Chronic mucocutaneous candidiasis (CMC) is an infectious phenotype characterized by recurrent or persistent infections caused by Candida species that affect the skin, nails, oral, and genital mucosae for a duration exceeding six months. Current research suggests that CMC is an immunodeficiency disease with a complex pathogenesis. Patients with CMC have various defects in nonspecific and/or specific immunity against Candida infection, resulting in the inability of patients to defend themselves against Candida infection. CMC can be stratified into primary CMC and secondary CMC based on etiology. Primary CMC is often associated with genetic mutations leading to immunodeficiencies in T helper cell 17 and interleukin-17, whereas secondary CMC is frequently linked to factors such as human immunodeficiency virus infection, diabetes mellitus, and immunosuppressive therapy. Primary CMC typically manifests as Candida infections, with distinct genetic mutations often correlating to varied concomitant symptoms. Secondary CMC may present with not only superficial mucosal Candida infections and manifestations of the underlying primary disease but also with invasive fungal infections. Diagnosing CMC requires an integration of medical history and clinical presentation, supplemented by the outcomes of auxiliary diagnostic procedures, including microscopic examination of fungal smear, fungal culture, immunological testing, and genetic sequencing and analysis. Furthermore, confirming primary CMC requires exclusion of the aforementioned secondary factors. At present, antifungal drugs such as triazoles, echinocandins, and polyenes are the main treatment for CMC. Moreover, immunotherapy with biologics such as Janus kinase (JAK) inhibitors provides more options for the clinical treatment of patients with CMC. Gene therapy also has potential clinical application value. In this review, we discuss the etiologies, pathogenesis, clinical manifestations, diagnosis, and treatments of CMC, aiming to provide a reference for the clinical diagnosis and treatment of CMC.

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.

Background/Aims: Lenvatinib resistance remains a critical barrier in advanced hepatocellular carcinoma (HCC) therapy. However, the underlying mechanisms and strategies for reversing resistance remain incompletely understood. Methods: Integrated transcriptomics of lenvatinib-resistant patient tumors and an acquired-resistance murine model identified a novel macrophage subpopulation. Functional validation employed CRISPR-SAM screening, conditioned medium (CM) assays, subcutaneous/orthotopic xenografts, patient-derived organoids (PDOs), and patient-derived xenografts (PDXs). Mechanistic studies included ChIP-qPCR, co-immunoprecipitation, and pharmacologic targeting. Clinical relevance was assessed in a retrospective cohort. Results: Resistant HCC exhibited significant enrichment of a COLEC12high TAM subset , which correlated with poor survival and treatment response. These TAMs secreted neuregulin-1 (NRG1) , activating HER2/HER3-AKT signaling in tumor cells to drive cancer stemness and lenvatinib resistance. Mechanistically, in TAMs COLEC12 sequestered STAT1 in the cytoplasm, preventing its phosphorylation, and thereby derepressing STAT3-mediated NRG1 transcription. Depletion of NRG1 reversed the stemness phenotypes and resensitized tumors to lenvatinib both in vitro and in vivo. Clinically, high NRG1 expression predicted an inferior lenvatinib response and shorter survival. Crucially, the bispecific anti-HER2/HER3 antibody zenocutuzumab restored lenvatinib efficacy in PDOs, PDXs, and murine models. Conclusions Our work establishes the COLEC12high TAM/NRG1 axis as a master regulator of therapeutic resistance and identifies NRG1 as a predictive biomarker, providing a clinically actionable strategy to overcome lenvatinib resistance in HCC.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

[Objective] To retrospectively analyze the detection results of alanine aminotransferase (ALT) unqualified repeat blood donors in Guangzhou, so as to provide evidence for further expanding the repeat blood donor pool, reducing the rate of blood discarding and improving the qualified rate of blood test. [Methods] Blood donors with unqualified ALT in Guangzhou Blood Center from January 2018 to April 2024 were selected as the research objects. The past blood donation and population characteristics were analyzed according to the number of blood donations and ALT unqualified times. [Results] Among repeat blood donors with previous ALT disqualification, 99.5% to 99.7% did not have reactive markers for transfusion-transmitted diseases (TTD), which was higher than the rate among first-time blood donors with unqualified ALT (95.8%) (P<0.05). The rate of single-item ALT disqualification in repeat blood donors was higher in males than in females (P<0.05); it also varied by age (18-25 years > 26-35 years > 36-45 years > over 45 years) (P<0.05); and by quarter (third and fourth quarters > first and second quarters) (P<0.05). The ALT unqualified rate was significantly higher whole blood donors than that of platelet donors and returning blood donors (P<0.05). The overall ALT level (51.0 U/L), individual ALT level (56.0 U/L) and individual ALT unqualified rate (66.7%) of repeat blood donors with multiple ALT disqualifications were higher than those of repeat blood donors with single-item ALT disqualifications (26.0 U/L, 38.5 U/L, and 33.3%, respectively) (P<0.05). Moreover, as the number of ALT disqualifications increased, the overall level of ALT in repeat blood donors also increased (P<0.05), and the average level of individual ALT and individual ALT unqualified ratio tended to increase. Repeat blood donors with frequent ALT disqualifications had higher ALT levels (69.0 U/L). [Conclusion] The ALT unqualified rates of repeat blood donors were mostly non-specific elevation without TTD. Repeat blood donors with multiple ALT disqualifications tend to have continuous high ALT. Moreover, and with the increase of ALT disqualifications times, the overall ALT levels the average individual ALT levels and individual ALT unqualified rates showed an increasing trend.

