Randomized Controlled Trial Investigating Perioperative Immunonutrition for Patients Undergoing Cytoreductive Surgery (CRS) and Hyperthermic Intraperitoneal Chemotherapy (HIPEC).

BACKGROUND: Immunonutrition has been shown to reduce hospital stay and postoperative morbidity in patients undergoing gastrointestinal, and head and neck surgery. However, its use has not been demonstrated in patients undergoing cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). This study aims to determine the effectiveness of perioperative immunonutrition on patients undergoing CRS-HIPEC in reducing length of hospitalization and postoperative complications. PATIENTS AND METHODS: From April 2017 to December 2018, patients undergoing CRS-HIPEC for peritoneal metastases in a single center were enrolled in a randomized controlled trial. Patients with evidence of intestinal obstruction or with diabetes mellitus were excluded. Patients were randomly assigned in a 1:1 fashion to receive perioperative oral immunonutrition or standard nutritional feeds. Length of hospital stay and rates of wound infection and complications were recorded and compared between the two groups in an intention-to-treat manner. RESULTS: A total of 62 patients were recruited and randomized into two groups. Compliance to nutritional feeds in the preoperative period was significantly higher in the standard nutrition group (95.2% versus 75.4%, p = 0.004). There was no difference in postoperative compliance rates. Length of hospital stay and rates of wound infection and postoperative complications were higher in the standard nutrition group when compared with patients on immunonutrition (15.5 versus 11.1 days, p = 0.186; 19% versus 9.7%, p = 0.473; 16% versus 9.7%, p = 0.653; respectively). CONCLUSIONS: Patients undergoing CRS-HIPEC who received perioperative immunonutrition had shorter hospitalization and less wound infections and postoperative complications, although the differences with the standard nutrition group were not statistically significant. Potential benefits of perioperative immunonutrition need to be further evaluated in larger studies.

Molecular design and rational optimization of synergistic effect between the two wings of a roughly orthogonal cation-π-π stacking system at nasopharyngeal carcinoma YAP1-TEAD4 parallel Helix-Helix interaction interface.

The Yes-associated protein-1 (YAP1) is an essential regulator of human Hippo signaling pathway and functions through interaction with TEA domain-4 (TEAD4) transcription factor involved in the tumorigenesis of nasopharyngeal cancer. Previously, a parallel helix-helix interaction (PHHI) was identified as the key hotspot at YAP1-TEAD4 complex interface and has been exploited as an attractive druggable target to disrupt the complex. In this study, we investigated a roughly orthogonal cation-π-π stacking system across the crystal PHHI packing interface by integrating computational modeling and binding assay, which forms between one YAP1 helical residue Phe69 and two TEAD4 helical residues Phe373/Lys376. A synergistic effect between cation-π and π-π interactions was observed; they separately represent two wings of the stacking system. The π-electron is primarily responsible for the synergistic effect. Combination between diverse aromatic/charged amino acids. as well as neutral alanine on the cation-π-π stacking, revealed that the presence of aromatic tryptophan and charged arginine at, respectively, the residues 373 and 376 of TEAD4 helix can considerably improve PHHI binding affinity by ~6-fold, whereas neutral alanine substitution on each residue and on both would reduce the affinity significantly, confirming a strong synergistic effect involved in the roughly orthogonal cation-π-π stacking system at YAP1-TEAD4 PHHI interface.

Anomalous Loss of Stiffness with Increasing Reinforcement in a Photo-Activated Nanocomposite.

