Semantic regularization of electromagnetic inverse problems.

Solving ill-posed inverse problems typically requires regularization based on prior knowledge. To date, only prior knowledge that is formulated mathematically (e.g., sparsity of the unknown) or implicitly learned from quantitative data can be used for regularization. Thereby, semantically formulated prior knowledge derived from human reasoning and recognition is excluded. Here, we introduce and demonstrate the concept of semantic regularization based on a pre-trained large language model to overcome this vexing limitation. We study the approach, first, numerically in a prototypical 2D inverse scattering problem, and, second, experimentally in 3D and 4D compressive microwave imaging problems based on programmable metasurfaces. We highlight that semantic regularization enables new forms of highly-sought privacy protection for applications like smart homes, touchless human-machine interaction and security screening: selected subjects in the scene can be concealed, or their actions and postures can be altered in the reconstruction by manipulating the semantic prior with suitable language-based control commands.

Trends and focal points in pelvic floor reconstruction for pelvic organ prolapse: A bibliometric analysis.

OBJECTIVE: This study aims to investigate the current research trends and focal points in the field of pelvic floor reconstruction for the management of pelvic organ prolapse (POP). METHODS: To achieve this objective, a bibliometric analysis was conducted on relevant literature using the Citespace database. The analysis led to the creation of a knowledge map, offering a comprehensive overview of scientific advancements in this research area. RESULTS: The study included a total of 607 publications, revealing a consistent increase in articles addressing pelvic floor reconstruction for POP treatment. Most articles originated from the United States (317 articles), followed by Chinese scholars (40 articles). However, it is important to note that the overall number of articles remains relatively low. The organization with the highest publication frequency was the Cleveland Clinic in Ohio, where Matthew D. Barber leads the academic group. Barber himself has the highest number of published articles (18 articles), followed by Zhu Lan, a Chinese scholar (10 articles). Key topics with high frequency and mediated centrality include stress urinary incontinence, quality of life, impact, and age. The journal with the largest number of papers from both domestic and international researchers is INT UROGYNECOL J. The study's hotspots mainly focus on the impact of pelvic floor reconstruction on the treatment and quality of life of POP patients. The United States leads in this field, but there is a lack of cooperation between countries, institutions, and authors. Moving forward, cross-institutional, cross-national, and cross-disciplinary exchanges and cooperation should be strengthened to further advance the field of pelvic floor reconstructive surgery for POP research.

Clinical Outcomes and Prognostic Factors in Stage III C Cervical Cancer Patients Treated with Radical Radiotherapy or Radiochemotherapy.

Objective: Since the update of the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging criteria, there have been few reports on the prognosis of stage III C cervical cancer. Moreover, some studies have drawn controversial conclusions, necessitating further verification. This study aims to evaluate the clinical outcomes and determine the prognostic factors for stage III C cervical cancer patients treated with radical radiotherapy or radiochemotherapy. Methods: The data of 117 stage III C cervical cancer patients (98 III C1 and 19 III C2) who underwent radical radiotherapy or radiochemotherapy were retrospectively analyzed. We evaluated 3-year overall survival (OS) and disease-free survival (DFS) using the Kaplan-Meier method. Prognostic factors were analyzed using the Log-rank test and Cox proportional hazard regression model. The risk of para-aortic lymph node metastasis (LNM) in all patients was assessed through Chi-squared test and logistic regression analysis. Results: For stage III C1 and III C2 patients, the 3-year OS rates were 77.6% and 63.2% (P = .042), and the 3-year DFS rates were 70.4% and 47.4% (P = .003), respectively. The pretreatment location of pelvic LNM, histological type, and FIGO stage was associated with OS (P = .033, .003, .042, respectively); the number of pelvic LNM and FIGO stage were associated with DFS (P = .015, .003, respectively). The histological type was an independent prognostic indicator for OS, and the numbers of pelvic LNM and FIGO stage were independent prognostic indicators for DFS. Furthermore, a pelvic LNM largest short-axis diameter ≥ 1.5 cm and the presence of common iliac LNM were identified as high-risk factors influencing para-aortic LNM in stage III C patients (P = .046, .006, respectively). Conclusions: The results of this study validated the 2018 FIGO staging criteria for stage III C cervical cancer patients undergoing concurrent chemoradiotherapy. These findings may enhance our understanding of the updated staging criteria and contribute to better management of patients in stage III C.

