Additively Manufactured Bionic Corrugated Lightweight Honeycomb Structures with Controlled Deformation Load-Bearing Properties.

The rapid development of additive manufacturing (AM) has facilitated the creation of bionic lightweight, energy-absorbing structures, enabling the implementation of more sophisticated internal structural designs. For protective structures, the utilization of artificially controlled deformation patterns can effectively reduce uncertainties arising from random structural damage and enhance deformation stability. This paper proposed a bionic corrugated lightweight honeycomb structure with controllable deformation. The force on the onset state of deformation of the overall structure was investigated, and the possibility of controlled deformation in the homogeneous structure was compared with that in the corrugated structure. The corrugated structures exhibited a second load-bearing capacity wave peak, with the load-bearing capacity reaching 60.7% to 117.29% of the first load-bearing peak. The damage morphology of the corrugated structure still maintained relative integrity. In terms of energy absorption capacity, the corrugated lightweight structure has a much stronger energy absorption capacity than the homogeneous structure due to the second peak of the load carrying capacity. The findings of this study suggested that the combination of geometric customization and longitudinal corrugation through additive manufacturing offers a promising approach for the development of high-performance energy-absorbing structures.

KNa2Lu(BO3)2: A Rare-Earth Borate Crystal Characterized by an Enhanced Birefringence and Wide Ultraviolet Transparency Range.

Borate materials are of significant interest due to their versatile structural configuration and competitive ultraviolet (UV) transparency range. In this study, we present a novel rare-earth borate crystal, KNa2Lu(BO3)2, synthesized for the first time through a facile spontaneous crystallization method. It adopts the centrosymmetric space group Pnma (no. 62) and yields a unique three-dimensional (3D) structural network formed by isolated [BO3] plane triangles and distorted [LuO7] polyhedra. This compound displays excellent thermal stability up to ∼990 °C, demonstrating a favorable congruent melting nature. Moreover, KNa2Lu(BO3)2 achieves a notably short UV absorption cutoff at approximately 204 nm, yielding a large band gap of 5.58 eV. Remarkably, it showcases an enlarged birefringence of 0.044 at 1064 nm, implying its potential as a birefringent material. Moreover, density functional theory calculations demonstrate that the optical characteristics are predominantly influenced by fundamental building blocks [BO3] triangles and distorted [LuO7] polyhedra. Our findings demonstrate the potential of KNa2Lu(BO3)2 in the development of a birefringent candidate and enrich the structural chemistry of rare-earth-based borates.

Simulation and Experimental Study of a Novel Negative-Pressure Flapper-Nozzle Mechanism.

As the manufacturing industry evolves, the significance of control valve positioners in chemical production escalates. The flapper-nozzle system, the heart of control valve positioners, directly influences the linearity of system control. Presently, studies on the flapper-nozzle system primarily focus on dynamic system modeling and computational fluid dynamics simulations. However, traditional flapper-nozzle mechanisms often fail to achieve linear control objectives. This paper proposes a novel negative-pressure nozzle structure to tackle this issue, combining computational fluid dynamics and experimental methods, and considering gas compressibility during high-speed flow. Both simulation and experimental results suggest that the new structure improves the supply air pressure and broadens the linear pressure output range of the system, showing significant potential for practical applications.

Efficient disinfection of SARS-CoV-2-like coronavirus, pseudotyped SARS-CoV-2 and other coronaviruses using cold plasma induces spike protein damage.

Coronavirus disease 2019 (COVID-19) has become a worldwide public health emergency, and the high transmission of SARS-CoV-2 variants has raised serious concerns. Efficient disinfection methods are crucial for the prevention of viral transmission. Herein, pulse power-driven cold atmospheric plasma (CAP), a novel sterilization strategy, was found to potently inactivate SARS-CoV-2-like coronavirus GX_P2V, six strains of major epidemic SARS-CoV-2 variants and even swine coronavirus PEDV and SADS-CoV within 300 s (with inhibition rate more than 99%). We identified four dominant short-lived reactive species, ONOO-, 1O2, O2- and·OH, generated in response to CAP and distinguished their roles in the inactivation of GX_P2V and SARS-CoV-2 spike protein receptor binding domain (RBD), which is responsible for recognition and binding to human angiotensin-converting enzyme 2 (hACE2). Our study provides detailed evidence of a novel surface disinfection strategy for SARS-CoV-2 and other coronaviruses.

