memory abilities.

Elucidating Facet-Dependent Photocatalytic Activities of Metastable CdS and Au@CdS Core-Shell Nanocrystals.

Controlling the crystal facets of semiconductor nanocrystals (NCs) has been proven as an effective approach to tune their physicochemical properties. However, the study on facet-engineering of metastable zinc blende CdS (zb-CdS) and its heterostructures is still not fully explored. In this study, the zb-CdS and Au@zb-CdS core-shell NCs with tunable terminating facets are controllably synthesized, and their photocatalytic performance for water splitting are evaluated. It is found that the {111} facets of the zb-CdS NCs display higher intrinsic activity than the {100} counterparts, which originates from these surfaces being much more efficient, facilitating electron transition to enhance the adsorption ability and the dissociation of the adsorbed water, as revealed by theoretical calculations. Moreover, the Au@zb-CdS core-shell NCs exhibit better photocatalytic performance than the zb-CdS NCs terminated with the same facets under visible light irradiation (≥400 nm), which is mainly ascribed to the accelerated electron separation at the interface, as demonstrated by femtosecond transient absorption (fs-TA) spectroscopy. Importantly, the quantum yield of plasmon-induced hot electron transfer quantified by fs-TA in the Au@zb-CdS core-shell octahedrons can be reached as high as 1.2% under 615 nm excitation, which is higher than that of the Au@zb-CdS core-shell cubes. This work unravels the face-dependent photocatalytic performance of the metastable semiconductor NCs via a combination of experiments and theoretical calculations, providing the understanding of the underlying mechanism of these photocatalysts.

Static study and numerical simulation of the influence of cement distribution in the upper and lower adjacent vertebrae on sandwich vertebrae in osteoporotic patients: Finite element analysis.

Objective: We analyzed the influence of the location of the upper and lower cement on the sandwich vertebrae (SV) by computer finite element analysis. Materials and Methods: A finite element model of the spinal segment of T11-L1 was constructed and 6 mL of cement was built into T11 and L1 simultaneously. According to the various distributions of bone cement at T11 and L1, the following four groups were formed: (i) Group B-B: bilateral bone cement reinforcement in both T11 and L1 vertebral bodies; (ii) Group L-B: left unilateral reinforcement in T11 and bilateral reinforcement in L1; (iii) Group L-R: unilateral cement reinforcement in both T11 and L1 (cross); (iv) Group L-L: unilateral cement reinforcement in both T11 and L1 (ipsilateral side). The maximum von Mises stress (VMS) and maximum displacement of the SV and intervertebral discs were compared and analyzed. Results: The maximum VMS of T12 was in the order of size: group B-B < L-B < L-R < L-L. Group B-B showed the lowest maximum VMS values for T12: 19.13, 18.86, 25.17, 25.01, 19.24, and 20.08 MPa in six directions of load flexion, extension, left and right lateral bending, and left and right rotation, respectively, while group L-L was the largest VMS in each group, with the maximum VMS in six directions of 21.55, 21.54, 30.17, 28.33, 19.88, and 25.27 MPa, respectively. Conclusion: Compared with the uneven distribution of bone cement in the upper and lower adjacent vertebrae (ULAV), the uniform distribution of bone cement in the ULAV reduced and uniformed the stress load on the SV and intervertebral disc. Theoretically, it can lead to the lowest incidence of sandwich vertebral fracture and the slowest rate of intervertebral disc degeneration.

Multi-dimensional cell-free DNA-based liquid biopsy for sensitive early detection of gastric cancer.

BACKGROUND: Gastric cancer is the fifth most common cancer type. Most patients are diagnosed at advanced stages with poor prognosis. A non-invasive assay for the detection of early-stage gastric cancer is highly desirable for reducing associated mortality. METHODS: We collected a prospective study cohort of 110 stage I-II gastric cancer patients and 139 non-cancer individuals. We performed whole-genome sequencing with plasma samples and profiled four types of cell-free DNA (cfDNA) characteristics, fragment size pattern, copy number variation, nucleosome coverage pattern, and single nucleotide substitution. With these differential profiles, we developed an ensemble model to detect gastric cancer signals. Further, we validated the assay in an in-house first validation cohort of 73 gastric cancer patients and 94 non-cancer individuals and an independent second validation cohort of 47 gastric cancer patients and 49 non-cancer individuals. Additionally, we evaluated the assay in a hypothetical 100,000 screening population by Monte Carlo simulation. RESULTS: Our cfDNA-based assay could distinguish early-stage gastric cancer from non-cancer at an AUROC of 0.962 (95% CI: 0.942-0.982) in the study cohort, 0.972 (95% CI: 0.953-0.992) in the first validation cohort and 0.937 (95% CI: 0.890-0.983) in the second validation cohort. The model reached a specificity of 92.1% (128/139) and a sensitivity of 88.2% (97/110) in the study cohort. In the first validation cohort, 91.5% (86/94) of non-cancer individuals and 91.8% (67/73) of gastric cancer patients were correctly identified. In the second validation cohort, 89.8% (44/49) of non-cancer individuals and 87.2% (41/47) of gastric cancer patients were accurately classified. CONCLUSIONS: We introduced a liquid biopsy assay using multiple dimensions of cfDNA characteristics that could accurately identify early-stage gastric cancer from non-cancerous conditions. As a cost-effective non-invasive approach, it may provide population-wide benefits for the early detection of gastric cancer. TRIAL REGISTRATION: This study was registered on ClinicalTrials.gov under the identifier NCT05269056 on March 7, 2022.

