Time-Dependent Diffusion MRI Helps Predict Molecular Subtypes and Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Time-dependent diffusion MRI has the potential to help characterize tumor cell properties; however, to the knowledge of the authors, its usefulness for breast cancer diagnosis and prognostic evaluation is unknown. Purpose To investigate the clinical value of time-dependent diffusion MRI-based microstructural mapping for noninvasive prediction of molecular subtypes and pathologic complete response (pCR) in participants with breast cancer. Materials and Methods Participants with invasive breast cancer who underwent pretreatment with time-dependent diffusion MRI between February 2021 and May 2023 were prospectively enrolled. Four microstructural parameters were estimated using the IMPULSED method (a form of time-dependent diffusion MRI), along with three apparent diffusion coefficient (ADC) measurements and a relative ADC diffusion-weighted imaging parameter. Multivariable logistic regression analysis was used to identify parameters associated with each molecular subtype and pCR. A predictive model based on associated parameters was constructed, and its performance was assessed using the area under the receiver operating characteristic curve (AUC) and compared by using the DeLong test. The time-dependent diffusion MRI parameters were validated based on correlation with pathologic measurements. Results The analysis included 408 participants with breast cancer (mean age, 51.9 years ± 9.1 [SD]). Of these, 221 participants were administered neoadjuvant chemotherapy and 54 (24.4%) achieved pCR. The time-dependent diffusion MRI parameters showed reasonable performance in helping to identify luminal A (AUC, 0.70), luminal B (AUC, 0.78), and triple-negative breast cancer (AUC, 0.72) subtypes and high performance for human epidermal growth factor receptor 2 (HER2)-enriched breast cancer (AUC, 0.85), outperforming ADC measurements (all P < .05). Progesterone receptor status (odds ratio [OR], 0.08; P = .02), HER2 status (OR, 3.36; P = .009), and the cellularity index (OR, 0.01; P = .02) were independently associated with the odds of achieving pCR. The combined model showed high performance for predicting pCR (AUC, 0.88), outperforming ADC measurements and the clinical-pathologic model (AUC, 0.73 and 0.79, respectively; P < .001). The time-dependent diffusion MRI-estimated parameters correlated well with the pathologic measurements (n = 100; r = 0.67-0.81; P < .001). Conclusion Time-dependent diffusion MRI-based microstructural mapping was an effective method for helping to predict molecular subtypes and pCR to neoadjuvant chemotherapy in participants with breast cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Partridge and Xu in this issue.

How does the SARS-CoV-2 reinfection rate change over time? The global evidence from systematic review and meta-analysis.

BACKGROUND: There is a significant increase in the number of SARS-CoV-2 reinfection reports in various countries. However, the trend of reinfection rate over time is not clear. METHODS: We searched PubMed, Web of Science, Medline, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Wanfang for cohort studies, case-control studies, and cross-sectional studies up to March 16, 2023, to conduct a meta-analysis of global SARS-CoV-2 reinfection rate. Subgroup analyses were performed for age, country, study type, and study population, and time-varying reinfection rates of SARS-CoV-2 were estimated using meta-regression. The risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute critical appraisal tool. RESULT: A total of 55 studies involving 111,846 cases of SARS-CoV-2 reinfection were included. The pooled SARS-CoV-2 reinfection rate was 0.94% (95% CI: 0.65 -1.35%). In the subgroup analyses, there were statistically significant differences in the pooled reinfection rates by reinfection variant, and study type (P < 0.05). Based on meta-regression, the reinfection rate fluctuated with time. CONCLUSION: Meta-regression analysis found that the overall reinfection rate increased and then decreased over time, followed by a period of plateauing and then a trend of increasing and then decreasing, but the peak of the second wave of reinfection rate was lower than the first wave. SARS-CoV-2 is at risk of reinfection and the Omicron variant has a higher reinfection rate than other currently known variants. The results of this study could help guide public health measures and vaccination strategies in response to the Coronavirus Disease 2019 (COVID-19) pandemic.

A Deep Learning-Based Method for Rapid 3D Whole-Heart Modeling in Congenital Heart Disease.

INTRODUCTION: This study aims to develop a deep learning-based method for generating three-dimensional heart mesh models for patients with congenital heart disease by integrating medical imaging and clinical diagnostic information. METHODS: A deep learning model was trained using CT and cardiac MRI (CMR) images, along with clinical data from 110 patients. The web-based platform automatically outputs STL files for 3D printing and Unity 3D OBJ files for virtual reality (VR) applications upon uploading the medical images and diagnostic information. The models were tested on three congenital heart disease cases, with corresponding 3D printed and VR heart models generated. RESULTS: The 3D printed and VR heart models received high praise from professional doctors for their anatomical accuracy and clarity. Evaluations indicated that the proposed method effectively and rapidly reconstructs complex congenital heart disease structures, proving useful for preoperative planning and diagnostic support. CONCLUSION: The 3D modeling approach has the potential to enhance the precision of surgical planning and diagnosis for congenital heart disease. Future studies should explore larger datasets and training models for different types of congenital heart disease to validate the model's broad applicability.

Effect and optimal exercise prescription of robot-assisted gait training on lower extremity motor function in stroke patients: a network meta-analysis.

OBJECTIVE: This study aimed to evaluate the effectiveness of robot-assisted gait training (RAGT) and explore the optimal exercise prescription using a network meta-analysis approach. DATA SOURCES: A comprehensive search was conducted on randomized controlled trials comparing robotic and conventional rehabilitation published up to January 2024 in PubMed, Web of Science, Cochrane Library, Embase, CNKI, VIP, Wanfang, and SinoMed databases. REVIEW METHODS: The evaluation parameters included Fugl-Meyer Assessment of Lower Extremity (FMA-LE), Functional Ambulation Category (FAC), Berg Balance Scale (BBS), and 6-Minute Walk Test (6MWT). Two investigators independently performed study screening, data extraction, and bias evaluation. Data were merged, analyzed, and plotted using Review Manager 5.4.1 and Stata 18.0 software. RESULTS: A total of 21 articles involving 822 subjects were included in the analysis. RAGT positively influenced FMA-LE score (MD = 3.74, 95%CI 3.02-4.46, P < 0.05), FAC score (MD = 0.31, 95%CI 0.1-0.53, P < 0.05), BBS score (MD = 3.63, 95%CI 2.46-4.80, P < 0.05), and 6MWT score (MD = 23.73, 95%CI 15.31-32.14, P < 0.05). Surface under the cumulative ranking curve (SUCRA) values indicated that an exercise time of 40-60 min/training (97.4%), exercise frequency of 2-5 times/week (87.6%), and exercise duration of 8-12 weeks (78.1%) were most effective in improving the FMA-LE score. CONCLUSIONS: RAGT can effectively improve lower limb motor function, walking function, balance function, and walking endurance in stroke patients. For optimal improvement in FMA-LE score, an exercise time of 40-60 min/training, exercise frequency of 2-5 times/week, and exercise duration of 8-12 weeks are recommended.

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution.

Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution.

Implications of neuromuscular electrical stimulation on gait ability, balance and kinematic parameters after stroke: a systematic review and meta-analysis.

INTRODUCTIN: Improper gait patterns, impaired balance and foot drop consistently plague stroke survivors, preventing them from walking independently and safely. Neuromuscular electrical stimulation (NMES) technology can help patients reactivate their muscles and regain motor coordination. This study aims to systematically review and summarize the evidence for the potential benefits of NMES on the improvement of gait patterns after stroke. EVIDENCE ACQUISITION: PubMed, Cochrane Library, Embase, Science Direct and Web of Science were systematically searched until April 2024, to identify randomized controlled trials with the following criteria: stroke survivors as participants; NMES as intervention; conventional rehabilitation as a comparator; and gait assessment, through scales or quantitative parameters, as outcome measures. EVIDENCE SYNTHESIS: 29 publications involving 1711 patients met the inclusion criteria. Meta-analysis showed no significant differences in Ten-meter walk test, Fugl-Meyer assessment lower extremity, Modified Ashworth Assessment and asymmetry between the NMES group and the control group. Besides, NMES was associated with changes in outcome indicators such as quantitative gait analysis speed [SMD = 0.53, 95% CI (0.20, 0.85), P = 0.001], cadence [SMD = 0.76, 95% CI (0.32, 1.20), P = 0.0008], affected side step length [SMD = 0.73, 95% CI (0.16, 1.31), P = 0.01], angle of ankle dorsiflexion [WMD = 1.57, 95% CI (0.80, 2.33), P < 0.0001], Six-Minute Walk Test [WMD = 14.83, 95% CI (13.55, 16.11), P<0.00001]. According to the PEDro scale, 21 (72.4%) studies were of high quality and 8 were of moderate quality (27.6%). CONCLUSIONS: Taken together, the review synthesis indicated that NMES might play a potential role in stroke-induced walking dysfunction. And NMES may be superior for survivors in the chronic phase than the acute and subacute phases, and the efficacy of short sessions received by patients was greater than that of those who participated in a longer session. Additionally, further comparisons of the effects of NMES with different types or stimulation frequencies may provide unexpected benefits.

XGBoost-based multiparameters from dual-energy computed tomography for the differentiation of multiple myeloma of the spine from vertebral osteolytic metastases.

OBJECTIVES: To evaluate the performance of extreme gradient boosting (XGBoost) combined with multiparameters from dual-energy computed tomography (mpDECT) to differentiate between multiple myeloma (MM) of the spine and vertebral osteolytic metastases (VOM). METHODS: For this retrospective study, 28 patients (83 lesions) with MM of the spine and 23 patients (54 lesions) with VOM who underwent DECT were included. The mpDECT for each lesion, including normalized effective atomic number, slope of the spectral Hounsfield unit curve, CT attenuation, and virtual noncalcium (VNCa), was obtained. Boruta was used to select the key parameters, and then subsequently merged with XGBoost to yield a prediction model. The lesions were divided into the training and testing group in a 3:1 ratio. The highest performance of the univariate analysis was compared with XGBoost using the Delong test. RESULTS: The mpDECT of MM was significantly lower than that of VOM (all p < 0.05). In univariate analysis, VNCa had the highest area under the receiver operating characteristic curve (AUC) in the training group (0.81) and testing group (0.87). Based on Boruta, 6 parameters of DECT were selected for XGBoost model construction. The XGBoost model achieved an excellent and stable diagnostic performance, as shown in the training group (AUC of 1.0) and testing group (AUC of 0.97), with a sensitivity of 80%, a specificity of 95%, and an accuracy of 88%, which was superior to VNCa (p < 0.05). CONCLUSIONS: XGBoost combined with mpDECT yielded promising performance in differentiating between MM of the spine and VOM. KEY POINTS: • The multiparameters obtained from dual-energy CT of multiple myeloma differed significantly from those of vertebral osteolytic metastases. • The virtual noncalcium offered the highest AUC in the univariate analysis to distinguish multiple myeloma from vertebral osteolytic metastases. • Extreme gradient boosting combined with multiparameters from dual-energy CT had a promising performance to distinguish multiple myeloma from vertebral osteolytic metastases.

Simultaneous Thermal Enhancement of Upconversion and Downshifting Luminescence by Negative Thermal Expansion in Nonhygroscopic ZrSc(WO4)2PO4:Yb/Er Phosphors.

Thermal quenching (TQ) is still a critical challenge for lanthanide (Ln3+)-doped luminescent materials. Herein, we report the novel negative thermal expansion nonhygroscopic phosphor ZrSc(WO4)2PO4:Yb3+/Er3+. Upon excitation with a 980 nm laser, a simultaneous thermal enhancement is realized on upconversion (UC) and downshifting (DS) emissions from room temperature to 573 K. In situ temperature-dependent X-ray diffraction and photoluminescence dynamics are used to reveal the luminescence mechanism in detail. The coexistence of the high energy transfer efficiency and the promoted radiative transition probability can be responsible for the thermally enhanced luminescence. On the basis of the luminescence intensity ratio of thermally coupled energy levels 2H11/2 and 4S3/2 at different temperatures, the relative and absolute sensitivities of the targeted samples reach 1.10% K-1 and 1.21% K-1, respectively, and the low-temperature uncertainty is approximately 0.1-0.4 K on the whole temperature with a high repeatability (98%). Our findings highlight a general approach for designing a hygro-stable, thermostable, and highly efficient Ln3+-doped phosphor with UC and DS luminescence.

Knowledge, attitudes and practices of patients with chronic pharyngitis toward laryngopharyngeal reflux in Suzhou, China.

BACKGROUND: This study aimed to investigate the knowledge, attitudes and practices (KAP) of patients with chronic pharyngitis in Suzhou, China toward laryngopharyngeal reflux (LPR). METHODS: This cross-sectional study was conducted in patients with chronic pharyngitis in Suzhou, China at the otolaryngology outpatient clinic of the First Affiliated Hospital of Soochow University between November, 2022, and May, 2023. Data was collected through a self-designed online questionnaire encompassing the sociodemographic characteristics and three dimensions of KAP. The questionnaire was administered using SoJump, and data were exported from this platform. Subsequently, statistical analysis, including Structural Equation Modeling, was performed using SPSS 22 software to evaluate the KAP scores. RESULTS: A total of 487 valid questionnaires were collected, with 275 (56.35%) female patients. The mean score of KAP were 4.76 ± 2.93 (possible range: 0-11), 33.10 ± 4.46 (possible range: 8-40), 31.29 ± 6.04 (possible range: 8-40), respectively. Pearson's correlation analysis showed significant positive correlations between knowledge and attitude dimensions (r = 0.413, P < 0.001), knowledge and practice dimensions (r = 0.355, P < 0.001), and attitude and practice dimensions (r = 0.481, P < 0.001). Structural equation modeling revealed that education exhibited positive effect on knowledge (ß = 0.476, P < 0.001) and attitude (ß = 0.600, P < 0.001), and having family history of chronic pharyngitis showed positive effect on knowledge (ß = 0.580, P = 0.047), experienced with reflux symptoms showed positive effect on knowledge (ß = 0.838, P = 0.001) and attitude (ß = 0.631, P = 0.085). Moreover, knowledge showed positive effect on attitude (ß = 0.555, P < 0.001) and practice (ß = 0.351, P < 0.001). Attitude, in turn, showed positive effect on practice (ß = 0.511, P < 0.001). CONCLUSION: Patients with chronic pharyngitis had inadequate knowledge, positive attitudes and suboptimal practices toward LPR. Education, family history of chronic pharyngitis, experienced with reflux symptoms might have effect on their KAP.

A Novel Approach to Fetal ECG Extraction Using Temporal Convolutional Encoder-Decoder Network (TCED-Net).

To extract weak fetal ECG signals from the mixed ECG signal on the mother's abdominal wall, providing a basis for accurately estimating fetal heart rate and analyzing fetal ECG morphology. First, based on the relationship between the maternal chest ECG signal and the maternal ECG component in the abdominal signal, the temporal convolutional encoder-decoder network (TCED-Net) model is trained to fit the nonlinear transmission of the maternal ECG signal from the chest to the abdominal wall. Then, the maternal chest ECG signal is nonlinearly transformed to estimate the maternal ECG component in the abdominal mixed signal. Finally, the estimated maternal ECG component is subtracted from the abdominal mixed signal to obtain the fetal ECG component. The simulation results on the FECGSYN dataset show that the proposed approach achieves the best performance in F1 score, mean square error (MSE), and quality signal-to-noise ratio (qSNR) (98.94%, 0.18, and 8.30, respectively). On the NI-FECG dataset, although the fetal ECG component is small in energy in the mixed signal, this method can effectively suppress the maternal ECG component and thus extract a clearer and more accurate fetal ECG signal. Compared with existing algorithms, the proposed method can extract clearer fetal ECG signals, which has significant application value for effective fetal health monitoring during pregnancy.

Enhanced effect of recombinant adenoviruses co-expression of ING4 and OSM on anti-tumour activity of laryngeal cancer.

The inhibitor of growth family member 4 (ING4) is one of the ING family genes, serves as a repressor of angiogenesis or tumour growth and suppresses loss of contact inhibition. Oncostatin M (OSM) is a multifunctional cytokine that belongs to the interleukin (IL)-6 subfamily with several biological activities. However, the role of recombinant adenoviruses co-expressing ING4 and OSM (Ad-ING4-OSM) in anti-tumour activity of laryngeal cancer has not yet been identified. Recombinant Ad-ING4-OSM was used to evaluate their combined effect on enhanced anti-tumour activity in Hep-2 cells of laryngeal cancer in vivo. Moreover, in vitro function assays of co-expression of Ad-ING4-OSM were performed to explore impact of co-expression of Ad-ING4-OSM on biological phenotype of laryngeal cancer cell line, that is Hep-2 cells. In vitro, Ad-ING4-OSM significantly inhibited the growth, enhanced apoptosis, altered cell cycle with G1 and G2/M phase arrest, and upregulated the expression of P21, P27, P53 and downregulated survivin in laryngeal cancer Hep-2 cells. Furthermore, in vivo functional experiments of co-expressing of Ad-ING4-OSM demonstrated that solid tumours in the nude mouse model were significantly suppressed, and the co-expressing Ad-ING4-OSM showed a significant upregulation expression of P21, P53, Bax and Caspase-3 and a downregulation of Cox-2, Bcl-2 and CD34. This study for the first time demonstrated the clinical value and the role of co-expressing Ad-ING4-OSM in biological function of laryngeal cancer. This work suggested that co-expressing Ad-ING4-OSM might serve as a potential therapeutic target for laryngeal cancer patients.

[Development of High-performance Hearing Test System].

This paper analyzes the shortcomings of the existing pure tone audiometers, and proposes a system to realize pure tone audiometry and speech audiometry with a new DSP processor. The pure tone test signal produced by the system has accurate frequency, high signal-to-noise ratio, and small harmonic distortion. The noise generator that comes with DSP adds a band-pass filter to realize the generation of narrow-band noise. At the same time, due to the modular structure of software design, the system has good ease of use and scalability. The test results show that the hearing test system has excellent performance and can be better used in hearing medical diagnosis.

Statistical Physics of Unsupervised Learning with Prior Knowledge in Neural Networks.

Integrating sensory inputs with prior beliefs from past experiences in unsupervised learning is a common and fundamental characteristic of brain or artificial neural computation. However, a quantitative role of prior knowledge in unsupervised learning remains unclear, prohibiting a scientific understanding of unsupervised learning. Here, we propose a statistical physics model of unsupervised learning with prior knowledge, revealing that the sensory inputs drive a series of continuous phase transitions related to spontaneous intrinsic-symmetry breaking. The intrinsic symmetry includes both reverse symmetry and permutation symmetry, commonly observed in most artificial neural networks. Compared to the prior-free scenario, the prior reduces more strongly the minimal data size triggering the reverse-symmetry breaking transition, and moreover, the prior merges, rather than separates, permutation-symmetry breaking phases. We claim that the prior can be learned from data samples, which in physics corresponds to a two-parameter Nishimori constraint. This Letter thus reveals mechanisms about the influence of the prior on unsupervised learning.

Learning Credit Assignment.

Deep learning has achieved impressive prediction accuracies in a variety of scientific and industrial domains. However, the nested nonlinear feature of deep learning makes the learning highly nontransparent, i.e., it is still unknown how the learning coordinates a huge number of parameters to achieve decision-making. To explain this hierarchical credit assignment, we propose a mean-field learning model by assuming that an ensemble of subnetworks, rather than a single network, is trained for a classification task. Surprisingly, our model reveals that apart from some deterministic synaptic weights connecting two neurons at neighboring layers, there exists a large number of connections that can be absent, and other connections can allow for a broad distribution of their weight values. Therefore, synaptic connections can be classified into three categories: very important ones, unimportant ones, and those of variability that may partially encode nuisance factors. Therefore, our model learns the credit assignment leading to the decision and predicts an ensemble of sub-networks that can accomplish the same task, thereby providing insights toward understanding the macroscopic behavior of deep learning through the lens of distinct roles of synaptic weights.

Turn-On Luminescent Sensor toward Fe3+, Cr3+, and Al3+ Based on a Co(II) Metal-Organic Framework with Open Functional Sites.

A novel Co-based metal-organic framework (MOF) with the formula of {[Co3(BIBT)3(BTC)2(H2O)2]·solvents}n (JXUST-2, where JXUST denotes Jiangxi University of Science and Technology, BIBT = 4,7-bi(1H-imidazol-1-yl)benzo-[2,1,3]thiadiazole, and H3BTC = 1,3,5-benzenetricarboxylic acid) has been solvothermally prepared, which takes 3D structure with a rare 3,4,6-c topology and contains intramolecular hydrogen bonds. Interestingly, the sensing investigations suggest that JXUST-2 could be considered as a multifunctional fluorescence sensor toward Fe3+, Cr3+, and Al3+ via a turn-on effect with good reusability and detection limits of 0.13, 0.10, and 0.10 µM, respectively. The turn-on effect of JXUST-2 could be ascribed to an absorbance caused enhancement (ACE) mechanism. Notably, JXUST-2 is the first turn-on MOF fluorescent sensor for Fe3+, Cr3+, and Al3+ simultaneously.

A blockchain-based scheme for privacy-preserving and secure sharing of medical data.

How to alleviate the contradiction between the patient's privacy and the research or commercial demands of health data has become the challenging problem of intelligent medical system with the exponential increase of medical data. In this paper, a blockchain-based privacy-preserving scheme is proposed, which realizes secure sharing of medical data between several entities involved patients, research institutions and semi-trusted cloud servers. And meanwhile, it achieves the data availability and consistency between patients and research institutions, where zero-knowledge proof is employed to verify whether the patient's medical data meets the specific requirements proposed by research institutions without revealing patients' privacy, and then the proxy re-encryption technology is adopted to ensure that research institutions can decrypt the intermediary ciphertext. In addition, this proposal can execute distributed consensus based on PBFT algorithm for transactions between patients and research institutions according to the prearranged terms. Theoretical analysis shows the proposed scheme can satisfy security and privacy requirements such as confidentiality, integrity and availability, as well as performance evaluation demonstrates it is feasible and efficient in contrast with other typical schemes.

MicroRNA-1225-5p acts as a tumor-suppressor in laryngeal cancer via targeting CDC14B.

This study aimed to investigate the role of miRNA-1225-5p (miR-1225) in laryngeal carcinoma (LC). We found that the expression of miR-1225 was suppressed in human LC samples, while CDC14B (cell division cycle 14B) expression was reinforced in comparison with surrounding normal tissues. We also demonstrated that enhanced expression of miR-1225 impaired the proliferation and survival of LC cells, and resulted in G1/S cell cycle arrest. In contrast, reduced expression of miR-1225 promoted cell survival. Moreover, miR-1225 resulted in G1/S cell cycle arrest and enhanced cell death. Further, miR-1225 targets CDC14B 3'-UTR and recovery of CDC14B expression counteracted the suppressive influence of miR-1225 on LC cells. Thus, these findings offer insight into the biological and molecular mechanisms behind the development of LC.

The electrochemical applications of rare earth-based nanomaterials.

As a series of important metal materials, rare earth elements and their related derivative compounds have received significant attention because of their narrow emission bands, noncytotoxicity, excitation of multiple fluorescence colors and electrochemical properties. These unique properties endow rare earth-based nanomaterials with great potential for application in various fields such as in supercapacitors, batteries, sensors, devices and solar cells. This review presents a general description of the electrochemical properties of rare earth-based nanomaterials. The developments and improvements of their electrochemical applications in electrochemical sensors for the detection of small biomolecules and DNA, supercapacitors, batteries and electrochemical catalysis towards the hydrogen evolution reaction have also been discussed.

A ZnII-Based Metal-Organic Framework with a Rare tcj Topology as a Turn-On Fluorescent Sensor for Acetylacetone.

A ZnII-based metal-organic framework (MOF) with a rare tcj topology has been solvothermally synthesized and displays relatively good thermal and chemical stabilities. Interestingly, the MOF can sensitively and selectively sense acetylacetone (acac) via a fluorescence enhancement effect with a detection limit of 0.10 ppm and good reusability, which demonstrates the first example of a MOF-based turn-on fluorescent sensor for acac.

Electrochemical sensor based on a nanocomposite prepared from TmPO4 and graphene oxide for simultaneous voltammetric detection of ascorbic acid, dopamine and uric acid.

A nanocomposite is described that consists of TmPO4 and graphene oxide (GO) and is used to modify a glassy carbon electrode (GCE) to obtain a sensor for simultaneous determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA). GO and TmPO4 were synthesized via the Hummers method and by a hydrothermal method, respectively. The nanocomposite was characterized by transmission electron microscopy, energy dispersive X-ray spectroscopy, powder X-ray diffraction and Fourier transform infrared spectroscopy. The electrochemical properties of the modified GCE were studied by electrochemical impedance spectroscopy and cyclic voltammetry. The good performance of the modified GCE results from the synergistic effects between GO with its good electrical conductivity and of TmPO4 as the electron mediator that accelerates the electron transfer rate. Compared to a bare GCE, a GO/GCE and a TmPO4/GCE, the GO/TmPO4/GCE exhibits three well-defined and separated oxidation peaks (at -0.05, +0.13 and + 0.26 V vs. SCE). Responses to AA, DA and UA are linear in the 0.1-1.0 mM, 2-20 µM and 10-100 µM concentration ranges, respectively. Graphical abstract Schematic presentation of a nanocomposite that consists TmPO4 and graphene oxide (GO) and is used to modify a glassy carbon electrode (GCE) to obtain a sensor for simultaneous determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA).