scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.

MOTIVATION: Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging. RESULTS: Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes. AVAILABILITY AND IMPLEMENTATION: The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

Combined transcriptome and proteome analysis reveals MSN and ARFIP2 as biomarkers for trastuzumab resistance of breast cancer.

PURPOSE: HER2-positive breast cancer (BC) accounts for 20-30% of all BC subtypes and is linked to poor prognosis. Trastuzumab (Tz), a humanized anti-HER2 monoclonal antibody, is a first-line treatment for HER2-positive breast cancer which faces resistance challenges. This study aimed to identify the biomarkers driving trastuzumab resistance. METHODS: Differential expression analysis of genes and proteins between trastuzumab-sensitive (TS) and trastuzumab-resistant (TR) cells was conducted using RNA-seq and iTRAQ. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used to study their functions. The prognostic significance and protein levels of ARFIP2 and MSN were evaluated using online tools and immunohistochemistry. Sensitivity of MSN and ARFIP2 to other therapies was assessed using public pharmacogenomics databases and the R language. RESULTS: Five genes were up-regulated, and nine genes were down-regulated in TR cells at both transcriptional and protein levels. Low ARFIP2 and high MSN expression linked to poor BC prognosis. MSN increased and ARFIP2 decreased in TR patients, correlating with shorter OS. MSN negatively impacted fulvestrant and immunotherapy sensitivity, while ARFIP2 had a positive impact. CONCLUSION: Our findings suggest that MSN and ARFIP2 could serve as promising biomarkers for predicting response to Tz, offering valuable insights for future research in the identification of diagnostic and therapeutic targets for BC patients with Tz resistance.

The alleviation of difenoconazole-induced kidney injury in common carp (Cyprinus carpio) by silybin: Involvement of inflammation, oxidative stress, and apoptosis.

Triazole fungicides, such as difenoconazole (DFZ), are frequently used to control fungus in crops that pollute water. The common carp (Cyprinus carpio) (hereafter referred to as "carp") is an excellent bio-indicator of water quality. The seeds of the silymarin plant contain a flavonolignan called silybin (SYB), which is used to treat liver disease. To explore SYB's involvement in DFZ-triggered kidney damage in carps, an H&E assay was conducted, and ROS level was also examined. The results demonstrated that SYB alleviated DFZ-induced destruction of kidney tissue structure in carps, as well as alleviating the elevation of kidney ROS level in carps. RT-qPCR and Western blot were used to detect inflammation-, oxidative stress- and apoptosis-related factors at mRNA level and protein level. The experimental findings indicated that relative to the DFZ group, SYB + DFZ co-treatment reduced inflammation-related mRNA level of il-6, il-1ß and tnf-α, elevated mRNA level of il-10. It also reduced protein expression levels of NF-κB and iNOS. In addition, SYB + DFZ co-treatment reduced DFZ-induced increase in the oxidative stress-related mRNA indicators sod and cat, and decreased the protein expression levels of Nrf2 and NQO1. SYB reduced the DFZ-induced increase in pro-apoptotic gene Bax mRNA and protein expression levels and the DFZ-induced decrease in anti-apoptotic gene Bcl-2 mRNA and protein expression levels. In summary, SYB potentially mitigates DFZ-induced kidney damage in carp by addressing inflammation, oxidative stress, and apoptosis. Our results establish a theoretical foundation for the clinical advancement of freshwater carp feeds.

Determining Reaction Paths by Evaluating Kinetic Isotopic Effects with Density Functional Theory: Example of Methane Thermogenesis.