The novel HLA-C*07:1089 allele, identified by Sanger dideoxy nucleotide sequencing in a Chinese individual.

HLA-C*07:1089 differs from HLA-C*07:02:01:01 by one nucleotide in exon 3.

Stem cell-derived extracellular vesicles in the therapeutic intervention of Alzheimer's Disease, Parkinson's Disease, and stroke.

With the increase in the aging population, the occurrence of neurological disorders is rising. Recently, stem cell therapy has garnered attention due to its convenient sourcing, minimal invasiveness, and capacity for directed differentiation. However, there are some disadvantages, such as poor quality control, safety assessments, and ethical and logistical issues. Consequently, scientists have started to shift their attention from stem cells to extracellular vesicles due to their similar structures and properties. Beyond these parallels, extracellular vesicles can enhance biocompatibility, facilitate easy traversal of barriers, and minimize side effects. Furthermore, stem cell-derived extracellular vesicles can be engineered to load drugs and modify surfaces to enhance treatment outcomes. In this review, we summarize the functions of native stem cell-derived extracellular vesicles, subsequently review the strategies for the engineering of stem cell-derived extracellular vesicles and their applications in Alzheimer's disease, Parkinson's disease, and stroke, and discuss the challenges and solutions associated with the clinical translation of stem cell-derived extracellular vesicles.

Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.

Background: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types. Method: We collected bronchoalveolar lavage fluid samples from 138 patients diagnosed with lower respiratory tract infections and conducted mNGS to characterize the lung microbiota. Employing various machine learning algorithms, we investigated the correlation of key bacteria in patients with concurrent bronchiectasis and developed a predictive model for hospitalization duration based on these identified key bacteria. Result: We observed variations in microbial communities across different age groups, diseases, and infection types. In the elderly group, Pseudomonas aeruginosa exhibited the highest relative abundance, followed by Corynebacterium striatum and Acinetobacter baumannii. Methylobacterium and Prevotella emerged as the dominant genera at the genus level in the younger group, while Mycobacterium tuberculosis and Haemophilus influenzae were prevalent species. Within the bronchiectasis group, dominant bacteria included Pseudomonas aeruginosa, Haemophilus influenzae, and Klebsiella pneumoniae. Significant differences in the presence of Pseudomonas phage JBD93 were noted between the bronchiectasis group and the control group. In the group with concomitant fungal infections, the most abundant genera were Acinetobacter and Pseudomonas, with Acinetobacter baumannii and Pseudomonas aeruginosa as the predominant species. Notable differences were observed in the presence of Human gammaherpesvirus 4, Human betaherpesvirus 5, Candida albicans, Aspergillus oryzae, and Aspergillus fumigatus between the group with concomitant fungal infections and the bacterial group. Machine learning algorithms were utilized to select bacteria and clinical indicators associated with hospitalization duration, confirming the excellent performance of bacteria in predicting hospitalization time. Conclusion: Our study provided a comprehensive description of the microbial characteristics among patients with lower respiratory tract infections, offering insights from various perspectives. Additionally, we investigated the advanced predictive capability of microbial community features in determining the hospitalization duration of these patients.

TNIK in disease: from molecular insights to therapeutic prospects.

TRAF2 and NCK interacting kinase (TNIK), a critical interacting protein kinase, is currently receiving wide attention. TNIK is found in various human body organs and tissues and participates in cell motility, proliferation, and differentiation. On the one hand, its aberrant expression is related to the onset and progression of numerous malignant tumors. On the other hand, TNIK is important in neuronal growth, proliferation, differentiation, and synaptic formation. Thus, the novel therapeutic strategies for targeting TNIK offer a promising direction for cancer, neurological or psychotic disorders. Here, we briefly summarized the biological information of TNIK, reviewed the role and regulatory mechanism in cancer and neuropsychiatric diseases, and introduced the research progress of inhibitors targeting TNIK. Taken together, this review hopes to contribute to the in-depth understanding of the function and regulatory mechanism of TNIK, which is of great significance for revealing the role of TNIK in the occurrence and treatment of diseases.

