Hybrid Double-Sided Tape with Asymmetrical Adhesion and Burst Pressure Tolerance for Abdominal Injury Treatment.

Patients with open abdominal (OA) wounds have a mortality risk of up to 30%, and the resulting disabilities would have profound effects on patients. Here, we present a novel double-sided adhesive tape developed for the management of OA wounds. The tape features an asymmetrical structure and employs an acellular dermal matrix (ADM) with asymmetric wettability as a scaffold. It is constructed by integrating a tissue-adhesive hydrogel composed of polydopamine (pDA), quaternary ammonium chitosan (QCS), and acrylic acid cross-linking onto the bottom side of the ADM. Following surface modification with pDA, the ADM would exhibit characteristics resistant to bacterial adhesion. Furthermore, the presence of a developed hydrogel ensures that the tape not only possesses tissue adhesiveness and noninvasive peelability but also effectively mitigates damage caused by oxidative stress. Besides, the ADM inherits the strength of the skin, imparting high burst pressure tolerance to the tape. Based on these remarkable attributes, we demonstrate that this double-sided (D-S) tape facilitates the repair of OA wounds, mitigates damage to exposed intestinal tubes, and reduces the risk of intestinal fistulae and complications. Additionally, the D-S tape is equally applicable to treating other abdominal injuries, such as gastric perforations. It effectively seals the perforation, promotes injury repair, and prevents the formation of postoperative adhesions. These notable features indicate that the presented double-sided tape holds significant potential value in the biomedical field.

Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning.

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.

Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder.

Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.

A vector projection similarity-based method for miRNA-disease association prediction.

[S U M M A R Y] Many miRNA-disease association prediction models incorporate Gaussian interaction profile kernel similarity (GIPS). However, the GIPS fails to consider the specificity of the miRNA-disease association matrix, where matrix elements with a value of 0 represent miRNA and disease relationships that have not been discovered yet. To address this issue and better account for the impact of known and unknown miRNA-disease associations on similarity, we propose a method called vector projection similarity-based method for miRNA-disease association prediction (VPSMDA). In VPSMDA, we introduce three projection rules and combined with logistic functions for the miRNA-disease association matrix and propose a vector projection similarity measure for miRNAs and diseases. By integrating the vector projection similarity matrix with the original one, we obtain the improved miRNA and disease similarity matrix. Additionally, we construct a weight matrix using different numbers of neighbors to reduce the noise in the similarity matrix. In performance evaluation, both LOOCV and 5-fold CV experiments demonstrate that VPSMDA outperforms seven other state-of-the-art methods in AUC. Furthermore, in a case study, VPSMDA successfully predicted 10, 9, and 10 out of the top 10 associations for three important human diseases, respectively, and these predictions were confirmed by recent biomedical resources.

Novel mesothelin-targeted chimeric antigen receptor-modified UNKT cells are highly effective in inhibiting tumor progression.

The design of chimeric antigen receptors (CAR) significantly enhances the antitumor efficacy of T cells. Although some CAR-T products have been approved by FDA in treating hematological tumors, adoptive immune therapy still faces many difficulties and challenges in the treatment of solid tumors. In this study, we reported a new strategy to treat solid tumors using a natural killer-like T (NKT) cell line which showed strong cytotoxicity to lyse 15 cancer cell lines, safe to normal cells and had low or no Graft-versus-host activity. We thus named it as universal NKT (UNKT). In both direct and indirect 3D tumor-like organ model, UNKT showed efficient tumor-killing properties, indicating that it could penetrate the microenvironment of solid tumors. In mesothelin (MSLN)-positive tumor cells (SKOV-3 and MCF-7), MSLN targeting CAR modified-UNKT cells had enhanced killing potential against MSLN positive ovarian cancer compared with the wild type UNKT, as well as MSLN-CAR-T cells. Compared with CAR-T, Single-cell microarray 32-plex proteomics revealed CAR-UNKT cells express more effector cytokines, such as perforin and granzyme B, and less interleukin-6 after activation. Moreover, our CAR-UNKT cells featured in more multifunctionality than CAR-T cells. CAR-UNKT cells also demonstrated strong antitumor activity in mouse models of ovarian cancer, with the ability to migrate and infiltrate the tumor without inducing immune memory. The fast-in and -out, enhanced and prolonged tumor killing properties of CAR-UNKT suggested a novel cure option of cellular immunotherapy in the treatment of MSLN-positive solid tumors.

LUNCRW: Prediction of potential lncRNA-disease associations based on unbalanced neighborhood constraint random walk.

Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are associated with various complex human diseases. They can serve as disease biomarkers and hold considerable promise for the prevention and treatment of various diseases. The traditional random walk algorithms generally exclude the effect of non-neighboring nodes on random walking. In order to overcome the issue, the neighborhood constraint (NC) approach is proposed in this study for regulating the direction of the random walk by computing the effects of both neighboring nodes and non-neighboring nodes. Then the association matrix is updated by matrix multiplication for minimizing the effect of the false negative data. The heterogeneous lncRNA-disease network is finally analyzed using an unbalanced random walk method for predicting the potential lncRNA-disease associations. The LUNCRW model is therefore developed for predicting potential lncRNA-disease associations. The area under the curve (AUC) values of the LUNCRW model in leave-one-out cross-validation and five-fold cross-validation were 0.951 and 0.9486 ± 0.0011, respectively. Data from published case studies on three diseases, including squamous cell carcinoma, hepatocellular carcinoma, and renal cell carcinoma, confirmed the predictive potential of the LUNCRW model. Altogether, the findings indicated that the performance of the LUNCRW method is superior to that of existing methods in predicting potential lncRNA-disease associations.

Smart implants: 4D-printed shape-morphing scaffolds for medical implantation.

Biomedical implants have recently shown excellent application potential in tissue repair and replacement. Applying three-dimensional (3D) printing to implant scaffold fabrication can help to address individual needs more precisely. Fourdimensional (4D) printing emerges rapidly based on the development of shape-responsive materials and design methods, which makes the production of dynamic functional implants possible. Smart implants can be pre-designed to respond to endogenous or exogenous stimuli and perform seamless integration with regular/ irregular tissue defects, defect-luminal organs, or curved structures via programmed shape morphing. At the same time, they offer great advantages in minimally invasive surgery due to the small-to-large volume transition. In addition, 4D-printed cellular scaffolds can generate extracellular matrix (ECM)-mimetic structures that interact with the contacting cells, expanding the possible sources of tissue/organ grafts and substitutes. This review summarizes the typical technologies and materials of 4D-printed scaffolds, and the programming designs and applications of these scaffolds are further highlighted. Finally, we propose the prospects and outlook of 4D-printed shape-morphing implants.

Subphenotyping heterogeneous patients with chronic critical illness to guide individualised fluid balance treatment using machine learning: a retrospective cohort study.

Background: The great heterogeneity of patients with chronic critical illness (CCI) leads to difficulty for intensive care unit (ICU) management. Identifying subphenotypes could assist in individualized care, which has not yet been explored. In this study, we aim to identify the subphenotypes of patients with CCI and reveal the heterogeneous treatment effect of fluid balance for them. Methods: In this retrospective study, we defined CCI as an ICU length of stay over 14 days and coexists with persistent organ dysfunction (cardiovascular Sequential Organ Failure Assessment (SOFA) score ≥1 or score in any other organ system ≥2) at Day 14. Data from five electronic healthcare record datasets covering geographically distinct populations (the US, Europe, and China) were studied. These five datasets include (1) subset of Derivation (MIMIC-IV v1.0, US) cohort (2008-2019); (2) subset Derivation (MIMIC-III v1.4 'CareVue', US) cohort (2001-2008); (3) Validation I (eICU-CRD, US) cohort (2014-2015); (4) Validation II (AmsterdamUMCdb/AUMC, Euro) cohort (2003-2016); (5) Validation III (Jinling, CN) cohort (2017-2021). Patients who meet the criteria of CCI in their first ICU admission period were included in this study. Patients with age over 89 or under 18 years old were excluded. Three unsupervised clustering algorithms were employed independently for phenotypes derivation and validation. Extreme Gradient Boosting (XGBoost) was used for phenotype classifier construction. A parametric G-formula model was applied to estimate the cumulative risk under different daily fluid management strategies in different subphenotypes of ICU mortality. Findings: We identified four subphenotypes as Phenotype A, B, C, and D in a total of 8145 patients from three countries. Phenotype A is the mildest and youngest subgroup; Phenotype B is the most common group, of whom patients showed the oldest age, significant acid-base abnormality, and low white blood cell count; Patients with Phenotype C have hypernatremia, hyperchloremia, and hypercatabolic status; and in Phenotype D, patients accompany with the most severe multiple organ failure. An easy-to-use classifier showed good effectiveness. Phenotype characteristics showed robustness across all cohorts. The beneficial fluid balance threshold intervals of subphenotypes were different. Interpretation: We identified four novel phenotypes that revealed the different patterns and significant heterogeneous treatment effects of fluid therapy within patients with CCI. A prospective study is needed to validate our findings, which could inform clinical practice and guide future research on individualized care. Funding: This study was funded by 333 High Level Talents Training Project of Jiangsu Province (BRA2019011), General Program of Medical Research from the Jiangsu Commission of Health (M2020052), and Key Research and Development Program of Jiangsu Province (BE2022823).