A Risk-Prediction Nomogram for Neutropenia or Febrile Neutropenia after Etoposide-Based Chemotherapy in Cancer Patients: A Retrospective Cohort Study.

INTRODUCTION: This study was conducted to develop and validate a nomogram for predicting the risk of neutropenia or febrile neutropenia (FN) in tumor patients in the first cycle of etoposide-based chemotherapy. METHODS: This retrospective cohort study used an information system to monitor patients with non-Hodgkin's lymphoma or solid tumors receiving an etoposide regimen in the first chemotherapy cycle in our hospital from 2009 to 2020. Binary logistic regression analysis was used to identify the influencing factors of patients with neutropenia or FN. Those factors were then used to develop a nomogram. RESULTS: A total of 1,554 patients were divided into the development group (n = 1,072) and validation group (n = 482). Variables used to predict neutropenia or FN were Karnofsky performance status (odds ratio [OR] = 0.85, 95% confidence interval [CI] = 0.81-0.89, p < 0.01), metastatic sites ≥3 (OR = 6.33, 95% CI = 2.66-15.11, p < 0.01), comorbidity of heart disease (OR = 4.88, 95% CI = 1.74-13.67, p < 0.01), recent surgery (OR = 7.96, 95% CI = 1.96-32.36, p < 0.01), administration of alkylating agents (OR = 4.50, 95% CI = 1.10-18.48, p < 0.01), total bilirubin ≥25 µmol/L (OR = 11.42, 95% CI = 4.00-32.61, p < 0.01), and lymphocyte count <0.7 × 109/L (OR = 4.22, 95% CI = 2.00-9.75, p < 0.01). CONCLUSION: This model can aid the early identification and screening of the potential risk of neutropenia or FN in the first cycle of treatment for patients using etoposide-based chemotherapy.

Development and Validation of a Nomogram for Predicting Drug-Induced Acute Kidney Injury in Hospitalized Patients: A Case-Control Study Based on Propensity-Score Matching.

Background: Drug-induced acute kidney injury (D-AKI) is associated with increased mortality and longer hospital stays. This study aims to establish a nomogram to predict the occurrence of D-AKI in hospitalized patients in a multi-drug environment. Methods: A single center retrospective study among adult hospitalized patients was conducted from July 2019 to September 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by our hospital. According to the propensity score matching algorithm, four controls per case were matched to eliminate the confounding bias caused by individual baseline variables. The predictors for D-AKI were obtained by logistic regression equation and used to establish the nomogram. Results: Among 51,772 hospitalized patients, 332 were diagnosed with D-AKI. After matching, 288 pairs and 1,440 patients were included in the study, including 1,005 cases in the development group and 435 cases in the validation group. Six variables were independent predictors for D-AKI: alcohol abuse, the concurrent use of nonsteroidal anti-inflammatory drugs or diuretics, chronic kidney disease, lower baseline red blood cell count and neutrophil count ≥7 × 109/L. The area under the curve (AUC) of the prediction model in the development group and validation group were 0.787 (95%CI, 0.752-0.823) and 0.788 (95%CI, 0.736-0.840), respectively. The GiViTI calibration belts showed that the model had a good prediction accuracy for the occurrence of D-AKI (p > 0.05). Conclusion: This nomogram can help identify patients at high risk of D-AKI, which was useful in preventing the progression of D-AKI and treating it in the early stages.

Incidence, characteristics and risk factors for drug-induced liver injury in hospitalized patients: A matched case-control study.