Molecular landscape and multi-omic measurements of heterogeneity in fetal adenocarcinoma of the lung.

Fetal adenocarcinoma of the lung (FLAC) is a rare form of lung adenocarcinoma and was divided into high-grade (H-FLAC) and low-grade (L-FLAC) subtypes. Despite the existence of some small case series studies, a comprehensive multi-omics study of FLAC has yet to be undertaken. In this study, we depicted the multi-omics landscapes of this rare lung cancer type by performing multi-regional sampling on 20 FLAC cases. A comparison of multi-omics profiles revealed significant differences between H-FLAC and L-FLAC in a multi-omic landscape. Two subtypes also showed distinct relationships between multi-layer intratumor heterogeneity (ITH). We discovered that a lower genetic ITH was significantly associated with worse recurrence-free survival and overall survival in FLAC patients, whereas higher methylation ITH in H-FLAC patients suggested a short survival. Our findings highlight the complex interplay between genetic and transcriptional heterogeneity in FLAC and suggest that different types of ITH may have distinct implications for patient prognosis.

Mechanisms underlying neutrophils adhesion to triple-negative breast cancer cells via CD11b-ICAM1 in promoting breast cancer progression.

BACKGROUND: Triple-negative breast cancer (TNBC) is recognized as the most aggressive and immunologically infiltrated subtype of breast cancer. A high circulating neutrophil-to-lymphocyte ratio (NLR) is strongly linked to a poor prognosis among patients with breast cancer, emphasizing the critical role of neutrophils. Although the involvement of neutrophils in tumor metastasis is well documented, their interactions with primary tumors and tumor cells are not yet fully understood. METHODS: Clinical data were analyzed to investigate the role of neutrophils in breast cancer. In vivo mouse model and in vitro co-culture system were used for mechanism researches. Blocking experiments were further performed to identify therapeutic agents against TNBC. RESULTS: TNBC cells secreted GM-CSF to sustain the survival of mature neutrophils and upregulated CD11b expression. Through CD11b, neutrophils specifically binded to ICAM1 on TNBC cells, facilitating adhesion. Transcriptomic sequencing combined with human and murine functional experiments revealed that neutrophils, through direct CD11b-ICAM1 interactions, activated the MAPK signaling pathway in TNBC cells, thereby enhancing tumor cell invasion and migration. Atorvastatin effectively inhibited ICAM1 expression in tumor cells, and tumor cells with ICAM1 knockout or treated with atorvastatin were unresponsive to neutrophil activation. The MAPK pathway and MMP9 expression were significantly inhibited in the tumor tissues of TNBC patients treated with atorvastatin. CONCLUSIONS: Targeting CD11b-ICAM1 with atorvastatin represented a potential clinical approach to reduce the malignant characteristics of TNBC.

One Case of Drug-Resistant Invasive Fungal Infection after Myelodysplastic Syndrome Chemotherapy.

BACKGROUND: The aim of this study was to evaluate the therapeutic regimen of a patient with myelodysplastic syndrome (MDS) who developed invasive fungal infections caused by drug-resistant Candida tropicalis after chemotherapy and to investigate the effect of drug treatment. METHODS: We referred to the Diagnostic Criteria and Treatment Principles of invasive fungal diseases in patients with hematological diseases and malignant tumors (2013, fourth revised edition) and the Expert Consensus on Clinical Application of Posaconazole (2022 Edition). In addition, the drug treatment regimens of drug-resistant Candida tropicalis were reviewed. The doctors in charge were involved in the drug treatment process, and the ra-tional drug use was selected according to evidence-based medicine. RESULTS: After 4 months of use, the nodules around the body disappeared, and there was no further fever during follow-up. After 6 months of use, posaconazole was discontinued, and the patient continued to follow-up for 1 month without further fever or nodules. CONCLUSIONS: The combination of posaconazole, amphotericin B liposome, and micafungin is effective in the treatment of fluconazole-resistant Candida tropicalis infection.

Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective.

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods. Five random transformation-based methods-jittering, scaling, warping, iterated amplitude adjusted Fourier transform (IAAFT), and masking-are proposed. Jittering, scaling, warping, and IAAFT directly process original signals, while masking operating on time-frequency spectrograms. Acoustic signals from a real-world WDS are augmented, and the efficacy is validated using convolutional neural network classifiers to identify the spectrograms of acoustic signals. Results indicate the importance of implementing data augmentation before data splitting to prevent data leakage and overly optimistic outcomes. Among the techniques, IAAFT stands out, significantly increasing data volume and diversity, improving recognition accuracy by over 7%. Masking enhances performance mainly by compelling the classifier to learn global features of the spectrograms. Sequential application of IAAFT and masking further strengthens leak detection performance. Furthermore, when applying a complex model to acoustic leakage detection through transfer learning, data augmentation can also enhance the effectiveness of transfer learning. These findings advance artificial intelligence-driven acoustic leak detection technology from a data-centric perspective towards more mature applications.