Transcatheter arterial embolization of the common hepatic artery for pseudoaneurysm after a laparoscopic-assisted pancreaticoduodenectomy: A case report.

Common hepatic artery pseudoaneurysm is a rare and potentially life-threatening complication after pancreaticoduodenectomy, and the possible cause is unclear. We report a case of intraperitoneal hemorrhage after pancreaticoduodenectomy who was discharged after embolization under digital subtraction angiography. We conside that this complication may be related to iatrogenic injury.

Self-Healing Supramolecular Hydrogels with Antibacterial Abilities for Wound Healing.

Wound healing due to skin defects is a growing clinical concern. Especially when infection occurs, it not only leads to impair healing of the wound but even leads to the occurrence of death. In this study, a self-healing supramolecular hydrogel with antibacterial abilities was developed for wound healing. The supramolecular hydrogels inherited excellent self-healing and mechanical properties are produced by the polymerization of N-acryloyl glycinamide monomers which carries a lot of amides. In addition, excellent antibacterial properties are obtained by integrating silver nanoparticles (Ag NPs) into the hydrogels. The resultant hydrogel has a demonstrated ability in superior mechanical properties, including stretchability and self-healing. Also, the good biocompatibility and antibacterial ability have been proven in hydrogels. Besides, the prepared hydrogels were employed as wound dressings to treat skin wounds of animals. It was found that the hydrogels could significantly promote wound repair, including relieving inflammation, promoting collagen deposition, and enhancing angiogenesis. Therefore, such self-healing supramolecular hydrogels with composite functional nanomaterials are expected to be used as new wound dressings in the field of healthcare.

Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation.

Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.

Predicting miRNA-Disease Associations via Node-Level Attention Graph Auto-Encoder.

Previous studies have confirmed microRNA (miRNA), small single-stranded non-coding RNA, participates in various biological processes and plays vital roles in many complex human diseases. Therefore, developing an efficient method to infer potential miRNA disease associations could greatly help understand operational mechanisms for diseases at the molecular level. However, during these early stages for miRNA disease prediction, traditional biological experiments are laborious and expensive. Therefore, this study proposes a novel method called AGAEMD (node-level Attention Graph Auto-Encoder to predict potential MiRNA Disease associations). We first create a heterogeneous matrix incorporating miRNA similarity, disease similarity, and known miRNA-disease associations. Then these matrixes are input into a node-level attention encoder-decoder network which utilizes low dimensional dense embeddings to represent nodes and calculate association scores. To verify the effectiveness of the proposed method, we conduct a series of experiments on two benchmark datasets (the Human MicroRNA Disease Database v2.0 and v3.2) and report the averages over 10 runs in comparison with several state-of-the-art methods. Experimental results have demonstrated the excellent performance of AGAEMD in comparison with other methods. Three important diseases (Colon Neoplasms, Lung Neoplasms, Lupus Vulgaris) were applied in case studies. The results comfirm the reliable predictive performance of AGAEMD.

DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

4D-printed bilayer hydrogel with adjustable bending degree for enteroatmospheric fistula closure.

Recently, four-dimensional (4D) shape-morphing structures, which can dynamically change shape over time, have attracted much attention in biomedical manufacturing. The 4D printing has the capacity to fabricate dynamic construction conforming to the natural bending of biological tissues, superior to other manufacturing techniques. In this study, we presented a multi-responsive, flexible, and biocompatible 4D-printed bilayer hydrogel based on acrylamide-acrylic acid/cellulose nanocrystal (AAm-AAc/CNC) network. The first layer was first stretched and then formed reversible coordination with Fe3+ to maintain this pre-stretched length; it was later combined with a second layer. The deformation process was actuated by the reduction of Fe3+ to Fe2+ in the first layer which restored it to its initial length. The deformation condition was to immerse the 4D construct in sodium lactate (LA-Na) and then expose it to ultraviolet (UV) light until maximal deformation was realized. The bending degree of this 4D construct can be programmed by modifying the pre-stretched lengths of the first layer. We explored various deformation steps in simple and complex constructs to verify that the 4D bilayer hydrogel can mimic the curved morphology of the intestines. The bilayer hydrogel can also curve in deionized water due to anisotropic volume change yet the response time and maximum bending degree was inferior to deformation in LA-Na and UV light. Finally, we made a 4D-printed bilayer hydrogel stent to test its closure effect for enteroatmospheric fistulas (EAFs) in vitro and in vivo. The results illustrate that the hydrogel plays a role in the temporary closure of EAFs. This study offers an effective method to produce curved structures and expands the potential applications of 4D printing in biomedical fields.

HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart.

Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.

Trocar Puncture With a Sump Drain for Crohn's Disease With Intra-Abdominal Abscess: A 10-Year Retrospective Cohort Study.

Background: Traditional percutaneous catheter drainage (PCD) and surgical intervention could not always achieve satisfactory results for patients with Crohn's disease (CD) who have complications with intra-abdominal abscess. We proposed a trocar puncture with sump drainage for the treatment of CD with intra-abdominal abscess and compared it with the conventional PCD and surgical intervention. Methods: Crohn's disease patients with intra-abdominal abscess and admitted to our hospital from 2011 to 2020 were identified by reviewing the electronic medical records. We divided them into Trocar, PCD, and fecal diverting (FD) groups, according to the ways of treating an abscess. Outcomes, risk factors for abscess recurrence, and postoperative complications were compared among the three groups. Results: A total of 69 patients were included and they were divided into Trocar (n = 18), PCD (n = 29), and FD (n = 22) groups. Four patients in the PCD group were transferred to receive the FD surgery due to the failure of initial treatment. The incidence of abscess recurrence was significantly higher in the PCD (48%) and FD (50%) groups compared to the patients using the trocar puncture with the sump drain (Trocar group) (16.7%). There were 8 patients in Trocar, 22 in PCD, and 20 s in the FD group who received enterectomy. None of the patients in the Trocar had an ultimate stoma and the incidence of postoperative complications was statistically lower [0% (Trocar) vs. 31.8% (PCD) vs. 45% (FD), P < 0.05]. The way of initial treating of the abscess was significantly correlated with the abscess recurrence and postoperative complications. Conclusions: Trocar puncture with a sump drain had a lower incidence of abscess recurrence, abdominal adhesions, postdrainage, and postoperative complications compared to the conventional PCD or surgical intervention.

Correction: Engineering an adhesive based on photosensitive polymer hydrogels and silver nanoparticles for wound healing.

Correction for 'Engineering an adhesive based on photosensitive polymer hydrogels and silver nanoparticles for wound healing' by Qinqing Tang et al., J. Mater. Chem. B, 2020, 8, 5756-5764, DOI: 10.1039/d0tb00726a.

Efficacy and safety of over-the-scope clips (OTSC®) for closure of gastrointestinal fistulas less than 2 cm.

BACKGROUND AND AIM: Gastrointestinal (GI) fistula is a complication of surgery associated with potential morbidity and mortality. The aim of this study was to evaluate the efficacy and safety of over-the-scope clips (OTSC®) for closing GI fistulas. METHODS: Patients with GI fistula who underwent endoscopic closure using OTSC® were enrolled. The clinical date, duration, location and diameter of the fistula, technical success of the OTSC®, complications, follow-up periods and clinical success were recorded. RESULTS: A total of 98 patients with GI fistula underwent OTSC® closure. Their median age was 50 years (range 16-88 years), and the median duration of the fistula was 185.5 days (range 12-3129 days). The mean diameter of fistula was 4.64 ± 1.16 mm. Technical success was achieved in 100% of the patients, and clinical success was achieved in 55.10% (54/98) of the patients after a median follow-up of 168.5 days (range 36-424 days). Based on the location of the fistula, the clinical success rate of treating a fistula in the esophagus and small intestine was 100%, followed by the rectum (70%, 7/10), anastomotic stoma (61.90%, 13/21), duodenum (53.33%, 8/15), colon (47.06%, 8/17), stomach (43.47%, 10/23) and appendix stump (33.33%, 2/6). The duration of the fistula (HR 3.609, 95% CI 1.387-9.387, P = 0.009) was a risk factor for clinical success by multivariate analysis. CONCLUSION: OTSC® is a safe and efficient treatment for GI fistula and is a potential alternative to the surgical approach. Before OTSC® placement, the duration of the fistula should be assessed since it is related to the successful closures with OTSC®.