Preparation of pH-sensitive porous polylactic acid-based medical dressing with self-pumping function.

Excessive exudation from the wound site and the difficulty of determining the state of wound healing can make medical management more difficult and, in extreme cases, lead to wound deterioration. In this study, we fabricated a pH-sensitive colorimetric chronic wound dressing with self-pumping function using electrostatic spinning technology. It consisted of three layers: a polylactic acid-curcumin (PCPLLA) hydrophobic layer, a hydrolyzed polyacrylonitrile (HPAN) transfer layer, and a polyacrylonitrile-purple kale anthocyanin (PAN-PCA) hydrophilic layer. The results showed that the preparation of porous PLLA fiber membrane loaded with 0.2 % Cur was achieved by adjusting the spinning-related parameters, which could ensure that the composite dressing had sufficient anti-inflammatory, antibacterial and antioxidant properties. The HPAN membrane treated with alkali for 30 min had significantly enhanced liquid wetting ability, and the unidirectional transport of liquid could be achieved by simple combination with the 20 um PCPLLA fiber membrane. In addition, the 4 % loaded PCA showed more obvious color difference than the colorimetric membrane. In vivo and ex vivo experiments have demonstrated the potential of multifunctional dressings for the treatment of chronic wounds.

Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships.

OBJECTIVE: Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist 90 (SCL-90) designed to minimize patient response burden. Utilizing machine learning algorithms, the study sought to construct classification models capable of distinguishing between depression and anxiety. METHODS: The study included 4262 individuals currently experiencing depression alone (n = 2998), anxiety alone (n = 716), or both depression and anxiety (n = 548). Counterfactual diagnosis was used to construct a causal network on the dataset. Employing a causal network, the SCL-90 was simplified. Items that have causality with only depression, only anxiety and both depression and anxiety were selected, and these streamlined items served as input features for four distinct machine learning algorithms, facilitating the creation of classification models for distinguishing depression and anxiety. RESULTS: Cross-validation demonstrated the performance of the classification models with the following metrics: (1) K-nearest neighbors (AUC = 0.924, Acc = 92.81 %); (2) support vector machine (AUC = 0.937, Acc = 94.38 %); (3) random forest (AUC = 0.918, Acc = 94.38 %); and (4) adaptive boosting (AUC = 0.882, Acc = 94.38 %). Notably, the support vector machine excelled, with the highest AUC and superior accuracy. CONCLUSION: Incorporating the simplified SCL-90 and machine learning presents a promising, efficient, and cost-effective tool for the precise identification of depression and anxiety.

Pyroptotic macrophages promote proliferation and chemotherapy resistance of peripheral T-cell lymphoma via TLR4 signaling pathway.

Peripheral T-cell lymphoma (PTCL) is a highly aggressive type of non-Hodgkin's lymphoma with a poor prognosis. Pyroptosis is a newly discovered procedural cell death mode, which has been implicated to occur in both tumor cells and immune cells. However, the occurrence and effect of pyroptosis on PTCL remain unclear. Here, we found that pyroptosis occurred in interstitial macrophages of PTCL rather than in tumor cells. In clinical specimens, macrophage pyroptosis was associated with a poor prognosis of PTCL. In vitro experiments and gene sequencing results showed that pyroptotic macrophages could upregulate the expression of TLR4 through secreting inflammatory cytokines IL-18. Upregulated TLR4 activated its downstream NF-κB anti-apoptotic signaling pathway, thus leading to malignant proliferation and chemotherapy resistance of tumor cells. Moreover, the expression of factors such as XIAP in the NF-κB anti-apoptotic pathway was downregulated after the knockdown of TLR4, and the malignant promotion effect of pyroptotic macrophages on PTCL cells was also reversed. Our findings revealed the mechanism of pyroptotic macrophages promoting the malignant biological behavior of PTCL and elucidated the key role of TLR4 in this process. In-depth analysis of this mechanism will contribute to understanding the regulatory effect of PTCL by the tumor microenvironment and providing new ideas for the clinical treatment of PTCL.

Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. / åŒºåˆ†èƒ¶è´¨æ¯ç»†èƒžç˜¤å’Œå•å‘æ€§è„‘è½¬ç§»ç˜¤çš„å¤šæ¨¡æ€èžåˆæ·±åº¦å­¦ä¹ æ¨¡åž‹çš„å¼€å‘å’ŒéªŒè¯.

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Surufatinib combined with photodynamic therapy induces ferroptosis to inhibit cholangiocarcinoma in vitro and in tumor models.

The curative effect of single therapy for advanced cholangiocarcinoma (CCA) is poor, thus investigating combined treatment strategies holds promise for improving prognosis. Surufatinib (SUR) is a novel multikinase inhibitor that has been confirmed to prolong survival of patients with advanced CCA. Photodynamic therapy (PDT) can also ablate advanced CCA and relieve biliary obstruction. In this study, we explored the anti-CCA effect of SUR combined with PDT, and explored the underlying mechanism. We found that SUR could effectively inhibit the abilities of proliferation, migration and metastasis in CCA cells (HUCCT-1, RBE). The ability of SUR to inhibit CCA was also confirmed by the HUCCT-1 cell xenograft model in Balb/c nude mice and CCA patient-derived organoids. SUR combined with PDT can significantly enhance the inhibitory effect on CCA, and can be alleviated by two ferroptosis inhibitors (Ferrostatin-1, Deferoxamine). By detecting the level of reactive oxygen species, lipid peroxides, malondialdehyde and glutathione, we further confirmed that SUR combined with PDT can inhibit CCA cells by inducing ferroptosis. Glutathione peroxidase 4 (GPX4) belongs to the glutathione peroxidase family and is mainly responsible for the metabolism of intracellular hydrogen peroxide. GPX4 inhibits ferroptosis by reducing cytotoxic lipid peroxides (L-OOH) to the corresponding alcohols (L-OH). Acyl-CoA synthetase long-chain family member 4 (ACSL4) is a member of the long-chain fatty acid coenzyme a synthetase family and is mainly involved in the biosynthesis and catabolism of fatty acids. ACSL4 induces ferroptosis by promoting the accumulation of lipid peroxides. Both SUR and PDT can induce ferroptosis by promoting ACSL4 and inhibiting GPX4. The regulation effect is found to be more significant in combined treatment group. In conclusion, SUR combined with PDT exerted an anti-CCA effect by inducing ferroptosis. Combination therapy provides a new idea for the clinical treatment of CCA.

AURKA emerges as a vulnerable target for KEAP1-deficient non-small cell lung cancer by activation of asparagine synthesis.

AURKA is an established target for cancer therapy; however, the efficacy of its inhibitors in clinical trials is hindered by differential response rates across different tumor subtypes. In this study, we demonstrate AURKA regulates amino acid synthesis, rendering it a vulnerable target in KEAP1-deficient non-small cell lung cancer (NSCLC). Through CRISPR metabolic screens, we identified that KEAP1-knockdown cells showed the highest sensitivity to the AURKA inhibitor MLN8237. Subsequent investigations confirmed that KEAP1 deficiency heightens the susceptibility of NSCLC cells to AURKA inhibition both in vitro and in vivo, with the response depending on NRF2 activation. Mechanistically, AURKA interacts with the eIF2α kinase GCN2 and maintains its phosphorylation to regulate eIF2α-ATF4-mediated amino acid biosynthesis. AURKA inhibition restrains the expression of asparagine synthetase (ASNS), making KEAP1-deficient NSCLC cells vulnerable to AURKA inhibitors, in which ASNS is highly expressed. Our study unveils the pivotal role of AURKA in amino acid metabolism and identifies a specific metabolic indication for AURKA inhibitors. These findings also provide a novel clinical therapeutic target for KEAP1-mutant/deficient NSCLC, which is characterized by resistance to radiotherapy, chemotherapy, and targeted therapy.

Microbial bile acid metabolite ameliorates mycophenolate mofetil-induced gastrointestinal toxicity through vitamin D3 receptor.

