Antifungal Activity and Multi-Target Mechanism of Action of Methylaervine on Candida albicans.

The discovery of a lead compound against Candida albicans is urgently needed because of the lack of clinically available antifungal drugs and the increase in drug resistance. Herein, a ß-carboline alkaloid methylaervine (MET) exhibited potential activity against C. albicans (MIC = 16-128 µg/mL), no hemolytic toxicity, and a low tendency to induce drug resistance. An antifungal mechanism study indicated that MET effectively inhibited the biofilm formation and disrupted the mature biofilm. Moreover, filamentation formation and spore germination were also weakened. The electron microscopy analysis revealed that MET could damage the cell structure, including the cell wall, membrane, and cytoplasm. In particular, the permeability and integrity of the cell membrane were destroyed. When it entered the fungi cell, it interfered with the redox homeostasis and DNA function. Overall, MET can inhibit the growth of C. albicans from multiple channels, such as biofilm, filamentation, cell structure, and intracellular targets, which are difficult to mutate at the same time to generate drug resistance. This work provides a promising lead compound for the creation of new antifungal agents against C. albicans.

Enhancing isoprene production by supplementing mevalonate pathway expressed in E. coli with immobilized enzymes.

Isoprene is an important component in rubber production, which can be produced using the E. coli mevalonic acid (MVA) pathway, and this method has the advantage of green environmental protection and sustainable. However, due to the excessive accumulation of intermediates, the growth of cells was inhibited and the enzyme activity decreased gradually, so it was difficult to increase the yield of isoprene. The immobilized enzyme has the characteristics of high stability and strong reusability, so in this study, the immobilized enzyme was added to the fermentation process of isoprene production by mevalonate metabolizing bacteria (PT-P), to explore the effect on isoprene synthesis. Under the optimum conditions, compared with PT-P fermentation alone, the enzyme catalyzes the conversion of MVA with an efficiency of up to 50.86%, and the yield of isoprene increased by about 30%, reaching 234.47 mg/L.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022.

AIM: To conduct a bibliometric analysis of research on artificial intelligence (AI) in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies. METHODS: Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved, covering the period from January 1, 2013, to December 31, 2022. In order to assess the contributions and co-occurrence relationships among different countries/regions, institutions, authors, and journals, CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified. RESULTS: A total of 750 English articles published between 2013 and 2022 were collected, and the number of publications exhibited an overall increasing trend. The majority of the articles were from China, followed by the United States and India. National University of Singapore, Chinese Academy of Sciences, and Sun Yat-sen University made significant contributions to the published works. Weinreb RN and Fu HZ ranked first among authors and cited authors. American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma. The disciplinary scope of this field includes ophthalmology, computer science, mathematics, molecular biology, genetics, and other related disciplines. The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma. Initially, the hot topics in this field were primarily "segmentation", "classification" and "diagnosis". However, in recent years, the focus has shifted to "deep learning", "convolutional neural network" and "artificial intelligence". CONCLUSION: With the rapid development of AI technology, scholars have shown increasing interest in its application in the field of glaucoma. Moreover, the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot. However, the reliability and interpretability of AI data remain pressing issues that require resolution.

Tuberculosis to lung cancer: application of tuberculosis signatures in identification of lung adenocarcinoma subtypes and marker screening.

Background: There is an association between LUAD and TB, and TB increases the risk of lung adenocarcinogenesis. However, the role of TB in the development of lung adenocarcinoma has not been clarified. Methods: DEGs from TB and LUAD lung samples were obtained to identify TB-LUAD-shared DEGs. Consensus Clustering was performed on the TCGA cohort to characterize unique changes in TB transcriptome-derived lung adenocarcinoma subtypes. Prognostic models were constructed based on TB signatures to explore the characterization of subgroups. Finally, experimental validation and single-cell analysis of potential markers were performed. Results: We characterized three molecular subtypes with unique clinical features, cellular infiltration, and pathway change manifestations. We constructed and validated TB-related Signature in six cohorts. TB-related Signature has characteristic alterations, and can be used as an effective predictor of immunotherapy response. Prognostically relevant novel markers KRT80, C1QTNF6, and TRPA1 were validated by RT-qPCR. The association between KRT80 and lung adenocarcinoma disease progression was verified in Bulk transcriptome and single-cell transcriptome. Conclusion: For the first time, a comprehensive bioinformatics analysis of tuberculosis signatures was used to identify subtypes of lung adenocarcinoma. The TB-related Signature predicted prognosis and identified potential markers. This result reveals a potential pathogenic association of tuberculosis in the progression of lung adenocarcinoma.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort.

