Cholesterol neutralized vemurafenib treatment by promoting melanoma stem-like cells via its metabolite 27-hydroxycholesterol.

Vemurafenib has been used as first-line therapy for unresectable or metastatic melanoma with BRAFV600E mutation. However, overall survival is still limited due to treatment resistance after about one year. Therefore, identifying new therapeutic targets for melanoma is crucial for improving clinical outcomes. In the present study, we found that lowering intracellular cholesterol by knocking down DHCR24, the limiting synthetase, impaired tumor cell proliferation and migration and abrogated the ability to xenotransplant tumors. More importantly, administration of DHCR24 or cholesterol mediated resistance to vemurafenib and promoted the growth of melanoma spheroids. Mechanistically, we identified that 27-hydroxycholesterol (27HC), a primary metabolite of cholesterol synthesized by the enzyme cytochrome P450 27A1 (CYP27A1), reproduces the phenotypes induced by DHCR24 or cholesterol administration and activates Rap1-PI3K/AKT signaling. Accordingly, CYP27A1 is highly expressed in melanoma patients and upregulated by DHCR24 induction. Dafadine-A, a CYP27A1 inhibitor, attenuates cholesterol-induced growth of melanoma spheroids and abrogates the resistance property of vemurafenib-resistant melanoma cells. Finally, we confirmed that the effects of cholesterol on melanoma resistance require its metabolite 27HC through CYP27A1 catalysis, and that 27HC further upregulates Rap1A/Rap1B expression and increases AKT phosphorylation. Thus, our results suggest that targeting 27HC may be a useful strategy to overcome treatment resistance in metastatic melanoma.

Automatic Electrochemiluminescence Method for the Detection of Cancerous Exosomes Incorporating Specific Aptamer-Magnetic Beads and Signal Nanoprobes.

Exosomes, as an emerging biomarker, have exhibited remarkable promise in early cancer diagnosis. Here, a highly sensitive, selective, and automatic electrochemiluminescence (ECL) method for the detection of cancerous exosomes was developed. Specific aptamer-(EK)4 peptide-tagged magnetic beads (MBs-(EK)4-aptamer) were designed as a magnetic capture probe in which the (EK)4 peptide was used to reduce the steric binding hindrance of cancerous exosomes with a specific aptamer. One new universal ECL signal nanoprobe (CD9 Ab-PEG@SiO2ϵRu(bpy)32+) was designed and synthesized by using microporous SiO2 nanoparticles as the carrier for loading ECL reagent Ru(bpy)32+, polyethylene glycol (PEG) layer, and anticluster of differentiation 9 antibody (CD9 Ab). A "sandwich" biocomplex was formed on the surface of the magnetic capture probe after mixing the capture probe, target exosomes, and ECL signal nanoprobe, and then it was introduced into an automated ECL analyzer for rapid and automatic ECL measurement. It was found that the designed signal nanoprobe shows a 270-fold improvement in the signal-to-noise ratio than that of the ruthenium complex-labeled CD9 antibody signal probe. The relative ECL intensity was proportional to MCF-7 exosomes as a model in the range of 102 to 104 particle/µL, with a detection limit of 11 particle/µL. Furthermore, the ECL method was employed to discriminate cancerous exosomes based on fingerprint responses using the designed multiple magnetic capture probes and the universal ECL signal nanoprobe. This work demonstrates that the utilization of a designed automated ECL tactic using the MBs-(EK)4-aptamer capture probe and the CD9 Ab-PEG@SiO2ϵRu(bpy)32+ signal nanoprobe will provide a unique and robust method for the detection and discrimination of cancerous exosomes.

On-Demand Picoliter-Level-Droplet Inkjet Printing for Micro Fabrication and Functional Applications.

