Bacteria-based cascade in situ near-infrared nano-optogenetically induced photothermal tumor therapy.

Rationale: Optogenetically engineered facultative anaerobic bacteria exhibit a favorable tendency to colonize at solid tumor sites and spatiotemporally-programmable therapeutics release abilities, attracting extensive attention in precision tumor therapy. However, their therapeutic efficacy is moderate. Conventional photothermal agents with high tumor ablation capabilities exhibit low tumor targeting efficiency, resulting in significant off-target side effects. The combination of optogenetics and photothermal therapy may offer both tumor-targeting and excellent tumor-elimination capabilities, which unfortunately has rarely been investigated. Herein, we construct a bacteria-based cascade near-infrared optogentical-photothermal system (EcNαHL-UCNPs) for enhanced tumor therapy. Methods: EcNαHL-UCNPs consists of an optogenetically engineered Escherichia coli Nissle 1917 (EcN) conjugated with lanthanide-doped upconversion nanoparticles (UCNPs), which are capable of locally secreting α-hemolysin (αHL), a pore-forming protein, in responsive to NIR irradiation. Anti-tumor effects of EcNαHL-UCNPs were determined in both H22 and 4T1 tumors. Results: The αHL not only eliminates tumor cells, but more importantly disrupts endothelium to form thrombosis as an in situ photothermal agent in tumors. The in situ formed thrombosis significantly potentiates the photothermic ablation of H22 tumors upon subsequent NIR light irradiation. Besides, αHL secreted by EcNαHL-UCNPs under NIR light irradiation not only inhibits 4T1 tumor growth, but also suppresses metastasis of 4T1 tumor via inducing the immune response. Conclusion: Our studies highlight bacteria-based cascade optogenetical-photothermal system for precise and effective tumor therapy.

Impact of the hydrated functional zone on the adsorption of ciprofloxacin to microplastics under the influence of UV aging.

The inevitable UV aging of microplastics (MPs) is one of the key factors affecting their interaction with antibiotics. In this study, polyethylene (PE) and polystyrene (PS) MPs were aged with UV irradiation. The adsorption isotherms and kinetics of ciprofloxacin (CIP) to virgin and aged MPs were investigated through various models, and the effects of pH on the adsorption amount were explored. Characterization revealed that the surfaces of aged MPs became rougher, and the hydrophilicity increased. These aged MPs were still in the early stage of aging on the basis of their carbonyl index (CI) (<0.2) and O/C (<0.04) values. The adsorption isotherms indicated that the adsorption mechanism of aged PE was different from that of virgin PE. Compared with virgin PE, the adsorption amount of aged PE increased by 87.80-95.45%, and the adsorption rate decreased by 65.52-80.74%. However, aging did not significantly affect the equilibrium adsorption amount or adsorption rate of aged PS. The external diffusion rate (Kext) (about 2.29-0.36 h-1) was almost 30 times greater than the internal diffusion rate (Kint) in the film-pore mass transfer (FPMT) model, indicating that CIP adsorption rate was dominated by external diffusion. A hydrated functional zone is thought to form around aged MPs, thus changing the adsorption mechanism and adsorption amount of aged PE. Therefore, more attention should be given to alterations in the hydrated functional zone in the early stage of MPs aging.

Construction of a competency evaluation model for Clinical Research Coordinators in China: a study based on the Delphi method and questionnaire survey.

PURPOSE: As the number of clinical trials in China continues to grow, the assessment of competency of Clinical Research Coordinators (CRCs), who play a crucial role in clinical trials, has become an important and challenging topic. This study aims to construct a competency model for CRCs tailored to the Chinese context, in order to promote the standardisation and regulated development of the CRC industry. STUDY DESIGN AND SETTING: This study was conducted in China, engaging CRCs as the primary subjects. A competency evaluation model for CRCs was constructed through literature review, semi-structured interviews, Delphi expert consultation and the analytic hierarchy process. A questionnaire survey was distributed to a broad sample of CRCs across China to evaluate the model's reliability and validity. RESULTS: The final model encompasses 4 core competency dimensions and 37 indicators, tailored to assess the competencies of CRCs in China. The questionnaire yielded an effective response rate of 81.83%, with high internal consistency(Cronbach's α>0.7). Factor analysis confirmed the model's structure, indicating good reliability and validity. CONCLUSION: This study represents a pioneering effort in constructing a competency model specifically designed for Chinese CRCs, complemented by a robust and valid assessment scale. The findings bear significant implications for the recruitment, training, development and management of CRCs.

