Triple-junction perovskite-perovskite-silicon solar cells with power conversion efficiency of 24.4.

The recent tremendous progress in monolithic perovskite-based double-junction solar cells is just the start of a new era of ultra-high-efficiency multi-junction photovoltaics. We report on triple-junction perovskite-perovskite-silicon solar cells with a record power conversion efficiency of 24.4%. Optimizing the light management of each perovskite sub-cell (∼1.84 and ∼1.52 eV for top and middle cells, respectively), we maximize the current generation up to 11.6 mA cm-2. Key to this achievement was our development of a high-performance middle perovskite sub-cell, employing a stable pure-α-phase high-quality formamidinium lead iodide perovskite thin film (free of wrinkles, cracks, and pinholes). This enables a high open-circuit voltage of 2.84 V in a triple junction. Non-encapsulated triple-junction devices retain up to 96.6% of their initial efficiency if stored in the dark at 85 °C for 1081 h.

Inkjet-printed optical interference filters.

Optical interference filters (OIFs) are vital components for a wide range of optical and photonic systems. They are pivotal in controlling spectral transmission and reflection upon demand. OIFs rely on optical interference of the incident wave at multilayers, which are fabricated with nanometer precision. Here, we demonstrate that these requirements can be fulfilled by inkjet printing. This versatile technology offers a high degree of freedom in manufacturing, as well as cost-affordable and rapid-prototyping features from the micron to the meter scale. In this work, via rational ink design and formulation, OIFs were fully inkjet printed in ambient conditions. Longpass, shortpass, bandpass, and dichroic OIFs were fabricated, and precise control of the spectral response in OIFs was realized. Subsequently, customized lateral patterning of OIFs by inkjet printing was achieved. Furthermore, upscaling of the printed OIFs to A4 size (29.7 × 21.0 cm²) was demonstrated.

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Co-delivery of fucoxanthin and Twist siRNA using hydroxyethyl starch-cholesterol self-assembled polymer nanoparticles for triple-negative breast cancer synergistic therapy.

INTRODUCTION: Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer with an extremely dismal prognosis and few treatment options. As a desmoplastic tumor, TNBC tumor cells are girdled by stroma composed of cancer-associated fibroblasts (CAFs) and their secreted stromal components. The rapidly proliferating tumor cells, together with the tumor stroma, exert additional solid tissue pressure on tumor vasculature and surrounding tissues, severely obstructing therapeutic agent from deep intratumoral penetration, and resulting in tumor metastasis and treatment resistance. OBJECTIVES: Fucoxanthin (FX), a xanthophyll carotenoid abundant in marine algae, has attracted widespread attention as a promising alternative candidate for tumor prevention and treatment. Twist is a pivotal regulator of epithelial to mesenchymal transition, and its depletion has proven to sensitize antitumor drugs, inhibit metastasis, reduce CAFs activation and the following interstitial deposition, and increase tumor perfusion. The nanodrug delivery system co-encapsulating FX and nucleic acid drug Twist siRNA (siTwist) was expected to form a potent anti-TNBC therapeutic cyclical feedback loop. METHODS AND RESULTS: Herein, our studies constituted a novel self-assembled polymer nanomedicine (siTwist/FX@HES-CH) based on the amino-modified hydroxyethyl starch (HES-NH2) grafted with hydrophobic segment cholesterol (CH). The MTT assay, flow cytometry apoptosis analysis, transwell assay, western blot, and 3D multicellular tumor spheroids growth inhibition assay all showed that siTwist/FX@HES-CH could kill tumor cells and inhibit their metastasis in a synergistic manner. The in vivo anti-TNBC efficacy was demonstrated that siTwist/FX@HES-CH remodeled tumor microenvironment, facilitated interstitial barrier crossing, killed tumor cells synergistically, drastically reduced TNBC orthotopic tumor burden and inhibited lung metastasis. CONCLUSION: Systematic studies revealed that this dual-functional nanomedicine that targets both tumor cells and tumor microenvironment significantly alleviates TNBC orthotopic tumor burden and inhibits lung metastasis, establishing a new paradigm for TNBC therapy.

Biotin-new indocyanine green conjugate: Synthesis, in vitro photocytotoxicity and in vivo biodistribution.