With the advent of Internet of Things (IoTs) and wearable devices, manufacturing requirements have shifted toward miniaturization, flexibility, environmentalization, and customization. Inkjet printing, as a non-contact picoliter-level droplet printing technology, can achieve material deposition at the microscopic level, helping to achieve high resolution and high precision patterned design. Meanwhile, inkjet printing has the advantages of simple process, high printing efficiency, mask-free digital printing, and direct pattern deposition, and is gradually emerging as a promising technology to meet such new requirements. However, there is a long way to go in constructing functional materials and emerging devices due to the uncommercialized ink materials, complicated film-forming process, and geometrically/functionally mismatched interface, limiting film quality and device applications. Herein, recent developments in working mechanisms, functional ink systems, droplet ejection and flight process, droplet drying process, as well as emerging multifunctional and intelligence applications including optics, electronics, sensors, and energy storage and conversion devices is reviewed. Finally, it is also highlight some of the critical challenges and research opportunities. The review is anticipated to provide a systematic comprehension and valuable insights for inkjet printing, thereby facilitating the advancement of their emerging applications.

Improvement of gram staining effect by ethanol pretreatment and quantization of staining image by unsupervised machine learning.

In this study, we propose an Ethanol Pretreatment Gram staining method that significantly enhances the color contrast of the stain, thereby improving the accuracy of judgement, and demonstrated the effectiveness of the modification by eliminating unaided-eye observational errors with unsupervised machine learning image analysis. By comparing the traditional Gram staining method with the improved method on various bacterial samples, results showed that the improved method offers distinct color contrast. Using multimodal assessment strategies, including unaided-eye observation, manual image segmentation, and advanced unsupervised machine learning automatic image segmentation, the practicality of ethanol pretreatment on Gram staining was comprehensively validated. In our quantitative analysis, the application of the CIEDE2000, and CMC color difference standards confirmed the significant effect of the method in enhancing the discrimination of Gram staining.This study not only improved the efficacy of Gram staining, but also provided a more accurate and standardized strategy for analyzing Gram staining results, which might provide an useful analytical tool in microbiological diagnostics.

A Unified Adsorption Kinetic Model Inspired by Epidemiological Model: Based on Adsorbates "Infect" Adsorbents.

Adsorption is a unit operation used in various fields, including the environmental, chemical, and pharmaceutical industries. Understanding the adsorption kinetics is crucial to designing efficient adsorption systems. However, existing empirical adsorption models are limited in providing insights into the mass transfer mechanisms. Additionally, the absence of a unified adsorption kinetic model hampers the effective comparison of different adsorption systems. Here, we viewed the adsorption as an "infectious process of adsorbates by adsorbents" akin to epidemiology. In epidemiology, individuals can be divided into susceptible, infected, and recovered compartments, ignoring the complexities of movement among individuals. Analogously, we have categorized the adsorbates as adsorbable, adsorbed, and removed compartments. Thus, we proposed a unified adsorption kinetic model (the monolayer-multilayer-adsorbable-adsorbed-removed model) that accommodates monolayer/multilayer adsorption. The model was designed to encompass diverse adsorption setups, including continuous and batch processes with fixed/dispersed adsorbents. The versatility and applicability of the model were demonstrated through validation using a diverse set of experimental data. This validation underscored its effectiveness in water/wastewater treatment, salt reduction, metal recovery, and drug purification. A MATLAB-based program for solving this model was made available to researchers for their utilization and further investigations. Overall, this study developed a versatile adsorption kinetic model that offers a comprehensive and unified understanding of adsorption kinetics across various applications.

Rational Design of FeCo-S/Ni2P/NF Heterojunction as a Robust Electrocatalyst for Water Splitting.