Introduction: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD. Methods: In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD. Results: We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma. Discussion: Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Genome-wide CRISPR screening identifies critical role of phosphatase and tensin homologous (PTEN) in sensitivity of acute myeloid leukemia to chemotherapy. / å ¨åŸºå› ç»„CRISPR筛选揭示PTENåŸºå› åœ¨æ€¥æ€§é«“ç³»ç™½è¡€ç— åŒ–ç–—æ•æ„Ÿæ€§ä¸­çš„å ³é”®ä½œç”¨.

Although significant progress has been made in the development of novel targeted drugs for the treatment of acute myeloid leukemia (AML) in recent years, chemotherapy still remains the mainstay of treatment and the overall survival is poor in most patients. Here, we demonstrated the antileukemia activity of a novel small molecular compound NL101, which is formed through the modification on bendamustine with a suberanilohydroxamic acid (SAHA) radical. NL101 suppresses the proliferation of myeloid malignancy cells and primary AML cells. It induces DNA damage and caspase 3-mediated apoptosis. A genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) library screen revealed that phosphatase and tensin homologous (PTEN) gene is critical for the regulation of cell survival upon NL101 treatment. The knockout or inhibition of PTEN significantly reduced NL101-induced apoptosis in AML and myelodysplastic syndrome (MDS) cells, accompanied by the activation of protein kinase B (AKT) signaling pathway. The inhibition of mammalian target of rapamycin (mTOR) by rapamycin enhanced the sensitivity of AML cells to NL101-induced cell death. These findings uncover PTEN protein expression as a major determinant of chemosensitivity to NL101 and provide a novel strategy to treat AML with the combination of NL101 and rapamycin.

Optimal treatment strategies for hepatoid adenocarcinoma of the lung: insights from a comprehensive analysis.

BACKGROUND: Hepatoid adenocarcinoma of the lung (HAL) is a distinctly uncommon subtype of lung adenocarcinoma (LAC), characterized by hepatoid features and an alarmingly low 5-year survival rate of approximately 8%. The scarcity of information on this condition has contributed to the absence of standardized treatment protocols, and the molecular underpinnings of its pathogenesis remain largely unexplored. To bridge these gaps, this study compiled data from 191 primary HAL patients to delineate treatment patterns, prognostic factors, and potential pathogenic mechanisms. METHODS: This study was divided into two cohorts: cohort 1, comprising 110 patients extracted from the Surveillance, Epidemiology, and End Results (SEER) database, and cohort 2, consisting of 70 patients identified through a comprehensive literature review via the PubMed, Web of Science, and Scopus databases, in addition to 11 patients from Tongji Hospital. The Cox proportional hazards regression model was employed to identify independent prognostic factors. Kaplan-Meier survival curves were generated to assess the impact of treatment modalities centered around surgery and chemotherapy. Moreover, this study evaluated the efficacy of first-line treatment regimens and conducted Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses on identified mutated genes. RESULTS: The demographic and clinical profile of HAL patients typically comprises older individuals who are smokers, with a predisposition for diagnosis at advanced disease stages, culminating in a high mortality rate. Key prognostic indicators identified included disease stage, chemotherapy and surgical interventions. The study suggests a treatment strategy that advocates chemotherapy for patients with stage IV HAL and surgery for those with non-stage IV disease. The combination of paclitaxel and platinum-based chemotherapy emerged as an efficacious first-line treatment, with the integration of immunotherapy and targeted therapies showing potential benefits. Genetic analysis underscored similarities between HAL and LAC, particularly highlighting aberrant kinase activity (serine, threonine, and tyrosine) and the activation of PI3K-Akt and MAPK signaling pathways as contributing factors to HAL pathogenesis. CONCLUSION: Despite its relatively rare occurrence, this study underscores the significance of treatment strategies and concludes probable prognostic factors. Due to limited reports, a deeper understanding of the molecular mechanisms driving tumorigenesis and progression in HAL is needed.

Impaired glymphatic function as a biomarker for subjective cognitive decline: An exploratory dual cohort study.

BACKGROUND: Subjective cognitive decline (SCD) has been recognized as a potential risk stage for progression to Alzheimer's disease (AD), while glymphatic dysfunction is considered an important characteristic of AD. We hypothesize that glymphatic dysfunction occurs during the SCD stage, aiming to discover potential biomarkers for SCD. METHODS: Participants from two independent studies, Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 654) and the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 650), representing different ethnicities and disease stages, were included to assess glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS). RESULTS: Abnormal glymphatic function occurs during the SCD stage, with the ALPS index demonstrating excellent classification performance for SCD and normal controls (area under the receiver operating characteristic curve [AUC]SILCODE = 0.816, AUCADNI = 0.797). Lower ALPS index indicates higher risk of cognitive progression, which is negatively correlated with Subjective Cognitive Decline Questionnaire 9 scores and amyloid positron emission tomography burden. DISSCUSION: Our study suggests the ALPS index has the potential to serve as a biomarker for SCD. HIGHLIGHTS: Glymphatic function characterized by the analysis along the perivascular space (ALPS) index becomes abnormal in subjective cognitive decline (SCD), the earliest symptomatic manifestation and preclinical stage of Alzheimer's disease (AD). The ALPS index demonstrates excellent classification performance for SCD and normal controls in the East Asian and Western cohorts. Participants with a lower ALPS index show a higher risk of clinical progression. The ALPS index is closely associated with serval cognitive scales and amyloid beta burden.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1.

Introduction: Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations. Methods: Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort. Results: Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells. Discussion: Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Folic acid capping Bi3+-doped Ag quantum dots for enzyme-like dual-mode recognition of toxic S2- and visual sensing of NO2.

BACKGROUND: NO2- and S2- are two kinds of common toxic anions widely distributed in environmental water, soil and food products. Human beings have suffered a lot of diseases from intake of excessive NO2- or S2-, i.e., infantile methemoglobin, cancer and even to death. Although tremendous efforts have been afforded to monitor NO2- and S2-, most were high instrument-depended with complex processing procedures. To keep food safety and to protect human health, it will be a huge challenge to develop a convenient and efficient way to monitor S2- and NO2- in practice. RESULTS: A kind of folic acid capping Bi3+-doped Ag quantum dots (FA@Bi3+-Ag QDs) was developed for the first time by one-pot homogeneous reduced self-assembly. Not only did FA@Bi3+-Ag QDs possess intrinsic fluorescent property, it expressed synergistic peroxidase-like activity to catalyze the redox of 3,3',5,5'-tetramethylbenzidine (TMB) and H2O2 with Km/vmax of 0.087 mM/6.61 × 10-8 M s-1 and 6.42 mM/6.25 × 10-7 M s-1 respectively. Interestingly, trace S2- could exclusively alter its fluorescent property and peroxidase-like activity, exhibiting significant hypochromic and "turn-on" fluorescent effects. While trace NO2- could make FA@Bi3+-Ag QDs-TMB-H2O2 system hyperchromic. Under the optimized conditions, FA@Bi3+-Ag QDs were applied for dual-mode recognition of S2- and visual sensing of NO2- in real food samples with satisfactory recoveries, i.e., 100.7-107.9 %/95.8-104.7 % and 97.2-104.8 % respectively. The synergistic enzyme-mimic mechanism of FA@Bi3+-Ag QDs and its selective response mechanisms to S2- and NO2- were also proposed. SIGNIFICANCE: This represents the first nanozyme-based FA@Bi3+-Ag QDs system for dual-mode recognition of S2- and visual sensing of NO2-, well meeting the basic requirement in drinking water set by WHO. It will offer a promising way for multi-mode monitoring of different pollution using the same nanozyme-based sensor.

Temporo-frontoparietal hypoconnectivity as a biomarker for isolated language impairment in mild cognitive impairment: A cross-cohort comparison.

INTRODUCTION: Whether brain functional connectivity (FC) is consistently disrupted in individuals with mild cognitive impairment (MCI) with isolated language impairment (ilMCI), and its potential to differentiate between MCI subtypes remains uncertain. METHODS: Cross-sectional data from 404 participants in two cohorts (the Chinese Preclinical Alzheimer's Disease Study and the Alzheimer's Disease Neuroimaging Initiative) were analyzed, including neuropsychological tests, resting-state functional magnetic resonance imaging (fMRI), cerebral amyloid positivity, and apolipoprotein E (APOE) status. RESULTS: Temporo-frontoparietal FC, particularly between the bilateral superior temporal pole and the left inferior frontal/supramarginal gyri, was consistently decreased in ilMCI compared to amnestic MCI (aMCI) and normal controls, which was correlated with semantic impairment. Using mean temporo-frontoparietal FC as a classifier could improve accuracy in identifying ilMCI subgroups with positive cerebral amyloid deposition and APOE risk alleles. DISCUSSION: Temporal-frontoparietal hypoconnectivity was observed in individuals with ilMCI, which may reflect semantic impairment and serve as a valuable biomarker to indicate potential mechanisms of underlying neuropathology. HIGHLIGHTS: Temporo-frontoparietal hypoconnectivity was observed in impaired language mild cognitive impairment (ilMCI). Temporo-frontoparietal hypoconnectivity may reflect semantic impairment. Temporo-frontoparietal functional connectivity can classify ilMCI subtypes.

Deoxyshikonin: a promising lead drug grass against drug resistance or sensitivity to Helicobacter pylori in an acidic environment.

Helicobacter pylori (H. pylori) is closely associated with the diseases such as gastric sinusitis, peptic ulcers, and gastric adenocarcinoma. Its drug resistance is very severe, and new antibiotics are urgently needed. Nine comfrey compounds were screened by antimicrobial susceptibility testing, among which deoxyshikonin had the best inhibitory effect, with a minimum inhibitory concentration (MIC) of 0.5-1 µg/mL. In addition, deoxyshikonin also has a good antibacterial effect in an acidic environment, it is highly safe, and H. pylori does not readily develop drug resistance. Through in vivo experiments, it was proven that deoxyshikonin (7 mg/kg) had a beneficial therapeutic effect on acute gastritis in mice infected with the multidrug-resistant H. pylori BS001 strain. After treatment with desoxyshikonin, colonization of H. pylori in the gastric mucosa of mice was significantly reduced, gastric mucosal damage was repaired, inflammatory factors were reduced, and the treatment effect was better than that of standard triple therapy. Therefore, deoxyshikonin is a promising lead drug to solve the difficulty of drug resistance in H. pylori, and its antibacterial mechanism may be to destroy the biofilm and cause an oxidation reaction.

Identification of protein biomarkers in wastewater linked to the incidence of COVID-19.

Wastewater-based epidemiological (WBE) surveillance is a viable disease surveillance technique capable of monitoring the spread of infectious disease agents in sewershed communities. In addition to detecting viral genomes in wastewater, WBE surveillance can identify other endogenous biomarkers that are significantly elevated and excreted in the saliva, urine, and/or stool of infected individuals. Human protein biomarkers allow the realization of real-time WBE surveillance using highly sensitive biosensors. In this study, we analyzed endogenous protein biomarkers present in wastewater influent through liquid chromatography-tandem mass spectrophotometry and scaffold data-independent acquisition to identify candidate target protein biomarkers for WBE surveillance of SARS-CoV-2. We found that out of the 1382 proteins observed in the wastewater samples, 44 were human proteins associated with infectious diseases. These included immune response substances such as immunoglobulins, cytokine-chemokines, and complements, as well as proteins belonging to antimicrobial and antiviral groups. A significant correlation was observed between the intensity of human infectious disease-related protein biomarkers in wastewater and COVID-19 case numbers. Real-time WBE surveillance using biosensors targeting immune response proteins, such as antibodies or immunoglobulins, in wastewater holds promise for expediting the implementation of relevant policies for the effective prevention of infectious diseases in the near future.

Effect of steam explosion pretreatment on the fermentation characteristics of polysaccharides from tea residue.

Green tea residues are the by-product of tea processing and they contain a large number of bioactive ingredients. Steam explosion has been recognized as one of the most innovative pretreatments for modifying the physicochemical characteristic of polysaccharides from lignocellulosic materials. However, the comparison of biological activity of steam exploded (SE-GTR) and unexploded (UN-GTR) green tea residue polysaccharides was still unclear, which prompted the determination of the efficacy of steam explosion in tea residue resource utilization. In this study, the effects of two extracted polysaccharides UN-GTR and SE-GTR on human gut microbiota in vitro fermentation were conducted. The results showed that after steam explosion pretreatment, SE-GTR displayed more loose and porous structures, resulting in higher polysaccharide content (2483.44±0.5 µg/mg) compared to UN-GTR (1903.56±2.6 µg/mg). In addition, after 24 h fermentation, gut microbiota produced more beneficial metabolites by SE-GTR. The largest SCFAs produced among samples was acetic acid, propionic acid and butyric acid. Furthermore, SE-GTR could regulate the composition and diversity of microbial community, increasing the abundance of beneficial bacteria, such as Bifidobacterium. These results revealed that steam explosion pretreatment could be a promising and efficient approach to enhance the antioxidant activity and bioavailability of polysaccharides isolated from tea residues.

Investigation of METTL3 as an inhibitor of kanamycin-induced ototoxicity via stress granule formation.

Background: Methyltransferase-like 3 (METTL3), a component of the N6-methyladenosine (m6A) methyltransferase family, exhibits significant expression in HEI-OC1 cells and cochlear explants. Aminoglycoside antibiotics, known for their ototoxic potential, frequently induce irreversible auditory damage in hair cells, predominantly through oxidative stress mechanisms. However, the specific role of METTL3 in kanamycin-induced hair cell loss remains unclear. Objective: This study aims to elucidate the mechanisms by which METTL3 contributes to kanamycin-induced ototoxicity. Methods and Results: In vivo experiments demonstrated a notable reduction in METTL3 expression within cochlear explants following kanamycin administration, concomitant with the formation of stress granules (SGs). Similarly, a 24-hour kanamycin treatment led to decreased METTL3 expression and induced SG formation both in HEI-OC1 cells and neonatal cochlear explants, corroborating the in vivo observations. Lentivirus-mediated transfection was employed to overexpress and knockdown METTL3 in HEI-OC1 cells. Knockdown of METTL3 resulted in increased reactive oxygen species (ROS) levels and apoptosis induced by kanamycin, while concurrently reducing SG formation. Conversely, overexpression of METTL3 attenuated ROS generation, decreased apoptosis rates, and promoted SG formation induced by kanamycin. Therefore, METTL3-mediated SG formation presents a promising target for mitigating kanamycin-induced ROS generation and the rate of apoptosis. Conclusion: This finding indicates that METTL3-mediated SG formation holds potential in mitigating kanamycin-induced impairments in cochlear hair cells by reducing ROS formation and apoptosis rates.

Psychological resilience is positively correlated with Habenula volume.

BACKGROUND: Psychological resilience is defined as the process and outcome of individuals' successful adaptation to challenging life experiences. The Habenula (Hb) is known to be involved in the stress response; however, the relationship between Hb volume and resilience in humans remains unclear. This study investigated the correlation among resilience, Hb volume, and depressive tendencies in adults. METHODS: Hb volumes were assessed using deep learning techniques applied to 110 healthy participants. Resilience and depression were evaluated using the Connor-Davidson Resilience Scale and Beck Depression Inventory-II, respectively. We examined the relationship between Hb volume and resilience and assessed the mediating effects of resilience on the relationship between Hb volume and depressive tendencies. RESULTS: Correlation analysis revealed a positive correlation between resilience and Hb volume (partial r = 0.176, p = 0.001), which was more pronounced in women (partial r = 0.353, p = 0.003). Hb volumes on the left and right sides exhibited significant lateralization (LI = 0.031, 95 % CI = [0.016, 0.046]). Despite Hb asymmetry, lateralization was not significantly associated with resilience. The mediation analysis shows significant indirect effect of resilience on the relationship between Hb volume and depressive tendencies (ß = -0.093, 95%CI = [-0.189, -0.019]). CONCLUSION: This study found that populations with lower resilience have smaller Hb volume. Previous research has shown that Hb volume decreased with the increasing severity of depression symptoms in patients. Our findings support this view and extend it to a population that has not been clinically diagnosed with depression. Additionally, we found that psychological resilience can be predicted by Hb volume and may serve as a mediating factor indirectly affecting depressive tendencies, even in healthy individuals. LIMITATIONS: Due to its cross-sectional design, this study was unable to analyze dynamic changes in Hb volume during the process of resilience adaptation.

Upregulation of Siglec-6 induces mitochondrial dysfunction by promoting GPR20 expression in early-onset preeclampsia.

BACKGROUND: Preeclampsia, especially early-onset preeclampsia (EO-PE), is a pregnancy complication that has serious consequences for the health of both the mother and the fetus. Although abnormal placentation due to mitochondrial dysfunction is speculated to contribute to the development of EO-PE, the underlying mechanisms have yet to be fully elucidated. METHODS: The expression and localization of Siglec-6 in the placenta from normal pregnancies, preterm birth and EO-PE patients were examined by RT-qPCR, Western blot and IHC. Transwell assays were performed to evaluate the effect of Siglec-6 on trophoblast cell migration and invasion. Seahorse experiments were conducted to assess the impact of disrupting Siglec-6 expression on mitochondrial function. Co-IP assay was used to examine the interaction of Siglec-6 with SHP1/SHP2. RNA-seq was employed to investigate the mechanism by which Siglec-6 inhibits mitochondrial function in trophoblast cells. RESULTS: The expression of Siglec-6 in extravillous trophoblasts is increased in placental tissues from EO-PE patients. Siglec-6 inhibits trophoblast cell migration and invasion and impairs mitochondrial function. Mechanismly, Siglec-6 inhibits the activation of NF-κB by recruiting SHP1/SHP2, leading to increased expression of GPR20. Notably, the importance of GPR20 function downstream of Siglec-6 in trophoblasts is supported by the observation that GPR20 downregulation rescues defects caused by Siglec-6 overexpression. Finally, overexpression of Siglec-6 in the placenta induces a preeclampsia-like phenotype in a pregnant mouse model. CONCLUSIONS: This study indicates that the regulatory pathway Siglec-6/GPR20 has a crucial role in regulating trophoblast mitochondrial function, and we suggest that Siglec-6 and GPR20 could serve as potential markers and targets for the clinical diagnosis and therapy of EO-PE.

Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.

The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.

Constructing Hydrangea-Like Iron-Cobalt Phosphides via Boron-Assisted strategy as an Efficient Catalyst for Water Splitting at High Current Density.

Finding suitable bifunctional catalysts for industrial hydrogen production is the key to fully building a hydrogen energy society. Here we report a modulation of the surface morphology of electrodeposited CoP to form hydrangea-like Cobalt-Iron bimetallic phosphide (B-CoFeP@CoP) via ion-exchange and NaBH4-assisted methods. This catalyst exhibited excellent bifunctional catalytic capability at high current densities, achieving a current density of 500 mA cm-2 at a small overpotential (387 mV for OER and 252 mV for HER). When assembled into an OWS electrolyzer, this catalyst showed a fairly low cell voltage (≈1.88 V) at 500 mA cm-2 current density.，Furthermore, B-CoFeP@CoP shows ceaseless durability over 120 h in both freshwater and seawater with almost no change in the cell voltage. A combined experimental and theoretical study identified that the unique hydrangea-like structure provided a larger electrochemically active surface area and more effective active sites. Further analysis indicates that during the OER process, phosphides ensure that bimetallic active sites adsorb more OOH * intermediates and further DFT calculations showed that B-Fe2P and B-Co2P acted as active centers for dissociation of H2O and desorption of H2, respectively, to synergistically catalyze the HER process.

Group-informed attentive framework for enhanced diabetes mellitus progression prediction.

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning's advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research.