Hydrogels are commonly doped with stiff nanoscale fillers to endow them with the strength and stiffness needed for engineering applications. Although structure-property relations for many polymer matrix nanocomposites are well established, modeling the new generation of hydrogel nanocomposites requires the study of processing-structure-property relationships because subtle differences in chemical kinetics during their synthesis can cause nearly identical hydrogels to have dramatically different mechanical properties. The authors therefore assembled a framework to relate synthesis conditions (including hydrogel and nanofiller mechanical properties and light absorbance) to gelation kinetics and mechanical properties. They validated the model against experiments on a graphene oxide (GO) doped oligo (ethylene glycol) diacrylate (OEGDA), a system in which, in apparent violation of laws from continuum mechanics, doping can reduce rather than increase the stiffness of the resulting hydrogel nanocomposites. Both model and experiment showed a key role light absorbance-dominated gelation kinetics in determining nanocomposite mechanical properties in conjunction with nanofiller reinforcement, with the nanofiller's attenuation of chemical kinetics sometimes outweighing stiffening effects to explain the observed, anomalous loss of stiffness. By bridging the chemical kinetics and mechanics of nanocomposite hydrogels, the authors' modeling framework shows promise for broad applicability to design of hydrogel nanocomposites.

Thromboelastography (TEG) in normal pregnancy and its diagnostic efficacy in patients with gestational hypertension, gestational diabetes mellitus, or preeclampsia.

BACKGROUND: Thromboelastography (TEG) provides global assessment of hemostatic function and has been recommended to monitor potential coagulopathies during pregnancy in which hypercoagulable state is favored. In present study, we established the reference intervals (RIs) of the TEG parameters (R, K, MA, and α-angle) with Chinese pregnant women of third trimester. In addition, we examined the diagnostic efficacies of the TEG parameters in the patients diagnosed of gestational hypertension (GH), gestational diabetes mellitus (GDM), or preeclampsia (PE). METHODS: With specified including and excluding criteria, non-pregnant controls, healthy pregnant women, and pregnant women with GH, GDM, or PE had their venous blood drawn at Beijing Obstetrics and Gynecology Hospital, followed by TEG tests performed in the clinical laboratory. RESULTS: The RIs determined with the healthy pregnant women (in third trimester) for R, K, MA, and α-angle were 4.0-7.7, 1.2-3.2, 51.9-70.1, and 41.4-74.4, respectively. When compared with the healthy pregnancy group, the K value was significantly decreased in GH patients but increased in PE patients; MA was significantly lower in the PE group. In the receiver operating characteristic curve (ROC) analyses, K value was able to efficiently distinguish normal pregnancy from the GH patients, with an AUC of 0.86 which is far better than those of R (AUC = 0.57) and MA (AUC = 0.56). For the PE patients, the AUC of MA (0.69) was significantly greater than that of R (0.50). CONCLUSIONS: Thromboelastography may provide more accurate experimental basis for monitoring coagulation functions especially in pregnant women with complications of GH and PE.

Spatiotemporally Controlled Photoresponsive Hydrogels: Design and Predictive Modeling from Processing through Application.

Photoresponsive hydrogels (PRHs) are soft materials whose mechanical and chemical properties can be tuned spatially and temporally with relative ease. Both photo-crosslinkable and photodegradable hydrogels find utility in a range of biomedical applications that require tissue-like properties or programmable responses. Progress in engineering with PRHs is facilitated by the development of theoretical tools that enable optimization of their photochemistry, polymer matrices, nanofillers, and architecture. This review brings together models and design principles that enable key applications of PRHs in tissue engineering, drug delivery, and soft robotics, and highlights ongoing challenges in both modeling and application.

Detection of non-criteria autoantibodies in women without apparent causes for pregnancy loss.