The rational design of nonnoble-metal-based catalysts with high electroactivity and long-term stability, featuring controllable active sites, remains a significant challenge for achieving effective water electrolysis. Herein, a heterogeneous catalyst with a FeCo-S and Ni2P heterostructure (denoted FeCo-S/Ni2P/NF) grown on nickel foam (NF) was synthesized by a solvothermal method and low-temperature phosphorization. The FeCo-S/Ni2P/NF catalyst shows excellent electrocatalytic performance and stability in alkaline solution. The FeCo-S/Ni2P/NF catalyst demonstrates low overpotentials (η) for both the hydrogen evolution reaction (HER) (49 mV@10 mA cm-2) and the oxygen evolution reaction (OER) (279 mV@100 mA cm-2). Assembling the FeCo-S/Ni2P/NF catalyst as both cathode and anode in an electrolytic cell for overall water splitting (OWS) needs an ultralow cell voltage of 1.57 V to attain a current density (CD) of 300 mA cm-2. Furthermore, it demonstrates excellent durability, significantly outperforming the commercial Pt/C∥IrO2 system. The results of experiments indicate that the heterostructure and synergistic effect of FeCo-S and Ni2P can significantly enhance conductivity, facilitate mass/ion transport and gas evolution, and expose more active sites, thereby improving the catalytic activity of the electrocatalyst for the OWS. This study provides a rational approach for the development of commercially promising dual-functional electrocatalysts.

Reducing Remifentanil Usage in Laparoscopic Rectal Cancer Surgery for Elderly Patients by Optimizing Dosing.

Background: Colorectal cancer is a prevalent and serious health concern globally, particularly among the elderly population. Laparoscopic surgery is a commonly used approach for colorectal cancer treatment. However, the use of appropriate anesthesia and muscle relaxants is essential to ensure optimal surgical outcomes. Elderly patients undergoing surgery often have unique physiological characteristics and comorbidities, such as hypertension, diabetes, and coronary heart disease. These factors can affect treatment efficiency and patient outcomes. Objective: This study aimed to investigate the impact of different target-controlled infusion concentrations of rocuronium bromide on elderly patients undergoing laparoscopic colorectal cancer surgery. Methods: This is a prospective randomized controlled study. Ninety senior adults who underwent laparoscopic colorectal cancer surgery at our hospital between September 2018 and May 2020 were selected as the eligible participants. They were randomly divided into three groups: the low-dose group (0.6 mg/L of rocuronium bromide), the middle-dose group (0.9 mg/L of rocuronium bromide), and the high-dose group (1.2 mg/L of rocuronium bromide). The purpose of this division was to administer target-controlled infusions of rocuronium bromide to maintain skeletal muscle relaxation during the surgical procedure. Data on various outcome measures, including skeletal muscle relaxation effectiveness, patient satisfaction, skeletal muscle relaxation recovery times and indices, extubation duration, and remifentanil dosage, were collected and analyzed. Results: The middle-dose group and the high-dose group exhibited notably higher levels of satisfaction with skeletal muscle relaxation compared to the low-dose group. As the rocuronium bromide dosage increased, the patients experienced prolonged recovery times and had higher skeletal muscle indices (P < .05). Additionally, the middle-dose group demonstrated significantly reduced extubation times and lower remifentanil dosages compared to the other groups (P < .05). The enhanced satisfaction levels in the middle-dose and high-dose groups, indicating that higher concentrations of rocuronium bromide may be more effective in achieving optimal skeletal muscle relaxation during laparoscopic colorectal cancer surgery. The prolonged recovery times and higher skeletal muscle indices associated with increased dosage suggest a dose-dependent effect on muscle relaxation. Conclusion: For elderly patients undergoing laparoscopic rectal cancer surgery, the use of a target-controlled infusion of 0.9 mg/L of rocuronium bromide appears to be a viable option. It maintains adequate skeletal muscle relaxation, shortens postoperative recovery time, and reduces the demand for remifentanil, demonstrating excellent potential for clinical application. These findings provide valuable insights for anesthesiologists and healthcare professionals involved in the perioperative management of elderly patients undergoing laparoscopic rectal cancer surgery. Implementing the optimized dosage of rocuronium bromide can contribute to enhanced surgical outcomes, improved patient satisfaction, and more efficient resource utilization in the clinical setting.

USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning.

We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive-evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment.

[Identification of a novel variant in a patient with Calsequestrin 1 related myopathy].