New indocyanine green (ICG) (IR820) is one of the ICG derivatives and attracts increasing attention for cancer management. However, the unsatisfactory tumor targeting ability of IR820 significantly limits its applications for cancer theranostics. Biotin receptor is overexpressed on the membrane of various tumor cells and biotin modified nanocarriers have been reported to enhance the tumor targeting ability on several tumor types. In this work, biotin-new ICG conjugate (Biotin-SS-IR820) was prepared for tumor-targeted IR820 delivery. Biotin and IR820 were coupled through cystamine. The synthesized Biotin-SS-IR820 was characterized by 1 H NMR, FT-IR and HRMS. The in vitro singlet oxygen generation study shows that Biotin-SS-IR820 exhibits similar singlet oxygen generation as compared to IR820 upon 660 nm laser irradiation (0.8 W/cm2 ). The cellular uptake study shows that Biotin-SS-IR820 shows enhanced cellular uptake amount as compared to IR820 on 4T1 cells. As a result, Biotin-SS-IR820 displays enhanced in vitro photodynamic therapeutic effect against 4T1 cells as compared to IR820. In in vivo biodistribution study, Biotin-SS-IR820 shows enhanced tumor accumulation as compared to IR820. Biotin-SS-IR820 developed in this work shows promising prospects for targeted delivery of IR820 to biotin receptor overexpressed tumors.

Synthesis and in vitro antitumor activity of galactosamine-docetaxel conjugates.

Docetaxel (DTX) is a semi-synthetic analogue of paclitaxel which has attracted extensive attention in the treatment of cancer. However, the current clinically used DTX formulations display low tumor targeting ability, leading to unsatisfactory therapeutic outcomes with adverse effects, which poses significant challenges to the clinical application. In this study, three galactosamine (Gal) and docetaxel conjugates with different linkers were synthesized, namely DTX-(suc-Gal)2, DTX-(DTDPA-Gal)2, and DTX-(DSeDPA-Gal)2. These three conjugates were characterized by 1H NMR, FT-IR and HRMS. The in vitro drug release study shows that DTX-(DTDPA-Gal)2 and DTX-(DSeDPA-Gal)2 exhibit glutathione (GSH)-responsive drug release and DTX-(DSeDPA-Gal)2 displays higher GSH-responsiveness. The in vitro antitumor activity study shows that DTX-(DTDPA-Gal)2 and DTX-(DSeDPA-Gal)2 exhibit enhanced cytotoxicity, cell apoptosis rate and G2/M phase arrest against HepG2 cells as compared to DTX-(suc-Gal)2, DTX-(DSeDPA-Gal)2 displays the highest cytotoxicity, cell apoptosis rate and G2/M phase arrest among these three conjugates. In addition, DTX-(DSeDPA-Gal)2 exhibits higher selectivity to HepG2 cells as compared to free DTX. The DTX-(DSeDPA-Gal)2 developed in this study has been proven to be an effective DTX conjugate for selective killing hepatoma cells.

Total metagenomes outperform viromes in recovering viral diversity from sulfuric soils.

