Mechanism of mesenchymal stem cells in liver regeneration: Insights and future directions.

Mesenchymal stem cells (MSCs) are a prevalent source for stem cell therapy and play a crucial role in modulating both innate and adaptive immune responses. Non-alcoholic fatty liver disease (NAFLD) is characterized by the accumulation of triglycerides in liver cells and involves immune system activation, leading to histological changes, tissue damage, and clinical symptoms. A recent publication by Jiang et al, highlighted the potential of MSCs to mitigate in NAFLD progression by targeting various molecular pathways, including glycolipid metabolism, inflammation, oxidative stress, endoplasmic reticulum stress, and fibrosis. In this editorial, we comment on their research and discuss the efficacy of MSC therapy in treating NAFLD.

National-scale distribution of protists associated with sorghum leaves and roots.

Protists, as integral constituents of the plant microbiome, are posited to confer substantial benefits to plant health and performance. Despite their significance, protists have received considerably less attention compared to other constituents of the plant microbiome, such as bacteria and fungi. To investigate the diversity and community structure of protists in sorghum leaves and roots, we employed amplicon sequencing of the eukaryotic 18S rRNA gene in 563 leaf and root samples collected from 57 locations across China. We found significant differences in the diversity and community structure of protists in sorghum leaves and roots. The leaf was taxonomically dominated by Evosea, Cercozoa and Ciliophora, while the root was dominated by Endomyxa, Cercozoa and Oomycota. The functional taxa of protists exhibited notable differences between leaves and roots, with the former being predominantly occupied by consumers and the latter by parasites. The community composition of protists in the leaf was predominantly influenced by mean annual precipitation, whereas soil pH played a more significant role in the root. The present study identified the most abundant and distributed protists in sorghum leaves and roots and elucidated the underlying factors that govern their community structure. The present study offers a novel perspective on the factors that shape plant-associated protist communities and their potential roles in enhancing the functionality of plant ecosystems.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Exploring hepatic fibrosis screening via deep learning analysis of tongue images.

Background: Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate the effectiveness of deep learning-based analysis of tongue images for hepatic fibrosis screening. Methods: A total of 1083 tongue images were collected from 741 patients and divided into training, validation, and test sets. DenseNet-201, a convolutional neural network, was employed to train the AI model using these tongue images. The predictive performance of AI was assessed and compared with that of FIB-4, using real-time two-dimensional shear wave elastography as the reference standard. Results: The proposed AI model achieved an accuracy of 0.845 (95% CI: 0.79-0.90) and 0.814 (95% CI: 0.76-0.87) in the validation and test sets, respectively, with negative predictive values (NPVs) exceeding 90% in both sets. The AI model outperformed FIB-4 in all aspects, and when combined with FIB-4, the NPV reached 94.4%. Conclusion: Tongue inspection, with the assistance of AI, could serve as a first-line screening method for hepatic fibrosis.

Environmental microbiome, human fungal pathogens, and antimicrobial resistance.

Traditionally, antifungal resistance (AFR) has received much less attention compared with bacterial resistance to antibiotics. However, global changes, pandemics, and emerging new fungal infections have highlighted global health consequences of AFR. The recent report of the World Health Organisation (WHO) has identified fungal priority pathogens, and recognised AFR among the greatest global health threats. This is particularly important given the significant increase in fungal infections linked to climate change and pandemics. Environmental factors play critical roles in AFR and fungal infections, as many clinically relevant fungal pathogens and AFR originate from the environment (mainly soil). In addition, the environment serves as a potential rich source for the discovery of new antifungal agents, including mycoviruses and bacterial probiotics, which hold promise for effective therapies. In this article, we summarise the environmental pathways of AFR development and spread among high priority fungal pathogens, and propose potential mechanisms of AFR development and spread. We identify a research priority list to address key knowledge gaps in our understanding of environmental AFR. Further, we propose an integrated roadmap for predictive risk management of AFR that is critical for effective surveillance and forecasting of public health outcomes under current and future climatic conditions.