OBJECTIVE: To explore the genetic basis of a myopathic patient with pathological characteristics including tubular aggregates and vacuoles. METHODS: Next generation sequencing was carried out for the patient, and candidate variant was verified by Sanger sequencing. RESULTS: Genetic testing revealed that the patient has harbored a heterozygous c.730G>C (p.D244H) variant of Calsequestrin 1 (CASQ1) gene. The same variant was not found in his unaffected parents. Based on guidelines from the American College of Medical Genetics and Genomics, the variant was rated as pathogenic (PS1+PM2+PP3). CONCLUSION: The novel c.730G>C (p.D244H) variant of the CASQ1 gene probably underlay the myopathy in this patient. Above finding has enriched the mutational spectrum of the CASQ1 gene.

A novel monolayer adsorption kinetic model based on adsorbates "infect" adsorbents inspired by epidemiological model.

Adsorption is a unit operation process with broad applications in environmental, pharmaceutical, and chemical fields, with its most significance in environmental fields for water and wastewater treatment. Adsorption involves continuous/batch modes with fixed/dispersed adsorbents, leading to diverse systems. The adsorption kinetic models provide essential insights for effectively designing these systems. However, many adsorption models are semi-empirical/empirical, making it challenging to identify the adsorption mechanisms. Additionally, a consistent method for modelling the adsorption kinetics of different processes would be helpful for the comparison and analysis of various adsorption systems, but no such unified model is available. In epidemiological modeling, populations are often categorized into susceptible, infected, and removed individuals, simplifying disease transmission dynamics without considering individual-level movement intricacies. Likewise, we have employed a similar approach within adsorption systems, classifying adsorbates into absorbable, adsorbed, and removed (to the effluent) segments, thus developing the Monolayer-Absorbable-Adsorbed-Removed (MPQR) kinetics model. This model is applicable to continuous/batch adsorption systems, regardless of whether fixed or dispersed adsorbents are employed. The model was validated using experimental data across water/wastewater treatment, drug separation/purification, metal recovery, and desalination. The results showed that our model successfully fitted the kinetic data from various adsorption systems. It outperformed commonly used models for continuous/batch adsorption. The model allowed us to directly compare the parameters among various adsorption processes. The solving method based on Excel was provided and can be used by the researchers. Our model offers a versatile and unified approach to model adsorption kinetics, enabling the analysis and design of various adsorption systems.

The Gompertz model and its applications in microbial growth and bioproduction kinetics: Past, present and future.

The Gompertz model, initially proposed for human mortality rates, has found various applications in growth analysis across the biotechnological field. This paper presents a comprehensive review of the Gompertz model's applications in the biotechnological field, examining its past, present, and future. The past of the Gompertz model was examined by tracing its origins to 1825, and then it underwent various modifications throughout the 20th century to increase its applicability in biotechnological fields. The Zwietering-modified version has proven to be a versatile tool for calculating the lag-time and maximum growth rate/quantity in microbial growth. In addition, the present applications of the Gompertz model to microbial growth kinetics and bioproduction (e.g., hydrogen, methane, caproate, butanol, and hexanol production) kinetics have been comprehensively summarized and discussed. We highlighted the importance of standardized citations and guidance on model selection. The Zwietering-modified Gompertz model and the Lay-modified Gompertz model are recommended for describing microbial growth kinetics and bioproduction kinetics, recognized for their widespread use and provision of valuable kinetics information. Finally, in response to the current Gompertz models' focus on internal mortality, the modified Makeham-Gompertz models that consider both internal/external mortality were introduced and validated for microbial growth and bioproduction kinetics with good fitting performance. This paper provides a perspective of the Gompertz model and offers valuable insights that facilitate the diverse applications of this model in microbial growth and bioproduction kinetics.

Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.

Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Transformer-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Transformer-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Transformer-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Transformer-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Transformer-DIA.

Sensing of dietary amino acids and regulation of calcium dynamics in adipose tissues through Adipokinetic hormone in Drosophila.