Palladium-Catalyzed Arylation of C(sp2)-H Bonds and C(sp3)-H Bonds with 4-Amino-benzotriazole as the Bidentate Directing Group.

The arylation of C(sp2)-H and C(sp3)-H bonds in carboxylic acids catalyzed by Pd(II) with 4-aminobentriazole as the directing group was investigated. In addition to activation of the C(sp2)-H bond, selective arylation of alkyl carboxylic acids and amino acids in the ß position can also be achieved. This strategy involved a 5,5-bicyclic Pd intermediate complex whose structure was determined by X-ray single crystal diffraction analysis. Importantly, the DG (directing group) can be easily removed under mild conditions.

Characterization of an agmatine N-acetyltransferase from Bactrocera dorsalis that modulates ovary development.

Agmatine N-acetyltransferase (AgmNAT), which catalyzes the formation of N-acetylagmatine from acetyl-CoA and agmatine, is a member of the GCN5-related N-acetyltransferase family. So far, knowledge of the physiological roles of AgmNAT in insects is limited. Here, we identified one gene encoding protein homologous to that of Drosophila AgmNAT using sequence information from an activity-verified Drosophila AgmNAT in a BLAST search of the Bactrocera dorsalis genome. We expressed and purified B. dorsalis AgmNAT in Escherichia coli and used the purified enzyme to define the substrate specificity for acyl-CoA and amine substrates. Our application of the screening strategy to BdorAgmNAT led to the identification of agmatine as the best amine substrate for this enzyme, with the highest kcat/Km value. We successfully obtained a BdorAgmNAT knockout strain based on a wild-type strain (WT) using the CRISPR/Cas9 technique. The ovary development of the BdorAgmNAT knockout mutants was delayed for 10 days compared with the WT specimens. Moreover, mutants had a much smaller mature ovary size and laid far fewer eggs than WT. Loss of function of BdorAgmNAT caused by RNAi with mature WT females did not affect their fecundity. These findings indicate that BdorAgmNAT is critical for oogenesis. Our data provide the first evidence for AgmNAT in regulating ovary development.

[Preparation technology comparison and performance evaluation of different protein A affinity chromatographic materials].

