Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study.

BACKGROUND: Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care. AIM: To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR. METHODS: A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models. RESULTS: Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model. CONCLUSION: Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.

The role of the CXCR6/CXCL16 axis in the pathogenesis of fibrotic disease.

CXC chemokine receptor 6 (CXCR6), a seven-transmembrane domain G-protein-coupled receptor, plays a pivotal regulatory role in inflammation and tissue damage through its interaction with CXC chemokine ligand 16 (CXCL16). This axis is implicated in the pathogenesis of various fibrotic diseases and correlates with clinical parameters that indicate disease severity, activity, and prognosis in organ fibrosis, including afflictions of the liver, kidney, lung, cardiovascular system, skin, and intestines. Soluble CXCL16 (sCXCL16) serves as a chemokine, facilitating the migration and recruitment of CXCR6-expressing cells, while membrane-bound CXCL16 (mCXCL16) functions as a transmembrane protein with adhesion properties, facilitating intercellular interactions by binding to CXCR6. The CXCR6/CXCL16 axis is established to regulate the cycle of damage and repair during chronic inflammation, either through modulating immune cell-mediated intercellular communication or by independently influencing fibroblast homing, proliferation, and activation, with each pathway potentially culminating in the onset and progression of fibrotic diseases. However, clinically exploiting the targeting of the CXCR6/CXCL16 axis requires further elucidation of the intricate chemokine interactions within fibrosis pathogenesis. This review explores the biology of CXCR6/CXCL16, its multifaceted effects contributing to fibrosis in various organs, and the prospective clinical implications of these insights.

A Molecular-Sieving Interphase Towards Low-Concentrated Aqueous Sodium-Ion Batteries.

Aqueous sodium-ion batteries are known for poor rechargeability because of the competitive water decomposition reactions and the high electrode solubility. Improvements have been reported by salt-concentrated and organic-hybridized electrolyte designs, however, at the expense of cost and safety. Here, we report the prolonged cycling of ASIBs in routine dilute electrolytes by employing artificial electrode coatings consisting of NaX zeolite and NaOH-neutralized perfluorinated sulfonic polymer. The as-formed composite interphase exhibits a molecular-sieving effect jointly played by zeolite channels and size-shrunken ionic domains in the polymer matrix, which enables high rejection of hydrated Na+ ions while allowing fast dehydrated Na+ permeance. Applying this coating to electrode surfaces expands the electrochemical window of a practically feasible 2 mol kg-1 sodium trifluoromethanesulfonate aqueous electrolyte to 2.70 V and affords Na2MnFe(CN)6//NaTi2(PO4)3 full cells with an unprecedented cycling stability of 94.9% capacity retention after 200 cycles at 1 C. Combined with emerging electrolyte modifications, this molecular-sieving interphase brings amplified benefits in long-term operation of ASIBs.

Systematic review of automatic post-stroke gait classification systems.

BACKGROUND: Gait classification is a clinically helpful task performed after a stroke in order to guide rehabilitation therapy. Gait disorders are commonly identified using observational gait analysis in clinical settings, but this approach is limited due to low reliability and accuracy. Data-driven gait classification can quantify gait deviations and categorise gait patterns automatically possibly improving reliability and accuracy; however, the development and clinical utility of current data driven systems has not been reviewed previously. RESEARCH QUESTION: The purpose of this systematic review is to evaluate the literature surrounding the methodology used to develop automatic gait classification systems, and their potential effectiveness in the clinical management of stroke-affected gait. METHOD: The database search included PubMed, IEEE Xplore, and Scopus. Twenty-one studies were identified through inclusion and exclusion criteria from 407 available studies published between 2015 and 2022. Development methodology, classification performance, and clinical utility information were extracted for review. RESULTS AND SIGNIFICANCE: Most of gait classification systems reported a classification accuracy between 80%-100%. However, collated studies presented methodological errors in machine learning (ML) model development. Further, many studies neglected model components such as clinical utility (e.g., predictions don't assist clinicians or therapists in making decisions, interpretability, and generalisability). We provided recommendations to guide development of future post-stroke automatic gait classification systems to better assist clinicians and therapists. Future automatic gait classification systems should emphasise the clinical significance and adopt a standardised development methodology of ML model.

Alkaline-based aqueous sodium-ion batteries for large-scale energy storage.

Aqueous sodium-ion batteries are practically promising for large-scale energy storage, however energy density and lifespan are limited by water decomposition. Current methods to boost water stability include, expensive fluorine-containing salts to create a solid electrolyte interface and addition of potentially-flammable co-solvents to the electrolyte to reduce water activity. However, these methods significantly increase costs and safety risks. Shifting electrolytes from near neutrality to alkalinity can suppress hydrogen evolution while also initiating oxygen evolution and cathode dissolution. Here, we present an alkaline-type aqueous sodium-ion batteries with Mn-based Prussian blue analogue cathode that exhibits a lifespan of 13,000 cycles at 10 C and high energy density of 88.9 Wh kg-1 at 0.5 C. This is achieved by building a nickel/carbon layer to induce a H3O+-rich local environment near the cathode surface, thereby suppressing oxygen evolution. Concurrently Ni atoms are in-situ embedded into the cathode to boost the durability of batteries.

A VR-based Treadmill Training System for Post-stroke Gait Rehabilitation.

Virtual reality (VR) has the potential to enhance rehabilitation by creating simulated multiple training environments, thereby maximizing the implementation of motor learning principles. However, previous use of VR-based treadmill training to improve post-stroke gait function is limited by high cost and a lack of adherence to post-stroke gait rehabilitation principles in system design. This paper describes the development of a gait rehabilitation system that integrates treadmill gait training with VR technology to create a virtual rehabilitation setting with gait training tasks and real-time performance feedback. The proposed system targets post-stroke patients and integrates low-cost sensor and rehabilitation principles to allow remote training and maximize training efficacy.Clinical Relevance-This system is developed with an emphasis on rehabilitation and motor learning principles.

Activity and Selectivity Roadmap for C-N Electro-Coupling on MXenes.

Electrochemical coupling between carbon and nitrogen species to generate high-value C-N products, including urea, presents significant economic and environmental potentials for addressing the energy crisis. However, this electrocatalysis process still suffers from limited mechanism understanding due to the complex reaction networks, which restricts the development of electrocatalysts beyond trial-and-error practices. In this work, we aim to improve the understanding of the C-N coupling mechanism. This goal was achieved by constructing the activity and selectivity landscape on 54 MXene surfaces by density functional theory (DFT) calculations. Our results show that the activity of the C-N coupling step is largely determined by the *CO adsorption strength (Ead-CO), while the selectivity relies more on the co-adsorption strength of *N and *CO (Ead-CO and Ead-N). Based on these findings, we propose that an ideal C-N coupling MXene catalyst should satisfy moderate *CO and stable *N adsorption. Through the machine learning-based approach, data-driven formulas for describing the relationship between Ead-CO and Ead-N with atomic physical chemistry features were further identified. Based on the identified formula, 162 MXene materials were screened without time-consuming DFT calculations. Several potential catalysts were predicted with good C-N coupling performance, such as Ta2W2C3. The candidate was then verified by DFT calculations. This study has incorporated machine learning methods for the first time to provide an efficient high-throughput screening method for selective C-N coupling electrocatalysts, which could be extended to a wider range of electrocatalytic reactions to facilitate green chemical production.