Nutrient sensing and the subsequent metabolic responses are fundamental functions of animals, closely linked to diseases such as type 2 diabetes and various obesity-related diseases. Drosophila melanogaster has emerged as an excellent model for investigating metabolism and its associated disorders. In this study, we used live-cell imaging to demonstrate that the fly functional homolog of mammalian glucagon, Adipokinetic hormone (AKH), secreted from AKH hormone-producing cells (APCs) in the corpora cardiaca, stimulates intracellular Ca 2+ waves in the larval fat body/adipose tissue to promote lipid metabolism. Further, we show that specific dietary amino acids activate the APCs, leading to increased intracellular Ca 2+ and subsequent AKH secretion. Finally, a comparison of Ca 2+ dynamics in larval and adult fat bodies revealed different mechanisms of regulation, highlighting the interplay of pulses of AKH secretion, extracellular diffusion of the hormone, and intercellular communication through gap junctions. Our study underscores the suitability of Drosophila as a powerful model for exploring real-time nutrient sensing and inter-organ communication dynamics.

CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.

Summary: Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing. Availability and implementation: This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.

Nutrition literacy level of medical personnel in tertiary hospitals: evidence from a cross-sectional study.

BACKGROUND: Nutrition literacy (NL), stemming from health literacy, profoundly influences dietary habits and chronic diseases. Despite their pivotal societal role, scant research exists on NL levels among medical personnel. This study examined NL levels among tertiary hospital medical staff in Bengbu and identified influencing factors. METHODS: Using cluster sampling, all Bengbu tertiary hospitals were selected, with 4-5 departments randomly chosen. A structured questionnaire assessed demographic characteristics, while the NL short-form scale (NL-SF12) evaluated NL. SPSS 26.0 and AMOS conducted statistical analysis, including confirmatory factor analysis and Cronbach's α for reliability. Chi-square tests and logistic regression analyzed group differences and influencing factors. RESULTS: The NL-SF12 demonstrated robust reliability and validity. Of participants, 34.22% were male and 65.78% female; 41.03% were doctors and 42.16% nurses. Overall, 45.68% exhibited high NL. Females showed higher total NL (OR = 1.47, 95% CI: 1.08-1.98), cognition (OR = 1.66, 95% CI: 1.22-2.24), skills (OR = 1.48, 95% CI: 1.09-2.00), and interactive NL (OR = 2.21, 95% CI: 1.53-3.19) than males. Those with a master's or higher had higher total NL (OR = 2.20, 95% CI: 1.33-3.65) and cognition (OR = 3.23, 95% CI: 1.94-5.37) than those with an associate degree or less. Pharmacists, inspectors, and technicians had higher total NL (OR = 1.55, 95% CI: 1.06-2.26) and functional NL (OR = 1.49, 95% CI: 1.02-2.17). Gender, education level, and career were the influencing factors of nutrition literacy among medical personnel. CONCLUSIONS: Female medical staff and those with a master's degree or higher showed higher nutrition literacy (NL), particularly in cognition and skills. Pharmacists, inspectors, and other technicians exhibited higher levels of total NL and functional NL. Gender, education level, and career were identified as significant influencing factors of nutrition literacy among medical personnel. Understanding and considering these factors are crucial for developing targeted strategies to enhance nutrition literacy among healthcare professionals. Future efforts to improve nutrition literacy through training and interventions should be tailored to the characteristics of different groups to effectively enhance the capabilities and proficiency of healthcare professionals in nutrition knowledge and practice.

Proteomic stable isotope probing with an upgraded Sipros algorithm for improved identification and quantification of isotopically labeled proteins.