TransNeural: An Enhanced-Transformer-Based Performance Pre-Validation Model for Split Learning Tasks.

While digital twin networks (DTNs) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for split learning (SL) tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreported resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 9.3% and convergence estimation accuracy by 22.4% compared to traditional equation-based methods.

Quercetin activates energy expenditure to combat metabolic syndrome through modulating gut microbiota-bile acids crosstalk in mice.

Abdominal obesity-related metabolic syndrome (MetS) has emerged as a significant global public health issue that affects human health. Flavonoids, such as quercetin, have been reported to exert obvious anti-obesity and lipid-lowering effects in both humans and animal models. However, the precise underlying mechanism remains elusive. In this study, we investigated the potential roles of gut microbiota-bile acids (BAs) interactions in quercetin-induced anti-obesity effects and metabolic benefits. Oral administration of quercetin significantly enhanced energy metabolism through activating thermogenesis of brown adipose tissues (BAT) and browning of white adipose tissues (WAT), thus mitigating metabolic dysfunctions in an abdominal obesity-related MetS mouse model. Further mechanistic studies demonstrated that quercetin treatment substantially promoted the generation of non-12α-hydroxylated BAs (non-12OH BAs), particularly ursodeoxycholic acid (UDCA) and lithocholic acid (LCA), in serum via regulating the overall structure of gut microbiota and enriching Lactobacillus. High level of non-12OH BAs bind to Takeda G protein-coupled receptor 5 (TGR5) on adipocytes to stimulate thermogenesis. Remarkably, fecal microbiota transplantation (FMT) from quercetin-treated mice replicated the effects of quercetin on non-12OH BAs generation and energy expenditure, which suggested gut microbiota reshape and concomitant BAs regulation were responsible for the benefits on energy metabolism of quercetin in the MetS mouse model. Our findings not only highlighted the critical role of gut microbiota-BAs crosstalk in mediating quercetin-induced energy expenditure, but also enriched the pharmacological mechanisms of quercetin in ameliorating MetS-related diseases.

Machine Learning-Based Prediction of Large-for-Gestational Age Infants in Mothers with Gestational Diabetes Mellitus.

CONTEXT: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited. OBJECTIVE: We aimed to develop and validate an artificial intelligence (AI) based model to determine the probability of women with GDM giving birth to LGA infants during pregnancy using CGM measurements together with demographic data and metabolic indicators. METHODS: A total of 371 women with GDM from a prospective cohort at a university hospital were included. CGM was performed during 20-34 gestational weeks, and glycemic fluctuations were evaluated and visualized in women with GDM who gave birth to LGA and non-LGA infants. A convolutional neural network (CNN)-based fusion model was developed to predict LGA. Comparisons among the novel fusion model and three conventional models were made using the area under the receiver-operating characteristic curve (AUCROC) and accuracy. RESULTS: Overall, 76 (20.5%) out of 371 GDM women developed LGA neonates. The visualized 24-h glucose profiles differed at midmorning. This difference was consistent among subgroups categorized by pregestational BMI, therapeutic protocol and CGM administration period. The AI based fusion prediction model using 24-h CGM data and 15 clinical variables for LGA prediction (AUCROC 0.852, 95% CI 0.680-0.966, accuracy 84.4%) showed superior discriminative power compared with the three classic models. CONCLUSIONS: We demonstrated better performance in predicting LGA infants among women with GDM using the AI based fusion model. The characteristics of the CGM profiles allowed us to determine the appropriate window for intervention.

Small molecule tyrosine kinase inhibitors approved for systemic therapy of advanced hepatocellular carcinoma: recent advances and future perspectives.

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death in the world, and hepatocellular carcinoma (HCC) is the most common form of liver cancer. More than half of the HCC patients are diagnosed at an advanced stage and often require systemic therapy. Dysregulation of the activity of receptor tyrosine kinases (RTKs) is involved in the development and progress of HCC, RTKs are therefore the potential targets for systemic therapy of advanced HCC (aHCC). Currently, a total of six small molecule tyrosine kinase inhibitors (TKIs) have been approved for aHCC, including first-line sorafenib, lenvatinib, and donafenib, and second-line regorafenib, cabozantinib, and apatinib. These TKIs improved patients survival, which are associated with disease stage, etiology, liver function, tumor burden, baseline levels of alpha-fetoprotein, and treatment history. This review focuses on the clinical outcomes of these TKIs in key clinical trials, retrospective and real-world studies and discusses the future perspectives of TKIs for aHCC, with an aim to provide up-to-date evidence for decision-making in the treatment of aHCC.