Mycophenolate mofetil (MMF) is one of the most used immunosuppressive drugs in organ transplantation, but frequent gastrointestinal (GI) side effects through unknown mechanisms limit its clinical use. Gut microbiota and its metabolites were recently reported to play a vital role in MMF-induced GI toxicity, but the specific mechanism of how they interact with the human body is still unclear. Here, we found that secondary bile acids (BAs), as bacterial metabolites, were significantly reduced by MMF administration in the gut of mice. Microbiome data and fecal microbiota transfer model supported a microbiota-dependent effect on the reduction of secondary BAs. Supplementation of the secondary BA lithocholic acid alleviated MMF-induced weight loss, colonic inflammation, and oxidative phosphorylation damage. Genetic deletion of the vitamin D3 receptor (VDR), which serves as a primary colonic BA receptor, in colonic epithelial cells (VDRΔIEC) abolished the therapeutic effect of lithocholic acid on MMF-induced GI toxicity. Impressively, we discovered that paricalcitol, a Food and Drug Administration-approved VDR agonist that has been used in clinics for years, could effectively alleviate MMF-induced GI toxicity. Our study reveals a previously unrecognized mechanism of gut microbiota, BAs, and VDR signaling in MMF-induced GI side effects, offering potential therapeutic strategies for clinics.

Oxidized cellulose-filled double thermo/pH-sensitive hydrogel for local chemo-photothermal therapy in breast cancer.

Lumpectomy plus radiation is a treatment option offering better survival than conventional mastectomy for patients with early-stage breast cancer. However, successive radioactive therapy remains tedious and unsafe with severe adverse reactions and secondary injury. Herein, a composite hydrogel with pH- and photothermal double-sensitive activity is developed via physical crosslinking. The composite hydrogel incorporated with tempo-oxidized cellulose nanofiber (TOCN), polyvinyl alcohol (PVA) and a polydopamine (PDA) coating for photothermal therapy (PTT) triggered in situ release of doxorubicin (DOX) drug was utilized to optimize postoperative strategies of malignant tumors inhibition. The incorporation of TOCN significantly affects the performance of composite hydrogels. The best-performing TOCN/PVA7 was selected for drug loading and polydopamine coating by rational design. In vitro studies have demonstrated that the composite hydrogel exhibited high NIR photothermal conversion efficiency, benign cytotoxicity to L929 cells, pH-dependent release profiles, and strong MCF-7 cell inhibitory effects. Then the TOCN/PVA7-PDA@DOX hydrogel is implanted into the tumor resection cavity for local in vivo chemo-photothermal synergistical therapy to ablate residue tumor tissues. Overall, this work suggests that such a chemo-photothermal hydrogel delivery system has great potential as a promising tool for the postsurgical management of breast cancer.

Realization of all-band-flat photonic lattices.

Flatbands play an important role in correlated quantum matter and have promising applications in photonic lattices. Synthetic magnetic fields and destructive interference in lattices are traditionally used to obtain flatbands. However, such methods can only obtain a few flatbands with most bands remaining dispersive. Here we realize all-band-flat photonic lattices of an arbitrary size by precisely controlling the coupling strengths between lattice sites to mimic those in Fock-state lattices. This allows us to go beyond the perturbative regime of strain engineering and group all eigenmodes in flatbands, which simultaneously achieves high band flatness and large usable bandwidth. We map out the distribution of each flatband in the lattices and selectively excite the eigenmodes with different chiralities. Our method paves a way in controlling band structure and topology of photonic lattices.

Prediction Model and Risk Factor Analysis of Adjacent Segment Disease After L4-5 Transforaminal Lumbar Interbody Fusion Through Preoperative Radiographic Features.

STUDY DESIGN: A retrospective study. OBJECTIVE: To investigate the risk of adjacent segment disease (ASD) after L4-5 transforaminal lumbar interbody fusion (TLIF) in patients diagnosed with lumbar spinal stenosis (LSS), a prediction model for ASD is established and validated. METHODS: A retrospective study was carried out on a sample of 290 patients who underwent L4-5 TLIF at Zhongda Hospital, Southeast University, from January 2015 to January 2021. The study collected baseline data and preoperative radiographic features of L3-4 and L5-S1. The determination of the outcome variable was based on X-ray results spanning over 24 months and JOA scores. Multivariate logistic regression was used to identify the risk factors in constructing a nomogram. RESULTS: Independent risk factors for L3-4 degeneration after TLIF included osteoarthritis of L3-4 facet joints, L3-4 foraminal stenosis, L4 upper endplate osteochondritis, L3-4 local lordosis angle, and L3-4 spinal stenosis. Independent risk factors for L5-S1 degeneration after TLIF included osteoarthritis of L5-S1 facet joints, L5-S1 intervertebral disc degeneration, L5-S1 spinal stenosis, L5-S1 coronal imbalance, and S1 upper endplate osteochondritis. A predictive model was developed. The AUC for the prediction models at L3-4 and L5-S1 were .945 and .956. The calibration curve demonstrated good consistency between the predicted and actual probabilities. The DCA curve indicated the clinical benefit and practical value of this predictive model. CONCLUSION: This study established nomograms for postoperative degeneration at L3-4 and L5-S1 based on selected preoperative radiographic features. These models provide a valuable auxiliary decision-making system for clinicians and aid in early surgical decisions.