BACKGROUND: Proteomic stable isotope probing (SIP) is used in microbial ecology to trace a non-radioactive isotope from a labeled substrate into de novo synthesized proteins in specific populations that are actively assimilating and metabolizing the substrate in a complex microbial community. The Sipros algorithm is used in proteomic SIP to identify variably labeled proteins and quantify their isotopic enrichment levels (atom%) by performing enrichment-resolved database searching. RESULTS: In this study, Sipros was upgraded to improve the labeled protein identification, isotopic enrichment quantification, and database searching speed. The new Sipros 4 was compared with the existing Sipros 3, Calisp, and MetaProSIP in terms of the number of identifications and the accuracy and precision of atom% quantification on both the peptide and protein levels using standard E. coli cultures with 1.07 atom%, 2 atom%, 5 atom%, 25 atom%, 50 atom%, and 99 atom% 13C enrichment. Sipros 4 outperformed Calisp and MetaProSIP across all samples, especially in samples with ≥ 5 atom% 13C labeling. The computational speed on Sipros 4 was > 20 times higher than Sipros 3 and was on par with the overall speed of Calisp- and MetaProSIP-based pipelines. Sipros 4 also demonstrated higher sensitivity for the detection of labeled proteins in two 13C-SIP experiments on a real-world soil community. The labeled proteins were used to trace 13C from 13C-methanol and 13C-labeled plant exudates to the consuming soil microorganisms and their newly synthesized proteins. CONCLUSION: Overall, Sipros 4 improved the quality of the proteomic SIP results and reduced the computational cost of SIP database searching, which will make proteomic SIP more useful and accessible to the border community. Video Abstract.

Plant and microbial response in constructed wetland treating tetracycline antibiotic polluted water: Evaluating the effects of microplastic size and concentration.

Microplastics (MPs) and antibiotics are novel water pollutants that have attracted increasing attention. Constructed wetlands (CWs) are widely applied treating various types of polluted water. How these two new pollutants affect plants and microorganisms in CWs, especially deciphering the unknown roles of MPs size and concentration, is of great essential. Here, five CW treatments with submerged macrophyte Myriophyllum aquaticum were established to treat oxytetracycline (OTC) antibiotic-polluted water. The effects of polystyrene (PS) nanoplastics (NPs) (700 nm) and MPs (90-110 µm) on plant and microbial communities at 10 µg/L and 1 mg/L, respectively, were systematically evaluated. PS reduced the nitrogen and phosphorus removal efficiencies and inhibited OTC removal. Low doses (10 µg/L) of NPs and high doses (1 mg/L) of MPs had the greatest effects on plant and microbial responses. The overall effect of MPs was greater than that of NPs. Compared with high NPs concentration (1 mg/L), low concentrations (10 µg/L) had higher catalase (CAT), superoxide dismutase (SOD), and malondialdehyde (MDA) content. However, the activity and content of MPs at low concentrations (10 µg/L) were lower than those at high concentrations (1 mg/L). The coexistence of OTC and MPs/NPs decreased the microbial diversity and abundance. Low doses of NPs and high doses of MPs decreased the relative abundance of Abditibacteriota, Deinococccota, and Zixibacteria. Redundancy and network analyses revealed a strong correlation between pollutant removal and plant and microbial responses. NH4+-N and OTC removal was positively and negatively correlated with CAT, SOD, and MDA content, respectively. MDA positively correlated to chlorophyll content, whereas SOD showed a negative correlation with Chloroflexi. This study highlighted the scale effect of MPs in wastewater treatment via CWs. It enhances our understanding of the response of plants and microorganisms to the remediation of water co-polluted with MPs and antibiotics.

A bibliometric analysis of interstitial cells of Cajal research.