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.

This study developed and validated a deep learning-based diagnostic model with uncertainty estimation to aid radiologists in the preoperative differentiation of pathological subtypes of renal cell carcinoma (RCC) based on computed tomography (CT) images. Data from 668 consecutive patients with pathologically confirmed RCC were retrospectively collected from Center 1, and the model was trained using fivefold cross-validation to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC). An external validation with 78 patients from Center 2 was conducted to evaluate the performance of the model. In the fivefold cross-validation, the area under the receiver operating characteristic curve (AUC) for the classification of ccRCC, pRCC, and chRCC was 0.868 (95% CI, 0.826-0.923), 0.846 (95% CI, 0.812-0.886), and 0.839 (95% CI, 0.802-0.88), respectively. In the external validation set, the AUCs were 0.856 (95% CI, 0.838-0.882), 0.787 (95% CI, 0.757-0.818), and 0.793 (95% CI, 0.758-0.831) for ccRCC, pRCC, and chRCC, respectively. The model demonstrated robust performance in predicting the pathological subtypes of RCC, while the incorporated uncertainty emphasized the importance of understanding model confidence. The proposed approach, integrated with uncertainty estimation, offers clinicians a dual advantage: accurate RCC subtype predictions complemented by diagnostic confidence metrics, thereby promoting informed decision-making for patients with RCC.

The Development of Ambiguity Processing Is Explained by an Inverted U-Shaped Curve.

Understanding the developmental trajectories for recognizing facial expressions is important for a better understanding of development of psychiatric disorders. In this study, we examined the recognition of emotional and neutral facial expressions in 93 typically developing adolescents and adults. The Emotion Intensity Rating task required participants to rate the intensity of emotional expression in happy, neutral, and sad faces on a scale from 1 to 9. A score of '5' had to be assigned to neutral faces, scores between '6' (slightly happy) and '9' (very happy) to happy faces, and scores between '4' (slightly sad) and '1' (very sad) to sad faces. Mixed effects models were used to examine the effects of age and emotion on recognition accuracy, reaction time (RT), and emotional intensity. Participants tended to misjudge neutral faces as sad. Adolescents were less accurate than adults for neutral face recognition. There were significant quadratic effects of age on accuracy (negative quadratic effect) and RT (positive quadratic effect). The most accurate and fastest performance was observed in 25- to 35-year-old subjects. This trajectory may be associated with prefrontal cortex maturation, which provides top-down control over the heightened amygdala response to ambiguity that may be misinterpreted as emotional content.

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound.

OBJECTIVES: To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS). METHODS: 527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists. RESULTS: In the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205). CONCLUSIONS: A CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Research status of polycystic ovary syndrome treatment: a mini review and a bibliometric analysis from 2010 to 2023.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in premenopausal women, often linked to abdominal obesity, insulin resistance, and metabolic issues. With its heterogeneous nature, PCOS treatment should be tailored to individual symptoms and patient preferences. This study examines collaboration networks among countries, institutions, authors, references, and journals related to PCOS treatment. METHODS: Web of Science data was analyzed using VOSviewer and CiteSpace for bibliometric visualization. Chinese and Western medicine treatments for PCOS were reviewed, emphasizing symptom-targeted solutions. RESULTS: Data from 4682 records authored by 400 individuals from 515 institutes in 62 countries revealed China as the leading contributor. Notable authors include Monash University and Richard S. Legro. Common research themes include adipocytes, inflammation, insulin sensitivity, oxidative stress, and the gut microbiome. Tailoring treatment to individual needs is essential, focusing on hyperandrogenism, ovulation, and insulin resistance, with lifestyle counseling to address obesity. CONCLUSION: This bibliometric analysis provides valuable insights into the research status of PCOS treatment. China has made significant contributions, and complementary and alternative therapies, such as traditional Chinese medicine and acupuncture, have also shown beneficial effects recently. The research on inflammation, oxidative stress, and the gut microbiome may provide new targets and strategies for the treatment of PCOS. The recognition of the metabolic problems in PCOS patients facilitates the formulation of more personalized treatment plans to improve the prognosis of patients.

PELO regulates erythroid differentiation through interaction with MYC to upregulate KLF10.

Erythropoiesis is a multistep process of erythroid cell production that is controlled by multiple regulatory factors. Ribosome rescue factor PELO plays a crucial role in cell meiotic division and mice embryonic development. However, the function of PELO in erythroid differentiation remains unclear. Here, we showed that knockdown of PELO increased hemin-induced erythroid differentiation of K562 and HEL cells, exhibiting a higher number of benzidine-positive cells and increased mRNA levels of erythroid genes. PELO knockdown inhibited the proliferation and cell cycle progression and promoted apoptosis of K562 cells. Mechanistically, PELO could regulate the expression of KLF10 through interaction with MYC. Moreover, KLF10 knockdown also enhanced erythroid differentiation of K562 and HEL cells induced by hemin. Collectively, our results demonstrated that PELO regulates erythroid differentiation and increases KLF10 expression levels by interacting with MYC.

Thickness Characterization of Steel Plate Coating Materials with Terahertz Time-Domain Reflection Spectroscopy Based on BP Neural Network.

Accurate monitoring of steel plate coating thickness is crucial in construction quality control and durability assessments. To address this challenge, this study introduces a terahertz time-domain reflection spectroscopy based on a BP neural network model to achieve a quantitative visualization characterization of coating thickness. The BP neural network eliminates the inherent dependence of terahertz reflection spectroscopy on the refractive index value in thickness calculation. This trained BP neural network model effectively establishes a functional relationship between signal feature parameters and the corresponding thickness values. Additionally, the proposed model can innovatively measure different coating materials' refractive indexes, revealing the corresponding values for the black paint, white paint, epoxy resin, and rubber as 2.212, 1.967, 1.924, and 2.185, respectively. The experimental results demonstrate the trained BP neural network model possesses remarkable accuracy in predicting coating thickness within the scanning area, achieving a precision level exceeding 96%. This method enables the visualization of coating thickness and the extraction of thickness characterization values. Furthermore, using the thickness imaging results as a reference, the method can accurately identify the thickness abnormalities across the scanning area, locating the position and size of potential defects such as internal scratches and foreign object defects. This innovative approach offers a superior means of monitoring and assessing the thickness distribution quality of the steel plate coating layer materials.

Direct Probe of Conical Intersection Photochemistry by Time-Resolved X-ray Magnetic Circular Dichroism.

The direct probing of photochemical dynamics by detecting the electronic coherence generated during passage through conical intersections is an intriguing challenge. The weak coherence signal and the difficulty in preparing purely excited wave packets that exclude coherence from other sources make it experimentally challenging. We propose to use time-resolved X-ray magnetic circular dichroism to probe the wave packet dynamics around the conical intersection. The magnetic field amplifies the relative strength of the electronic coherence signal compared to populations through the magnetic field response anisotropy. More importantly, since the excited state relaxation through conical intersections involves a change of parity, the magnetic coupling matches the symmetry of the response function with the electronic coherence, making the coherence signal only sensitive to the conical intersection induced coherence and excludes the pump pulse induced coherence between the ground state and excited state. In this theoretical study, we apply this technique to the photodissociation dynamics of a pyrrole molecule and demonstrate its capability of probing electronic coherence at a conical intersection as well as population transfer. We demonstrate that a magnetic field can be effectively used to extract novel information about electron and nuclear molecular dynamics.

Synthesis of curcumin derivatives targeting androgen receptor for castration-resistant prostate cancer therapy.

In this work, a series of curcumin derivatives (1a-1h, 2a-2g, and 3a-3c) were synthesized for the suppression of castration-resistant prostate cancer cells. All synthesized compounds were characterized by 1H NMR, 13C NMR, HRMS, and melting point. The in vitro cytotoxicity study shows that compounds 1a, 1e, 1f, 1h, 2g, 3a, and 3c display similar or enhanced cytotoxicity against 22Rv1 and C4-2 cells as compared to ASC-J9, other synthesized compounds display reduced cytotoxicity against 22Rv1 and C4-2 cells as compared to ASC-J9. Molecular docking simulation was performed to study the binding affinity and probable binding modes of the synthesized compounds with androgen receptor. The results show that all synthesized compounds exhibit higher cdocker interaction energies as compared to ASC-J9. Compounds 1h, 2g, and 3c not only show strong cytotoxicity against 22Rv1 and C4-2 cells but also exhibit high binding affinity with androgen receptor. In androgen receptor suppression study, compounds 1f and 2g show similar androgen receptor suppression effect as compared to ASC-J9 on C4-2 cells, compound 3c displays significantly enhanced AR suppression effect as compared to ASC-J9, 1f and 2g. Compounds 1a, 1e, 1f, 1h, 2g, 3a and 3c prepared in this work have significant potential for castration-resistant prostate cancer therapy.

Effect of meddling ARBs on ARGs dynamics in fungal infested soil and their selective dispersal along spatially distant mycelial networks.

During the recent times, environmental antibiotic resistance genes (ARGs) and their potential transfer to other bacterial hosts of pathogenic importance are of serious concern. However, the dissemination strategies of such ARGs are largely unknown. We tested that saprotrophic soil fungi differentially enriched antibiotic resistant bacteria (ARBs) and subsequently contributed in spatial distribution of selective ARGs. Wafergen qPCR analysis of 295 different ARGs was conducted for manure treated pre-sterilized soil incubated or not with selected bacterial-fungal consortia. The qPCR assay detected unique ARGs specifically found in the mycosphere of ascomycetous and basidiomycetous fungi. Both fungi exerted potentially different selection pressures on ARBs, resulting in different patterns of ARGs dissemination (to distant places) along their respective growing fungal highways. The relative abundance of mobile genetic elements (MGEs) was significantly decreased along fungal highways compared to the respective inoculation points. Moreover, the decrease in MGEs and ARGs (along fungal highways) was more prominent over time which depicts the continuous selection pressure of growing fungi on ARBs for enrichment of particular ARGs in mycosphere. Such data also indicate the potential role of saprotrophic soil fungi to facilitate horizontal gene transfer within mycospheric environmental settings. Our study, therefore, advocates to emphasize the future investigations for such (bacteria-fungal) interactive microbial consortia for potential (spatial) dissemination of resistance determinants which may ultimately increase the exposure risks of ARGs.

Red blood cell count in cerebrospinal fluid was correlated with inflammatory markers on the seventh postoperative day and all associated with the outcome of aneurysmal subarachnoid hemorrhage patients.

Background: Exploring factors associated with the outcome of patients with aneurysmal subarachnoid hemorrhage (aSAH) has become a hot focus in research. We sought to investigate the associations of inflammatory markers and blood cell count in cerebrospinal fluid with the outcome of aSAH patients. Methods: We carried a retrospective study including 200 patients with aSAH and surgeries. The associations of neutrophil, lymphocyte, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune inflammation index (SII), system inflammation response index (SIRI), and blood cell count in cerebrospinal fluid on the 1st and 7th postoperative days with the outcome of aSAH patients were investigated by univariate analysis and multivariate logistic regression model. Results: According to the modified Rankin scale (mRS) score, there were 147 patients with good outcome and 53 patients with poor outcome. The neutrophil, NLR, SIRI, and SII levels on the seventh postoperative day in patients with poor outcome were all significantly higher than patients with good outcome, P < 0.05. The multivariate logistic regression model including inflammatory markers and blood cell counts in cerebrospinal fluid on the 1st postoperative day confirmed that red blood cell count in cerebrospinal fluid (≥177 × 109/L; OR: 7.227, 95% CI: 1.160-45.050, P = 0.034) was possibly associated with poor outcome of aSAH patients, surgical duration (≥169 min), Fisher grade (III-IV), hypertension, and infections were also possibly associated with the poor outcome. The model including inflammatory markers and blood cell counts in cerebrospinal fluid on the 7th postoperative day confirmed that red blood cell count in cerebrospinal fluid (≥54 × 109/L; OR: 39.787, 95% CI: 6.799-232.836, P < 0.001) and neutrophil-lymphocyte ratio (≥8.16; OR: 6.362, 95% CI: 1.424-28.428, P = 0.015) were all possibly associated with poor outcome of aSAH patients. The NLR (r = 0.297, P = 0.007) and SIRI (r = 0.325, P = 0.003) levels were all correlated with the count of red blood cells in cerebrospinal fluid. Discussion: Higher neutrophil-lymphocyte ratio and higher red blood cell count in cerebrospinal fluid were all possibly associated with poor outcome of patients with aneurysmal subarachnoid hemorrhage. However, we need a larger sample study.

Nitrogenous fertilizer plays a more important role than cultivars in shaping sorghum-associated microbiomes.

The plant microbiome plays a crucial role in facilitating plant growth through enhancing nutrient cycling, acquisition and transport, as well as alleviating stresses induced by nutrient limitations. Despite its significance, the relative importance of common agronomic practices, such as nitrogenous fertilizer, in shaping the plant microbiome across different cultivars remains unclear. This study investigated the dynamics of bacterial and fungal communities in leaf, root, rhizosphere, and bulk soil in response to nitrogenous fertilizer across ten sorghum varieties, using 16S rRNA and ITS gene amplicon sequencing, respectively. Our results revealed that nitrogen addition had a greater impact on sorghum-associated microbial communities compared to cultivar. Nitrogen addition significantly reduced bacterial diversity in all compartments except for the root endophytes. However, N addition significantly increased fungal diversity in both rhizosphere and bulk soils, while significantly reducing fungal diversity in the root endophytes. Furthermore, N addition significantly altered the community composition of bacteria and fungi in all four compartments, while cultivars only affected the community composition of root endosphere bacteria and fungi. Network analysis revealed that fertilization significantly reduced microbial network complexity and increased fungal-related network complexity. Collectively, this study provides empirical evidence that sorghum-associated microbiomes are predominantly shaped by nitrogenous fertilizer rather than by cultivars, suggesting that consistent application of nitrogenous fertilizer will ultimately alter plant-associated microbiomes regardless of cultivar selection.

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

BACKGROUND: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. OBJECTIVE: To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. METHODS: The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. RESULTS: Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. CONCLUSION: The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions.

Danger in the Dark: Stability of Perovskite Solar Cells with Varied Stoichiometries and Morphologies Stressed at Various Conditions.

The long-term stability of perovskite solar cells (PSCs) remains a bottleneck for commercialization. While studies on the stoichiometry and morphology of PSCs with regard to performance are prevalent, understanding the influence of these factors on their long-term stability is lacking. In this work, we evaluate the impact of stoichiometry and morphology on the long-term stability of cesium formamidinium-based PSCs. We demonstrate that the lead iodide (PbI2) to formamidinium iodide (FAI) ratio influences stability under various stress factors (elevated temperature and light). A high molar ratio (PbI2/FAI > 1.1) in the perovskite precursor displays drastic degradation under ISOS-L1 (100 mW/cm2, 25 °C, maximum power point tracking) conditions. However, postdegradation analysis contradicts these results. Devices with PbI2/FAI ≤ 1.1 are stable under light, but intermittent current density-voltage characterizations indicate that device performance decreases during storage in the dark. Migration of iodide (I-) ions to the electron-transport layer (ETL) and iodine vacancies (VI-+) to the hole-transport layer (HTL) forms localized shunts in the absorber layer. Pinhole formation, surrounded by FA+-rich regions, explains the extent of damage in comparably aged films. In summary, this work emphasizes the importance of reporting stability under different stress conditions, coupled with postdegradation and dark recovery analyses of PSCs to better understand the complexities of perovskite instability under real-life conditions such as expected during outdoor operation.

