NiCo2O4@SnS2nanosheets on carbon cloth as efficient bi-functional material for high performance supercapacitor and sensor applications.

Bi-functional materials provide an opportunity for the development of high-performance devices. Up till now, bi-functional performance of NiCo2O4@SnS2nanosheets is rarely investigated. In this work, NiCo2O4@SnS2nanosheets were synthesized on carbon cloth by utilizing a simple hydrothermal technique. The developed electrode (NiCo2O4@SnS2/CC) was investigated for the detection of L-Cysteine and supercapacitors applications. As a non-enzymatic sensor, the electrode proved to be highly sensitive for the detection of L-cysteine. The electrode exhibits a reproducible sensitivity of 4645.82µA mM-1cm-2in a wide linear range from 0.5 to 5 mM with a low limit of detection (0.005µM). Moreover, the electrode shows an excellent selectivity and long-time stability. The high specific surface area, enhanced kinetics, good synergy and distinct architecture of NiCo2O4@SnS2nanosheets produce a large number of active sites with substantial energy storage potential. As a supercapacitor, the electrode exhibits improve capacitance of 655.7 F g-1at a current density of 2 A g-1as compare to NiCo2O4/CC (560 F g-1). Moreover, the electrode achieves 95.3% of its preliminary capacitance after 10 000 cycles at 2 A g-1. Our results show that NiCo2O4@SnS2/CC nanosheets possess binary features could be attractive electrode material for the development of non-enzymatic biosensors as well as supercapacitors.

Mesoporous Co3O4@CdS nanorods as anode for high-performance lithium ion batteries with improved lithium storage capacity and cycle life.

Transition metal oxides based anodes are facing crucial problems of capacity fading at long cycles and high rates due to electrode degradations. In this prospective, an effective strategy is employed to develop advanced electrode materials for lithium-ion batteries (LIBs). In the present work, a mesoporous Co3O4@CdS hybrid sructure is developed and investigated as anode for LiBs. The hybrid structure owning porous nature and large specific surface area, provides an opportunity to boost the lithium storage capabilities of Co3O4 nanorods. The Co3O4@CdS electrode delivers an initial discharge capacity of 1292 mA h g-1 at 0.1C and a very stable reversible capacity of 760 mA h g-1 over 200 cycles with a capacity retention rate of 92.7%. In addition, the electrode exhibits excellent cyclic stability even after 800 cycles and good rate performance as compared to previously reported electrodes. Moreover, density functional theory (DFT) and electrochemical impedance spectroscopy (EIS) confirm the enhanced kinetics of the Co3O4@CdS electrode. The efficient performance of the electrode may be due to the increased surface reactivity, abundant active sites/interfaces for rapid Li+ ion diffusion and the synergy between Co3O4 and CdS NPs. This work demonstrates that Co3O4@CdS hybrid structures have great potential for high performance batteries.

Early warning of hepatocellular carcinoma in cirrhotic patients by three-phase CT-based deep learning radiomics model: a retrospective, multicentre, cohort study.

Background: The diagnosis of hepatocellular carcinoma (HCC) often experiences latency, ultimately leading to unfavorable patient outcomes due to delayed therapeutic interventions. Our study is designed to develop and validate a model that employs triple-phase computerized tomography (CT)-based deep learning radiomics and clinical variables for early warning of HCC in patients with cirrhosis. Methods: We studied 1858 patients with cirrhosis primarily from the PreCar cohort (NCT03588442) between June 2018 and January 2020 at 11 centres, and collected triple-phase CT images and laboratory results 3-12 months prior to HCC diagnosis or non-HCC final follow-up. Using radiomics and deep learning techniques, early warning model was developed in the discovery cohort (n = 924), and then validated in an internal validation cohort (n = 231), and an external validation cohort from 10 external centres (n = 703). Findings: We developed a hybrid model, named ALARM model, which integrates deep learning radiomics with clinical variables, enabling early warning of the majority of HCC cases. The ALARM model effectively predicted short-term HCC development in cirrhotic patients with area under the curve (AUC) of 0.929 (95% confidence interval 0.918-0.941) in the discovery cohort, 0.902 (0.818-0.987) in the internal validation cohort, and 0.918 (0.898-0.961) in the external validation cohort. By applying optimal thresholds of 0.21 and 0.65, the high-risk (n = 221, 11.9%) and medium-risk (n = 433, 23.3%) groups, which covered 94.4% (84/89) of the patients who developed HCC, had significantly higher rates of HCC occurrence compared to the low-risk group (n = 1204, 64.8%) (24.3% vs 6.4% vs 0.42%, P < 0.001). Furthermore, ALARM also demonstrated consistent performance in subgroup analysis. Interpretation: The novel ALARM model, based on deep learning radiomics with clinical variables, provides reliable estimates of short-term HCC development for cirrhotic patients, and may have the potential to improve the precision in clinical decision-making and early initiation of HCC treatments. Funding: This work was supported by National Key Research and Development Program of China (2022YFC2303600, 2022YFC2304800), and the National Natural Science Foundation of China (82170610), Guangdong Basic and Applied Basic Research Foundation (2023A1515011211).

Cell-free DNA testing for early hepatocellular carcinoma surveillance.

BACKGROUND: Liver cirrhosis (LC) is the highest risk factor for hepatocellular carcinoma (HCC) development worldwide. The efficacy of the guideline-recommended surveillance methods for patients with LC remains unpromising. METHODS: A total of 4367 LCs not previously known to have HCC and 510 HCCs from 16 hospitals across 11 provinces of China were recruited in this multi-center, large-scale, cross-sectional study. Participants were divided into Stage Ⅰ cohort (510 HCCs and 2074 LCs) and Stage Ⅱ cohort (2293 LCs) according to their enrollment time and underwent Tri-phasic CT/enhanced MRI, US, AFP, and cell-free DNA (cfDNA). A screening model called PreCar Score was established based on five features of cfDNA using Stage Ⅰ cohort. Surveillance performance of PreCar Score alone or in combination with US/AFP was evaluated in Stage Ⅱ cohort. FINDINGS: PreCar Score showed a significantly higher sensitivity for the detection of early/very early HCC (Barcelona stage A/0) in contrast to US (sensitivity of 51.32% [95% CI: 39.66%-62.84%] at 95.53% [95% CI: 94.62%-96.38%] specificity for PreCar Score; sensitivity of 23.68% [95% CI: 14.99%-35.07%] at 99.37% [95% CI: 98.91%-99.64%] specificity for US) (P < 0.01, Fisher's exact test). PreCar Score plus US further achieved a higher sensitivity of 60.53% at 95.08% specificity for early/very early HCC screening. INTERPRETATION: Our study developed and validated a cfDNA-based screening tool (PreCar Score) for HCC in cohorts at high risk. The combination of PreCar Score and US can serve as a promising and practical strategy for routine HCC care. FUNDING: A full list of funding bodies that contributed to this study can be found in Acknowledgments section.