Recent metagenomic advancements have offered unprecedented insights into soil viral ecology. However, it remains a challenge to select the suitable metagenomic method for investigating soil viruses under different environmental conditions. Here, we assessed the performance of viral size-fraction metagenomes (viromes) and total metagenomes in capturing viral diversity from hypersulfidic soils with neutral pH and sulfuric soils with pH <3.3. Viromes effectively enhanced the sequencing coverage of viral genomes in both soil types. Viomes of hypersulfidic soils outperformed total metagenomes by recovering a significantly higher number of viral operational taxonomic units (vOTUs). However, total metagenomes of sulfuric soils recovered ~4.5 times more vOTUs than viromes on average. Altogether, our findings suggest that the choice between viromes and total metagenomes for studying soil viruses should be carefully considered based on the specific environmental conditions.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an end-to-end convolutional neural network model. Results A total of 1161 US images of thyroid nodules (498 benign, 663 malignant) from 725 patients (mean age, 42.2 years ± 14.1 [SD]; 516 women) were evaluated. ChatGPT 4.0 and Bard displayed substantial to almost perfect intra-LLM agreement (κ range, 0.65-0.86 [95% CI: 0.64, 0.86]), while ChatGPT 3.5 showed fair to substantial agreement (κ range, 0.36-0.68 [95% CI: 0.36, 0.68]). ChatGPT 4.0 had an accuracy of 78%-86% (95% CI: 76%, 88%) and sensitivity of 86%-95% (95% CI: 83%, 96%), compared with 74%-86% (95% CI: 71%, 88%) and 74%-91% (95% CI: 71%, 93%), respectively, for Bard. Moreover, with ChatGPT 4.0, the image-to-text-LLM strategy exhibited an AUC (0.83 [95% CI: 0.80, 0.85]) and accuracy (84% [95% CI: 82%, 86%]) comparable to those of the human-LLM interaction strategy with two senior readers and one junior reader and exceeding those of the human-LLM interaction strategy with one junior reader. Conclusion LLMs, particularly integrated with image-to-text approaches, show potential in enhancing diagnostic medical imaging. ChatGPT 4.0 was optimal for consistency and diagnostic accuracy when compared with Bard and ChatGPT 3.5. © RSNA, 2024 Supplemental material is available for this article.

Microbial species pool-mediated diazotrophic community assembly in crop microbiomes during plant development.

Plant-associated diazotrophs strongly relate to plant nitrogen (N) supply and growth. However, our knowledge of diazotrophic community assembly and microbial N metabolism in plant microbiomes is largely limited. Here we examined the assembly and temporal dynamics of diazotrophic communities across multiple compartments (soils, epiphytic and endophytic niches of root and leaf, and grain) of three cereal crops (maize, wheat, and barley) and identified the potential N-cycling pathways in phylloplane microbiomes. Our results demonstrated that the microbial species pool, influenced by site-specific environmental factors (e.g., edaphic factors), had a stronger effect than host selection (i.e., plant species and developmental stage) in shaping diazotrophic communities across the soil-plant continuum. Crop diazotrophic communities were dominated by a few taxa (~0.7% of diazotrophic phylotypes) which were mainly affiliated with Methylobacterium, Azospirillum, Bradyrhizobium, and Rhizobium. Furthermore, eight dominant taxa belonging to Azospirillum and Methylobacterium were identified as keystone diazotrophic taxa for three crops and were potentially associated with microbial network stability and crop yields. Metagenomic binning recovered 58 metagenome-assembled genomes (MAGs) from the phylloplane, and the majority of them were identified as novel species (37 MAGs) and harbored genes potentially related to multiple N metabolism processes (e.g., nitrate reduction). Notably, for the first time, a high-quality MAG harboring genes involved in the complete denitrification process was recovered in the phylloplane and showed high identity to Pseudomonas mendocina. Overall, these findings significantly expand our understanding of ecological drivers of crop diazotrophs and provide new insights into the potential microbial N metabolism in the phyllosphere.IMPORTANCEPlants harbor diverse nitrogen-fixing microorganisms (i.e., diazotrophic communities) in both belowground and aboveground tissues, which play a vital role in plant nitrogen supply and growth promotion. Understanding the assembly and temporal dynamics of crop diazotrophic communities is a prerequisite for harnessing them to promote plant growth. In this study, we show that the site-specific microbial species pool largely shapes the structure of diazotrophic communities in the leaves and roots of three cereal crops. We further identify keystone diazotrophic taxa in crop microbiomes and characterize potential microbial N metabolism pathways in the phyllosphere, which provides essential information for developing microbiome-based tools in future sustainable agricultural production.

Two-Dimensional SEC-SEC-UV-MALS-dRI Workflow for Streamlined Analysis and Characterization of Biopharmaceuticals.