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.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Approximately 300 million tons of plastic are produced globally each year,which has a serious impact on human health,marine life and the livestock industry.Microplastics have also been detected in meat and milk samples.Research has shown that nanoplastics(NP)(＜1 μm)and mi-croplastics(MP)(1 μm-5 mm)can affect the digestive,immune and reproductive systems of ani-mals.This experiment aims to investigate whether NP and MP regulate autophagy and damage the nervous system and spatial memory of animals.This experiment was divided into control group,nanoplastic group(PS-NP group,0.1 μm)and microplastic group(PS-MP group,1 μm),with 20 mice in each group.The mice were given 0.5 mL of PS-NP and PS-MP every day for 35 consecutive days,followed by neck amputation and brain analysis.The results showed that NPs and MPs of dif-ferent diameters caused varying degrees of damage to the brains of mice.In the behavioral tests of new object recognition,barnes maze and Y-shaped maze spatial memory,compared with the control group,the PS-NP group and PS-MP group showed a significant decrease in spatial memory ability of mice.HE staining results showed that neuronal cells in the PS-NP and PS-MP groups of mice exhibited shrinkage,decreased cell volume and deepened staining.The number of Nissl bodies de-creased,leading to dissolution and disappearance.RT-PCR and Western blot results showed that compared with the control group,the expression of glutamate receptors NR1,NR2A and NR2B in-creased in mice administered NP and MP orally,while the expression of autophagy related proteins Parkin,LC3B and Beclin1 was inhibited.In summary,this study suggests that nanoplastics and mi-croplastics stimulate glutamate receptors in mice by inhibiting the autophagy pathway,leading to impaired spatial memory.

OBJECTIVE To explore the ameliorative effect of Tiaozhi Xiaoban Mixture on atherosclerosis and the potential role of long non-coding RNA(Linc RNA)in anti-atherosclerosis.METHODS A model of atherosclerosis was established in SD rats subjec-ted to a high-fat diet.At 4 weeks post-modeling,thoracic aortic tissues from atherosclerotic rats were collected for hematoxylin-eosin(HE)staining to systematically evaluate the anti-atherosclerotic effects of Tiaozhi Xiaoban Mixture at different doses.Biochemical kits were utilized to assess relevant indices related to blood lipid levels as well as liver and kidney function,thereby evaluating the impact of Tiaozhi Xiaoban Mixture on these parameters.Enzyme-linked immunosorbent assay(ELISA)was employed to measure serum inflam-mation markers influenced by Tiaozhi Xiaoban Mixture.Additionally,TUNEL staining and Western blot analysis were conducted to ex-amine the apoptotic effects of Tiaozhi Xiaoban Mixture on thoracic aorta tissue.Finally,qPCR was used to detect the expression levels of Line-HC,MALAT1,etc.,in order to evaluate how Tiaozhi Xiaoban Mixture affecting these specific RNA molecules.RESULTS Following treatment with Tiaozhi Xiaoban Mixture,the blood lipid profiles indicated that total cholesterol(TC),triglycerides(TG),and low-density lipoprotein cholesterol(LDL-C)were significantly down-regulated(P＜0.05,P＜0.01),while high-density lipopro-tein cholesterol(HDL-C)levels were up-regulated in the atherosclerotic rats.Moreover,serum levels of liver and kidney function markers such as aspartate aminotransferase(AST),alanine aminotransferase(ALT),blood urea nitrogen(BUN),and creatinine(Cr)exhibited down-regulation(P＜0.05,P＜0.01).Additionally,pro-inflammatory factors including interleukin-6(IL-6),interleukin-1 beta(IL-1β),tumor necrosis factor-alpha(TNF-α),high-sensitivity C-reactive protein(hs-CRP),and matrix metallopeptidase 9(MMP-9)were also reduced(P＜0.01),whereas the anti-inflammatory factor interleukin-10(IL-10)was found to be elevated(P＜0.01).Furthermore,after oral administration of Tiaozhi Xiaoban Mixture,expression levels of apoptosis-related factors NLRP3,ASC,Cleaved Caspase-1,Cleaved IL-1 β,Puma,Bax,Noxa,and MDM2 in thoracic aorta tissues from the atherosclerotic rats showed sig-nificant down-regulation(P＜0.05,P＜0.01).Notably,following treatment with Tiaozhi Xiaoban Mixture,mRNA levels of Linc-HC decreased while mRNA expression of MALAT1 increased(P＜0.05,P＜0.01).CONCLUSION Tiaozhi Xiaoban Mixture may inhibit the expression of Linc-HC and up-regulate the expression of MALAT1 to reduce the formation of atherosclerotic plaque,improve ab-normal blood lipids and liver and kidney function,alleviate inflammation and inhibit apoptosis.