BACKGROUND: Some of the non-criteria autoantibodies, especially non-conventional antiphospholipid (aPL) antibodies, were present with high prevalence in sporadic miscarriages and recurrent pregnant loss. However, whether these autoantibodies are associated with miscarriage patients without apparent causes remain unclear. METHODS: The subjects were recruited from the female patients visiting the Infertility Center at the Beijing Obstetrics and Gynecology Hospital from January 2017 to March 2018. The women who experienced one sporadic miscarriage (n = 89) or recurrent pregnancy loss (n = 125) were enrolled. The control participants (n = 59) were those women with normal pregnancy history and with no miscarriage or thrombosis experience. The collected serum specimens from above patients and controls were subjected to the 13 non-criteria autoantibody examinations, targeting non-conventional phospholipids, thyroid, sperm, endometrial, and anti-nuclear antigens. RESULTS: When compared with the controls, the following non-criteria antibodies stood out in present study with significantly increased frequency and were listed in the order of decreasing positive rates: aPE IgM (40.0%), ANA (15.2%), aEM IgG (13.6%), aPE IgG (12.8%), and aPT IgM (10.4%). Except for ANA, the presence of aPE IgM, aEM IgG, aPE IgG, and aPT IgM was not associated with positivity of LA tests. In receiver operating characteristic analyses, the combined aPE IgG and aEM IgG biomarker panel had the best discriminating power between miscarriage patients and healthy controls. CONCLUSION: Our findings suggested that the non-criteria could be included as part of the pregnancy loss evaluation when apparent causes are absent, and the conventional aPLs tests failed to provide interpretations.

Usage of glaucoma-specific patient-reported outcome measures (PROMs) in the Singapore context: a qualitative scoping exercise.

BACKGROUND: Despite the increasing emphasis on the role of glaucoma-specific patient-reported outcome measures (PROMs) as relevant outcome measures for the impact of glaucoma and its intervention on patients' daily lives, the feasibility of implementing PROMs in the routine clinical setting in Singapore remains undefined. We aim to evaluate the comprehensibility, acceptability, and relevance of four glaucoma-specific PROMs at healthcare professionals' and patients' level in a Singapore context. METHODS: Sixteen ophthalmic healthcare professionals and 24 glaucoma patients, with average age 60 years (SD = 15), were invited from a tertiary hospital in Singapore. Semi-structured interviews were conducted to explore participants' perceptions on the content and administration of four glaucoma-specific PROMs - the Glaucoma Quality of Life-15, Glaucoma Symptom Identifier, Independent Mobility Questionnaire and Treatment Satisfaction Survey of Intra-ocular Pressure. Semi-structured interviews were hand transcribed, and analysed thematically. Each participant filled out a feasibility survey at the end of interview. RESULTS: 79% of glaucoma patients and 94% of glaucoma healthcare professionals felt selected PROMs relevant to patients. 63% of glaucoma patients and 50% of healthcare professionals felt that selected PROMs were sufficiently comprehensive for clinical use. 46% of glaucoma patients and 56% of healthcare professionals felt selected PROMs were user-friendly. CONCLUSIONS: Using PROMs in the Singapore clinical setting receives promising support from both healthcare professionals and patients. The identified potential barriers tailored to Singapore clinical setting will help successful implementation of PROMs into routine clinical care.

Rational Molecular Design of Potent PLK1 PBD Domain-binding Phosphopeptides Using Preferential Amino Acid Building Blocks.

Polo-like kinase 1 (PLK1) is an important regulator in diverse aspects of the cell cycle and proliferation. The protein has a highly conserved polo-box domain (PBD) present in C-terminal noncatalytic region, which exhibits a relatively broad sequence specificity in recognizing and binding phosphorylated substrates to control substrate phosphorylation by the kinase. In order to elucidate the structural basis, thermodynamic property, and biological implication underlying PBD-substrate recognition and association, a systematic amino acid preference profile of phosphopeptide interaction with PLK1 PBD domain was established via virtual mutagenesis analysis and mutation energy calculation, from which the contribution of different amino acids at each residue position of two reference phosphopeptides to domain-peptide binding was characterized comprehensively and quantitatively. With the profile, we are able to determine the favorable, neutral, and unfavorable amino acid types for each position of PBD-binding phosphopeptides, and we also explored the molecular origin of the broad sequence specificity in PBD-substrate recognition. To practice computational findings, the profile was further employed to guide rational design of potent PBD binders; three 6-mer phosphopeptides (i.e., IQSpSPC, LQSpTPF, and LNSpTPT) were successfully developed, which can efficiently target PBD domain with high affinity (Kd = 5.7 ± 1.1, 0.75 ± 0.18, and 7.2 ± 2.6 µm, resp.) as measured by a fluorescence anisotropy assay. The complex structure of PLK1 PBD domain with a newly designed, potent phosphopeptide LQSpTPF as well as diverse noncovalent chemical forces, such as H-bonds and hydrophobic interactions at the complex interface, were examined in detail to reveal the molecular mechanism of high affinity and stability of the complex system.

