ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Objective:To analyze the etiological composition and clinical characteristics of infant hypertension，and provide reference for its diagnosis and treatment.Methods:This is a retrospective case-control study.Retrospective investigation and analysis were conducted on the clinical data of infants discharged from Beijing Children's Hospital Affiliated to Capital Medical University with a diagnosis of "hypertension" from June 1，2016 to September 30，2021，including clinical manifestations，auxiliary examinations，treatment plans，and prognosis.Results:A total of 74 eligible children were collected，including 42 male infants（56.8%）and 32 female infants（43.2%）.A total of 67 cases（90.5%）had clear secondary factors，including 35 cases of kidney disease（47.3%），12 cases of connective tissue disease（16.2%），and 9 cases of hematological tumor disease（12.2%）.At the beginning of the disease，cardiac ultrasound showed that 54 cases（73.0%）had ventricular wall thickening，including mild thickening in 31 cases（57.4%），moderate thickening in 11 cases（20.3%），and severe thickening in 12 cases（22.2%）.After grouping by etiology，the incidence of proteinuria and severe hypertension in the renal hypertension group，as well as those receiving multiple antihypertensive drugs，was significantly higher than that in the non-renal hypertension group（ χ 2=28.493， P<0.001； χ 2=17.283， P<0.001； χ 2=17.358， P<0.001）；Renal disease was risk factor for severe hypertension in infants according to univariate and multivariate logistic regression analysis respectively（ OR=11.176，95% CI：2.882~43.339， P<0.001； OR=11.669，95% CI：2.921~46.624， P<0.001）.Thirty-one children had follow-up records for 6 months or more，and 13（41.9%）still required antihypertensive treatment，of whom 26（83.9%）were no longer recorded as having elevated blood pressure. Conclusion:Infant hypertension is mainly secondary，with a high proportion of renal factors and predisposition to severe hypertension，which requires multiple antihypertensive drugs for control.Active antihypertensive treatment and removal of secondary factors during the acute phase are helpful for controlling hypertension in infants，but further research is needed on treatment options and long-term prognosis.

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*

This study aims to explore the effects and action mechanisms of the active ingredients in Buyang Huanwu Decoction(BYHWD), namely tetramethylpyrazine(TMP) and hydroxy-safflor yellow A(HSYA), on oxygen-glucose deprivation/reglucose-reoxygenation(OGD/R)-induced inflammation and oxidative stress of microglia(MG). Network pharmacology was used to screen the effective monomer ingredients of BYHWD and determine the safe concentration range for each component. Inflammation and oxidative stress models were established to further screen the best ingredient combination and optimal concentration ratio with the most effective anti-inflammatory and antioxidant effects. OGD/R BV2 cell models were constructed, and BV2 cells in the logarithmic growth phase were divided into a normal group, a model group, an HSYA group, a TMP group, and an HSYA + TMP group. Enzyme-linked immunosorbent assay(ELISA) was used to detect the levels of inflammatory cytokines such as interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and interleukin-6(IL-6). Oxidative stress markers, including superoxide dismutase(SOD), nitric oxide(NO), and malondialdehyde(MDA), were also measured. Western blot was used to analyze the protein expression of both inflammation-related pathway [Toll-like receptor 4(TLR4)/nuclear factor-kappa B(NF-κB)] and oxidative stress-related pathway [nuclear factor erythroid 2-related factor 2(Nrf2)/heme oxygenase-1(HO-1)]. Immunofluorescence was used to assess the expression of proteins such as inducible nitric oxide synthase(iNOS) and arginase-1(Arg-1). The most effective ingredients for anti-inflammatory and antioxidant effects in BYHWD were TMP and HSYA. Compared to the normal group, the model group showed significantly increased levels of IL-1β, TNF-α, IL-6, NO, and MDA, along with significantly higher protein expression of NF-κB, TLR4, Nrf2, and HO-1 and significantly lower SOD levels. The differences between the two groups were statistically significant. Compared to the model group, both the HSYA group and the TMP group showed significantly reduced levels of IL-1β, TNF-α, IL-6, NO, and MDA, lower expression of NF-κB and TLR4 proteins, higher levels of SOD, and significantly increased protein expression of Nrf2 and HO-1. Additionally, the expression of the M1-type MG marker iNOS was significantly reduced, while the expression of the M2-type MG marker Arg-1 was significantly increased. The results of the HSYA group and the TMP group had statistically significant differences from those of the model group. Compared to the HSYA group and the TMP group, the HSYA + TMP group showed further significant reductions in IL-1β, TNF-α, IL-6, NO, and MDA levels, along with significant reductions in NF-κB and TLR4 protein expression, an increase in SOD levels, and elevated Nrf2 and HO-1 protein expression. Additionally, the expression of the M1-type MG marker iNOS was reduced, while the M2-type MG marker Arg-1 expression increased significantly in the HSYA + TMP group compared to the TMP or HSYA group. The differences in the results were statistically significant between the HSYA + TMP group and the TMP or HSYA group. The findings indicated that the combined use of HSYA and TMP, the active ingredients of BYHWD, can effectively inhibit OGD/R-induced inflammation and oxidative stress of MG, showing superior effects compared to the individual use of either component.
