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Real-time sensing of nitric oxide (NO) in physiological environments is critically important in monitoring neurotransmission, inflammatory responses, cardiovascular systems, etc. Conventional approaches for NO detection relying on indirect colorimetric measurement or built with rigid and permanent materials cannot provide continuous monitoring and/or require additional surgical retrieval of the implants, which comes with increased risks and hospital cost. Herein, we report a flexible, biologically degradable and wirelessly operated electrochemical sensor for real-time NO detection with a low detection limit (3.97 nmol), a wide sensing range (0.01–100 μM), and desirable anti-interference characteristics. The device successfully captures NO evolution in cultured cells and organs, with results comparable to those obtained from the standard Griess assay. Incorporated with a wireless circuit, the sensor platform achieves continuous sensing of NO levels in living mammals for several days. The work may provide essential diagnostic and therapeutic information for health assessment, treatment optimization and postsurgical monitoring. Real-time continuous sensing of biological analytes is of importance in a range of biomedical applications. Here, the authors report on a flexible and physically transient sensor for the detection of nitric oxide and demonstrate applications in nitric oxide sensing in organs ex vivo and in vivo.

As nitric oxide (NO) plays significant roles in a variety of physiological processes, the capability for real-time and accurate detection of NO in live organisms is in great demand. Traditional assessments of NO rely on indirect colorimetric techniques or electrochemical sensors that often comprise rigid constituent materials and can hardly satisfy sensitivity and spatial resolution simultaneously. Here, we report a flexible and highly sensitive biosensor based on organic electrochemical transistors (OECTs) capable of continuous and wireless detection of NO in biological systems. By modifying the geometry of the active channel and the gate electrodes of OECTs, devices achieve optimum signal amplification of NO. The sensor exhibits a low response limit, a wide linear range, high sensitivity, and excellent selectivity, with a miniaturized active sensing region compared with a conventional electrochemical sensor. The device demonstrates continuous detection of the nanomolar range of NO in cultured cells for hours without significant signal drift. Real-time and wireless measurement of NO is accomplished for 8 d in the articular cavity of New Zealand White rabbits with anterior cruciate ligament (ACL) rupture injuries. The observed high level of NO is associated with the onset of osteoarthritis (OA) at the later stage. The proposed device platform could provide critical information for the early diagnosis of chronic diseases and timely medical intervention to optimize therapeutic efficacy.

Runoff prediction is a complex hydrological, nonlinear time-series problem. Many machine learning methods have been put forth in recent years to predict runoff. A sliding window method is typically used to preprocess the data in order to rebuild the time series of runoff data into a standard machine learning dataset. The size of the window is a variable parameter that is commonly referred to as the time step. With developments in computer and AI technology, data-driven models have demonstrated tremendous potential for runoff prediction. And AI technology has opened up a new avenue for weather prediction, with Pangu-Weather demonstrating considerable improvements in both accuracy and processing efficiency. This study creates two novel prediction models, LSTM-Pangu and GRU-Pangu, by combining Pangu with Long Short-Term Memory (LSTM) and the Gate Recurrent Unit (GRU). We concentrated on the Beipanjiang River Basin in China, using Guizhou Qianyuan Power Company Limited’s daily runoff data and meteorological satellite data from the Climate Data Store platform to forecast daily runoff. These models were used to anticipate runoff on various future days (known as the lead time). The results show that regardless of time step, both LSTM-Pangu and GRU-Pangu outperform the LSTM and GRU models. Furthermore, this advantage is more evident as the advance time increases. When the time step is 7 and the lead time is 5, the Nash–Sutcliffe Efficiency (NSE) of the LSTM-Pangu model improves by 8.1% compared to the LSTM model, while the NSE of the GRU-Pangu model improves by 11.7% compared to the GRU model. Furthermore, LSTM-Pangu and GRU-Pangu outperform LSTM and GRU models in terms of the predictive accuracy under high-flow conditions, highlighting their significant advantages in flood forecasting. This integration strategy displays great transferability and may be expanded to other typical data-driven models.

Following the publication of the article, a concerned reader drew to the authors' attention that, in Fig. 1B and C on p. 316, two pairs of the data panels showing the results from invasion and migration assay experiments appeared to be overlapping, such that they would have been derived from the same original sources where they were intended to show the results from different experiments; moreover, on p. 1698, the '17‑AAG / MG‑63' data panels in Fig. 3B and C were also overlapping, albeit the images were presented at a different scale and in a slightly different orientation. After having examined their original data, the authors have realized that these figures were inadvertently assembled incorrectly. The corrected versions of Figs. 1 and 3, now showing the correct data in Fig. 1C (where the errors made in compiling the figure had occurred) and the correct data for the '17‑AAG / MG‑63' data panel in Fig. 3C, are shown on the next two pages. These corrections do not grossly affect either the results or the conclusions reported in this work. The authors all agree to the publication of this Corrigendum, and are grateful to the Editor of for granting them the opportunity to correct the errors that were made during the assembly of these figures. Lastly, the authors apologize to the readership for any inconvenience these errors may have caused. [Oncology Reports 44: 313‑324, 2020; DOI: 10.3892/or.2020.7597].

Multiple drug resistance is a major obstacle to the successful treatment of osteosarcoma (OS). Recent studies have demonstrated that a subset of cells, referred to as OS stem cells (OSCs), play a crucial role in the acquisition of multiple drug resistance. Therefore, an improved understanding of OS biology and pathogenesis is required to advance the development of targeted therapies aimed at eradicating this particular subset of cells in order to reverse acquired chemoresistance in OS. The aim of the present study was to assess the anti-OSC effects of 17-AAG and determine the underlying molecular mechanism. Heat shock protein 90 expression was found to be increased in sarcosphere cells and was positively associated with cancer stem cell characteristics. In addition, 17-AAG was able to suppress the stem cell-like phenotype of OS cells. Mechanistically, 17-AAG inhibited OSC-like properties and chemoresistance through glycogen synthase kinase (GSK) 3β inactivation-mediated repression of the Hedgehog signaling pathway. The findings of the present study provided comprehensive evidence for the inhibition of OSC properties and chemoresistance by 17-AAG through repression of the GSK3β/Hedgehog signaling pathway, suggesting that 17-AAG may be a promising therapeutic agent for targeting OSCs.

Piezoelectric materials provide a unique platform for bioelectronic interfaces, enabling dynamic sensing and electroactive therapies through bidirectional transduction between biomechanical and bioelectrical signals. However, the development of bioresorbable piezoelectric materials that combine high functional performance with mechanical compliance remains a critical challenge for seamless integration with soft biological tissues, while eliminating the need for retrieval surgeries and long-term material retention. Here, we report a bioresorbable, flexible piezoelectric composite composed of Rochelle salt (RS) crystals embedded within poly(L-lactic acid) (PLLA) nanofibers. Fabricated via electrospinning and uniaxial compression, centimeter-scale biodegradable nanofiber films are achieved, exhibiting excellent effective piezoelectric coefficient of 43.1 pC N and piezoelectric voltage coefficient of 1909.2 mV m N , surpassing the piezoelectric performance of previously reported biodegradable flexible materials. Ultrasound-driven scaffold devices derived from these bioresorbable piezoelectric materials markedly enhance sciatic nerve regeneration in rodents. Additionally, a biodegradable piezoelectric strain sensor enables wireless, real-time monitoring of intestinal motility, facilitating diagnosis of colonic dysfunction. Together, these findings establish a prominent materials paradigm for biodegradable piezoelectric electronics, offering a versatile platform for bioelectronic applications in regenerative medicine, neuromodulation, and physiological monitoring.

Following the publication of the article, a concerned reader drew to the authors' attention that, in Fig. 1B and C on p. 316, two pairs of the data panels showing the results from invasion and migration assay experiments appeared to be overlapping, such that they would have been derived from the same original sources where they were intended to show the results from different experiments; moreover, on p. 1698, the '17‑AAG / MG‑63' data panels in Fig. 3B and C were also overlapping, albeit the images were presented at a different scale and in a slightly different orientation. After having examined their original data, the authors have realized that these figures were inadvertently assembled incorrectly. The corrected versions of Figs. 1 and 3, now showing the correct data in Fig. 1C (where the errors made in compiling the figure had occurred) and the correct data for the '17‑AAG / MG‑63' data panel in Fig. 3C, are shown on the next two pages. These corrections do not grossly affect either the results or the conclusions reported in this work. The authors all agree to the publication of this Corrigendum, and are grateful to the Editor of Oncology Reports for granting them the opportunity to correct the errors that were made during the assembly of these figures. Lastly, the authors apologize to the readership for any inconvenience these errors may have caused. [Oncology Reports 44: 313‑324, 2020; DOI: 10.3892/or.2020.7597].Following the publication of the article, a concerned reader drew to the authors' attention that, in Fig. 1B and C on p. 316, two pairs of the data panels showing the results from invasion and migration assay experiments appeared to be overlapping, such that they would have been derived from the same original sources where they were intended to show the results from different experiments; moreover, on p. 1698, the '17‑AAG / MG‑63' data panels in Fig. 3B and C were also overlapping, albeit the images were presented at a different scale and in a slightly different orientation. After having examined their original data, the authors have realized that these figures were inadvertently assembled incorrectly. The corrected versions of Figs. 1 and 3, now showing the correct data in Fig. 1C (where the errors made in compiling the figure had occurred) and the correct data for the '17‑AAG / MG‑63' data panel in Fig. 3C, are shown on the next two pages. These corrections do not grossly affect either the results or the conclusions reported in this work. The authors all agree to the publication of this Corrigendum, and are grateful to the Editor of Oncology Reports for granting them the opportunity to correct the errors that were made during the assembly of these figures. Lastly, the authors apologize to the readership for any inconvenience these errors may have caused. [Oncology Reports 44: 313‑324, 2020; DOI: 10.3892/or.2020.7597].

Pursuing and developing effective methodologies to construct highly active catalytic sites to maximize the atomic and energy efficiency by material engineering are attractive. Relative to the tremendous researches of carbon-based single atom systems, the construction of bio-applicable single atom materials is still in its infancy. Herein, we propose a facile and general interfacial-confined coordination strategy to construct high-quality single-atom nanotherapeutic agent with Fe single atoms being anchored on defective carbon dots confined in a biocompatible mesoporous silica nanoreactor. Furthermore, the efficient energy conversion capability of silica-based Fe single atoms system has been demonstrated on the basis of the exogenous physical photo irradiation and endogenous biochemical reactive oxygen species stimulus in the confined mesoporous network. More importantly, the highest photothermal conversion efficiency with the mechanism of increased electron density and narrow bandgap of this single atom structure in defective carbon was proposed by the theoretical DFT calculations. The present methodology provides a scientific paradigm to design and develop versatile single atom nanotherapeutics with adjustable metal components and tune the corresponding reactions for safe and efficient tumor therapeutic strategy. Developing single atom systems with improved catalytic potential for bio-application has major therapeutic potential. Here, the authors report on the development of a metal single-atom on a carbon dot support confined within mesoporous silica for the development of therapeutic agents.

A summertime hypoxia sporadically occurred in the lower Pearl River Estuary (PRE) for more than three decades. Although its mechanism has already been extensively studied, the topic on why seasonal hypoxia is persistent in patchy waters is still an open question. Here, we presented the investigation of physical dynamics structures and dissolved oxygen (DO) processes for controlling the spatial distribution and maintenance of coastal hypoxia. Field observations were conducted in the 2015 summer in the PRE and adjacent shelf sea. High river discharge forms intense haloclines in the river plume, while salinity intrusion of shelf benthic waters results in a notable pycnocline at the top of salt wedge. A mid-depth transitional layer with the weakest mixing over water column functions as a barrier for DO vertical exchange between river plume and shelf salt wedge. A benthic hypoxia in the 2015 summer appears at the overlapping zone between river plume and shelf salt wedge. Based on physical and biological processes, a DO budget for the hypoxic system was established. The DO advection by gravitational circulation from shelf benthic waters is roughly balanced by bacterial respiration in water column. The DO diffusion from river plume to benthic hypoxia is completely inhibited by the barrier layer. The patchy distribution of benthic hypoxia for the 30-yr period in the PRE can be satisfactorily predicted by the numerical simulations of the overlapping zones between river plume and shelf salt wedge. These findings will have an important implication for predicting and mitigating coastal hypoxia. Physical structures and processes of DO dynamics were investigated to understand the spatial distribution and maintenance of coastal hypoxia. Summertime hypoxia appear near the head of shelf salinity intrusion, where a mid-depth barrier layer inhibits the vertical exchange between river plume and shelf salt wedge. DO advection by gravitational circulation from DO-rich shelf benthic waters is roughly balanced by bacterial respiration in water column. The spatial distribution of coastal hypoxia can be well predicted by the overlapping zone between river plume and shelf salt wedge.

Background Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. Methods We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein–protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. Results We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. Conclusions Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.