Transfer of visual perceptual learning over a task-irrelevant feature through feature-invariant representations: Behavioral experiments and model simulations.

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a "task-relevant" feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a "task-irrelevant" feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a "task-irrelevant" feature.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study.

BACKGROUND: Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear. OBJECTIVE: This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare. METHODS: Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions. RESULTS: Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions. CONCLUSIONS: Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

A mitochondria-specific NIR fluorescence probe for dual-detection of sulfur dioxide and viscosity in living cells and mice.

The level of sulfur dioxide (SO2) and viscosity in mitochondria play vital roles in various physiological and pathological processes. Abnormalities in mitochondrial SO2 and viscosity are closely associated with numerous biological diseases. It is of great significance to develop novel fluorescence probes for simultaneous detection of SO2 and viscosity within mitochondria. Herein, we have developed a water-soluble, mitochondrial-targeted and near-infrared fluorescent probe, CMBT, for the simultaneous detection of SO2 and viscosity. The probe CMBT incorporates benzothiazolium salt as a mitochondrial targeting moiety and 7-diethylaminocoumarin as a rotor for viscosity detection, respectively. Based on the prompt reaction between nucleophilic HSO3-/SO32- and the backbone of the benzothiazolium salt derivative, probe CMBT displayed high sensitivity and selectivity toward SO2 with a limit of detection as low as 0.17 µM. As viscosity increased, the twisted intramolecular charge transfer (TICT) process was restricted, resulting in fluorescence emission enhancement at 690 nm. Moreover, probe CMBT demonstrated exceptional mitochondrial targeting ability and was successfully employed to image variations of SO2 and viscosity in living cells and mice. The work highlights the great potential of the probe as a convenient tool for revealing the relationship between SO2 and viscosity in biological systems.

Identification of microbial communities and multi-species biofilms contamination in seafood processing environments with different hygiene conditions.

Biofilms formed by spoilage and pathogenic bacteria increase microbial persistence, causing an adverse influence on the quality of seafood. The mono-species biofilms are widely reported, however, the contamination of multi-species biofilms and their matrix in food environments are still not fully understood. Here, we assessed the contamination of multi-species biofilms in three seafood processing environments with different hygiene levels by detecting bacterial number and three biofilm matrix components (carbohydrates, extracellular DNA (eDNA), and proteins). Samples comprising seven food matrix surfaces and eight food processing equipment surfaces were collected from two seafood processing plants (XY and XC) and one seafood market (CC). The results showed that the bacterial counts ranged from 1.89 to 4.91 CFU/cm2 and 5.68 to 9.15 BCE/cm2 in these surfaces by cultivation and real-time PCR, respectively. Six biofilm hotspots were identified, including four in CC and two in XY. Among the three processing environments, the amplicon sequence variants (ASVs) of Proteobacteria, Bacteroidetes, and Actinobacteria decreased with improved processing hygiene, while Firmicutes showed a decrease in the four most abundant phyla. The most prevalent bacteria belonged to genera Psychrobacter, Acinetobacter, and Pseudomonas, demonstrating the significant differences and alteration in bacterial community composition during different environments. From the biofilm hotspots, 15 isolates with strong biofilm forming ability were identified, including 7 Pseudomonas, 7 Acinetobacter, and 1 Psychrobacter. The Pseudomonas isolates exhibited the highest production of EPS components and three strong motilities, whose characteristics were positively correlated. Thus, this study verified the presence of multi-species biofilms in seafood processing environments, offering preliminary insights into the diversity of microbial communities during processing. It highlights potential contamination sources and emphasizes the importance of understanding biofilms composition to control biofilms formation in seafood processing environments.

Improving iconic memory through contrast detection training with HOA-corrected vision.

Iconic memory and short-term memory are not only crucial for perception and cognition, but also of great importance to mental health. Here, we first showed that both types of memory could be improved by improving limiting processes in visual processing through perceptual learning. Normal adults were trained in a contrast detection task for ten days, with their higher-order aberrations (HOA) corrected in real-time. We found that the training improved not only their contrast sensitivity function (CSF), but also their iconic memory and baseline information maintenance for short-term memory, and the relationship between memory and CSF improvements could be well-predicted by an observer model. These results suggest that training the limiting component of a cognitive task with visual perceptual learning could improve visual cognition. They may also provide an empirical foundation for new therapies to treat people with poor sensory memory.