Oxidative Stress/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Mice ; Glucose/metabolism* ; Cell Line ; Inflammation/genetics* ; Oxygen/metabolism* ; Pyrazines/pharmacology* ; Microglia/metabolism* ; NF-E2-Related Factor 2/immunology* ; NF-kappa B/immunology* ; Toll-Like Receptor 4/immunology* ; Anti-Inflammatory Agents/pharmacology* ; Humans

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective To study the protective effects and mechanism of sacubitril(Sac)/valsartan(Val)on cardiomyocytes of rabbits with heart failure induced by doxorubicin(DOX).Methods Thirty New Zealand rabbits were selected to establish DOX-induced heart failure rabbit model.Twenty-five rabbits with successful modeling were randomly assigned to model group(DOX group,n=9),DOX+Val group(n=8),and DOX+Sac/Val group(n=8);and another 8 New Zealand rabbits were selected as blank group.The DOX+Val group was gavaged with 4.65 mg/kg Val suspension each time,the DOX+Sac/Val group was gavaged with 9.3 mg/kg Sac/Val suspension each time,and the blank group and DOX group were gavaged with equal volume of distilled water each time.Each group was gavaged twice a day for 8 weeks.After 8 weeks of administration,echocardiography was used to measure left ventricular end-diastolic diameter(LVDD),left ventricular end-systolic diameter(LVSD),left ventricular ejection fraction(LVEF),and left ventricular fractional shortening(LVFS).The heart mass index(HMI)and left ventricular mass index(LVMI)were calculated.The pathological morphology and myocardial fibrosis of myocardial tissue were observed by hematoxylin-eosin(H-E)and Masson staining.The ultrastructure of cardiomyocytes was observed by transmission electron microscope.Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling(TUNEL)staining was used to observe cardiomyocytes apoptosis and apoptosis rate was calculated.Enzyme-linked immunosorbent assay(ELISA)was used to detect the levels of N-terminal pro-brain natriuretic peptide(NT-proBNP),high-sensitivity cardiac troponin I(Hs-cTNI),angiotensin Ⅱ(Ang Ⅱ),aldosterone(ALD),atrial natriuretic peptide(ANP),brain natriuretic peptide(BNP),cyclic guanosine monophosphate(cGMP),and protein kinase G(PKG)in serum.Quantitative polymerase chain reaction(qPCR)was used to detect the expression of natriuretic peptide receptor A(NPR-A),cGMP-specific phosphodiesterase 5A(PDE5A[cGMP]),PKG,B-cell lymphoma 2(Bcl-2),Bcl-2 associated X protein(Bax),and cysteine aspartate protease 3(caspase 3)mRNA in myocardial tissue.Western blotting was used to detect the expression of phosphorylated cAMP response element-binding protein(p-CREB),phosphorylated Bcl-2 related death promoting factor(p-Bad),Bcl-2,Bax,and caspase 3 proteins in myocardial tissue.Results Compared with the blank group,the LVDD and LVSD in the DOX group were increased(both P<0.01),the LVEF and LVFS were decreased(both P<0.01)and the HMI and LVMI were increased(both P<0.01);the apoptosis and apoptosis rate of cardiomyocytes were increased(P<0.01);the levels of NT-proBNP,Hs-cTNI,Ang Ⅱ,ALD,ANP,BNP,cGMP and PKG and the expression of NPR-A,PDE5A(cGMP),PKG,p-CREB,Bax and caspase 3 were all increased(all P<0.01),while the expression of Bcl-2 was decreased(P<0.01),and the expression of p-Bad had no significant difference(P>0.05).Compared with the DOX group,the LVDD and LVSD of the DOX+Sac/Val group and DOX+Val group were decreased(all P<0.01),the LVEF and LVFS were increased(all P<0.01)and the HMI and the LVMI were decreased(all P<0.01);the apoptosis and apoptosis rate of cardiomyocytes were decreased(all P<0.01);the levels of NT-proBNP,Hs-cTNI,Ang Ⅱ,ALD,ANP,BNP,cGMP and PKG and the expression of NPR-A,PDE5A(cGMP),PKG,Bax and caspase 3 were all decreased(all P<0.01),while the expression of Bcl-2 was increased(P<0.01);and the expression of p-CREB and p-Bad was increased in the DOX+Sac/Val group(both P<0.01),but there was no significant difference in the DOX+Val group(both P>0.05).Compared with the DOX+Val group,the DOX+Sac/Val group showed a decrease in all indicators except for LVEF,LVFS,NPR-A,ANP,BNP,cGMP,PDE5A(cGMP),PKG,p-CREB,p-Bad,and Bcl-2,which were all elevated(all P<0.05).Myocardial pathology and transmission electron microscopy showed that Sac/Val effectively protected cardiomyocytes,reduced cardiomyocytes apoptosis and myocardial fibrosis,and these effects were significantly better than those of Val.Conclusion Sac/Val can effectively reduce cardiomyocytes apoptosis,improve cardiac function and reduce myocardial fibrosis in rabbits with heart failure,and these effects are superior to Val.Its mechanism may be related to activating the NPR-A/cGMP/PKG signaling pathway and inhibiting renin-angiotensin-aldosterone system.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Endosomes are characterized by the presence of various phosphoinositides that are essential for defining the membrane properties. However, the interplay between endosomal phosphoinositides metabolism and innate immunity is yet to be fully understood. Here, our findings highlight the evolutionary continuity of RAB-10/Rab10's involvement in regulating innate immunity. Upon infection of Caenorhabditis elegans with Pseudomonas aeruginosa, an increase in RAB-10 activity was observed in the intestine. Conversely, when RAB-10 was absent, the intestinal diacylglycerols (DAGs) decreased, and the animal's response to the pathogen was impaired. Further research revealed that UNC-16/JIP3 acts as an RAB-10 effector, facilitating the recruitment of phospholipase EGL-8 to endosomes. This leads to a decrease in endosomal phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) and an elevation of DAGs, as well as the activation of the PMK-1/p38 MAPK innate immune pathway. It is noteworthy that the dimerization of UNC-16 is a prerequisite for its interaction with RAB-10(GTP) and the recruitment of EGL-8. Moreover, we ascertained that the rise in RAB-10 activity, due to infection, was attributed to the augmented expression of LET-413/Erbin, and the nuclear receptor NHR-25/NR5A1/2 was determined to be indispensable for this increase. Hence, this study illuminates the significance of endosomal PI(4,5)P2 catabolism in boosting innate immunity and outlines an NHR-25-mediated mechanism for pathogen detection in intestinal epithelia.
Animals ; Caenorhabditis elegans/genetics* ; Endosomes/immunology* ; Caenorhabditis elegans Proteins/immunology* ; Phosphatidylinositol 4,5-Diphosphate/immunology* ; Immunity, Innate ; Pseudomonas aeruginosa/immunology* ; rab GTP-Binding Proteins/genetics* ; Diglycerides/metabolism*