HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification.

Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.

Single-cell profiling reveals transcriptomic signatures of vascular endothelial cells in non-healing diabetic foot ulcers.

Background: The treatment of diabetic foot ulcers (DFUs) poses a challenging medical problem that has long plagued individuals with diabetes. Clinically, wounds that fail to heal for more than 12 weeks after the formation of DFUs are referred to as non-healing/chronic wounds. Among various factors contributing to the non-healing of DFUs, the impairment of skin microvascular endothelial cell function caused by high glucose plays a crucial role. Our study aimed to reveal the transcriptomic signatures of non-healing DFUs endothelial cells, providing novel intervention targets for treatment strategies. Methods: Based on the GEO dataset (GSE165816), we selected DFU-Healer, DFU-Non-healer, and healthy non-diabetic controls as research subjects. Single-cell RNA transcriptomic sequencing technology was employed to analyze the heterogeneity of endothelial cells in different skin tissue samples and identify healing-related endothelial cell subpopulations. Immunofluorescence was applied to validate the sequencing results on clinical specimens. Results: The number of endothelial cells and vascular density showed no significant differences among the three groups of skin specimens. However, endothelial cells from non-healing DFUs exhibited apparent inhibition of angiogenesis, inflammation, and immune-related signaling pathways. The expression of CCND1, ENO1, HIF1α, and SERPINE1 was significantly downregulated at the transcriptomic and histological levels. Further analysis demonstrated that healing-related endothelial cell subpopulations in non-healing DFUs has limited connection with other cell types and weaker differentiation ability. Conclusion: At the single-cell level, we uncovered the molecular and functional specificity of endothelial cells in non-healing DFUs and highlighted the importance of endothelial cell immune-mediated capability in angiogenesis and wound healing. This provides new insights for the treatment of DFUs.

PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI.

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks.

Graph representation learning has re-emerged as a fascinating research topic due to the successful application of graph convolutional networks (GCNs) for graphs and inspires various downstream tasks, such as node classification and link prediction. Nevertheless, existing GCN-based methods for graph representation learning mainly focus on static graphs. Although some methods consider the dynamic characteristics of networks, the global structure information, which helps a node to gain worthy features from distant but valuable nodes, has not received enough attention. Moreover, these methods generally update the features of the nodes by averaging the features of neighboring nodes, which may not effectively consider the importance of different neighboring nodes during the aggregation. In this article, we propose a novel representation learning for dynamic graphs based on the GCNs, called DGCN. More specifically, the long short-term memory (LSTM) is utilized to update the weight parameters of GCN for capturing the global structure information across all time steps of dynamic graphs. Besides, a new Dice similarity is proposed to overcome the problem that the influence of directed neighbors is unnoticeable, which is further used to guide the aggregation. We evaluate the performance of the proposed method in the field of node clustering and link prediction, and the experimental results show a generally better performance of our proposed DGCN than baseline methods.

Comprehensive analysis identifies IFI16 as a novel signature associated with overall survival and immune infiltration of skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is the most common skin tumor with high mortality. The unfavorable outcome of SKCM urges the discovery of prognostic biomarkers for accurate therapy. The present study aimed to explore novel prognosis-related signatures of SKCM and determine the significance of immune cell infiltration in this pathology. METHODS: Four gene expression profiles (GSE130244, GSE3189, GSE7553 and GSE46517) of SKCM and normal skin samples were retrieved from the GEO database. Differentially expressed genes (DEGs) were then screened, and the feature genes were identified by the LASSO regression and Boruta algorithm. Survival analysis was performed to filter the potential prognostic signature, and GEPIA was used for preliminary validation. The area under the receiver operating characteristic curve (AUC) was obtained to evaluate discriminatory ability. The Gene Set Variation Analysis (GSVA) was performed, and the composition of the immune cell infiltration in SKCM was estimated using CIBERSORT. At last, paraffin-embedded specimens of primary SKCM and normal skin tissues were collected, and the signature was validated by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC). RESULTS: Totally 823 DEGs and 16 feature genes were screened. IFI16 was identified as the signature associated with overall survival of SKCM with a great discriminatory ability (AUC > 0.9 for all datasets). GSVA noticed that IFI16 might be involved in apoptosis and ultraviolet response in SKCM, and immune cell infiltration of IFI16 was evaluated. At last, FISH and IHC both validated the differential expression of IFI16 in SKCM. CONCLUSIONS: In conclusion, our comprehensive analysis identified IFI16 as a signature associated with overall survival and immune infiltration of SKCM, which may play a critical role in the occurrence and development of SKCM.

Protective Effects of Chicken Egg Yolk Immunoglobulins (IgYs) against Vibrio vulnificus Infections.

Vibrio (V.) vulnificus infection is a rare disease whose death rates exceed 50% despite aggressive antibiotic treatment and surgical debridement. The aim of this study was to assess the ability of specific anti-V. vulnificus immunoglobulins Y (IgYs) for preventing and treating V. vulnificus infections. IgYs were produced by immunizing egg laying hens with inactivated whole cell bacteria. Peritoneal cytokines, blood's bacterial load, and survival curves were obtained from both prophylactic and therapeutic mouse models. The results showed that the specific IgYs (i) inhibited the growth of V. vulnificus in vitro, (ii) dramatically reduced the inflammatory response and blood's bacterial load, and (iii) improved the survival rate of V. vulnificus-infected mice. These results prove that anti-V. vulnificus IgYs can be markedly effective means for the prophylaxis and the therapy of V. vulnificus infections.

Study on the Effect of the Five-in-One Comprehensive Limb Salvage Technologies of Treating Severe Diabetic Foot.

Objective: To explore the clinical efficacy and advantages of five-in-one comprehensive limb salvage technologies for the treatment of severe diabetic foot ulcer (DFU). Approach: Clinical data for 120 patients with severe DFU treated between January 2012 and December 2017 were analyzed retrospectively. The control group (48 cases) was treated with traditional therapies, including controlling blood sugar, improving microcirculation, preserving nerve function, and dressing changes, whereas the experimental group (72 cases) was treated with traditional therapy combined with additional techniques, such as early and thorough debridement, negative pressure wound therapy, revascularization, and skin graft or flap. Ankle-brachial index (ABI), transcutaneous oxygen pressure (TcPO2), wound healing rate, healing time, ulcer recurrence rate, and amputation rate were recorded. Results: Compared with the control group, the experimental group significantly improved wound healing rate (93.1% vs. 72.9%; p < 0.01), decreased wound healing time (16.2 ± 5.4 days vs. 32.2 ± 7.8 days; p < 0.05), reduced major limb amputation rate (1.4% vs. 10.4%, p < 0.05), and ulcer recurrence rate (5.6% vs. 14.6%; p < 0.05). There were no significant differences in amputation rate between experimental and control group (29.2% vs. 33.3%, p = 0.628). After revascularization, the revascularization group showed significantly improved ABI (0.75 ± 0.21 vs. 0.35 ± 0.16, p < 0.05) and TcPO2 (36 ± 6 mmHg vs. 15 ± 4 mmHg, p < 0.05). Innovation: We propose a five-in-one comprehensive treatment method, which provides a multidisciplinary cooperative model for comprehensive medical and surgical treatments for DFU. Conclusion: The five-in-one comprehensive limb salvage treatment technologies played a vital role in enhancing the healing rate of severe DFU, shortening the healing time, and reducing the rate of recurrence and major amputation, thus improving the overall quality of life.

Reconstruction of lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveals functional lncRNAs in skin cutaneous melanoma.

BACKGROUND: Human skin cutaneous melanoma is the most common and dangerous skin tumour, but its pathogenesis is still unclear. Although some progress has been made in genetic research, no molecular indicators related to the treatment and prognosis of melanoma have been found. In various diseases, dysregulation of lncRNA is common, but its role has not been fully elucidated. In recent years, the birth of the "competitive endogenous RNA" theory has promoted our understanding of lncRNAs. METHODS: To identify the key lncRNAs in melanoma, we reconstructed a global triple network based on the "competitive endogenous RNA" theory. Gene Ontology and KEGG pathway analysis were performed using DAVID (Database for Annotation, Visualization, and Integration Discovery). Our findings were validated through qRT-PCR assays. Moreover, to determine whether the identified hub gene signature is capable of predicting the survival of cutaneous melanoma patients, a multivariate Cox regression model was performed. RESULTS: According to the "competitive endogenous RNA" theory, 898 differentially expressed mRNAs, 53 differentially expressed lncRNAs and 16 differentially expressed miRNAs were selected to reconstruct the competitive endogenous RNA network. MALAT1, LINC00943, and LINC00261 were selected as hub genes and are responsible for the tumorigenesis and prognosis of cutaneous melanoma. CONCLUSIONS: MALAT1, LINC00943, and LINC00261 may be closely related to tumorigenesis in cutaneous melanoma. In addition, MALAT1 and LINC00943 may be independent risk factors for the prognosis of patients with this condition and might become predictive molecules for the long-term treatment of melanoma and potential therapeutic targets.

Selective debridement of burn wounds using hydrosurgery system.

In recent years, hydrosurgery is a technology that has been applied more and more in debridement procedures. However, the selectivity of hydrosurgery to cutaneous necrotic tissues has not been proved. This study was designed to investigate the possible tissue selectivity of hydrosurgery in the debridement in burn wounds. Deep partial-thickness burns were produced on the back of porcine, and 48 hours later, both burn wounds and normal skin were debrided using the hydrosurgery system. Then tissue samples were taken, and histological staining was performed and observed under microscope. Burn wound resection rates and the normal skin damaged rates were measured. Our result indicated that the burn wounds were significantly more sensitive than the normal skin when the water pressure produced by the hydrosurgery system was set between 3000 and 5000 psi (pounds per square inch), that is, the necrotic tissue portions were debrided more easily than the normal skin tissue. Based on these data, we suggest that 3000 to 5000 psi of water pressure in the hydrosurgery system has a skin tissue selectivity in burn wounds.