Zoonotic infections by avian influenza virus: changing global epidemiology, investigation, and control.

Avian influenza virus continues to pose zoonotic, epizootic, and pandemic threats worldwide, as exemplified by the 2020-23 epizootics of re-emerging H5 genotype avian influenza viruses among birds and mammals and the fatal jump to humans of emerging A(H3N8) in early 2023. Future influenza pandemic threats are driven by extensive mutations and reassortments of avian influenza viruses rooted in frequent interspecies transmission and genetic mixing and underscore the urgent need for more effective actions. We examine the changing global epidemiology of human infections caused by avian influenza viruses over the past decade, including dramatic increases in both the number of reported infections in humans and the spectrum of avian influenza virus subtypes that have jumped to humans. We also discuss the use of advanced surveillance, diagnostic technologies, and state-of-the-art analysis methods for tracking emerging avian influenza viruses. We outline an avian influenza virus-specific application of the One Health approach, integrating enhanced surveillance, tightened biosecurity, targeted vaccination, timely precautions, and timely clinical management, and fostering global collaboration to control the threats of avian influenza viruses.

[Impacts of Anthropogenic Emission Reduction on Urban Atmospheric Oxidizing Capacity During the COVID-19 Lockdown].

In recent years， regional compound air pollution events caused by fine particles （PM2.5） and ozone （O3） have occurred frequently in economically developed areas of China， in which atmospheric oxidizing capacity （AOC） has played an important role. In this study， the WRF-CMAQ model was used to study the impacts of anthropogenic emission reduction on AOC during the COVID-19 lockdown period. Three representative cities in eastern China （Shijiazhuang， Nanjing， and Guangzhou） were selected for an in-depth analysis to quantify the contribution of meteorology and emissions to the changes in AOC and oxidants and to discuss the impact of AOC changes on the formation of secondary pollutants. The results showed that， compared with that in the same period in 2019， the urban average AOC in Shijiazhuang， Nanjing， and Guangzhou in 2020 increased by 60%， 48.7%， and 12.6%， respectively. The concentrations of O3， hydroxyl radical （·OH）， and nitrogen trioxide （NO3·ï¼‰ increased by 1.6%-26.4%， 14.8%-73.3%， and 37.9%-180%， respectively. The AOC in the three cities increased by 0.06×10-4， 0.12×10-4， and 0.33×10-4 min-1， respectively， due to emission reduction. The meteorological change increased AOC in Shijiazhuang and Nanjing by 20% and 17.9%， respectively， but decreased AOC in Guangzhou by -9.3%. Enhanced AOC led to an increase in the nitrogen oxidation ratio （NOR） and VOCs oxidation ratio （VOR） and promoted the transformation of primary pollutants to secondary pollutants. This offset the effects of primary emission reduction and resulted in a nonlinear decline in secondary pollutants compared to emissions during the COVID-19 lockdown.

Fecal microbiota transplantation ameliorates abdominal obesity through inhibiting microbiota-mediated intestinal barrier damage and inflammation in mice.

Abdominal obesity (AO), characterized by the excessive abdominal fat accumulation, has emerged as a significant public health concern due to its metabolic complications and escalating prevalence worldwide, posing a more pronounced threat to human health than general obesity. While certain studies have indicated that intestinal flora contributed to diet-induced general obesity, the precise involvement of gut microbiota in the development of AO, specifically the accumulation of abdominal fat, remains inadequately explored. In this study, the 16 S rDNA sequencing was employed to analyze gut flora alterations, and the intestinal microbiota dysbiosis characterized by a vanishing decline of Akkermansia was found in the AO group. Along with notable gut microbiota changes, the intestinal mucosal barrier damage and metabolic inflammation were detected, which collectively promoted metabolic dysregulation in AO. Furthermore, the metabolic inflammation and AO were ameliorated after the intestinal microbiota depletion with antibiotics (ABX) drinking, underscoring a significant involvement of gut microbiota dysbiosis in the progression of AO. More importantly, our findings demonstrated that the transplantation of healthy intestinal flora successfully reversed the gut microbiota dysbiosis, particularly the decline of Akkermansia in the AO group. The gut flora reshaping has led to the repair of gut barrier damage and mitigation of metabolic inflammation, which ultimately ameliorated abdominal fat deposition. Our study established the role of interactions between gut flora, mucus barrier, and metabolic inflammation in the development of AO, thereby offering a theoretical foundation for the clinical application of fecal microbiota transplantation (FMT) as a treatment for AO.