Learning Student Network Under Universal Label Noise.

Data-free knowledge distillation aims to learn a small student network from a large pre-trained teacher network without the aid of original training data. Recent works propose to gather alternative data from the Internet for training student network. In a more realistic scenario, the data on the Internet contains two types of label noise, namely: 1) closed-set label noise, where some examples belong to the known categories but are mislabeled; and 2) open-set label noise, where the true labels of some mislabeled examples are outside the known categories. However, the latter is largely ignored by existing works, leading to limited student network performance. Therefore, this paper proposes a novel data-free knowledge distillation paradigm by utilizing a webly-collected dataset under universal label noise, which means both closed-set and open-set label noise should be tackled. Specifically, we first split the collected noisy dataset into clean set, closed noisy set, and open noisy set based on the prediction uncertainty of various data types. For the closed-set noisy examples, their labels are refined by teacher network. Meanwhile, a noise-robust hybrid contrastive learning is performed on the clean set and refined closed noisy set to encourage student network to learn the categorical and instance knowledge inherited by teacher network. For the open-set noisy examples unexplored by previous work, we regard them as unlabeled and conduct self-supervised learning on them to enrich the supervision signal for student network. Intensive experimental results on image classification tasks demonstrate that our approach can achieve superior performance to state-of-the-art data-free knowledge distillation methods.

Deep Supervised Multi-View Learning With Graph Priors.

This paper presents a novel method for supervised multi-view representation learning, which projects multiple views into a latent common space while preserving the discrimination and intrinsic structure of each view. Specifically, an apriori discriminant similarity graph is first constructed based on labels and pairwise relationships of multi-view inputs. Then, view-specific networks progressively map inputs to common representations whose affinity approximates the constructed graph. To achieve graph consistency, discrimination, and cross-view invariance, the similarity graph is enforced to meet the following constraints: 1) pairwise relationship should be consistent between the input space and common space for each view; 2) within-class similarity is larger than any between-class similarity for each view; 3) the inter-view samples from the same (or different) classes are mutually similar (or dissimilar). Consequently, the intrinsic structure and discrimination are preserved in the latent common space using an apriori approximation schema. Moreover, we present a sampling strategy to approach a sub-graph sampled from the whole similarity structure instead of approximating the graph of the whole dataset explicitly, thus benefiting lower space complexity and the capability of handling large-scale multi-view datasets. Extensive experiments show the promising performance of our method on five datasets by comparing it with 18 state-of-the-art methods.

Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning.

3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a sophisticated "detect-then-describe" pipeline, which builds explicit relation modules upon a 3D detector with numerous hand-crafted components. While these methods have achieved initial success, the cascade pipeline tends to accumulate errors because of duplicated and inaccurate box estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding. Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture. To this end, we propose an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features. Additionally, we introduce the iterative spatial refinement strategy to vote queries for faster convergence and better localization performance. We also insert additional spatial information to the caption head for more accurate descriptions. Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and Vote2Cap-DETR++ surpass conventional "detect-then-describe" methods by a large margin.

Resolving tumor evolution: a phylogenetic approach.

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Moesin as a key cytoskeleton regulator in corneal fibrosis.