Gestational Exposure to PM2.5 and Specific Constituents, Meconium Metabolites, and Neonatal Neurobehavioral Development: A Cohort Study.

Exposure to fine particulate matter (PM2.5) during pregnancy has been inversely associated with neonatal neurological development. However, the associations of exposure to specific PM2.5 constituents with neonatal neurological development remain unclear. We investigated these associations and examined the mediating role of meconium metabolites in a Chinese birth cohort consisting of 294 mother-infant pairs. Our results revealed that exposure to PM2.5 and its specific constituents (i.e., organic matter, black carbon, sulfate, nitrate, and ammonium) in the second trimester, but not in the first or third trimester, was inversely associated with the total neonatal behavioral neurological assessment (NBNA) scores. The PM2.5 constituent mixture in the second trimester was also inversely associated with NBNA scores, and sulfate was identified as the largest contributor. Furthermore, meconium metabolome analysis identified four metabolites, namely, threonine, lysine, leucine, and saccharopine, that were associated with both PM2.5 constituents and NBNA scores. Threonine was identified as an important mediator, accounting for a considerable proportion (14.53-15.33%) of the observed inverse associations. Our findings suggest that maternal exposure to PM2.5 and specific constituents may adversely affect neonatal behavioral development, in which meconium metabolites may play a mediating role.

Programmable Colloids with Analogous Hypercoordination Complex Architectures.

Colloidal molecule clusters (CMCs) are promising building blocks with molecule-like symmetry, offering exceptional synergistic properties for applications in plasmonics and catalysis. Traditional CMC fabrication has been limited to simple molecule-like structures utilizing isotropic particles. Here, we employ molecular dynamics simulation to investigate the co-assembly of anisotropic nanorods (NRs) and the stimulus-responsive polymer (SRP) via reversible adsorption. The results of the simulation show that it is possible to fabricate hypercoordination complex structures with high symmetry from the co-assembly of NRs and the SRP, even in analogy to the Th(BH4)4 structure. The coordination number of these CMCs can be precisely programmed by adjusting the shape and size of the ends of the NRs and the SRP cohesion energy. Furthermore, a finite-difference time-domain simulation indicates these hypercoordination structures exhibit significantly enhanced optical activity and plasmonic coupling effects. These findings introduce a new design approach for complex molecule-like structures utilizing anisotropic nanoparticles and may expand the applications of CMCs in photonics.

Associations of cold-inducible RNA-binding protein with bacterial load, proinflammatory cytokines and mortality from pneumonia.

Cold-inducible RNA-binding protein (CIRP) is a damage-associated molecular pattern that plays a critical role in triggering inflammatory responses. It remains unknown whether CIRP is strongly associated with bacterial load, inflammatory response, and mortality in sepsis model. Pneumonia was induced in specific pathogen-free 8-9-week old male rats by injecting bacteria via puncture of the tracheal cartilage. The expressions of CIRP and proinflammatory cytokines [tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and IL-1ß] in lung tissues, alveolar macrophages (AMs), plasma, and bronchoalveolar lavage fluid (BALF) were determined by reverse transcription-polymerase chain reaction, western blotting, and enzyme-linked immunosorbent assay. The numbers of bacteria recovered from the lungs were correlated with the bacterial loads injected and mortality. The expressions of CIRP increased sharply as the bacterial loads increased in the lung tissues and AMs. The amounts of TNF-α, IL-6 and IL-1ß proteins synthesized were dependent on the bacterial load in the lung tissues. Releases of CIRP, TNF-α, IL-6, and IL-1ß increased with the bacterial load in the blood plasma. The proteins confirmed similar patterns in the BALF. CIRP was strongly associated with the releases of TNF-α, IL-6, and IL-1ß in the lung tissues, blood plasma, and BALF, and showed a close correlation with mortality. CIRP demonstrated a strong association with bacterial load, which is new evidence, and close correlations with proinflammatory cytokines and mortality of pneumonia in rats, suggesting that it might be an interesting pneumonic biomarker for monitoring host response and predicting mortality, and a promising target for immunotherapy.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Novel Quantitative Contrast Sensitivity Function Enhances the Prediction of Treatment Outcome and Recurrence in Amblyopia.

Purpose: Although effective amblyopia treatments are available, treatment outcome is unpredictable, and the condition recurs in up to 25% of the patients. We aimed to evaluate whether a large-scale quantitative contrast sensitivity function (CSF) data source, coupled with machine learning (ML) algorithms, can predict amblyopia treatment response and recurrence in individuals. Methods: Visual function measures from traditional chart vision acuity (VA) and novel CSF assessments were used as the main predictive variables in the models. Information from 58 potential predictors was extracted to predict treatment response and recurrence. Six ML methods were applied to construct models. The SHapley Additive exPlanations was used to explain the predictions. Results: A total of 2559 consecutive records of 643 patients with amblyopia were eligible for modeling. Combining variables from VA and CSF assessments gave the highest accuracy for treatment response prediction, with the area under the receiver operating characteristic curve (AUC) of 0.863 and 0.815 for outcome predictions after 3 and 6 months, respectively. Variables from the VA assessment alone predicted the treatment response, with AUC values of 0.723 and 0.675 after 3 and 6 months, respectively. Variables from the CSF assessment gave rise to an AUC of 0.909 for recurrence prediction compared to 0.539 for VA assessment alone, and adding VA variables did not improve predictive performance. The interocular differences in CSF features are significant contributors to recurrence risk. Conclusions: Our models showed CSF data could enhance treatment response prediction and accurately predict amblyopia recurrence, which has the potential to guide amblyopia management by enabling patient-tailored decision making.

The cardiovascular system at high altitude: A bibliometric and visualization analysis.

BACKGROUND: When exposed to high-altitude environments, the cardiovascular system undergoes various changes, the performance and mechanisms of which remain controversial. AIM: To summarize the latest research advancements and hot research points in the cardiovascular system at high altitude by conducting a bibliometric and visualization analysis. METHODS: The literature was systematically retrieved and filtered using the Web of Science Core Collection of Science Citation Index Expanded. A visualization analysis of the identified publications was conducted employing CiteSpace and VOSviewer. RESULTS: A total of 1674 publications were included in the study, with an observed annual increase in the number of publications spanning from 1990 to 2022. The United States of America emerged as the predominant contributor, while Universidad Peruana Cayetano Heredia stood out as the institution with the highest publication output. Notably, Jean-Paul Richalet demonstrated the highest productivity among researchers focusing on the cardiovascular system at high altitude. Furthermore, Peter Bärtsch emerged as the author with the highest number of cited articles. Keyword analysis identified hypoxia, exercise, acclimatization, acute and chronic mountain sickness, pulmonary hypertension, metabolism, and echocardiography as the primary research hot research points and emerging directions in the study of the cardiovascular system at high altitude. CONCLUSION: Over the past 32 years, research on the cardiovascular system in high-altitude regions has been steadily increasing. Future research in this field may focus on areas such as hypoxia adaptation, metabolism, and cardiopulmonary exercise. Strengthening interdisciplinary and multi-team collaborations will facilitate further exploration of the pathophysiological mechanisms underlying cardiovascular changes in high-altitude environments and provide a theoretical basis for standardized disease diagnosis and treatment.

Codelivery of CPT and siPHB1 with GSH/ROS Dual-Responsive Hybrid Nanoparticles Based on a [12]aneN3-Derived Lipid for Synergistic Lung Cancer Therapy.

The combination of small-interfering RNA (siRNA)-mediated gene silencing and chemotherapeutic agents for lung cancer treatment has attracted widespread attention in terms of a greater therapeutic effect, minimization of systemic toxicity, and inhibition of multiple drug resistance (MDR). In this work, three amphiphiles, CBN1-CBN3, were first designed and synthesized as a camptothecin (CPT) conjugate and gene condensation agents by the combination of CPT prodrugs and di(triazole-[12]aneN3) through the ROS-responsive phenylborate ester and different lengths of alkyl chains (with 6, 9, 12 carbon chains for CBN1-CBN3, respectively). CBN1-CBN3 were able to be self-assembled into liposomes with an average diameter in the range of 320-240 nm, showing the ability to effectively condense siRNA. Among them, CBN2, with a nine-carbon alkyl chain, displayed the best anticancer efficiency in A549 cells. In order to give nanomedicines a stealth property and PEGylation/dePEGylation transition, a GSH-responsive PEGylated TPE derivative containing a disulfide linkage (TSP) was further designed and prepared. A combination of CBN2/siRNA complexes and DOPE with TSP resulted in GSH/ROS dual-responsive lipid-polymer hybrid nanoparticles (CBN2-DP/siRNA NPs). In present GSH and H2O2, CBN2-DP/siRNA NPs were decomposed, resulting in the controlled release of CPT drug and siRNA. In vitro, CBN2-DP/siPHB1 NPs showed the best anticancer activity for suppression of about 75% of A549 cell proliferation in a serum medium. The stability of CBN2-DP/siRNA NPs was significantly prolonged in blood circulation, and they showed effective accumulation in the A549 tumor site through an enhanced permeability and retention (EPR) effect. In vivo, CBN2-DP/siPHB1 NPs demonstrated enhanced synergistic cancer therapy efficacy and tumor inhibition as high as 71.2%. This work provided a strategy for preparing lipid-polymer hybrid NPs with GSH/ROS dual-responsive properties and an intriguing method for lung cancer therapy.

Ultrasmall Single-Chain Nanoparticles Derived from Amphiphilic Alternating Copolymers.

The collapse or folding of an individual polymer chain into a nanoscale particle gives rise to single-chain nanoparticles (SCNPs), which share a soft nature with biological protein particles. The precise control of their properties, including morphology, internal structure, size, and deformability, are a long-standing and challenging pursuit. Herein, a new strategy based on amphiphilic alternating copolymers for producing SCNPs with ultrasmall size and uniform structure is presented. SCNPs are obtained by folding the designed alternating copolymer in N,N-dimethylformamide (DMF) and fixing it through a photocatalyzed cycloaddition reaction of anthracene units. Molecular dynamics simulation confirms the solvophilic outer corona and solvophobic inner core structure of SCNPs. Furthermore, by adjusting the length of PEG units, precise control over the mean size of SCNPs is achieved within the range of 2.8 to 3.9 nm. These findings highlight a new synthetic strategy that enables enhanced control over morphology and internal structure while achieving ultrasmall and uniform size for SCNPs.

Acupuncture alleviates inflammatory pain by activating CXCL1/CXCR2 signaling in the primary somatosensory cortex in adjuvant induced arthritis rats. / S1脑区CXCL1/CXCR2å‚ä¸Žé’ˆåˆºæ”¹å–„ä½å‰‚æ€§å ³èŠ‚ç‚Žå¤§é¼ ç‚Žæ€§ç—›çš„ä½œç”¨æœºåˆ¶.

OBJECTIVES: To observe whether acupuncture up-regulates chemokine CXC ligand 1 (CXCL1) in the brain to play an analgesic role through CXCL1/chemokine CXC receptor 2 (CXCR2) signaling in adjuvant induced arthritis (AIA) rats, so as to reveal its neuro-immunological mechanism underlying improvement of AIA. METHODS: BALB/c mice with relatively stable thermal pain reaction were subjected to planta injection of complete Freund adjuvant (CFA) for establishing AIA model, followed by dividing the AIA mice into simple AF750 (fluorochrome) and AF750+CXCL1 groups (n=2 in each group). AF750 labeled CXCL1 recombinant protein was then injected into the mouse's tail vein to induce elevation of CXCL1 level in blood for simulating the effect of acupuncture stimulation which has been demonstrated by our past study. In vivo small animal imaging technology was used to observe the AF750 and AF750+CXCL1-labelled target regions. After thermal pain screening, the Wistar rats with stable pain reaction were subjected to AIA modeling by injecting CFA into the rat's right planta, then were randomized into model and manual acupuncture groups (n=12 in each group). Other 12 rats that received planta injection of saline were used as the control group. Manual acupuncture (uniform reinforcing and reducing manipulations) was applied to bilateral "Zusanli" (ST36) for 4×2 min, with an interval of 5 min between every 2 min, once daily for 7 days. The thermal pain threshold was assessed by detecting the paw withdrawal latency (PWL) using a thermal pain detector. The contents of CXCL1 in the primary somatosensory cortex (S1), medial prefrontal cortex, nucleus accumbens, amygdala, periaqueductal gray and rostroventromedial medulla regions were assayed by using ELISA, and the expression levels of CXCL1, CXCR2 and mu-opioid receptor (MOR) mRNA in the S1 region were detected using real time-quantitative polymerase chain reaction. The immune-fluorescence positive cellular rate of CXCL1 and CXCR2 in S1 region was observed after immunofluorescence stain. The immunofluorescence double-stain of CXCR2 and astrocyte marker glial fibrillary acidic protein (GFAP) or neuron marker NeuN or MOR was used to determine whether there is a co-expression between them. RESULTS: In AIA mice, results of in vivo experiments showed no obvious enrichment signal of AF750 or AF750+CXCL1 in any organ of the body, while in vitro experiments showed that there was a stronger fluorescence signal of CXCL1 recombinant protein in the brain. In rats, compared with the control group, the PWL from day 0 to day 7 was significantly decreased (P<0.01) and the expression of CXCR2 mRNA in the S1 region significantly increased in the model group (P<0.05), while in comparison with the model group, the PWL from day 2 to day 7, CXCL1 content, CXCR2 mRNA expression and CXCR2 content, and MOR mRNA expression in the S1 region were significantly increased in the manual acupuncture group (P<0.05, P<0.01). Immunofluorescence stain showed that CXCR2 co-stained with NeuN and MOR in the S1 region, indicating that CXCR2 exists in neurons and MOR-positive neurons but not in GFAP positive astrocytes. CONCLUSIONS: Acupuncture can increase the content of CXCL1 in S1 region, up-regulate CXCR2 on neurons in the S1 region and improve MOR expression in S1 region of AIA rats, which may contribute to its effect in alleviating inflammatory pain.

The effects of antioxidant supplementation on short-term mortality in sepsis patients.

Background: The occurrence and development of sepsis are related to the excessive production of oxygen free radicals and the weakened natural clearance mechanism. Further dependable evidence is required to clarify the effectiveness of antioxidant therapy, especially its impact on short-term mortality. Objectives: The purpose of this systematic review and meta-analysis was to evaluate the effect of common antioxidant therapy on short-term mortality in patients with sepsis. Methods: According to PRISMA guidelines, a systematic literature search on antioxidants in adults sepsis patients was performed on PubMed/Medline, Embase, and the Cochrane Library from the establishment of the database to November 2023. Antioxidant supplements can be a single-drug or multi-drug combination: HAT (hydrocortisone, ascorbic acid, and thiamine), ascorbic acid, thiamine, N-acetylcysteine and selenium. The primary outcome was the effect of antioxidant treatment on short-term mortality, which included 28-day mortality, in-hospital mortality, intensive care unit mortality, and 30-day mortality. Subgroup analyses of short-term mortality were used to reduce statistical heterogeneity and publication bias. Results: Sixty studies of 130,986 sepsis patients fulfilled the predefined criteria and were quantified and meta-analyzed. Antioxidant therapy reduces the risk of short-term death in sepsis patients by multivariate meta-analysis of current data, including a reduction of in-hospital mortality (OR = 0.81, 95% CI 0.67 to 0.99; P = 0.040) and 28-day mortality (OR = 0.81, 95% CI 0.69 to 0.95]; P = 0.008). Particularly in subgroup analyses, ascorbic acid treatment can reduce in-hospital mortality (OR = 0.66, 95% CI 0.90 to 0.98; P = 0.006) and 28-day mortality (OR = 0.43, 95% CI 0.24 to 0.75; P = 0.003). However, the meta-analysis of RCTs found that antioxidant therapy drugs, especially ascorbic acid, did substantially reduce short-term mortality(OR = 0.78, 95% CI 0.62 to 0.98; P = 0.030; OR = 0.57, 95% CI 0.36 to 0.91; P = 0.020). Conclusions: According to current data of RCTs, antioxidant therapy, especially ascorbic acid, has a trend of improving short-term mortality in patients with sepsis, but the evidence remains to be further demonstrated.

Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties.

The coarse-grained (CG) model serves as a powerful tool for the simulation of polymer systems; its reliability depends on the accurate representation of both structural and dynamical properties. However, strong correlations between structural and dynamical properties on different scales and also a strong memory effect, enforced by chain connectivity between monomers in polymer systems, render developing a chemically specific systematic CG model a formidable task. In this study, we report a systematic CG approach that combines the iterative Boltzmann inversion (IBI) method and the generalized Langevin equation (GLE) dynamics. Structural properties are ensured by using conservative CG potentials derived from the IBI method. To retrieve the correct dynamical properties in the system, we demonstrate that using a combination of a Rouse-type delta function and a time-dependent short-time kernel in the GLE simulation is practically efficient. The former can be used to adjust the long-time diffusion dynamics, and the latter can be reconstructed from an iterative procedure according to the velocity autocorrelation function (ACF) from all-atomistic (AA) simulations. Taking the polystyrene as an example, we show that not only structural properties of radial distribution function, intramolecular bond, and angle distributions can be reproduced but also dynamical properties of mean-square displacement, velocity ACF, and force ACF resulted from our CG model have quantitative agreement with the reference AA model. In addition, reasonable agreements are observed in other collective properties between our GLE-CG model and the AA simulations as well.

A distinct tumor microenvironment makes anaplastic thyroid cancer more lethal but immunotherapy sensitive than papillary thyroid cancer.

Both anaplastic thyroid cancer (ATC) and papillary thyroid cancer (PTC) originate from thyroid follicular epithelial cells, but ATC has a significantly worse prognosis and shows resistance to conventional therapies. However, clinical trials found that immunotherapy works better in ATC than late-stage PTC. Here, we used single-cell RNA sequencing (scRNA-Seq) to generate a single-cell atlas of thyroid cancer. Differences in ATC and PTC tumor microenvironment components (including malignant cells, stromal cells, and immune cells) leading to the polarized prognoses were identified. Intriguingly, we found that CXCL13+ T lymphocytes were enriched in ATC samples and might promote the development of early tertiary lymphoid structure (TLS). Last, murine experiments and scRNA-Seq analysis of a treated patient's tumor demonstrated that famitinib plus anti-PD-1 antibody could advance TLS in thyroid cancer. We displayed the cellular landscape of ATC and PTC, finding that CXCL13+ T cells and early TLS might make ATC more sensitive to immunotherapy.

Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer.

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer.Approach. This retrospective study included 322 women with invasive breast cancer who underwent preoperative DCE-MRI. A generative adversarial network (GAN) based postcontrast image synthesis (GANPIS) model with perceptual loss was proposed to generate contrast-enhanced MR images from precontrast images. The quality of the synthesized images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The diagnostic performance of the generated images was assessed using a convolutional neural network to predict Ki-67, luminal A and histological grade with the area under the receiver operating characteristic curve (AUC). The patients were divided into training (n= 200), validation (n= 60), and testing sets (n= 62).Main results. Quantitative analysis revealed strong agreement between the generated and real postcontrast images in the test set, with PSNR and SSIM values of 36.210 ± 2.670 and 0.988 ± 0.006, respectively. The generated postcontrast images achieved AUCs of 0.918 ± 0.018, 0.842 ± 0.028 and 0.815 ± 0.019 for predicting the Ki-67 expression level, histological grade, and luminal A subtype, respectively. These results showed a significant improvement compared to the use of precontrast images alone, which achieved AUCs of 0.764 ± 0.031, 0.741 ± 0.035, and 0.797 ± 0.021, respectively.Significance. This study proposed a GAN-based MR image synthesis method for breast cancer that aims to generate postcontrast images from precontrast images, allowing the use of contrast-free images to simulate kinetic features for improved diagnosis.