This study demonstrates the determination of reaction pathways by evaluating the carbon kinetic isotopic effect and interpreting isotopic fractionations based on quantum chemical calculations. The reaction under investigation is methane thermogenesis from the decomposition of kerogen, which is a geochemical reaction under temperatures below 150 °C and lasts for tens of millions of years. Investigating its mechanism requires theoretical simulations because lab experiments at practical time-lengths require elevated temperatures, which introduce unwanted side reactions. Density functional theory and kinetic simulations were conducted on isotopic fractionations with the use of two possible pathways (free-radical versus carbonium), and the results were compared to field data sets. Different molecular sizes of kerogen were investigated to account for the hindrance of translation and rotation in modeling a reactant in the solid phase. Both pathways have low reaction barriers, implying that the reaction rates are limited by the concentration of active species (hydrated protons and free radicals). The results support the carbonium pathway and rule out the free-radical pathway as the 13CH4 from the latter would be 30‰ more depleted than the observed data. Additionally, simulations were conducted on hydrocarbon isotope fractionation of the carbonium pathway with consideration of hydrogen exchange between methane and water, successively reproducing the observed abundances of deuterium-containing isotopologues (13CH3D, 13CH3D, and 12CH2D2).

miR-19a may function as a biomarker of oral squamous cell carcinoma (OSCC) by regulating the signaling pathway of miR-19a/GRK6/GPCRs/PKC in a Chinese population.

BACKGROUND: In this study, we aimed to investigate the potential of miR-19a as a biomarker of OSCC and its underlying molecular mechanisms. METHODS: We collected serum and saliva samples from 66 OSCC patients and 66 healthy control subjects. Real-time PCR analysis, bioinformatic analysis and luciferase assays were performed to establish a potential signaling pathway of miR-19a/GRK6/GPCRs/PKC. Flowcytometry and Transwell assays were performed to observe the changes in cell apoptosis, metastasis and invasion. RESULTS: We found that miR-19a, GPR39 mRNA and PKC mRNA were upregulated while GRK6 mRNA was downregulated in the serum and saliva samples collected from OSCC patients. Moreover, in silico analysis confirmed a potential binding site of miR-19a on the 3'UTR of GRK6 mRNA, and the subsequent luciferase assays confirmed the molecular binding between GRK6 and miR-19a. We further identified that the over-expression of miR-19a could regulate the signaling between GRK6, GPR39 and PKC via the signaling pathway of miR-19a/GRK6/GPR39/PKC, which accordingly resulted in suppressed cell apoptosis and promoted cell migration and invasion. CONCLUSION: Collectively, the findings of our study propose that miR-19a is a crucial mediator in the advancement of OSCC, offering a potential avenue for the development of innovative therapeutic interventions aimed at regulating GRK6 and its downstream signaling pathways.

An interval water demand prediction method to reduce uncertainty: A case study of Sichuan Province, China.

Effective prediction of water demand is a prerequisite for decision makers to achieve reliable management of water supply. Currently, the research on water demand prediction focuses on point prediction method. In this study, we constructed a GA-BP-KDE hybrid interval water demand prediction model by combining non-parametric estimation and point prediction. Multiple metaheuristic algorithms were used to optimize the Back-Propagation Neural Network (BP) and Kernel Extreme Learning Machine (KELM) network structures. The performance of the water demand point prediction models was compared by the Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Kling-Gupta Efficiency (KGE), computation time, and fitness convergence curves. The kernel density estimation method (KDE) and the normal distribution method were used to fit the distribution of errors. The probability density function with the best fitting degree was selected based on the index G. The shortest confidence interval under 95% confidence was calculated according to the asymmetry of the error distribution. We predicted the impact indicator values for 2025 using the exponential smoothing method, and obtained water demand prediction intervals for various water use sectors. The results showed that the GA-BP model was the optimal model as it exhibited the highest computational efficiency, algorithmic stability, and prediction accuracy. The three prediction intervals estimated after adjusting the KDE bandwidth parameter covered most of the sample points in the test set. The prediction intervals of the four water use sectors were evaluated as F values of 1.6845, 1.3294, 1.6237, and 1.3600, which indicates high accuracy and quality of the prediction intervals. The mixed water demand interval prediction based on GA-BP-KDE reduces the uncertainty of the point prediction results and can provide a basis for water resource management by decision makers.

Spectroscopic Investigation into the Binding of Ferulic Acid with Sodium Deoxycholate: Hydrophobic Force Versus Hydrogen Bonding.