A novel microRNA miR-4433a-3p as a potential diagnostic biomarker for lung adenocarcinoma.

Background: Lung adenocarcinoma is one of the leading causes of cancer-related deaths because of the lack of early specific clinical indicators. MicroRNAs (miRNAs) have become the focus in lung cancer diagnosis. Further studies are required to explore miRNA expression in the serum of lung adenocarcinoma patients and their correlation with therapy and analyse specific messenger RNA targets to improve the specificity and sensitivity of early diagnosis. Methods: The Toray 3D-Gene miRNA array was used to compare the expression levels of various miRNAs in the sera of patients with lung adenocarcinoma and healthy volunteers. Highly expressed miRNAs were selected for further analysis. To verify the screening results, serum and pleural fluid samples were analysed using qRT-PCR. Serum levels of the miRNAs and their correlation with the clinical information of patients with lung adenocarcinoma were analysed. The functions of miRNAs were further analysed using the Kyoto Encyclopedia of Gene and Genomes and Gene Ontology databases. Results: Microarray analysis identified 60 and 50 miRNAs with upregulated and downregulated expressions, respectively, in the serum of patients with lung adenocarcinoma compared to those in healthy individuals. Using qRT-qPCR to detection of miRNAs expression in the serum or pleural effusion of patients with early and advanced lung adenocarcinoma, we found that miR-4433a-3p could be used as a diagnostic marker and therapeutic evaluation indicator for lung adenocarcinoma. Serum of miR-4433a-3p levels significantly correlated with the clinical stage. miR-4433a-3p may be more suitable than other tumour markers for the early diagnosis and evaluation of therapeutic effects in lung adenocarcinoma. miR-4433a-3p may affect tumour growth and metastasis by acting on target genes (PIK3CD, UBE2J2, ICMT, PRDM16 and others) and regulating tumour-related signalling pathways (MAPK signal pathway, Ras signalling pathway and others). Conclusion: miR-4433a-3p may serve as a biomarker for the early diagnosis of lung adenocarcinoma and monitoring of therapeutic effects.

Indocyanine green-mediated fabrication of urchin-like hydroxyethyl starch nanocarriers for enhanced drug tumor EPR and deep penetration effects.

Effective EPR and tumor penetration are bottlenecks in current nanomedicine therapy. Comosol software was utilized to analyze the motion process of nanoparticles (NPs) with different shapes, from blood vessels to tumor tissue, to address this. By calculation, urchin-like NPs experienced higher drag forces than spherical NPs, facilitating their EPR and tumor penetration effects. Thus, urchin-like indocyanine green-loaded hydroxyethyl starch-cholesterol (ICG@HES-CH) NPs were prepared by leveraging the instability of ICG responding to near-infrared light (NIR). Upon NIR exposure, ICG degraded and partly disintegrated ICG@HES-CH NPs, and its morphology transformed from spherical to urchin-like. Vincristine (VC), as a model drug, was loaded in urchin-like ICG@HES-CH NPs for the treatment of lymphoma. A20 lymphoma cells and 3T3-A20 tumor organoids were employed to investigate the influence of shape on NPs' cellular uptake, penetration pathway, and cytotoxicity. It demonstrated that urchin-like ICG@HES-CH NPs mainly transport across the extracellular matrix through intercellular pathways, easily reaching the deep tumor sites and achieving higher cytotoxicity. In vivo VC distribution and anti-tumor results indicated that urchin-like NPs increased VC EPR and penetration ability, lowering VC neurotoxicity and superior anti-tumor effect. Therefore, urchin-like ICG@HES-CH NPs have great translational potential to be used as chemotherapeutic nanocarriers in anticancer therapy.