Protein A affinity chromatographic materials are widely used in clinical medicine and biomedicine because of their specific interactions with immunoglobulin G (IgG). Both the characteristics of the matrix, such as its structure and morphology, and the surface modification method contribute to the affinity properties of the packing materials. The specific, orderly, and oriented immobilization of protein A can reduce its steric hindrance with the matrix and preserve its bioactive sites. In this study, four types of affinity chromatographic materials were obtained using agarose and polyglycidyl methacrylate (PGMA) spheres as substrates, and multifunctional epoxy and maleimide groups were used to fix protein A. The effects of the ethylenediamine concentration, reaction pH, buffer concentration, and other conditions on the coupling efficiency of protein A and adsorption performance of IgG were evaluated. Multifunctional epoxy materials were prepared by converting part of the epoxy groups of the agarose and PGMA matrices into amino groups using 0.2 and 1.6 mol/L ethylenediamine, respectively. Protein A was coupled to the multifunctional epoxy materials using 5 mmol/L borate buffer (pH 8) as the reaction solution. When protein A was immobilized on the substrates by maleimide groups, the agarose and PGMA substrates were activated with 25% (v/v) ethylenediamine for 16 h to convert all epoxy groups into amino groups. The maleimide materials were then converted into amino-modified materials by adding 3 mg/mL 3-maleimidobenzoyl-N-hydroxysuccinimide ester (MBS) dissolved in dimethyl sulfoxide (DMSO) and then suspended in 5 mmol/L borate buffer (pH 8). The maleimide groups reacted specifically with the C-terminal of the sulfhydryl group of recombinant protein A to achieve highly selective fixation on both the agarose and PGMA substrates. The adsorption performance of the affinity materials for IgG was improved by optimizing the bonding conditions of protein A, such as the matrix type, matrix particle size, and protein A content, and the adsorption properties of each affinity material for IgG were determined. The column pressure of the protein A affinity materials prepared using agarose or PGMA as the matrix via the maleimide method was subsequently evaluated at different flow rates. The affinity materials prepared with PGMA as the matrix exhibited superior mechanical strength compared with the materials prepared with agarose. Moreover, an excellent linear relationship between the flow rate and column pressure of 80 mL/min was observed for this affinity material. Subsequently, the effect of the particle size of the PGMA matrix on the binding capacity of IgG was investigated. Under the same protein A content, the dynamic binding capacity of the affinity materials on the PGMA matrix was higher when the particle size was 44-88 µm than when other particle sizes were used. The properties of the affinity materials prepared using the multifunctional epoxy and maleimide-modified materials were compared by synthesizing affinity materials with different protein A coupling amounts of 1, 2, 4, 6, 8, and 10 mg/mL. The dynamic and static binding capacities of each material for bovine IgG were then determined. The prepared affinity material was packed into a chromatographic column to purify IgG from bovine colostrum. Although all materials showed specific adsorption selectivity for IgG, the affinity material prepared by immobilizing protein A on the PGMA matrix with maleimide showed significantly better performance and achieved a higher dynamic binding capacity at a lower protein grafting amount. When the protein grafting amount was 15.71 mg/mL, the dynamic binding capacity of bovine IgG was 32.23 mg/mL, and the dynamic binding capacity of human IgG reached 54.41 mg/mL. After 160 cycles of alkali treatment, the dynamic binding capacity of the material reached 94.6% of the initial value, indicating its good stability. The developed method is appropriate for the production of protein A affinity chromatographic materials and shows great potential in the fields of protein immobilization and immunoadsorption material synthesis.

Selenium nanoparticles stabilized by Sargassum fusiforme polysaccharides: Synthesis, characterization and bioactivity.

Selenium nanoparticles (SeNPs) are a potential tumor therapeutic drug and have attracted widespread attention due to their high bioavailability and significant anticancer activity. However, the poor water solubility and degradability of selenium nanoparticles severely limit their application. In this study, spherical selenium nanoparticles with a particle size of approximately 50 nm were prepared by using Sargassum fusiforme polysaccharide (SFPS) as a modifier and Tween-80 as a stabilizer. The results of in vitro experiments showed that Sargassum fusiforme polysaccharide-Tween-80-Selenium nanoparticles (SFPS-Tw-SeNPs) had a significant inhibitory effect on A549 cells, with an IC50 value of 6.14 µg/mL, and showed antitumor cell migration and invasion ability against A549 cells in scratch assays and cell migration and invasion assays (transwell assays). Western blot experiments showed that SFPS-Tw-SeNPs could inhibit the expression of tumor migration- and invasion-related proteins. These results suggest that SFPS-Tw-SeNPs may be potential tumor therapeutic agents, especially for the treatment of human lung cancer.

Coexisting RET/PTC and TERT Promoter Mutation Predict Poor Prognosis but Effective RET and MEK Targets in Thyroid Cancer.

PURPOSE: To investigate the role of coexisting RET/PTC rearrangement and TERT promoter mutation in the prognosis and therapeutic targeting in papillary thyroid cancer (PTC). METHODS: A total of 669 PTC patients with complete clinical follow-up and genetic data were pooled from thyroid cancer datasets TCGA, MSK MetTropism, and MSK-IMPACT, from whom 163 patients (112 women and 47 men, 4 unknown) with wild-type BRAF/RAS were identified, with median age (IQR) of 46.00 (33.00, 61.00) years and median follow-up time (IQR) of 16.13 (8.09, 27.91) months for comparative genotype cohort analysis of mortality. RESULTS: There was a significant concurrence index between RET/PTC and TERT promoter mutations, being 2.040 (95% CI 1.110-3.747, P = 0.023). Mortality occurred in 5/100 (5%) patients harboring neither mutation, 2/18 (11.1%) patients harboring TERT promoter mutation alone, 0/31 (0%) patients harboring RET/PTC alone, and 7/14 (50%) patients harboring both genetic alterations, corresponding to HRs (95% CI) of 1 (Reference), 2.469 (0.405-14.02), 3.296e-09 (0-inf), and 9.019 (2.635-30.87), respectively, which remained essentially unchanged after adjustment for patient race, sex, and age. Similar results were observed with BRAF/RAS and TERT promoter mutations. Mechanistically, RET/PTC used the MAP kinase pathway to upregulate the mutated TERT, but not the wild-type TERT, and, correspondingly, targeting RET and MEK could suppress mutated TERT but not the wild-type TERT. CONCLUSION: Coexisting RET/PTC and TERT promoter mutation identify PTC as a unique clinical entity with high mortality, providing new implications for genetic-based prognostication and potential therapeutic targeting of RET and MEK guided by RET/PTC and TERT status.

Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition.

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher's gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher's skeleton and then inputs the recognized sequence of the teacher's skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.

Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric.

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.

RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria.

Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.

Functional diversity of apoptotic vesicle subpopulations from bone marrow mesenchymal stem cells in tissue regeneration.

Apoptosis releases numerous apoptotic vesicles that regulate processes such as cell proliferation, immunity, and tissue regeneration and repair. Now, it has also emerged as an attractive candidate for biotherapeutics. However, apoptotic vesicles encompass a diverse range of subtypes, and it remains unclear which specific subtypes play a pivotal role. In this study, we successfully isolated different apoptotic vesicle subtypes based on their sizes and characterized them using NTA and TEM techniques, respectively. We compared the functional variances among the distinct subtypes of apoptotic vesicles in terms of stem cell proliferation, migration, and differentiation, as well as for endothelial cell and macrophage function, effectively identifying subtypes that exhibit discernible functional differences. ApoSEV (with diameter <1000 nm) promoted stem cell proliferation, migration, and multi-potent differentiation, and accelerated skin wound healing of diabetes mouse model, while apoBD (with diameter >1000 nm) played the opposite effect on cell function and tissue regeneration. Lastly, employing protein analysis and gene sequencing techniques, we elucidated the intrinsic mechanisms underlying these differences between different subtypes of apoEVs. Collectively, this study identified that apoptotic vesicle subtypes possessed distinct bio-functions in regulating stem cell function and behaviour and modulating tissue regeneration, which primarily attribute to the distinct profiling of protein and mRNA in different subtypes. This comprehensive analysis of specific subtypes of apoEVs would provide novel insights for potential therapeutic applications in cell biology and tissue regeneration.

Developmental stage and infection status may affect drug distribution in the prostate of rats.

Prostate inflammation is often treated with drugs which are ineffective. Antibacterial agents fail to reach the prostate epithelium, and the blood-prostate barrier (BPB) may affect the drug transport process. Factors affecting drug efficacy remain unclear.Rats were categorised into groups A and B, corresponding to adulthood and puberty, respectively. Group C included the model of chronic prostate infection. Dialysates of levofloxacin and cefradine were collected from the prostate gland and jugular vein and evaluated. Pharmacokinetic analysis was conducted.The free concentrations of antimicrobials in the prostate and plasma samples of all groups peaked at 20 min, then gradually decreased. The mean AUC0-tprostate/AUC0-tplasma ratio in the levofloxacin group were 0.86, 0.53, and 0.95, and the mean values of AUC0-∞prostate/AUC0-∞plasma ratio were 0.85, 0.63, and 0.97. The corresponding values in the cefradine group were 0.67, 0.30 and 0.84, and 0.66, 0.31, and 0.85, respectively. The mean values in group B were lower than those in group A, and those in group C were higher than those in group B.The maturity of the prostate may affect the ability of the drug to cross the BPB. Infection may disrupt the BPB, affecting drug permeability.

[Effect of Emotion Regulation on Anxiety/Depression Symptoms in Children With Attention-Deficit/Hyperactivity Disorder: The Mediating Role of Social Problems and the Moderating Role of Family Functioning]. / æƒ ç»ªè°ƒèŠ‚ä¸Žæ³¨æ„ç¼ºé™·å¤šåŠ¨éšœç¢æ‚£å„¿ç„¦è™‘/抑郁症状：社交问题的中介效应和家庭功能的调节.

Objective: To investigate the mediating effect of social problems in the effect pathway of emotional dysregulation influencing anxiety/depression emotions in children with attention-deficit/hyperactivity disorder (ADHD) and to explore the potential moderating effect of family functionality. Methods: A total of 235 children diagnosed with ADHD were enrolled in the study. The paticipants' age ranged from 6 to 12. Emotion Regulation Checklist, Achenbach's Child Behavior Checklist (CBCL) Social Problems Subscale, CBCL Anxious/Depressed Subscale, and Family Assessment Device were used to evaluate the emotional regulation, social problems, anxiety/depression emotions, and family functionality of the participants. A moderated mediation model was employed to analyze whether social problems and family functionality mediate and moderate the relationship between emotional regulation and anxiety/depression emotions. Results: Social problems partially mediated the impact of emotional dysregulation on anxiety/depression emotions in ADHD children, with the direct effect being 0.26 (95% confidence interval [CI]: [0.17, 0.36], P<0.001), the indirect effect being 0.13 (95% CI: [0.07, 0.19], P<0.001), and the mediating effect accounting for 33% of the total effect. Family functionality exhibited a positive moderating effect on the relationship between social problems and anxiety/depression emotions. Conclusion: This study contributes to the understanding of complex factors influencing anxiety/depression in children with ADHD, providing reference for the further development of targeted interventions for children with ADHD and the improvement of prognosis.

[Clinical Analysis of Mitoxantrone Liposome in the Treatment of Children with High-Risk Acute Myeloid Leukemia].

OBJECTIVE: To investigate the safety and efficacy of mitoxantrone liposome in the treatment of children with high-risk acute myeloid leukemia (AML). METHODS: The children with high-risk AML who received the mitoxantrone liposome regimen at Wuhan Children's Hospital from January 2022 to February 2023 were collected as the observation group, and the children with high-risk AML who received idarubicin regimen were enrolled as controls, and their clinical data were analyzed. Time to bone marrow recovery, the complete remission rate of bone marrow cytology, the clearance rate of minimal residual disease, and treatment-related adverse reactions were compared between the two groups. RESULTS: The patients treated with mitoxantrone liposome showed shorter time to recovery of leukocytes(17 vs 21 day), granulocytes(18 vs 24 day), platelets(17 vs 24 day), and hemoglobin(20 vs 26 day) compared with those treated with idarubicin, there were statistical differences (P <0.05). The effective rate and MRD turning negative rate in the observation group were 90.9% and 72.7%, respectively, while those in the control group were 94.1% and 76.4%, with no statistical difference (P >0.05). The overall response rate of the two groups of patients was similar. CONCLUSION: The efficacy of mitoxantrone liposome is not inferior to that of idarubicin in children with high-risk AML, but mitoxantrone liposome allows a significantly shorter duration of bone marrow suppression and the safety is better.

Distribution patterns and co-occurrence network of eukaryotic algae in different salinity waters of Yuncheng Salt Lake, China.

The community structure and co-occurrence pattern of eukaryotic algae in Yuncheng Salt Lake were analyzed based on marker gene analysis of the 18S rRNA V4 region to understand the species composition and their synergistic adaptations to the environmental factors in different salinity waters. The results showed indicated that the overall algal composition of Yuncheng Salt Lake showed a Chlorophyta-Pyrrophyta-Bacillariophyta type structure. Chlorophyta showed an absolute advantage in all salinity waters. In addition, Cryptophyta dominated in the least saline waters; Pyrrophyta and Bacillariophyta were the dominant phyla in the waters with salinity ranging from 13.2 to 18%. Picochlorum, Nannochloris, Ulva, and Tetraselmis of Chlorophyta, Biecheleria and Oxyrrhis of Pyrrophyta, Halamphora, Psammothidium, and Navicula of Bacillariophyta, Guillardia and Rhodomonas of Cryptophyta were not observed in previous surveys of the Yuncheng Salt Lake, suggesting that the algae are undergoing a constant turnover as the water environment of the Salt Lake continues to change. The network diagram demonstrated that the algae were strongly influenced by salinity, NO3-, and pH, changes in these environmental factors would lead to changes in the algal community structure, thus affecting the stability of the network structure.

Label-Free Light Scattering Imaging with Purified Brownian Motion Differentiates Small Extracellular Vesicles in Cell Microenvironments.

Small extracellular vesicles (sEVs) are heterogeneous biological nanoparticles (NPs) with wide biomedicine applications. Tracking individual nanoscale sEVs can reveal information that conventional microscopic methods may lack, especially in cellular microenvironments. This usually requires biolabeling to identify single sEVs. Here, we developed a light scattering imaging method based on dark-field technology for label-free nanoparticle diffusion analysis (NDA). Compared with nanoparticle tracking analysis (NTA), our method was shown to determine the diffusion probabilities of a single NP. It was demonstrated that accurate size determination of NPs of 41 and 120 nm in diameter is achieved by purified Brownian motion (pBM), without or within the cell microenvironments. Our pBM method was also shown to obtain a consistent size estimation of the normal and cancerous plasma-derived sEVs without and within cell microenvironments, while cancerous plasma-derived sEVs are statistically smaller than normal ones. Moreover, we showed that the velocity and diffusion coefficient are key parameters for determining the diffusion types of the NPs and sEVs in a cancerous cell microenvironment. Our light scattering-based NDA and pBM methods can be used for size determination of NPs, even in cell microenvironments, and also provide a tool that may be used to analyze sEVs for many biomedical applications.

Bioinspired Touch-Responsive Hydrogels for On-Demand Adhesion on Rough Surfaces.

Reversible adhesives are widely needed in our daily lives and industrial applications. However, robust and switchable adhesion on rough surfaces with on-demand attachment and detachment remains highly challenging. Here, we report a snail-mucus-inspired touch-responsive hydrogel (TRH), whose universal and robust adhesion is triggered by simple contact with the attaching surface. TRH is composed of a polymeric hydrogel and saturated sodium acetate (NaAc) and is prepared by one-pot synthesis. At room temperature, TRH remains in an amorphous and soft state, which allows it to conformally adapt to rough surfaces. The contact with the target surface triggers the crystallization of NaAc, which increases the modulus of TRH by an order of magnitude and interlocks with the target surfaces, achieving an adhesion of up to 204.84 ± 53.98 kPa. Upon heating, TRH returns to a soft state, facilitating easy detachment with adhesion of 5.12 ± 1.34 kPa. Meanwhile, the detached TRH is ready for the next adhesion without the need to be maintained at high temperature. TRH finds applications as a smart material for light-triggered adhesion switching, information encryption, and temperature sensors.

Machine Learning-Based Characterization and Identification of Tertiary Lymphoid Structures Using Spatial Transcriptomics Data.

Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells in non-lymphoid tissues and are associated with a favorable prognosis in tumors. However, TLS markers remain inconsistent, and the utilization of machine learning techniques for this purpose is limited. To tackle this challenge, we began by identifying TLS markers through bioinformatics analysis and machine learning techniques. Subsequently, we leveraged spatial transcriptomic data from Gene Expression Omnibus (GEO) and built two support vector classifier models for TLS prediction: one without feature selection and the other using the marker genes. The comparable performances of these two models confirm the efficacy of the selected markers. The majority of the markers are immunoglobulin genes, demonstrating their importance in the identification of TLSs. Our research has identified the markers of TLSs using machine learning methods and constructed a model to predict TLS location, contributing to the detection of TLS and holding the promising potential to impact cancer treatment strategies.