Objective: The significance of interstitial cells of Cajal (ICC) in the gastrointestinal tract has garnered increasing attention. In recent years, approximately 80 articles on ICC have been published annually in various journals. However, no bibliometric study has specifically focused on the literature related to ICC. Therefore, we conducted a comprehensive bibliometric analysis of ICC to reveal dynamic scientific developments, assisting researchers in exploring hotspots and emerging trends while gaining a global perspective. Methods: We conducted a literature search in the Web of Science Core Collection (WoSCC) from January 1, 2013, to December 31, 2023, to identify relevant literature on ICC. We employed bibliometric software, namely VOSviewer and CiteSpace, to analyze various aspects including annual publication output, collaborations, research hotspots, current status, and development trends in this domain. Results: A total of 891 English papers were published in 359 journals by 928 institutions from 57 countries/regions. According to the keyword analysis of the literature, researchers mainly focused on "c-Kit," "expression," "smooth muscle," and "nitric oxide" related to ICC over the past 11 years. However, with "SIP syncytium," "ANO1," "enteric neurons," "gastrointestinal stromal tumors (GIST)," and "functional dyspepsia (FD)," there has been a growing interest in the relationship between ANO1, SIP syncytium, and ICC, as well as the role of ICC in the treatment of GIST and FD. Conclusion: Bibliometric analysis has revealed the current status of ICC research. The association between ANO1, SIP syncytium, enteric neurons and ICC, as well as the role of ICC in the treatment of GIST versus FD has become the focus of current research. However, further research and collaboration on a global scale are still needed. Our analysis is particularly valuable to researchers in gastroenterology, oncology, and cell biology, providing insights that can guide future research directions.

Dietary Amino Acids Promote Glucagon-like Hormone Release to Generate Novel Calcium Waves in Adipose Tissues.

Nutrient sensing and the subsequent metabolic responses are fundamental functions of animals, closely linked to diseases such as type 2 diabetes and various obesity-related morbidities. Among different metabolic regulatory signals, cytosolic Ca2+ plays pivotal roles in metabolic regulation, including glycolysis, gluconeogenesis, and lipolysis. Recently, intercellular calcium waves (ICWs), the propagation of Ca2+ signaling through tissues, have been found in different systems to coordinate multicellular responses. Nevertheless, our understanding of how ICWs are modulated and operate within living organisms remains limited. In this study, we explore the real-time dynamics, both in organ culture and free-behaving animals, of ICWs in Drosophila larval and adult adipose tissues. We identified Adipokinetic hormone (AKH), the fly functional homolog of mammalian glucagon, as the key factor driving Ca2+ activities in adipose tissue. Interestingly, we found that AKH, which is released in a pulsatile manner into the circulating hemolymph from the AKH-producing neurosecretory cells (APCs) in the brain, stimulates ICWs in the larval fat by a previously unrecognized gap-junction-independent mechanism to promote lipolysis. In the adult fat body, however, gap-junction-dependent random ICWs are triggered by a presumably uniformly diffused AKH. This highlights the stage-specific interplay of hormone secretion, extracellular diffusion, and intercellular communication in the regulation of Ca2+ dynamics. Additionally, we discovered that specific dietary amino acids activate the APCs, leading to increased intracellular Ca2+ and subsequent AKH secretion. Altogether, our findings identify that dietary amino acids regulate the release of AKH peptides from the APCs, which subsequently stimulates novel gap-junction-independent ICWs in adipose tissues, thereby enhancing lipid metabolism.

A novel phosphotriesterase hybrid nanoflower-hydrogel sensor equipped with a smartphone detector for real-time on-site monitoring of organophosphorus pesticides.

Designing efficient and rapid methods for the detection of organophosphorus pesticides (OPs) residue is a prerequisite to mitigate their negative health impacts. In this study, we propose the concept of an enzyme catalysis system-based hydrogel kit integrated with a smartphone detector for in-field screening of OPs. Here, we rapidly prepared phosphotriesterase hybrid nanoflowers (PTE-HNFs) using a self-assembly strategy by adding external energy and embedded the nanocomposite in sodium alginate (SA) hydrogel to construct a target-responsive hydrogel kit. The color response of the kit is induced by catalyzing methyl parathion (MP) to produce p-nitrophenol. For on-site quantification, the color variations of the portable kit are converted into digital information through a smartphone, which exhibits an applicable linear range towards OPs. The hydrogel sensing platform demonstrates a wide linear range (1-150 µM) and low detection limit (0.15 µM) for MP while maintaining high reliability, excellent long-term stability, and ease of operation. Overall, the PTE-HNFs-based SA hydrogel kit provides a useful strategy for simple and sensitive detection of MP and holds great potential for applications in detecting OPs in food and environmental water.