Helicase POLQ-like (HELQ) as a novel indicator of platinum-based chemoresistance for epithelial ovarian cancer.

OBJECTIVE: To investigate the role of HELQ in chemo-resistance of epithelial ovarian carcinoma (EOC), which is a critical factor of patients' prognosis. METHODS: Immunohistochemistry, survival analysis of our 87 EOC patients and bioinformatics analysis of The Cancer Genome Atlas (TCGA) datasets (Nature, 2011) disclosed the clinical importance of HELQ expression. Quantitative reverse transcription polymerase chain reaction (qRT-PCR), and Western Blot analyses of EOC tissue were used to confirm it. Ectopic overexpression and RNA interference knockdown of HELQ were carried out in OVCAR3 and A2780 cell lines, respectively, to determine the effect of altered HELQ expression on cellular response to cisplatin by CCK8 assay. The DNA repair capacity of these cells was evaluated by using host-cell reactivation assay. Western Blot analyses were carried out to determine the effect of HLEQ on the DNA repair genes by using cells with altered HELQ expression. RESULTS: HELQ expression associates with response of EOC patients to platinum-based chemotherapy and their overall survival (OS), disease free survival (DFS). HELQ overexpression or knockdown, respectively, increased and decreased the cellular resistance to cisplatin, DNA repair activity, and expression of DNA repair proteins of Nucleotide excision repair (NER) pathway. CONCLUSIONS: HELQ plays an important role in regulating the expression of DNA repair proteins NER pathway which, in turn, contributes to cellular response to cisplatin and patients' response to platinum-based chemotherapy. Our results demonstrated that HELQ could serve as a novel indicator for chemo-resistance of EOC, which can predict the prognosis of the disease.

Preliminary screening and identification of differentially expressed metastasis-related ncRNAs in ovarian cancer.

Ovarian cancer (OC) is an aggressive disease with few valuable biomarkers and effective therapies. In this study, we aimed to elucidate biomarkers associated with OC metastasis into the omentum. We performed comprehensive screening of non-coding RNAs (ncRNAs) between matched primary OC and omental metastasis using the Agilent human lncRNA Array V3.0 microarray. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used to validate the microarray results at the mRNA level. Microarray revealed 235 ncRNAs changes, and we validated the top four differential changed genes in an additional 27 paired samples with RT-qPCR. We found that myocardial infarction associated transcript (MIAT) expression increased in the omentum tissue, while small nucleolar RNA, C/D Box 114 cluster (SNORD114) family members SNORD114-10, SNORD114-2 and SNORD114-11 were downregulated when compared with OC tissue. However, there is no significant difference in SNORD114-2 and SNORD114-11 levels. We thus infer that differential expression of MIAT and SNORD114-10 could play an important role during OC metastasis. These ncRNAs might be useful as pre-diagnostic biomarkers at the early stage of cancer metastasis.

Development and characterization of novel antimicrobial bilayer films based on Polylactic acid (PLA)/Pickering emulsions.

Biodegradable food packaging is sustainable and has a great application prospect. PLA is a promising alternative for petroleum-derived polymers. However, PLA packaging suffers from poor barrier properties compared with petroleum-derived ones. To address this issue, we designed bilayer films based on PLA and Pickering emulsions. The formed bilayer films were compact and uniform and double layers were combined firmly. This strategy enhanced mechanical resistance, ductility and moisture barrier of Pickering emulsion films, and concomitantly enhanced the oxygen barrier for PLA films. Thymol loadings in Pickering emulsion layer endowed them with antimicrobial and antioxidant activity. The release profile of thymol was well fitted with Fick's second law. The antimicrobial activity of the films depended on film types, and Pickering emulsion layer presented larger inhibition zone than PLA layer, hinting that the films possessed directional releasing role. This study opens a promising route to fabricate bilayer architecture creating synergism of each layer.

Development of antioxidant Pickering high internal phase emulsions (HIPEs) stabilized by protein/polysaccharide hybrid particles as potential alternative for PHOs.

We report for the first time the usage of mono-dispersed gliadin/chitosan hybrid particles as a particulate emulsifier for Pickering high internal phase emulsions (HIPEs) development. The hybrid particles with partial wettability were fabricated at pH 5.0 using a facile anti-solvent route. Stable Pickering HIPEs with internal phases of up to 83% can be prepared with low particle concentrations (0.5-2%). The hybrid latexes were effectively adsorbed and anchored at the oil-water interface to exert steric hindrance against coalescence. Concomitantly, the compressed droplets in Pickering HIPEs to form a percolating 3D-network framework endowed the emulsions viscoelastic and self-standing features. The protective effect of Pickering HIPEs on curcumin was confirmed, and the content of primary oxidation products in HIPEs was slightly lower than that in bulk oil. This work opens an attractive strategy to convert liquid oils to viscoelastic soft solids without artificial trans fats, as a potential alternative for PHOs.