The emergence of complex biological modalities in the biopharmaceutical industry entails a significant expansion of the current analytical toolbox to address the need to deploy meaningful and reliable assays at an unprecedented pace. Size exclusion chromatography (SEC) is an industry standard technique for protein separation and analysis. Some constraints of traditional SEC stem from its restricted ability to resolve complex mixtures and notoriously long run times while also requiring multiple offline separation conditions on different pore size columns to cover a wider molecular size distribution. Two-dimensional liquid chromatography (2D-LC) is becoming an important tool not only to increase peak capacity but also to tune selectivity in a single online method. Herein, an online 2D-LC framework in which both dimensions utilize SEC columns with different pore sizes is introduced with a goal to increase throughput for biomolecule separation and characterization. In addition to improving the separation of closely related species, this online 2D SEC-SEC approach also facilitated the rapid analysis of protein-based mixtures of a wide molecular size range in a single online experimental run bypassing time-consuming deployment of different offline SEC methods. By coupling the second dimension with multiangle light scattering (MALS) and differential refractive index (dRI) detectors, absolute molecular weights of the separated species were obtained without the use of calibration curves. As illustrated in this report for protein mixtures and vaccine processes, this workflow can be used in scenarios where rapid development and deployment of SEC assays are warranted, enabling bioprocess monitoring, purity assessment, and characterization.

National-scale investigation reveals the dominant role of phyllosphere fungal pathogens in sorghum yield loss.

Fungal plant pathogens threaten crop production and sustainable agricultural development. However, the environmental factors driving their diversity and nationwide biogeographic model remain elusive, impacting our capacity to predict their changes under future climate scenarios. Here, we analyzed potential fungal plant pathogens from 563 samples collected from 57 agricultural fields across China. Over 28.0% of fungal taxa in the phyllosphere were identified as potential plant pathogens, compared to 22.3% in the rhizosphere. Dominant fungal plant pathogen groups were Cladosporium (in the phyllosphere) and Fusarium (in the rhizosphere), with higher diversity observed in the phyllosphere than in rhizosphere soil. Deterministic processes played an important role in shaping the potential fungal plant pathogen community assembly in both habitats. Mean annual precipitation and temperature were the most important factor influencing phyllosphere fungal plant pathogen richness. Significantly negative relationships were found between fungal pathogen diversity and sorghum yield. Notably, compared to the rhizosphere, the phyllosphere fungal plant pathogen diversity played a more crucial role in sorghum yield. Together, our work provides novel insights into the factors governing the spatial patterns of fungal plant pathogens in the crop microbiome, and highlights the potential significance of aboveground phyllosphere fungal plant pathogens in crop productivity.

Assisting role of carbonaceous skeleton in sludge thermal hydrolysis and press filtration.

As a key step in disposal and reutilization, sludge dewatering is very difficult, since extracellular polymers substances (EPS) binds the water, and compressible organic matter deforms and causes water filtration channels to collapse. Sludge dewaterability was demonstrated to enhance by carbonaceous skeleton (CSkel)-assisted thermal hydrolysis in our previously study. This work further investigated the assisting role of different types of CSkel in EPS decomposition during sludge thermal hydrolysis stage and channels reformation during press filtration stage. Two major types of CSkel, lignocellulosic waste (waste sawdust, waste straw, processing by-product) and protein-rich waste (shrimp shells, jatropha oil cake), were selected. The experimental results showed that in the thermal hydrolysis stage, the decomposition of lignocellulosic waste would increase fatty acids production by 28%, resulting in an acidic environment that reduced the total amount of three hydrophilic amino acids, i.e., glycine, serine and threonine. These promoted the release of water from the sludge. In the press filtration stage, average pore size of sludge was reduced by approximately 87% and nanoscale holes began to appear and increase. Assisting of CSkel rebuilt the filtration channels which brought good connectivity between the pores in sludge cake. Lignocellulosic waste proved significantly more effective than protein-rich waste in achieving a water removal rate of 88.63% under 1 MPa. This study provided a basis for selecting suitable CSkel to optimize sludge dewatering for subsequent utilization.

Higher stochasticity in comammox Nitrospira community assembly in upland soils than the adjacent paddy soils at a regional scale.