Deep pan-cancer analysis and multi-omics evidence reveal that ALG3 inhibits CD8+ T cell infiltration by suppressing chemokine secretion and is associated with 5-fluorouracil sensitivity.

BACKGROUND: α-1,3-mannosyltransferase (ALG3) holds significance as a key member within the mannosyltransferase family. Nevertheless, the exact function of ALG3 in cancer remains ambiguous. Consequently, the current research aimed to examine the function and potential mechanisms of ALG3 in various types of cancer. METHODS: Deep pan-cancer analyses were conducted to investigate the expression patterns, prognostic value, genetic variations, single-cell omics, immunology, and drug responses associated with ALG3. Subsequently, in vitro experiments were executed to ascertain the biological role of ALG3 in breast cancer. Moreover, the link between ALG3 and CD8+ T cells was verified using immunofluorescence. Lastly, the association between ALG3 and chemokines was assessed using qRT-PCR and ELISA. RESULTS: Deep pan-cancer analysis demonstrated a heightened expression of ALG3 in the majority of tumors based on multi-omics evidence. ALG3 emerges as a diagnostic and prognostic biomarker across diverse cancer types. In addition, ALG3 participates in regulating the tumor immune microenvironment. Elevated levels of ALG3 were closely linked to the infiltration of bone marrow-derived suppressor cells (MDSC) and CD8+ T cells. According to in vitro experiments, ALG3 promotes proliferation and migration of breast cancer cells. Moreover, ALG3 inhibited CD8+ T cell infiltration by suppressing chemokine secretion. Finally, the inhibition of ALG3 enhanced the responsiveness of breast cancer cells to 5-fluorouracil treatment. CONCLUSION: ALG3 shows potential as both a prognostic indicator and immune infiltration biomarker across various types of cancer. Inhibition of ALG3 may represent a promising therapeutic strategy for tumor treatment.

Two new oleanane triterpenes from Maytenus hookeri.

Two new triterpenes mayteneri A (1), mayteneri B (2), and seven known compounds (3-9) were isolated from stems of Maytenus hookeri Loes. The chemical structures of compounds 1 and 2 were established by 1D, 2D NMR, HRESIMS analysis, and calculating electronic circular dichroism (ECD). The structures of known compounds 3-9 were determined by comparison of their spectral with those reported. Compounds 4-7 showed significant inhibitory activity for NLRP3 inflammasome, with the IC50 values of 2.36-3.44 µM.

Eliminating the stabilizer antagonistic effects for efficiently stabilizing Pb and As co-contaminated soil by innovative stepwise steam flash heating.

Chemical stabilization is frequently used to stabilize lead (Pb) or arsenate (As), but faces challenges in Pb-As co-contaminated soils because of the antagonistic reactions between chemical stabilizers and contaminants. In this work, we innovated an effective and cost-efficient stepwise steam flash heating (SSFH) strategy to simultaneously immobilize Pb and As, and unraveled the underlying mechanisms. The combination of 1.5% Ca(H2PO4)2 and 2% Fe2(SO4)3 only decreased 1.99% Pb by toxicity characteristic leaching procedure (TCLP-Pb) but increased 17.8% of TCLP-As due to the antagonistic effects. SSFH with Ca(H2PO4)2 in the first step and Fe2(SO4)3 in the second step achieved the minimal TCLP-Pb and TCLP-As of 0.778 and 0.327 mg/L, respectively. It also reduced 69.8% of leachable As in 100-year acid rain simulation, indicating a favorable long-term stabilization performance. Additionally, SSFH approach reduced 43.2% stabilizer dosage and 14.9% cost. X-ray absorption near edge structure (XANES) documented that the stepwise SFH promoted the transformation of Pb(NO3)2 and NaAsO2/NaAsO3/As2O3/As2O5 into stable Pb3(PO4)2 and FeAsO4, preventing the formation of AsO43- and FePO4. Our findings proved the state-of-the-art SSFH approach and unraveled its mechanisms to stabilize Pb and As co-contamination in soils, offering a green and sustainable remediation alternative for the management of heavy metal contaminated sites. ENVIRONMENTAL IMPLICATION: A novel stepwise SFH approach can be applied to overcome the stabilizer antagonist effects by separately immobilizing Pb and As in two sequential steps. It also decreased 43.2% of stabilizer dosage and 14.9% of cost comparing to conventional chemical stabilization. This approach can be used for other metal co-contaminated soils facing similar antagonistic challenges, and our work raises a state-of-the-art solution for cost-effective, green and sustainable remediation practices.