PURPOSE: : Corneal fibrosis is the third leading cause of blindness worldwide. α-Smooth muscle actin (SMA), a marker of fibrosis, is closely regulated through an intermediate group of submembrane molecules - cytoskeleton regulators. The purpose of this study was to elucidate the role of specific cytoskeleton regulators in a mouse model of corneal fibrosis. METHODS: : A mouse model of corneal fibrosis was developed using anterior keratectomy (AK) and the topical application of transforming growth factor (TGF)-ß1 (1 µg/ml). The RT² Profiler™ PCR Array for cytoskeleton regulators was used to assay changes in levels of specific members of this class of proteins. Moesin siRNA was delivered into the corneal stroma by iontophoresis in vivo. Transformation of the corneal keratocyte-to-myofibroblast in corneal fibrosis, as defined by the expression of α-SMA, was determined by Western blot. RESULTS: : After AK and topical application of TGF-ß1, moesin was the most highly upregulated gene among 84 cytoskeleton regulator genes; iontophoresing moesin siRNA into the corneal stroma reduced the expression of α-SMA to 0.22-, 0.52-, and 0.31-fold of control at postoperative (PO) day 1, 3, and 5, respectively; also, upregulation of phospho-Smad 2 induced by TGF-ß1 was reduced by moesin siRNA to 0.59-, 0.56-, and 0.31-fold of control and expression of phospho-Smad 3 was reduced to 0.58-, 0.53-, and 0.47-fold of control at the same PO days. CONCLUSIONS: : Moesin may be a potential drug target for inhibiting corneal fibrosis, and the details of moesin-related signaling pathways would be critical for understanding corneal fibrosis.

A cascaded nonlinear fault-tolerant control for fixed-wing aircraft with wing asymmetric damage.

This paper investigates the flight dynamics of the aircraft with wing asymmetric damage and the fault-tolerant control problem to improve the stability and flight quality of damaged aircraft. A high-fidelity wing asymmetric damaged aircraft nonlinear model is developed, as well as the impact of wing asymmetric damage on the physical and aerodynamic properties of the aircraft is also analyzed. The trim strategies for damaged aircraft are investigated to achieve a rapid estimation of trim states after damage occurs. This paper presents a robust cascaded nonlinear fault-tolerant control framework that integrates the incremental nonlinear dynamic inversion control with improved piecewise-constant-based nonlinear L1 adaptive control for the stability control to enhance the stability and tracking performance of the damaged aircraft. Theoretical analysis proves that the presented fault-control structure is robust to disturbances and can decouple rapidity and robustness while guaranteeing steady-state and transient performance. Finally, the hardware-in-the-loop flight control experiment platform is developed to validate the cascaded nonlinear fault-tolerant controller. In the experiment, the proposed controller is verified under wing asymmetric damage and compared with existing methods. Experimental results show that the proposed fault-tolerant control is able to overcome wing asymmetric damage and significantly improve the tracking performance of the damaged aircraft even with 27.2% of the severe damage to the left-wing.

Dual-Stream Contrastive Learning for Channel State Information Based Human Activity Recognition.

WiFi-based human activity recognition (HAR) has been extensively studied due to its far-reaching applications in health domains, including elderly monitoring, exercise supervision and rehabilitation monitoring, etc. Although existing supervised deep learning techniques have achieved remarkable performances for these tasks, they are however data-hungry and hence are notoriously difficult due to the privacy and incomprehensibility of WiFi-based HAR data. Existing contrastive learning models, mainly designed for computer vision, cannot guarantee their performance on channel state information (CSI) data. To this end, we propose a new dual-stream contrastive learning model that can process and learn the raw WiFi CSI data in a self-supervised manner. More specifically, our proposed method, coined as DualConFi, takes raw WiFI CSI data as input and incorporates channel and temporal streams to learn highly-discriminative spatiotemporal features under a mutual information constraint using unlabeled data. We exhibit the effectiveness of our model on three publicly available CSI data sets in various experiment settings, including linear evaluation, semi-supervised, and transfer learning. We show that DualConFi is able to perform favourably against challenging baselines in each setting. Moreover, by studying the effects of different transform functions on CSI data, we finally verify the effectiveness of highly-discriminative features.

Acute toxicity, biochemical and transcriptomic analysis of Procambarus clarkii exposed to avermectin.

BACKGROUND: Pesticides are extensively applied globally. Pesticide residues induce calamitous effects on the environment and untargeted organisms. Public concerns for the safety of freshwater organisms and the challenges posed by aquatic contaminants remain high. In the present study, the acute toxicity of avermectins (AVMs) to the crayfish, Procambarus clarkii was evaluated. We also evaluated the potential effects of AVM on the biochemical and transcriptomic status of the hepatopancreas and gastrointestinal tract in P. clarkii. RESULTS: The 24, 48, 72, 96 h median lethal concentrations (LC50 ) of AVM on crayfish were 2.626, 1.162, 0.723, 0.566 mg L-1 , respectively. The crayfish were then exposed to 0.65 mg L-1 of AVM for 96 h. AVM significantly altered biochemical parameters including AChE and CAT activities in the hepatopancreas, and AChE, SOD and Na + -K + -ATPase activities in the gastrointestinal tract at several time points. Furthermore, transcriptomic analysis identified 953 and 1851 differentially-expressed genes (DEGs) in the hepatopancreas and gastrointestinal tract, respectively. KEGG enrichment showed that the gene expression profiles of the hepatopancreas and gastrointestinal tract were distinct from each other. The DEGs in the hepatopancreas were mostly enriched with stress-response pathways, while the majority of the DEGs in the gastrointestinal tract belonged to metabolism-related pathways. CONCLUSION: We demonstrated that the AVM induced acute toxicity, oxidative stress, osmoregulation disturbance, neurotoxicity and transcriptome imbalance in crayfish. These findings unraveled the detrimental effects of AVMs exposure on crayfish. © 2022 Society of Chemical Industry.

Unsupervised Contrastive Cross-Modal Hashing.

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we propose a novel momentum optimizer that performs hashing operation learnable in CL, thus making on-the-shelf deep cross-modal hashing possible. In other words, our method does not involve binary-continuous relaxation like most existing methods, thus enjoying better retrieval performance; ii) to alleviate the influence brought by false-negative pairs (FNPs), we propose a Cross-modal Ranking Learning loss (CRL) which utilizes the discrimination from all instead of only the hard negative pairs, where FNP refers to the within-class pairs that were wrongly treated as negative pairs. Thanks to such a global strategy, CRL endows our method with better performance because CRL will not overuse the FNPs while ignoring the true-negative pairs. To the best of our knowledge, the proposed method could be one of the first successful contrastive hashing methods. To demonstrate the effectiveness of the proposed method, we carry out experiments on five widely-used datasets compared with 13 state-of-the-art methods. The code is available at https://github.com/penghu-cs/UCCH.

Establishment of trimester-specific reference intervals of renal function tests and their predictive values in pregnant complications and perinatal outcomes: A population-based cohort study.

Objectives: In this study, we aimed to establish the trimester-specific RIs of renal function tests (RFTs) in singleton pregnant women and investigate the associations between adverse perinatal outcomes and abnormal renal function laboratory results. Methods: The results of RFTs and the associated medical records were retrieved from 16489 singleton pregnant women who underwent first- and third-trimester prenatal screening and gave a live birth at out institute between August 2018 and December 2019. The RFTs were performed on the automated immunochemistry platform ARCHITECT ci16200 (Abbott Laboratories Ltd, Abbott Park, Illinois, US) in the clinical laboratory of our institute. The nonparametric 2.5th-97.5th percentile intervals and the indirect Hoffmann methods were used to define the trimester-specific RIs. The associations between abnormal RFTs and adverse pregnancy outcomes was assessed statistically by logistic regression. Results: There was no significant difference between the direct observational and the indirect Hoffmann methods in establishing RIs of RFTs. Compared with RFTs in the first trimester, the concentrations of serum BUN and Crea were slightly decreased (p < 0.001), and the serum UA and Cys C levels were significantly elevated in the third trimester (p < 0.001). In the logistic regression analysis, high concentrations of UA, Crea, and Cys C in late pregnancy were associated with an increased risk of postpartum hemorrhage. Meanwhile, early pregnancy UA was associated with a modestly increased risk of GDM, GH, and PE. Conclusion: It is necessary to establish trimester-specific RIs for RFTs, in order to appropriately interpret laboratory results and to identify women with high risks of developing various adverse outcomes.