Effects of In Vitro Fermentation of Polysialic Acid and Sialic Acid on Gut Microbial Community Composition and Metabolites in Healthy Humans.

The influence of polysialic acid (PSA) and sialic acid (SA) on the gut microbial community composition and metabolites in healthy humans was investigated using a bionic gastrointestinal reactor. The results indicated that PSA and SA significantly changed the gut microbiota and metabolites to different degrees. PSA can increase the relative abundances of Faecalibacterium and Allisonella, whereas SA can increase those of Bifidobacterium and Megamonas. Both can significantly increase the content of short-chain fatty acids. The results of metabolome analysis showed that PSA can upregulate ergosterol peroxide and gallic acid and downregulate the harmful metabolite N-acetylputrescine. SA can upregulate 4-pyridoxic acid and lipoic acid. PSA and SA affect gut microbiota and metabolites in different ways and have positive effects on human health. These results will provide a reference for the further development of PSA- and SA-related functional foods and health products.

Aurora kinase A-mediated phosphorylation triggers structural alteration of Rab1A to enhance ER complexity during mitosis.

Morphological rearrangement of the endoplasmic reticulum (ER) is critical for metazoan mitosis. Yet, how the ER is remodeled by the mitotic signaling remains unclear. Here, we report that mitotic Aurora kinase A (AURKA) employs a small GTPase, Rab1A, to direct ER remodeling. During mitosis, AURKA phosphorylates Rab1A at Thr75. Structural analysis demonstrates that Thr75 phosphorylation renders Rab1A in a constantly active state by preventing interaction with GDP-dissociation inhibitor (GDI). Activated Rab1A is retained on the ER and induces the oligomerization of ER-shaping protein RTNs and REEPs, eventually triggering an increase of ER complexity. In various models, from Caenorhabditis elegans and Drosophila to mammals, inhibition of Rab1AThr75 phosphorylation by genetic modifications disrupts ER remodeling. Thus, our study reveals an evolutionarily conserved mechanism explaining how mitotic kinase controls ER remodeling and uncovers a critical function of Rab GTPases in metaphase.

Z-Type Heterojunction MnO2@g-C3N4 Photocatalyst-Activated Peroxymonosulfate for the Removal of Tetracycline Hydrochloride in Water.

A Z-type heterojunction MnO2@g-C3N4 photocatalyst with excellent performance was synthesized by an easy high-temperature thermal polymerization approach and combined with peroxymonosulfate (PMS) oxidation technology for highly efficient degrading of tetracycline hydrochloride (TC). Analysis of the morphological structural and photoelectric properties of the catalysts was achieved through different characterization approaches, showing that the addition of MnO2 heightened visible light absorption by g-C3N4. The Mn1-CN1/PMS system showed the best degradation of TC wastewater, with a TC degradation efficiency of 96.97% following 180 min of treatment. This was an approximate 38.65% increase over the g-C3N4/PMS system. Additionally, the Mn1-CN1 catalyst exhibited excellent stability and reusability. The active species trapping experiment indicated •OH and SO4•- remained the primary active species to degrade TC in the combined system. TC degradation pathways and intermediate products were determined. The Three-Dimensional Excitation-Emission Matrix (3DEEM) was employed for analyzing changes in the molecular structure in TC photocatalytic degradation. The biological toxicity of TC and its degradation intermediates were investigated via the Toxicity Estimation Software Test (T.E.S.T.). The research offers fresh thinking for water environment pollution treatment.

A novel antagonist of the CCL5/CCR5 axis suppresses the tumor growth and metastasis of triple-negative breast cancer by CCR5-YAP1 regulation.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC) with a high mortality rate, and few effective therapeutic strategies are available. CCL5/CCR5 is an appealing immunotherapeutic target for TNBC. However, its signaling mechanism is poorly understood and its direct antagonists have not been reported. Here, we developed a high-throughput screening (HTS) assay for discovering its antagonists. Verteporfin was identified as a more selective and potent antagonist than the known CCR5 antagonist maraviroc. Without photodynamic therapy, verteporfin demonstrated significant inhibition on TNBC tumor growth through immune regulation, remarkable suppression of lung metastasis by cell-intrinsic mechanism, and a significant extension of overall survival in vivo. Mechanistically, CCR5 was found to be essential for expression of the key hippo effector YAP1. It promoted YAP1 transcription via HIF-1α and exerted further control over the migration of CD8+ T, NK, and MDSC immune cells through chemokines CXCL16 and CXCL8 which were identified from RNA-seq. Moreover, the CCR5-YAP1 axis played a vital role in promoting metastasis by modulating ß-catenin and core epithelial-mesenchymal transition transcription factors ZEB1 and ZEB2. It is noteworthy that the regulatory relationship between CCR5 and YAP1 was observed across various BC subtypes, TNBC patients, and showed potential relevance in fifteen additional cancer types. Overall, this study introduced an easy-to-use HTS assay that streamlines the discovery of CCL5/CCR5 axis antagonists. Verteporfin was identified as a specific molecular probe of this axis with great potentials as a therapeutic agent for treating sixteen malignant diseases characterized by heightened CCR5 and YAP1 levels.

Interferon-γ and its response are determinants of antibody-mediated rejection and clinical outcomes in patients after renal transplantation.

Interferon-γ (IFN-γ) is an important cytokine in tissue homeostasis and immune response, while studies about it in antibody-mediated rejection (ABMR) are very limited. This study aims to comprehensively elucidate the role of IFN-γ in ABMR after renal transplantation. In six renal transplantation cohorts, the IFN-γ responses (IFNGR) biological process was consistently top up-regulated in ABMR compared to stable renal function or even T cell-mediated rejection in both allografts and peripheral blood. According to single-cell analysis, IFNGR levels were found to be broadly elevated in most cell types in allografts and peripheral blood with ABMR. In allografts with ABMR, M1 macrophages had the highest IFNGR levels and were heavily infiltrated, while kidney resident M2 macrophages were nearly absent. In peripheral blood, CD14+ monocytes had the top IFNGR level and were significantly increased in ABMR. Immunofluorescence assay showed that levels of IFN-γ and M1 macrophages were sharply elevated in allografts with ABMR than non-rejection. Importantly, the IFNGR level in allografts was identified as a strong risk factor for long-term renal graft survival. Together, this study systematically analyzed multi-omics from thirteen independent cohorts and identified IFN-γ and IFNGR as determinants of ABMR and clinical outcomes in patients after renal transplantation.

Construction of an economical xylose-utilizing Saccharomyces cerevisiae and its ethanol fermentation.

Traditional industrial Saccharomyces cerevisiae could not metabolize xylose due to the lack of a specific enzyme system for the reaction from xylose to xylulose. This study aims to metabolically remould industrial S. cerevisiae for the purpose of utilizing both glucose and xylose with high efficiency. Heterologous gene xylA from Piromyces and homologous genes related to xylose utilization were selected to construct expression cassettes and integrated into genome. The engineered strain was domesticated with industrial material under optimizing conditions subsequently to further improve xylose utilization rates. The resulting S. cerevisiae strain ABX0928-0630 exhibits a rapid growth rate and possesses near 100% xylose utilization efficiency to produce ethanol with industrial material. Pilot-scale fermentation indicated the predominant feature of ABX0928-0630 for industrial application, with ethanol yield of 0.48 g/g sugars after 48 hours and volumetric xylose consumption rate of 0.87 g/l/h during the first 24 hours. Transcriptome analysis during the modification and domestication process revealed a significant increase in the expression level of pathways associated with sugar metabolism and sugar sensing. Meanwhile, genes related to glycerol lipid metabolism exhibited a pattern of initial increase followed by a subsequent decrease, providing a valuable reference for the construction of efficient xylose-fermenting strains.

Abnormal functional connectivity within the prefrontal cortex is associated with multiple plasma lipid species in major depressive disorder.

BACKGROUND: Abnormalities in functional connectivity (FC) in major depressive disorder (MDD) have been widely reported. Analysis of the relationship between FC and plasma lipid profiles would be meaningful in the exploration of pathophysiological mechanisms and helpful for the identification of biomarkers for MDD. METHODS: Patients with MDD (n = 49) and healthy controls (HC, n = 87) were recruited. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected for FC construction. The plasma lipid profiles were acquired using ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS) analysis and clustered as co-expression modules. The differential FC and lipid modules between HCs and patients with MDD were identified, and then the association between FC and lipid co-expression modules was analyzed using correlation analysis. The modules associated molecular function was explored using metabolite set enrichment analysis (MSEA). RESULTS: MDD-associated FC and lipid co-expression modules were identified. One module was associated with FC values between the right orbital part of the middle frontal gyrus and the opercular part of the left inferior frontal gyrus, which was enriched in lipid sets of diacylglycerols and fatty alcohols; another module was associated with FC values between the right middle frontal gyrus and the right anterior cingulate and paracingulate gyri, which was enriched in lipid sets of glycerophosphocholines and glycerophosphoethanolamines. CONCLUSION: Our results indicated that abnormal FC in the prefrontal cortex is associated with multiple plasma lipid species, which may provide novel clues for exploring the pathophysiology of MDD.

Dual consistency regularization with subjective logic for semi-supervised medical image segmentation.

Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical image analysis. It significantly reduces the time and cost involved in labeling data. Current methods primarily focus on consistency regularization and the generation of pseudo labels. However, due to the model's poor awareness of unlabeled data, aforementioned methods may misguide the model. To alleviate this problem, we propose a dual consistency regularization with subjective logic for semi-supervised medical image segmentation. Specifically, we introduce subjective logic into our semi-supervised medical image segmentation task to estimate uncertainty, and based on the consistency hypothesis, we construct dual consistency regularization under weak and strong perturbations to guide the model's learning from unlabeled data. To evaluate the performance of the proposed method, we performed experiments on three widely used datasets: ACDC, LA, and Pancreas. Experiments show that the proposed method achieved improvement compared with other state-of-the-art (SOTA) methods.

Changes of structural functional connectivity coupling and its correlations with cognitive function in patients with major depressive disorder.

BACKGROUND: Previous neuroimaging studies have reported structural and functional brain abnormalities in major depressive disorder (MDD). This study aimed to explore whether the coherence of structural-functional networks was affected by disease and investigate its correlation with clinical manifestations. METHODS: The severity of symptoms and cognitive function of 121 MDD patients and 139 healthy controls (HC) were assessed, and imaging data, including diffusion tensor imaging, T1 structural magnetic resonance imaging (MRI) and resting-state functional MRI, were collected. Spearman correlation coefficients of Kullback-Leibler similarity (KLS), fiber number (FN), fractional anisotropy (FA) and functional connectivity (FC) were calculated as coupling coefficients. Double-weight median correlation analysis was conducted to investigate the correlations between differences in brain networks and clinical assessments. RESULTS: The percentage of total correct response of delayed matching to sample and the percentage of delayed correct response of pattern recognition memory was lower in MDD. Compared with the HC, KLS-FC coupling between the parietal lobe and subcortical area, FA-FC coupling between the temporal and parietal lobe, and FN-FC coupling in the frontal lobe was lower in MDD. Several correlations between structural-functional connectivity and clinical manifestations were identified. LIMITATIONS: First, our study lacks longitudinal follow-up data. Second, the sample size was relatively small. Moreover, we only used the Anatomical Automatic Labeling template to construct the brain network. Finally, the validation of the causal relationship of neuroimaging-behavior factors was still insufficient. CONCLUSIONS: The alternation in structural-functional coupling were related to clinical characterization and might be involved in the neuropathology of depression.