The binding of ferulic acid (FA) with sodium deoxycholate (NaDC) has been investigated using fluorescence and absorption measurements. The fluorescence probe technique of pyrene reveals that the presence of FA favors the micellization of NaDC, leading to the decreased critical micelle concentrations for the formation of NaDC micelles. As NaDC molecules change gradually from monomers via primary micelles into secondary micelles, the intensities of absorption and fluorescence spectra of FA increase at low NaDC concentrations, but decrease suddenly at intermediate NaDC concentrations, and finally increase again at high NaDC concentrations. These results corroborated well with FA fluorescence lifetime data suggesting that the aryl ring of FA hydrophobically binds to the convex surface of NaDC monomers, whereas the hydrogen bonding between FA and NaDC is significantly involved in NaDC primary micelles, which is gradually overcome by the hydrophobic interaction between FA and NaDC secondary micelles. The absorption and fluorescence spectra as well as the binding constant value of FA indicate the strong binding of FA in the large hydrophobic core of NaDC secondary micelles. At low FA concentrations, the measurement of FA anisotropy suggests that FA can increase the packing order of hydrophobic surfaces in NaDC secondary micelles, whereas the high amount of FA can greatly disrupt the packing structure of NaDC secondary micelles which is ascribed to the formation of FA dimers. The spectroscopic experiments outlined here present the binding events of FA with NaDC monomers and primary and secondary micelles, which are significantly related with the hydrophobic force and hydrogen bonding as well as the unique structural characteristics of bile salt.

Unfolding the Determinants of COVID-19 Vaccine Acceptance in China.

BACKGROUND: China is at the forefront of global efforts to develop COVID-19 vaccines and has five fast-tracked candidates at the final-stage, large-scale human clinical trials testing phase. Vaccine-promoting policymaking for public engagement is a prerequisite for social mobilization. However, making an informed and judicious choice is a dilemma for the Chinese government in the vaccine promotion context. OBJECTIVE: In this study, public opinions in China were analyzed via dialogues on Chinese social media, based on which Chinese netizens' views on COVID-19 vaccines and vaccination were investigated. We also aimed to develop strategies for promoting vaccination programs in China based on an in-depth understanding of the challenges in risk communication and social mobilization. METHODS: We proposed a novel behavioral dynamics model, SRS/I (susceptible-reading-susceptible/immune), to analyze opinion transmission paradigms on Chinese social media. Coupled with a meta-analysis and natural language processing techniques, the emotion polarity of individual opinions was examined in their given context. RESULTS: We collected more than 1.75 million Weibo messages about COVID-19 vaccines from January to October 2020. According to the public opinion reproduction ratio (R0), the dynamic propagation of those messages can be classified into three periods: the ferment period (R01=1.1360), the revolution period (R02=2.8278), and the transmission period (R03=3.0729). Topics on COVID-19 vaccine acceptance in China include price and side effects. From September to October, Weibo users claimed that the vaccine was overpriced, making up 18.3% (n=899) of messages; 38.1% (n=81,909) of relevant topics on Weibo received likes. On the contrary, the number of messages that considered the vaccine to be reasonably priced was twice as high but received fewer likes, accounting for 25.0% (n=53,693). In addition, we obtained 441 (47.7%) positive and 295 (31.9%) negative Weibo messages about side effects. Interestingly, inactivated vaccines instigated more heated discussions than any other vaccine type. The discussions, forwards, comments, and likes associated with topics related to inactivated vaccines accounted for 53% (n=588), 42% (n=3072), 56% (n=3671), and 49% (n=17,940), respectively, of the total activity associated with the five types of vaccines in China. CONCLUSIONS: Most Chinese netizens believe that the vaccine is less expensive than previously thought, while some claim they cannot afford it for their entire family. The findings demonstrate that Chinese individuals are inclined to be positive about side effects over time and are proud of China's involvement with vaccine development. Nevertheless, they have a collective misunderstanding about inactivated vaccines, insisting that inactivated vaccines are safer than other vaccines. Reflecting on netizens' collective responses, the unfolding determinants of COVID-19 vaccine acceptance provide illuminating benchmarks for vaccine-promoting policies.

COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog.

The outbreak of a novel coronavirus (COVID-19) aroused great public opinion in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we analyze the real data of COVID-19 information and propose a comprehensive susceptible-reading-forwarding-immune (SRFI) model to understand the patterns of key information propagation considering both public contact and participation. We develop the SRFI model, based on the public reading quantity and forwarding quantity that denote contact and participation respectively, and take into account the behavior that users may re-enter another related topic during the attention phase or the participation phase freely. Data fitting using the real data of both reading quantity and forwarding quantity obtained from Chinese Sina-microblog can parameterize the model to make an accurate prediction of the COVID-19 public opinion trend until the next major news item occurs, and the sensitivity analysis provides the basic strategies for communication.

A pilot study: a possible implication of Candida as an etiologically endogenous pathogen for oral lichen planus.

BACKGROUND: The aim of this study was to investigate the prevalence and genotypic profiles of Candida albicans in patients with oral lichen planus (OLP). MATERIALS AND METHODS: Positive rates and genotypic profiles of Candida albicans strains from OLP patients and healthy controls were analyzed. Random amplified polymorphic DNA and internal transcribed spacer of ribosome DNA polymerase chain reactions were used to sequence the DNA of these strains, and then their genetic similarity was measured using BLAST, UIV Band, and Vector NTI Suite Sequence Analyses Software. RESULTS: The prevalence of C. albicans strains detected from erosive-OLP, non-erosive OLP, and normal individuals was 18.87, 18.75, and 7.92%, respectively. Four different genotypes were revealed by the two methods. To be specific, type I was found only in the healthy subjects; type II a and II b were found in non-erosive OLP, and type III was identified in erosive OLP. Intragroup similarity coefficients, i.e. SAB were 100%, and inter-groups similarity coefficients, i.e. SAB were less than 30%. CONCLUSIONS: The genotypic results of C. albicans in OLP revealed an endogenous rather than exogenous infection of C. albicans. In addition, a possible pathogenic role of C. albicans in OLP, with the etiologic sense contributing to a more proper recognition on the pathogenesis, development, and progression of OLP, as well as some strategies for its diagnosis and treatment were identified.

Treatment ideas of acupuncture and moxibustion for adenomyosis based on "etiology, location, nature and development of disease". / 基于"ç— å› ã€ç— ä½ã€ç— æ€§ã€ç— åŠ¿"æµ æžå­å®«è ºè‚Œç—‡çš„é’ˆç¸æ²»ç–—æ€è·¯.

Focusing on the syndrome/pattern differentiation to determine treatment, the approaches to the diagnosis and treatment of acupuncture and moxibustion for adenomyosis are explored by identifying the etiology, location, nature and development of disease. The syndromes/patterns of adenomyosis are differentiated in view of both zangfu and meridian theories. The treatment is delivered complying with the menstrual cycle and the basic rule of treatment, "treating the symptoms in the acute stage, while the root causes in the recovery stage". During menstrual period, stopping pain and eliminating stasis are dominant; while during the other days of menstrual cycle, regulating zangfu dysfunction (excess or deficiency) is emphasized. In general, the functions of the thoroughfare vessel and the conception vessel should be specially considered and adjusted, and the principles of treatment include strengthening the spleen, regulating the kidney and soothing the liver. Acupoints are selected mainly from the spleen meridian of foot-taiyin, the kidney meridian of foot-shaoyin and the conception vessel. Ciliao (BL 32), Shiqizhui (EX-B 8), Zigong (EX-CA 1), Diji (SP 8) and four-gate points (bilateral Hegu [LI 4] and Taichong [LR 3]) are used in menstrual period; Zusanli (ST 36), Sanyinjiao (SP 6) and Taixi (KI 3) in postmenstrual phase; Guanyuan (CV 4), Luanchao (Ovary, Extra) and Qihai (CV 6) in intermenstrual phase; while, Guanyuan (CV 4), Qihai (CV 6) and Shenque (CV 8), combined with Gongsun (SP 4), Neiguan (PC 6) and Jianshi (PC 5) in premenstrual phase. According to the dynamic development of patient's conditions, the reinforcing or reducing techniques of acupuncture and moxibustion are feasibly applied in treatment of adenomyosis.

Utilizing AfDesign for Developing a Small Molecule Inhibitor of PICK 1-PDZ.

INTRODUCTION: The PICK1 PDZ domain has been identified as a potential drug target for neurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5, have been discovered experimentally to bind to the PDZ domain with a relatively high binding affinity. With the rapid growth of computational research, there is an urgent need for more efficient computational methods to design viable ligands that target proteins. METHOD: Recently, a newly developed program called AfDesign (part of ColabDesign) at https:// github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has been suggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitating the ligand development process. To evaluate the performance of this program, we explored its ability to target the PICK1 PDZ domain, given our current understanding of it. We found that the designated length of the ligand and the number of recycles play vital roles in generating ligands with optimal properties. RESULTS: Utilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparable ligands to that of prior identified ligands. Moreover, these designed ligands displayed significantly lower binding energy compared to manually created sequences. CONCLUSION: This work demonstrated that AfDesign can potentially be a powerful tool to facilitate the exploration of the ligand space for the purpose of targeting PDZ domains.

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Vegetation degradation reduces aggregate associated carbon by reducing both labile and stable carbon fraction in Northeast China.

Vegetation changes can affect soil organic carbon (SOC) content and storage by altering the inputs of plant biomass and the catabolism and anabolism of soil microorganisms. However, influence of vegetation degradation on aggregate associated carbon fractions and the contribution of different aggregates to total SOC in bulk soil remains poorly understood. In this study, undisturbed soil samples were collected from three types of grassland in Songnen grassland: an undegraded grassland (LEY, Leymus chinensis), a moderately degraded grassland (CHL, Chloris virgata), and a severely degraded grassland (SUA, Suaeda heteroptera). Three soil aggregates including macroaggregate (> 0.25 mm), microaggregate (0.053-0.25 mm) and silt and clay fraction (< 0.053 mm) were separated using wet sieving. Contents of total SOC, soil labile and stable carbon in bulk soil and different soil aggregates were measured. Compared with LEY, the mean weight diameter and geometric mean diameter under the degraded vegetation communities reduced by 39.42 % and 28.47 %, respectively. The reduction in SOC contents in bulk soil, macroaggregate, microaggregate and silt and clay fraction resulting from vegetation degradation was 49.81 %, 26.00 %, 76.17 % and 43.65 %, respectively. Under the degraded vegetation communities, contents of soil labile and stable carbon in bulk soil (45.73 % and 52.61 %, respectively), macroaggregate (17.38 % and 31.61 %, respectively), microaggregate (77.83 % and 74.18 %, respectively), and silt and clay fraction (21.20 % and 53.45 %, respectively) were significantly lower than those under LEY. The contribution of macroaggregate, microaggregate and silt and clay fraction to total SOC was 13.27 %, 23.61 % and 63.12 %, respectively. The contribution of soil aggregates to total SOC following vegetation degradation reduced by 53.63 % for microaggregate, but increased by 47.10 % for silt and clay fraction. These findings collectively indicate that vegetation degradation reduces the aggregate associated carbon content by reducing both labile and stable carbon fraction in Songnen grassland, and sustainable vegetation restoration strategies are need to enhance soil carbon storage in Northeast China.

A rapid near-infrared turn on fluorescence probe for the detection of sulfite in food and its application in biological imaging.

A near-infrared fluorescent "turn on" probe DTMI featuring simple skeleton was constructed easily. It undergoes a structure transformation from an A-π-A to a D-π-A framework towards SO32-. Besides, DTMI is capable of distinctive sensing sulfite with a fast response and a significant Stokes shift as well as with high sensitivity, excellent selectivity, long-term stability of fluorescence signals, and good anti-interference ability. The detection limit (LOD) of DTMI for sulfite within the linear concentration range of 0.5-10 µM is 27.39 nM. More importantly, DTMI has been favorably utilized for detecting sulfite in food samples such as red wine and vermicelli. Based on its low biotoxicity, DTMI has been successfully applied in imaging experiments involving HeLa cells, onion inner epidermal cells, and zebrafish. Therefore, the results show that the presented probe possesses potential sensing activity towards sulfite in complex biological system and food samples.

Using geotagged facial expressions to visualize and characterize different demographic groups' emotion in theme parks.

Tourism is an emotional sphere, and researchers focus on emotions to optimize tourism experiences. Tourism studies on emotions mostly ignore differences in emotions across demographic tourist groups by gender and age, thus limiting the understanding of emotions to the explicit characteristics of tourists' emotions. On the basis of geotagged facial expressions on social media platforms, this study aims to visualize the emotions of groups in scenic spots and then reveal the variations between groups' emotions within theme parks. By employing a facial recognition algorithm, an emotion distribution graph was proposed to represent groups' emotions in detail. Some analytical methods were combined to characterize of the emotion distribution of each group. Through a comprehensive comparison, the results suggest that there are unique characteristics of emotion distribution for each group and considerable variations between them. This study helps researchers achieve a deeper understanding of tourists' emotional differences and enhances the theorization of emotions. This research also highlights the advantages and significant practical implications of our method framework.

Analysis of Immune and Prognostic-Related lncRNA PRKCQ-AS1 for Predicting Prognosis and Regulating Effect in Sepsis.

Background: Sepsis was a high mortality and great harm systemic inflammatory response syndrome caused by infection. lncRNAs were potential prognostic marker and therapeutic target. Therefore, we expect to screen and analyze lncRNAs with potential prognostic markers in sepsis. Methods: Transcriptome sequencing and limma was used to screen dysregulated RNAs. Key RNAs were screened by correlation analysis, lncRNA-mRNA co-expression and weighted gene co-expression network analysis. Immune infiltration, gene set enrichment analysis and gene set variation analysis were used to analyze the immune correlation. Kaplan-Meier curve, receiver operator characteristic curve, Cox regression analysis and nomogram were used to analyze the correlation between key RNAs and prognosis. Sepsis model was established by lipopolysaccharide-induced HUVECs injury, and then cell viability and migration ability were detected by cell counting kit-8 and wound healing assay. The levels of apoptosis-related proteins and inflammatory cytokines were detected by RT-qPCR and Western blot. Reactive Oxygen Species and superoxide dismutase were detected by commercial kit. Results: Fourteen key differentially expressed lncRNAs and 663 key differentially expressed genes were obtained. And these lncRNAs were closely related to immune cells, especially T cell activation, immune response and inflammation. Subsequently, Subsequently, lncRNA PRKCQ-AS1 was identified as the regulator for further investigation in sepsis. RT-qPCR results showed that PRKCQ-AS1 expression was up-regulated in clinical samples and sepsis model cells, which was an independent prognostic factor in sepsis patients. Immune correlation analysis showed that PRKCQ-AS1 was involved in the immune response and inflammatory process of sepsis. Cell function tests confirmed that PRKCQ-AS1 could inhibit sepsis model cells viability and promote cell apoptosis, inflammatory damage and oxidative stress. Conclusion: We constructed immune-related lncRNA-mRNA regulatory networks in the progression of sepsis and confirmed that PRKCQ-AS1 is an important prognostic factor affecting the progression of sepsis and is involved in immune response.

Evaluation of hemostatic, anti-inflammatory, wound healing, skin irritation and allergy, and antimicrobial properties of active fraction from the ethanol extract of Chromolaena odorata (L.) R.M. King & H. Rob.

ETHNOPHARMACOLOGICAL RELEVANCE: Chromolaenaodorata (L.) R.M. King & H. Rob, a perennial herb, has been traditionally utilized as a herbal remedy for treating leech bites, soft tissue wounds, burn wounds, skin infections, and dento-alveolitis in tropical and subtropical regions. AIM OF THE STUDY: The present study was to analyze the active fraction of C. odorata ethanol extract and investigate its hemostatic, anti-inflammatory, wound healing, and antimicrobial properties. Additionally, the safety of the active fraction as an external preparation was assessed through skin irritation and allergy tests. MATERIALS AND METHODS: The leaves and stems of C. odorata were initially extracted with ethanol, followed by purification through AB-8 macroporous adsorption resin column chromatography to yield different fractions. These fractions were then screened for hemostatic activity in mice and rabbits to identify the active fraction. Subsequently, the hemostatic effect of the active fraction was assessed through the bleeding time of the rabbit ear artery in vivo and the coagulant time of rabbit blood in vitro. The anti-inflammatory activity of the active fraction was tested on mice ear edema induced by xylene and rat paw edema induced by carrageenin. Furthermore, the active fraction's promotion effect on wound healing was evaluated using a rat skin injury model, and skin safety tests were conducted on rabbits and guinea pigs. Lastly, antimicrobial activities against two Gram-positive bacteria (G+, Staphylococcus aureus and S. epidermidis) and three Gram-negative bacteria (G-, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were determined using the plate dilution method. RESULTS: The ethanol extract of C. odorata leaves and stems was fractionated into 30%, 60%, and 90% ethanol eluate fractions. These fractions demonstrated hemostatic activity, with the 30% ethanol eluate fraction (30% EEF) showing the strongest effect, significantly reducing bleeding time (P < 0.05). A concentration of 1.0 g/mL of the 30% EEF accelerated cutaneous wound healing in rats on the 3rd, 6th, and 9th day post-operation, with the healing effect increasing over time. No irritation or allergy reactions were observed in rabbits and guinea pigs exposed to the 30% EEF. Additionally, the 30% EEF exhibited mild inhibitory effect on mice ear and rat paw edema, as well as antimicrobial activity against tested bacteria, with varying minimal inhibitory concentration (MIC) values. CONCLUSIONS: The 30% EEF demonstrated a clear hemostatic effect on rabbit bleeding time, a slight inhibitory effect on mice ear edema and rat paw edema, significant wound healing activity in rats, and no observed irritation or allergic reactions. Antibacterial activity was observed against certain clinically isolated bacteria, particularly the G- bacteria. This study lays the groundwork for the potential development and application of C. odorata in wound treatment.

Global hotspots and trends in gut mycological research: a visual analytics and bibliometric approach.

Background: Recent findings highlight the significant impact of intestinal fungi on the complex makeup of the gut microbiota and human health, challenging past oversights. However, a lack of thorough systematic and quantitative analyses remains. This study aims to address this gap by thoroughly examining the current research on gut fungi. Through analyzing developments and unique features in this area, our goal is to foster a deeper understanding and identify future research pathways. Methods: We performed an extensive bibliometric analysis on documents from 2000 to 2023, sourced from the Web of Science Core Collection (WoSCC). Utilizing advanced visualization tools such as VOSviewer, CiteSpace, and Bibliometrix R, we meticulously examined and illustrated the data in scientific landscapes and networks. Results: A total of 1434 papers were analyzed, revealing a substantial increase in publication volume over the past two decades, particularly in 2020. Contributions came from 67 countries, 2178 institutions, and 8,479 authors. China led in publication output with 468 articles, followed by the University of California with 84 articles, and ZHANG F as the most prolific author with 17 articles. Emerging research areas such as "Fungal-Bacteria Interactions," "Gut Fungus and Gut-Brain Axis," and "Gut Fungus and Immunity" are expected to attract growing interest in the future. Conclusion: This extensive bibliometric analysis offers a current overview of scholarly efforts concerning intestinal fungi, highlighting the predominant landscape in this field. These insights can assist scholars in identifying appropriate publication avenues, forming collaborative relationships, and enhancing understanding of key themes and emerging areas, thereby stimulating future research endeavors.

Ablation of porcine subcutaneous fat and porcine aorta tissues by a burst-mode nanosecond-pulsed laser at 355 nm.

High-energy laser pulses used in laser angioplasty are challenging the laser cost, delivery system damage, efficiency, and laser catheter operating time. 355 nm nanosecond-pulsed laser in burst mode has shown potentials in reducing the system complexity and selective ablation of tissues. In this paper, burst mode laser ablation of porcine subcutaneous fat and porcine aorta is investigated. A histopathological analysis demonstrates that porcine subcutaneous fat can be ablated at a rate of greater than 0.2 mm/s when the number of pulses per burst is 1500 (corresponding to a fluence of 0.12 mJ/mm2 per pulse and 180 mJ/mm2 per burst), and the temperature of tissue during lasing is lower than 45°C. The porcine aorta remains nearly unaffected at the same laser parameter, and the tissue temperature during lasing is lower than 35°C. It shows the feasibility of using a burst-mode laser for selective ablation of tissue.