AIMS: The diagnosis of drug-induced liver injury (DILI) is relatively complex and involves a wide variety of drugs. The purpose of this study was to use algorithms to quickly screen DILI patients, determine its incidence and identify risk factors. METHODS: The Adverse Drug Events Active Surveillance and Assessment System-2 was used to extract the data of patients hospitalized in 2019 according to the set standards and the Roussel Uclaf Causality Assessment Method was used to evaluate patients who met the standards. A retrospective case-control study was conducted according to suspected drugs, length of hospital stay and height- and weight-matched controls, and logistic regression was used to identify risk factors. RESULTS: Among the 156 570 hospitalized patients, 480 patients (499 cases) with DILI were confirmed and the incidence of DILI was 0.32%. Anti-infective agents, antineoplastic agents and nonsteroidal anti-inflammatory drugs were the major categories of drugs causing DILI, and the highest incidence of DILI was due to voriconazole. The latency period and hospital stay of patients with cholestasis were both relatively long. Patients with hyperlipidaemia (adjusted odds ratio [AOR] 1.884), cardiovascular disease (AOR 1.465), pre-existing liver disease (AOR 1.827) and surgical history (AOR 1.312) were at higher risk for DILI. CONCLUSIONS: The incidence of DILI in hospitalized patients was uncommon (0.32%) and its pathogenic drugs were widely distributed. The incidence of DILI for many drugs has been seriously underestimated. It is recommended to focus on patients with hyperlipidaemia, cardiovascular disease, pre-existing liver disease and surgical history.

CTNN: A Convolutional Tensor-Train Neural Network for Multi-Task Brainprint Recognition.

Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the ability to extract high-level features and latent dependencies, deep learning can effectively overcome the limitation of specific tasks, but numerous samples are required for model training. Therefore, brainprint recognition in realistic scenes with multiple individuals and small amounts of samples in each class is challenging for deep learning. This article proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples. Firstly, local temporal and spatial features of the brainprint are extracted by the convolutional neural network (CNN) with depthwise separable convolution mechanism. Afterwards, we implement the TensorNet (TN) via low-rank representation to capture the multilinear intercorrelations, which integrates the local information into a global one with very limited parameters. The experimental results indicate that CTNN has high recognition accuracy over 99% on all four datasets, and it exploits brainprint under multi-task efficiently and scales well on training samples. Additionally, our method can also provide an interpretable biomarker, which shows specific seven channels are dominated for the recognition tasks.

Clinical Characteristics of Hospitalized Patients with Drug-Induced Acute Kidney Injury and Associated Risk Factors: A Case-Control Study.

BACKGROUND: Drug-induced acute kidney injury (D-AKI) is increasingly common and can extend the hospital length of stay and increase mortality. This study is aimed at analyzing the clinical characteristics of hospitalized patients with D-AKI and the associated risk factors in a multidrug environment. METHODS: A retrospective study among hospitalized patients was conducted in July 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by the authors. Four controls were matched with each case according to the matching criteria. The risk factors for D-AKI were identified by binary multivariate logistic regression. RESULTS: A total of 23,073 patients were hospitalized in July 2019, 21,131 of whom satisfied the inclusion criteria. The independent risk factors for D-AKI consisted of alcohol abuse (odds ratio (OR), 2.05; 95% confidence interval (CI), 1.04-4.07), nonsteroidal anti-inflammatory drug (NSAID) use (OR, 2.39; 95% CI, 1.25-4.58), diuretic use (OR, 2.64; 95% CI, 1.42-4.92), prior anemia (OR, 4.10; 95% CI, 1.94-8.67), and prior chronic kidney disease (OR, 2.33; 95% CI, 1.07-5.08). CONCLUSIONS: The occurrence of D-AKI in hospitalized patients had significant associations with alcohol abuse, combination therapy with NSAIDs or diuretics, and prior anemia or chronic kidney disease. Clinicians should meticulously follow patients with the above characteristics.