Understanding the assembly mechanisms of microbial communities, particularly comammox Nitrospira, in agroecosystems is crucial for sustainable agriculture. However, the large-scale distribution and assembly processes of comammox Nitrospira in agricultural soils remain largely elusive. We investigated comammox Nitrospira abundance, community structure, and assembly processes in 16 paired upland peanuts and water-logged paddy soils in south China. Higher abundance, richness, and network complexity of comammox Nitrospira were observed in upland soils than in paddy soils, indicating a preference for upland soils over paddy soils among comammox Nitrospira taxa in agricultural environments. Clade A.2.1 and clade A.1 were the predominant comammox Nitrospira taxa in upland and paddy soils, respectively. Soil pH was the most crucial factor shaping comammox Nitrospira community structure. Stochastic processes were found to predominantly drive comammox Nitrospira community assembly in both upland and paddy soils, with deterministic processes playing a more important role in paddy soils than in upland soils. Overall, our findings demonstrate the higher stochasticity of comammox Nitrospira in upland soils than in the adjacent paddy soils, which may have implications for autotrophic nitrification in acidic agricultural soils.

Lipid Isobar and Isomer Imaging Using Nanospray Desorption Electrospray Ionization Combined with Triple Quadrupole Mass Spectrometry.

Mass spectrometry imaging (MSI) is widely used for examining the spatial distributions of molecules in biological samples. Conventional MSI approaches, in which molecules extracted from the sample are distinguished based on their mass-to-charge ratio, cannot distinguish between isomeric species and some closely spaced isobars. To facilitate isobar separation, MSI is typically performed using high-resolution mass spectrometers. Nevertheless, the complexity of the mixture of biomolecules observed in each pixel of the image presents a challenge, even for modern mass spectrometers with the highest resolving power. Herein, we implement nanospray desorption electrospray ionization (nano-DESI) MSI on a triple quadrupole (QqQ) mass spectrometer for the spatial mapping of isobaric and isomeric species in biological tissues. We use multiple reaction monitoring acquisition mode (MRM) with unit mass resolution to demonstrate the performance of this new platform by imaging lipids in mouse brain and rat kidney tissues. We demonstrate that imaging in MRM mode may be used to distinguish between isobaric phospholipids requiring a mass resolving power of 3,800,000. Additionally, we have been able to image eicosanoid isomers, a largely unexplored class of signaling molecules present in tissues at low concentrations, in rat kidney tissue. This new capability substantially enhances the specificity and selectivity of MSI, enabling spatial localization of species that remain unresolved in conventional MSI experiments.

Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling.

BACKGROUND: There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. METHODS: We recruited patients with respiratory failure at the First People's Hospital of Yancheng and the People's Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness. RESULTS: The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value. CONCLUSIONS: A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

Mechanisms of Bushen Tiaoxue Granules against controlled ovarian hyperstimulation-induced abnormal morphology of endometrium based on network pharmacology.

Bushen Tiaoxue Granules (BTG) is an empirical Chinese herbal formula that has been used for the treatment of subfertility. The protective effect of BTG on controlled ovarian hyperstimulation (COH)-induced impaired endometrial receptivity has been reported in our previous study. This study aims to explore the mechanisms of BTG on ameliorating abnormal morphology of endometrium based on network pharmacology. Active compounds of BTG were identified via the traditional Chinese medicine systems pharmacology and UPLC-MS technology. The SwissTargetPrediction platform and HERB database were used to screen out the putative targets of BTG. Potential targets of endometrial dysfunction caused by COH were obtained from three GEO databases. Through the STRING database, the protein-protein interaction was carried out according to the cross-common targets of diseases and drugs. GO terms and KEGG pathways enrichment analyses were conducted via the Metascape database. AutoDock Vina was used for docking validation of the affinity between active compounds and potential targets. Finally, in vivo experiments were used to verify the potential mechanisms derived from network pharmacology study. A total of 141 effective ingredients were obtained from TCMSP and nine of which were verified in UPLC-MS. Six genes were selected through the intersection of 534 disease related genes and 165 drug potential targets. Enrichment analyses showed that BTG might reverse endometrial dysfunction by regulating adherens junction and arachidonic acid metabolism. Hematoxylin-eosin staining revealed that BTG ameliorated the loose and edematous status of endometrial epithelium caused by COH. The protein expression of FOXO1A, ß-Catenin and COX-2 was decreased in the COH group, and was up-regulated by BTG. BTG significantly alleviates the edema of endometrial epithelium caused by COH. The mechanisms may be related to adheren junctions and activation of arachidonic acid metabolism. The potential active compounds quercetin, taxifolin, kaempferol, eriodictyol, and isorhamnetin identified from the BTG exhibit marginal cytotoxicity. Both high and low concentrations of kaempferol, eriodictyol, and taxifolin are capable of effectively ameliorating impaired hESC cellular activity.

