A bioactive calcium silicate nanowire-containing hydrogel for organoid formation and functionalization.

Organoids, which are 3D multicellular constructs, have garnered significant attention in recent years. Existing organoid culture methods predominantly utilize natural and synthetic polymeric hydrogels. This study explored the potential of a composite hydrogel mainly consisting of calcium silicate (CS) nanowires and methacrylated gelatin (GelMA) as a substrate for organoid formation and functionalization, specifically for intestinal and liver organoids. Furthermore, the research delved into the mechanisms by which CS nanowires promote the structure formation and development of organoids. It was discovered that CS nanowires can influence the stiffness of the hydrogel, thereby regulating the expression of the mechanosensory factor yes-associated protein (YAP). Additionally, the bioactive ions released by CS nanowires in the culture medium could accelerate Wnt/ß-catenin signaling, further stimulating organoid development. Moreover, bioactive ions were found to enhance the nutrient absorption and ATP metabolic activity of intestinal organoids. Overall, the CS/GelMA composite hydrogel proves to be a promising substrate for organoid formation and development. This research suggested that inorganic biomaterials hold significant potential in organoid research, offering bioactivities, biosafety, and cost-effectiveness.

Boosting the Piezoelectric Response and Interfacial Compatibility in Flexible Piezoelectric Composites via DET-Doping BT Nanoparticles.

With the advent of the Internet of Things, self-powered wearable sensors have become increasingly prevalent in our daily lives. The utilization of piezoelectric composites to harness and sense surrounding mechanical vibrations has been extensively investigated during the last decades. However, the poor interface compatibility between ceramics nanofillers and polymers matrix, as well as low piezoelectric performance, still serves as a critical challenge. In this work, we employed Di(dioctylpyrophosphato) ethylene titanate (DET) as the coupling agent for modifying barium titanate (BTO) nanofillers. Compared to the BTO/PVDF counterpart, the DET-BTO/PVDF nanofibers exhibit an augmented content of piezoelectric ß phase (~85.7%) and significantly enhanced stress transfer capability. The piezoelectric coefficient (d33) is up to ~40 pC/N, which is the highest value among reported BTO/PVDF composites. The piezoelectric energy harvesters (PEHs) present benign durability and attain a high instantaneous power density of 276.7 nW/cm2 at a matched load of 120 MΩ. Furthermore, the PEHs could sense various human activities, with the sensitivity as high as 0.817 V/N ranging from 0.05-0.1 N. This work proposes a new strategy to boosting the piezoelectric performance of PVDF-based composites via DET-doping ceramics nanoparticles, and in turn show significantly improved energy harvesting and sensing capability.

Correction: Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.

[This corrects the article DOI: 10.2196/37881.].

Ultrafast universal fabrication of configurable porous silicone-based elastomers by Joule heating chemistry.

Silicone-based elastomers (SEs) have been extensively applied in numerous cutting-edge areas, including flexible electronics, biomedicine, 5G smart devices, mechanics, optics, soft robotics, etc. However, traditional strategies for the synthesis of polymer elastomers, such as bulk polymerization, suspension polymerization, solution polymerization, and emulsion polymerization, are inevitably restricted by long-time usage, organic solvent additives, high energy consumption, and environmental pollution. Here, we propose a Joule heating chemistry method for ultrafast universal fabrication of SEs with configurable porous structures and tunable components (e.g., graphene, Ag, graphene oxide, TiO2, ZnO, Fe3O4, V2O5, MoS2, BN, g-C3N4, BaCO3, CuI, BaTiO3, polyvinylidene fluoride, cellulose, styrene-butadiene rubber, montmorillonite, and EuDySrAlSiOx) within seconds by only employing H2O as the solvent. The intrinsic dynamics of the in situ polymerization and porosity creation of these SEs have been widely investigated. Notably, a flexible capacitive sensor made from as-fabricated silicone-based elastomers exhibits a wide pressure range, fast responses, long-term durability, extreme operating temperatures, and outstanding applicability in various media, and a wireless human-machine interaction system used for rescue activities in extreme conditions is established, which paves the way for more polymer-based material synthesis and wider applications.

Photoinduced Selective B-H Activation of nido-Carboranes.

The development of new synthetic methods for B-H bond activation has been an important research area in boron cluster chemistry, which may provide opportunities to broaden the application scope of boron clusters. Herein, we present a new reaction strategy for the direct site-selective B-H functionalization of nido-carboranes initiated by photoinduced cage activation via a noncovalent cage···π interaction. As a result, the nido-carborane cage radical is generated through a single electron transfer from the 3D nido-carborane cage to a 2D photocatalyst upon irradiation with green light. The resulting transient nido-carborane cage radical could be directly probed by an advanced time-resolved EPR technique. In air, the subsequent transformations of the active nido-carborane cage radical have led to efficient and selective B-N, B-S, and B-Se couplings in the presence of N-heterocycles, imines, thioethers, thioamides, and selenium ethers. This protocol also facilitates both the late-stage modification of drugs and the synthesis of nido-carborane-based drug candidates for boron neutron capture therapy (BNCT).

Targeting optimal power consumption: Optimizing operational parameters for orchard furrowing and fertilizing machine.

In response to the problem of excessive power consumption during the furrowing operation of orchard furrowing fertilizer machines, an optimization experiment of furrowing operation parameters for orchard furrowing fertilizer machine was conducted based on discrete element simulations. This research focused on the impact of furrowing device operation parameters on furrowing power consumption under full machine operating conditions. Firstly, a kinematics analysis of the soil granules during cutting was done. The mathematical model of soil granules through three movement processes of rising, detachment, and falling was established to determine the main factors affecting the power consumption of furrowing. Secondly, in assessing the furrowing power consumption, the stability coefficient of the furrowing depth, and the percentage of soil cover, alongside the key parameters of furrowing depth, forward propulsion velocity, and furrowing blade rotation speed, a comprehensive quadratic orthogonal rotation regression experiment was meticulously conducted. It was established that test metrics and test parameters regress. Finally, the test parameters were comprehensively optimized after analyzing each factor's impact on the test metrics. The orchard furrowing fertilizer machine's optimal operating parameters were determined, and the verification test was performed. According to the field test findings, the forward propulsion velocity was 785 m/h, and the furrowing blade rotation speed was 190 r/min when the furrowing depth was 275 mm. At this point, the furrowing power consumption was 2.39 kW, the soil cover percentage was 69.06%, and the furrowing depth stability coefficient was 95.08%. These results were in line with the requirements of orchard furrowing operation. The findings of the study can be utilized as a guide for structural changes to orchard furrowing equipment and the management of furrowing operation parameters.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Bioprinting of inorganic-biomaterial/neural-stem-cell constructs for multiple tissue regeneration and functional recovery.

Tissue regeneration is a complicated process that relies on the coordinated effort of the nervous, vascular and immune systems. While the nervous system plays a crucial role in tissue regeneration, current tissue engineering approaches mainly focus on restoring the function of injury-related cells, neglecting the guidance provided by nerves. This has led to unsatisfactory therapeutic outcomes. Herein, we propose a new generation of engineered neural constructs from the perspective of neural induction, which offers a versatile platform for promoting multiple tissue regeneration. Specifically, neural constructs consist of inorganic biomaterials and neural stem cells (NSCs), where the inorganic biomaterials endows NSCs with enhanced biological activities including proliferation and neural differentiation. Through animal experiments, we show the effectiveness of neural constructs in repairing central nervous system injuries with function recovery. More importantly, neural constructs also stimulate osteogenesis, angiogenesis and neuromuscular junction formation, thus promoting the regeneration of bone and skeletal muscle, exhibiting its versatile therapeutic performance. These findings suggest that the inorganic-biomaterial/NSC-based neural platform represents a promising avenue for inducing the regeneration and function recovery of varying tissues and organs.

Flexible and Simply Degradable MXene-Methylcellulose Piezoresistive Sensor for Human Motion Detection.

Flexible pressure sensors are intensively demanded in various fields such as electronic skin, medical and health detection, wearable electronics, etc. MXene is considered an excellent sensing material due to its benign metal conductivity and adjustable interlayer distance. Exhibiting both high sensitivity and long-term stability is currently an urgent pursuit in MXene-based flexible pressure sensors. In this work, high-strength methylcellulose was introduced into the MXene film to increase the interlayer distance of 2D nanosheets and fundamentally overcome the self-stacking problem. Thus, concurrent improvement of the sensing capability and mechanical strength was obtained. By appropriately modulating the ratio of methylcellulose and MXene, the obtained pressure sensor presents a high sensitivity of 19.41 kPa-1 (0.88-24.09 kPa), good stability (10000 cycles), and complete biodegradation in H2O2 solution within 2 days. Besides, the sensor is capable of detecting a wide range of human activities (pulse, gesture, joint movement, etc.) and can precisely recognize spatial pressure distribution, which serves as a good candidate for next-generation wearable electronic devices.

Robust Ionics Reinforced Fiber As Implantable Sensor for Early Operando Monitoring Cell Thermal Safety of Commercial Lithium-Ion Batteries.

Commercial batteries have been largely applied in mobile electronics, electric vehicles, and scalable energy storage systems. However, thermal runaway of batteries still obstructs the reliability of electric equipment. Considering this, building upon recent investigations of energy thermal safety, commercially available organogel fiber-based implantable sensors have been developed through 3D printing technology for first operando implantable monitoring of cell temperature. The printed fibers present excellent reliability and superelasticity because of internal supramolecular cross-linking. High temperature sensitivity (-39.84% °C-1/-1.557% °C-1) within a wide range (-15 to 80 °C) is achieved, and the corresponding mechanism is clarified based on in situ temperature-dependent Raman technology. Furthermore, taking the pouch cell as an example, combined with finite element analysis, the real-time observation system of cell temperature is successfully demonstrated through an implanted sensor with wireless Bluetooth transmission. This enlightening approach paves the way for achieving safety monitoring and smart warnings for various electric equipment.

Robust Fiber-Shaped Flexible Temperature Sensors for Safety Monitoring with Ultrahigh Sensitivity.

Flexible temperature sensors capable of detecting and transmitting temperature data from the human body, environment, and electronic devices hold significant potential for applications in electronic skins, human-machine interactions, and disaster prevention systems. Nonetheless, fabricating flexible temperature sensors with exceptional sensing performance remains a formidable task, primarily due to the intricate process of constructing an intrinsically flexible sensing element with high sensitivity. In this study, a facile in situ two-step synthetic method is introduced for fabricating flexible fiber-shaped NiO/carbon nanotube fiber (CNTF) composites. The resulting NiO/CNTF flexible temperature sensors demonstrate outstanding deformability and temperature sensing characteristics, encompassing a broad working range (-15 to 60 °C) and high sensitivity (maximum TCR of -20.2% °C-1 and B value of 3332 K). Importantly, the mechanical and thermal behaviors of the sensor in various application conditions are thoroughly examined using finite element analysis simulations. Moreover, the temperature sensors can effectively capture diverse thermal signals in wearable applications. Notably, a temperature monitoring and warning system is developed to prevent fire accidents resulting from abnormal thermal runaway in electronic devices.

A Promising Application of Injectable Hydrogels in Nerve Repair and Regeneration for Ischemic Stroke.

Ischemic stroke, a condition that often leads to severe nerve damage, induces complex pathological and physiological changes in nerve tissue. The mature central nervous system (CNS) lacks intrinsic regenerative capacity, resulting in a poor prognosis and long-term neurological impairments. There is no available therapy that can fully restore CNS functionality. However, the utilization of injectable hydrogels has emerged as a promising strategy for nerve repair and regeneration. Injectable hydrogels possess exceptional properties, such as biocompatibility, tunable mechanical properties, and the ability to provide a supportive environment for cell growth and tissue regeneration. Recently, various hydrogel-based tissue engineering approaches, including cell encapsulation, controlled release of therapeutic factors, and incorporation of bioactive molecules, have demonstrated great potential in the treatment of CNS injuries caused by ischemic stroke. This article aims to provide a comprehensive review of the application and development of injectable hydrogels for the treatment of ischemic stroke-induced CNS injuries, shedding light on their therapeutic prospects, challenges, recent advancements, and future directions. Additionally, it will discuss the underlying mechanisms involved in hydrogel-mediated nerve repair and regeneration, as well as the need for further preclinical and clinical studies to validate their efficacy and safety.

Thrombectomy alone vs thrombectomy with over 2/3-dose intravenous thrombolysis pretreatment in the DIRECT-MT trial.

BACKGROUND: The DIRECT-MT trial showed that endovascular thrombectomy (EVT) alone was noninferior to EVT preceded by intravenous alteplase. However, the infusion of intravenous alteplase was uncompleted before the initiation of EVT in most cases of this trial. Therefore, the additional benefit and risk of over 2/3-dose intravenous alteplase pretreatment remain to be assessed. METHODS: We assessed patients with acute anterior circulation ischemic stroke who received EVT alone or with over 2/3-dose intravenous alteplase pretreatment from the DIRECT-MT trial. Patients were assigned to the thrombectomy-alone group and the alteplase pretreatment group. The primary outcome was the distribution of modified Rankin Scale (mRS) at 90 days. The interaction of treatment allocation and collateral capacity was assessed. RESULTS: A total of 393 patients (thrombectomy alone: 315; alteplase pretreatment: 78) were identified. The thrombectomy alone was comparable with alteplase pretreatment prior to the thrombectomy on the distribution of mRS at 90 days without significant effect modification by collateral capacity (adjusted common odds ratio (acOR), 1.12; 95% CI, 0.72-1.74; adjusted P for interaction = 0.83). Successful reperfusion before thrombectomy and the number of passes in the thrombectomy alone group differed significantly from the alteplase pretreatment group (2.6% vs. 11.5%; corrected P = 0.02 and 2 vs. 1; corrected P = 0.003). There was no interaction between treatment allocation and collateral capacity on all outcomes. CONCLUSIONS: EVT alone and EVT preceded by over 2/3-dose intravenous alteplase might have equal efficacy and safety for patients with acute anterior circulation large vessel occlusion, except for successful perfusion before thrombectomy and the number of passes.

Health-related quality of life assessed by EQ-5D-5L and its determinants among rural adults: result from the Henan rural cohort study.

OBJECTIVES: Evidence on the health-related quality of life (HRQoL) among Chinese rural populations and its influence factors were limited. This study aimed to describe the distribution of HRQoL assessed by EQ-5D-5L and its determinants among a Chinese rural population. METHODS: A total of 23,510 eligible participants (9542 men and 13,968 women) from the Henan rural cohort study were included. Tobit regression and generalized linear models were performed to investigate the associations between demographic characteristics, lifestyle factors, common chronic diseases, and HRQoL. RESULTS: Of all respondents, the most frequently reported problem was pain/discomfort dimension (23.05%), followed by mobility (12.72%), anxiety/depression (7.77%), and usual activities (6.45%), while the least reported was the self-care dimension (3.84%). The more problems reported in all dimensions with age increased except anxiety/depression. The mean (standard deviation) utility index and VAS score were 0.954 (0.111) and 78.34 (14.80), respectively. The regression analysis indicated that increasing age, former drinkers, poor sleep quality, and suffering from common chronic diseases were significantly associated with low HRQoL, while high education level, average monthly income, and physical activity were significantly associated with high HRQoL. CONCLUSION: This study described the distribution of HRQoL and its influence factors among the Chinese rural population, which was helpful for medical institutions and policymakers to allocate medical resources and better understand HRQoL among the Chinese rural population.

Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.

BACKGROUND: The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan's early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions. OBJECTIVE: This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period. METHODS: We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords "corona," "COVID-19," and "new pneumonia" from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups. RESULTS: The analysis revealed a mix of positive and negative sentiments, with "joy" significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as "worry" and "disgust," were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group. CONCLUSIONS: This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis.

Disruption of LPA-LPAR1 pathway results in lung tumor growth inhibition by downregulating B7-H3 expression in fibroblasts.

BACKGROUND: Lysophosphatidic acids (LPAs) belong to a class of bioactive lysophospholipids with multiple functions including immunomodulatory roles in tumor microenvironment (TME). LPA exerts its biological effects via its receptors that are highly expressed in fibroblasts among other cell types. As cancer-associated fibroblasts (CAFs) are a key component of the TME, it is important to understand LPA signaling and regulation of receptors in fibroblasts or CAFs and associated regulatory roles on immunomodulation-related molecules. METHODS: Cluster analysis, immunoblotting, real-time quantitative-PCR, CRISPR-Cas9 gene editing system, immunohistochemical staining, coculture model, and in vivo xenograft model were used to investigate the effects of LPA-LPAR1 on B7-H3 in tumor promotion of CAFs. RESULTS: In this study, we found that LPAR1 and CD276 (B7-H3) were generally highly expressed in fibroblasts with good expression correlation. LPA induced B7-H3 up-expression through LPAR1, and stimulated fibroblasts proliferation that could be inhibited by silencing LPAR1 or B7-H3 as well as small molecule LPAR1 antagonist (Ki16425). Using engineered fibroblasts and non-small cell lung carcinoma (NSCLC) cell lines, subsequent investigations demonstrated that CAFs promoted the proliferation of NSCLC in vitro and in vivo, and such effect could be inhibited by knocking out LPAR1 or B7-H3. CONCLUSION: The present study provided new insights for roles of LPA in CAFs, which could lead to the development of innovative therapies targeting CAFs in the TME. It is also reasonable to postulate a combinatory approach to treat malignant fibrous tumors (such as NSCLC) with LPAR1 antagonists and B7-H3 targeting therapies.

TaCHP encoding C1-domain protein stably enhances wheat yield in saline-alkaline fields.

Overexpression of the zinc finger gene TaCHP stably enhanced wheat yield in saline-alkaline conditions in a multi-year, three-site field trial, and the genetic variations in its promoter contribute to saline-alkaline tolerance of wheat accessions. TaCHP and its tolerant haplotype have great potential for molecular breeding of stress-tolerant wheat.

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM.

Introduction: At present, machine learning and image processing technology are widely used in plant disease diagnosis. In order to address the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves. Methods: The study proposes a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of detection during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted and a multiple regression analysis model was used to screen for key features. Linear discriminant analysis was then employed to optimize the data and obtain the optimal shape and color feature factors of apple tree leaves during each growth period. Various machine-learning methods, including SVM, DT, and KNN, were used for the diagnosis of potassium deficiency. Results: The MLR-LDA-SVM model was found to be the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods. Discussion: The model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards.

Identification of potential therapeutic drugs targeting core genes for systemic lupus erythematosus (SLE) and coexisting COVID-19: Insights from bioinformatic analyses.

OBJECTIVE: Systemic lupus erythematosus (SLE) patients are at risk during the COVID-19 pandemic, yet the underlying molecular mechanisms remain incompletely understood. This study sought to analyze the potential molecular connections between COVID-19 and SLE, employing a bioinformatics approach to identify effective drugs for both conditions. METHODS: The data sets GSE100163 and GSE183071 were utilized to determine share differentially expressed genes (DEGs). These DEGs were later analyzed by various bioinformatic methods, including functional enrichment, protein-protein interaction (PPI) network analysis, regulatory network construction, and gene-drug interaction construction. RESULTS: A total of 50 common DEGs were found between COVID-19 and SLE. Gene ontology (GO) functional annotation revealed that "immune response," "innate immune response," "plasma membrane," and "protein binding" were most enriched in. Additionally, the pathways that were enriched include "Th1 and Th2 cell differentiation." The study identified 48 genes/nodes enriched with 292 edges in the PPI network, of which the top 10 hub genes were CD4, IL7R, CD3E, CD5, CD247, KLRB1, CD40LG, CD7, CR2, and GZMK. Furthermore, the study found 48 transcription factors and 8 microRNAs regulating these hub genes. Finally, four drugs namely ibalizumab (targeted to CD4), blinatumomab (targeted to CD3E), muromonab-CD3 (targeted to CD3E), and catumaxomab (targeted to CD3E) were found in gene-drug interaction. CONCLUSION: Four possible drugs that targeted two specific genes, which may be beneficial for COVID-19 patients with SLE.

Photocatalytic degradation of multiple-organic-pollutant under visible light by graphene oxide modified composite: degradation pathway, DFT calculation and mechanism.

Wastewater containing antibiotics, organic dyes, and waterborne bacteria is a severe threat to human health and the environment. Amoxicillin has a slow metabolism rate in humans. Methylene blue is mutagenic and carcinogenic. In addition, Salmonella causes serious diarrhea. In this study, an effective 2D/2D photocatalyst with excellent elimination of these pollutants was fabricated by combining graphene oxide (GO), Bi2WO6, BiPO4 and Ag species. GO was applied at varying loading contents (0.8, 1.6, 2.4, 3.2 wt%) to improve the properties of the photocatalyst toward the removal of representative pollutants. The chemical structures, morphology, light absorption and charge mobility were investigated by different GO loading samples. The results indicated that when the wt% of GO was 2.4%, the photocatalyst showed excellent photocatalytic properties and removal rates for typical pollutants. Amoxicillin and methylene blue were mineralized into CO2, H2O, and small molecules, while Salmonella was disinfected with excellent photocatalytic efficiency. Furthermore, the possible photodecomposition pathways of amoxicillin and methylene blue were proposed by DFT calculations and intermediates identified by LCMS. The mechanism of the photocatalytic process was investigated by radical trapping experiments, ESR spectroscopy, and Motty-Schottky plots. The free radicals could be produced constantly during the photocatalytic process, leading to mineralization of amoxicillin and methylene blue, and disinfection of Salmonella. In this work, a new perspective on GO modified Bi2WO6 with different loading contents and the degradation pathways of antibiotics and dyes was proposed.