Application of mesenchymal stem cell therapy for premature ovarian insufficiency: Recent advances from mechanisms to therapeutics.

The incidence of premature ovarian insufficiency (POI) is increasing worldwide, particularly among younger women, posing a significant challenge to fertility. In addition to menopausal symptoms, POI leads to several complications that profoundly affect female reproductive function and overall health. Unfortunately, current clinical treatment strategies for this condition are limited and often yield unsatisfactory outcomes. These approaches typically involve hormone replacement therapy combined with psychological support. Recently, mesenchymal stem cell (MSC) therapies for POI have garnered considerable attention in global research. MSCs can restore ovarian reproductive and endocrine functions through diverse mechanisms, including controlling differentiation, promoting angiogenesis, regulating ovarian fibrosis, inhibiting apoptosis, enhancing autocrine and paracrine effects, suppressing inflammation, modulating the immune system, and genetic regulation. This editorial offers a succinct summary of the application of MSC therapy in the context of POI, providing evidence for groundbreaking medical approaches that have potential to enhance reproductive health and overall well-being for women.

Spätzle maintains homeostasis of hemolymph microbiota in Scylla paramamosain through Toll2.

The Toll pathway is crucial for innate immune responses in organisms (including Drosophila and mammals). The Spätzle protein outside of cells acts as a ligand for Toll receptors, enabling the transfer of signals from outside the cell to the inside. However, the function of Spätzle in the immune system of mud crab (Scylla paramamosain) remains unclear. This research discovered a novel Spätzle gene (Sp-Spz) in mud crab, which showed extensive expression in all the tissues that were examined. The RNA interference exhibited the correlation between Sp-Spz and the anti-lipopolysaccharide factors (ALFs). Knockdown of Sp-Spz decreased the expression of Sp-Toll2 but not Sp-Toll1. In Drosophila Schneider 2 cells, Sp-Spz was found interacted with Sp-Toll2. Moreover, the depletion of Sp-Spz caused the separation of hepatic lobules from the basement membrane, resulting in the disruption of the structural coherence of hepatopancreatic cells. Additionally, the knockdown of Sp-Spz resulted in changes to the composition of the hemolymph microbiota, specifically affecting the proportions of different phylum and family levels. The findings indicated that Sp-Spz may promote the synthesis of ALFs via Sp-Toll2, thereby influencing the homeostasis of microbiota in the hemolymph. In this study, novel insights into mud crab immunity are provided.

Accelerating Pharmaceutical Process Development with an Acoustic Droplet Ejection-Multiple Reaction Monitoring-Mass Spectrometry Workflow.

Fast-paced pharmaceutical process developments (e.g., high-throughput experimentation, directed evolution, and machine learning) involve the introduction of fast, sensitive, and accurate analytical assays using limited sample volumes. In recent years, acoustic droplet ejection (ADE) coupled with an open port interface has been invented as a sampling technology for mass spectrometry, providing high-throughput nanoliter analytical measurements directly from the standard microplates. Herein, we introduce an ADE-multiple reaction monitoring-mass spectrometry (ADE-MRM-MS) workflow to accelerate pharmaceutical process research and development (PR&D). This systematic workflow outlines the selection of MRM transitions and optimization of assay parameters in a data-driven manner using rapid measurements (1 sample/s). The synergy between ADE sampling and MRM analysis enables analytical assays with excellent sensitivity, selectivity, and speed for PR&D reaction screenings. This workflow was utilized to develop new ADE-MRM-MS assays guiding a variety of industrial processes, including (1) screening of Ni-based catalysts for C-N cross-coupling reaction at 1 Hz and (2) high-throughput regioisomer analysis-enabled enzyme library screening for peptide ligation reaction. ADE-MRM-MS assays were demonstrated to deliver accurate results that are comparable to conventional liquid chromatography (LC) experiments while providing >100-fold throughput enhancement.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. In this study, we reorganized a fundamental dataset of parasitic disease-drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-view heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms five state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .