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Abstract Background Patients with lung cancer and malignant pleural effusion (MPE) often have poor prognoses. Accurate prognostic tools are needed to guide interventions and improve outcomes. Methods We retrospectively analyzed clinical and imaging data from MPE patients at two medical centers. A nomogram was developed and externally validated. Clinical and imaging features were refined using least absolute shrinkage and selection operator (LASSO), and independent predictors were identified via multivariate logistic regression. Predictors were integrated into the nomogram, whose predictive performance, calibration, and clinical utility were evaluated using statistical analyses, including receiver operating characteristic (ROC) curves, calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Survival curves illustrated prognostic differences among risk groups. Results The final nomogram included five variables: Lactate Dehydrogenase (LDH) levels in pleural fluid, clarity of pleural effusion, treatment regimen, presence of pericardial effusion, and total volume of pleural effusion. In both cohorts, the nomogram demonstrated strong predictive accuracy (Area Under the Curve (AUC): 0.929 and 0.941, respectively) and excellent calibration (Hosmer-Lemeshow test p-values: 0.944 and 0.425, respectively). DCA confirmed the nomogram’s clinical utility. Risk stratification revealed significant survival disparities among patients. Conclusion Our nomogram accurately predicts the prognosis of lung cancer patients with MPE at initial diagnosis, incorporating key variables such as LDH levels in pleural fluid, clarity of pleural effusion, treatment regimen, pericardial effusion, and total volume of pleural effusion. Its robust predictive performance, calibration, and clinical utility support its use in guiding clinical decision-making for this patient population.

Abstract Progestogens commonly used in the clinic include levonorgestrel, etonogestrel, medroxyprogesterone, hydroxyprogesterone, progesterone, desogestrel, and megestrol. Progestogens are widely used for contraception and the treatment of endometriosis, threatened abortion and other diseases. However, the correlation between progestogen use and depression is not clear. Therefore, this study used data from the FDA Adverse Event Reporting System (FAERS) database to assess the relationship between progestogen levels and depression. In this study, all data from the first quarter of 2004 to the third quarter of 2024 were extracted and imported into SAS 9.4 software for data cleaning and analysis. The reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-item Gamma Poisson Shrinker (MGPS) were used for Bayesian analysis and disproportionation analysis. Levonorgestrel, medroxyprogesterone, etonogestrel and desogestrel presented positive signals for depression, and medroxyprogesterone also presented positive signals for major depression. Although none of the progestogens presented positive signals for suicide or self-harm, medroxyprogesterone presented a positive signal for suicidal ideation. Conclusion Analysis of data from the FAERS database revealed that levonorgestrel, medroxyprogesterone, etonogestrel, and desogestrel were correlated with depression. These findings provide real-world evidence of the potential risk of progestogen-related depression.

Abstract Biological organisms can perform flexible and controllable multimodal motion under external stimuli owing to the hierarchical assembly of anisotropic structures across multiple length scales. However, artificial soft actuators exhibit the limited response speed, deformation programmability and movement capability especially in harsh environments because of insufficient anisotropic hierarchy and precision in structural design. Here, we report a programmed assembly directed confinement polymerization method for the fabrication of environmentally tolerant and fast responsive hydrogels with lamellar assembly-confined cellular structure interpenetrated with highly aligned nanopillars by the directional freezing-assisted polymerization in the predesigned anisotropic laminar scaffold. The obtained hydrogel exhibits ultrafast responsiveness and anisotropic deformation exposed to temperature/light/solvent stimulation, maintaining highly consistent responsive deformation capability in all-polarity solvents over 100 days of soaking. Moreover, the hydrogels implement photoactive programmable multi-gait locomotion whose amplitude and directionality are precisely regulated by the intrinsic structure, including controlled crawling and rotation in water and non-polar solvents, and 3D self-propulsion floating and swimming in polar solvents. Thus, this hydrogel with hierarchically ordered structure and dexterous locomotion may be suitable for flexible intelligent actuators serving in harsh solvent environments.

PurposeThis study explored the association between the C-reactive protein-albumin-lymphocyte (CALLY) index and erectile dysfunction (ED).Patients and methodsData from 2,128 participants in the 2001–2004 National Health and Nutrition Examination Survey (NHANES) were analyzed and classified into ED and non-ED groups.Additionally, a separate analysis of complete erectile dysfunction was conducted.A weighted multiple logistic regression model was used to assess the association between CALLY and ED, while smooth curve fitting was applied to explore their linear relationship.ROC analysis was conducted to compare the predictive accuracy (AUC) of CALLY, Systemic Inflammation Response Index (SIRI), Systemic Immune-Inflammation Index (SII), Aggregate Index of Systemic Inflammation (AISI), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), and the product of platelet count and neutrophil count (PPN) for ED.ResultsAfter adjustment, Ln-CALLY was negatively associated with ED (OR = 0.77, 95% CI: 0.69–0.85, p < 0.0001) and complete ED (OR = 0.88, 95% CI: 0.78–1.00, p = 0.0450).The highest Ln-CALLY tertile (Q3) was associated with a significantly lower risk of ED compared to Q1 (OR = 0.40, 95% CI: 0.30–0.55, p < 0.0001).A similar trend was observed for complete ED (OR = 0.57, 95% CI: 0.38–0.85, p = 0.006).Curve fitting revealed a negative correlation between CALLY and both types of ED.Subgroup analysis confirmed the consistent and independent association.CALLY exhibited superior predictive performance for ED (AUC = 0.6512) and complete ED (AUC = 0.6237) compared to other markers.ConclusionHigher CALLY levels were linked to a reduced ED risk and proved a superior predictor compared to other inflammatory markers.

Prosocial behaviors (PB), referring to voluntary acts intended to benefit others, have become increasingly prevalent online due to advancements in Internet and technology, providing opportunities to benefit people globally. Moreover, previous research suggests that age is a crucial determinant of PB, although the findings are mixed. This study explored the types of online prosocial behaviors (OPB) preferred by different age groups among a sample of 31 Hong Kong Chinese aged 20–70. The participants included a roughly equal number of females and males, recruited through social media platforms. Participants engaged in four focus group discussions, sharing their experiences and thoughts on OPB. The thematic analysis was guided by a recently developed classification of prosociality, distinguishing between interpersonal prosociality (direct PB with immediate feedback) and ideological prosociality (indirect benefits toward collectives without immediate outcomes). Inductive codes that could not be allocated to either type were grouped as a new theme. Three themes emerged: (i) interpersonal OPB (e.g., helping others online for specific goals), (ii) ideological OPB (e.g., concern about injustice and environmental issues), and (iii) mixed OPB (e.g., saving animals, updating COVID-19 information). We found that attention to interpersonal prosociality was highest among older adults (aged 60+), while younger adults (aged 18–29) exhibited greater concern for ideological OPB compared to their older counterparts. Our findings contribute to the conceptual framework of prosociality and underscore the importance of age-related factors in future quantitative research on OPB and on the design of online charity campaigns.

Conclusions: THSWT combined with the regulation of the H19/miR-675-5p axis effectively improved LIRI by modulating the Wnt3a/Ca2+ signaling pathway, providing insights for potential therapeutic strategies for LIRI.

Nav1.7 is considered a promising target for developing next-generation analgesic drugs, given its critical role in human pain pathologies. Although most reported inhibitors with strong in vitro activity and high selectivity share the aryl sulfonamide scaffold, they failed to demonstrate marked clinical efficacy. Therefore, exploring new Nav1.7-selective antagonists is quite urgent to the development of next-generation analgesic drugs. Here, we report a highly effective 1H-indole-3-propionamide inhibitor, WN2, identified through an integrated drug discovery strategy. Notably, the structure of WN2 is quite different from previously reported aryl sulfonamide inhibitors. Molecular dynamics simulations and experimental findings reveal that the R configuration of WN2 (WN2-R) is the preferred form (IC50 = 24.7 ± 9.4 nM) within the VSDIV pocket of Nav1.7. WN2-R exhibits impressive analgesic effects in acute and chronic inflammatory pain, as well as neuropathic pain models in mice. Additionally, it displays favorable subtype selectivity and positive drug safety in acute toxicity studies. Pharmacokinetic studies indicate that WN2-R has high bioavailability (F = 20.29%), highlighting its considerable potential for drug development. Our study establishes WN2-R as a novel Nav1.7-selective inhibitor with a unique structural scaffold, offering a promising candidate for the next generation of analgesic drugs.

Bile acids are liver-derived signaling molecules that can be found in the brain, but their role there remains largely unknown. We found increased brain chenodeoxycholic acid (CDCA) in mice with absent 12α-hydroxylase (Cyp8b1), a bile acid synthesis enzyme. In these Cyp8b1−/−, and in Wt mice administered CDCA, stroke infarct area was reduced. Elevated glutamate-induced excitotoxicity mediated by aberrant N-methyl-D-aspartate receptor (NMDAR) overactivation contributes to neuronal death in ischemic stroke. We found reduced glutamate-induced excitotoxicity in neurons from Cyp8b1−/− and CDCA-treated Wt mice. CDCA decreased NMDAR-mediated excitatory post-synaptic currents by reducing over-activation of NMDAR subunit GluN2B in Wt brains. Synaptic NMDAR activity was also decreased in Cyp8b1−/− brains. Expression and synaptic distribution of GluN2B were unaltered in Cyp8b1−/− mice, suggesting CDCA may directly antagonize GluN2B-containing NMDARs. Supporting our findings, in a case-control cohort of acute ischemic stroke patients, we found lower circulatory CDCA. Together, our data suggest that CDCA, acting in the liver-brain axis, decreases GluN2B-containing NMDAR overactivation, contributing to neuroprotection in stroke.

Abstract Background Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization. Method We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization. Results The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making. Conclusion This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.

Abstract Background The TetR family of transcriptional regulators (TFRs), serving as crucial regulators of diverse cellular processes, undergo conformational changes induced by small-molecule ligands, which either inhibit or activate them to modulate target gene expression. Some ligands of TFRs in actinomycetes and their regulatory effects have been identified and studied; however, regulatory mechanisms of the TetR family in the lincomycin-producing Streptomyces lincolnensis remain poorly understood. Results In this study, we found that AbrT (SLCG_1979), a TetR family regulator, plays a pivotal role in regulating lincomycin production and morphological development in S. lincolnensis. Deletion of abrT gene resulted in increased lincomycin A (Lin-A) production, but delayed mycelium formation and sporulation on solid media. AbrT directly or indirectly repressed the expression of lincomycin biosynthetic (lin) cluster genes and activated that of the morphological developmental genes amfC, whiB, and ftsZ. We demonstrated that AbrT bound to two motifs (5′-CGCGTACTCGTA-3′ and 5′-CGTACGATAGCT-3′) present in the bidirectional promoter between abrT and SLCG_1980 genes. This consequently repressed abrT itself and its adjacent gene SLCG_1980 that encodes an arabinose efflux permease. D-arabinose, not naturally occurring as L-arabinose, was identified as the effector molecule of AbrT, reducing its binding affinity to abrT-SLCG_1980 intergenic region. Furthermore, based on functional analysis of the AbrT homologue in Saccharopolyspora erythraea, we inferred that the TetR family regulator AbrT may play an important role in regulating secondary metabolism in actinomycetes. Conclusions AbrT functions as a regulator for governing lincomycin production and morphological development of S. lincolnensis. Our findings demonstrated that D-arabinose acts as a ligand of AbrT to mediate the regulation of lincomycin biosynthesis in S. lincolnensis. Our findings provide novel insights into ligand-mediated regulation in antibiotic biosynthesis.

Abstract The inhibition of Nav1.7 is a promising strategy for the development of analgesic treatments. Spider venom‐derived peptide toxins are recognized as significant sources of Nav1.7 inhibitors. However, their development has been impeded by limited selectivity. In this study, eight peptide toxins from three distinct spider venom Nav channel families demonstrated robust inhibition of hNav1.7, rKv4.2, and rKv4.3 (rKv4.2/4.3) currents, exhibiting a similar mode of action. The analysis of structure and function relationship revealed a significant overlap in the pharmacophore responsible for inhibiting hNav1.7 and rKv4.2 by HNTX‐III, although Lys25 seems to play a more pivotal role in the inhibition of rKv4.2/4.3. Pharmacophore‐guided rational design is employed for the development of an mGpTx1 analogue, mGpTx1‐SA, which retains its inhibition of hNav1.7 while significantly reducing its inhibition of rKv4.2/4.3 and eliminating cardiotoxicity. Moreover, mGpTx1‐SA demonstrates potent analgesic effects in both inflammatory and neuropathic pain models, accompanied by an improved in vivo safety profile. The results suggest that off‐target inhibition of rKv4.2/4.3 by specific spider peptide toxins targeting hNav1.7 may arise from a conserved binding motif. This insight promises to facilitate the design of hNav1.7‐specific analgesics, aimed at minimizing rKv4.2/4.3 inhibition and associated toxicity, thereby enhancing their suitability for therapeutic applications.

Abstract Full-length p53 (p53α) plays a pivotal role in maintaining genomic integrity and preventing tumor development. Over the years, p53 was found to exist in various isoforms, which are generated through alternative splicing, alternative initiation of translation, and internal ribosome entry site. p53 isoforms, either C-terminally altered or N-terminally truncated, exhibit distinct biological roles compared to p53α, and have significant implications for tumor development and therapy resistance. Due to a lack of part and/or complete C- or N-terminal domains, ectopic expression of some p53 isoforms failed to induce expression of canonical transcriptional targets of p53α like CDKN1A or MDM2, even though they may bind their promoters. Yet, p53 isoforms like Δ40p53α still activate subsets of targets including MDM2 and BAX. Furthermore, certain p53 isoforms transactivate even novel targets compared to p53α. More recently, non-canonical functions of p53α in DNA repair and of different isoforms in DNA replication unrelated to transcriptional activities were discovered, amplifying the potential of p53 as a master regulator of physiological and tumor suppressor functions in human cells. Both regarding canonical and non-canonical functions, alternative p53 isoforms frequently exert dominant negative effects on p53α and its partners, which is modified by the relative isoform levels. Underlying mechanisms include hetero-oligomerization, changes in subcellular localization, and aggregation. These processes ultimately influence the net activities of p53α and give rise to diverse cellular outcomes. Biological roles of p53 isoforms have implications for tumor development and cancer therapy resistance. Dysregulated expression of isoforms has been observed in various cancer types and is associated with different clinical outcomes. In conclusion, p53 isoforms have expanded our understanding of the complex regulatory network involving p53 in tumors. Unraveling the mechanisms underlying the biological roles of p53 isoforms provides new avenues for studies aiming at a better understanding of tumor development and developing therapeutic interventions to overcome resistance.

Abstract Background Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict associations between variables, whereas causal graph learning models causality dynamics through graphs. However, building personalized causal graphs for each individual is challenging due to the limited amount of data available for each patient. Method In this study, we present a new algorithmic framework using meta-learning for learning personalized causal graphs in biomedicine. Our framework extracts common patterns from multiple patient graphs and applies this information to develop individualized graphs. In multi-task causal graph learning, the proposed optimized initial guess of shared commonality enables the rapid adoption of knowledge to new tasks for efficient causal graph learning. Results Experiments on one real-world biomedical causal graph learning benchmark data and four synthetic benchmarks show that our algorithm outperformed the baseline methods. Our algorithm can better understand the underlying patterns in the data, leading to more accurate predictions of the causal graph. Specifically, we reduce the structural hamming distance by 50-75%, indicating an improvement in graph prediction accuracy. Additionally, the false discovery rate is decreased by 20-30%, demonstrating that our algorithm made fewer incorrect predictions compared to the baseline algorithms. Conclusion To the best of our knowledge, this is the first study to demonstrate the effectiveness of meta-learning in personalized causal graph learning and cause inference modeling for biomedicine. In addition, the proposed algorithm can also be generalized to transnational research areas where integrated analysis is necessary for various distributions of datasets, including different clinical institutions.

Abstract Background Curcubita ficifolia Bouché (Cucurbitaceae) has high value as a food crop and medicinal plant, and also has horticultural value as rootstock for other melon species. China is home to many different cultivars, but the genetic diversity of these resources and the evolutionary relationships among them, as well as the differences between C. ficifolia and other Cucurbita species, remain unclear. Results We investigated the chloroplast (cp) genomes of 160 C. ficifolia individuals from 31 populations in Yunnan, a major C. ficifolia production area in China. We found that the cp genome of C. ficifolia is ~151 kb and contains 128 genes, of which 86 are protein coding genes, 34 encode tRNA, and eight encode rRNAs. We also identified 64 SSRs, mainly AT repeats. The cp genome was found to contain a total of 204 SNP and 57 indels, and a total of 21 haplotypes were found in the 160 study individuals. The reverse repeat (IR) region of C. ficifolia contained a few differences compared with this region in the six other Cucurbita species. Sequence difference analysis demonstrated that most of the variable regions were concentrated in the single copy (SC) region. Moreover, the sequences of the coding regions were found to be more similar among species than those of the non-coding regions. The phylogenies reconstructed from the cp genomes of 61 representative species of Cucurbitaceae reflected the currently accepted classification, in which C. ficifolia is sister to the other Cucurbita species, however, different interspecific relationships were found between Cucurbita species. Conclusions These results will be valuable in the classification of C. ficifolia genetic resources and will contribute to our understanding of evolutionary relationships within the genus Cucurbita.

Abstract The wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterward, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.

The present study examined the effect of two dietary regimens with elevated salt concentrations (4% and 8% salt) on hemorheological functions of SD rats, and explored the underlying mechanisms mainly through microbiome-metabolome analysis. An 8% HSD substantially altered the hemorheological parameters, and compromised intestinal barrier integrity and reduced the short-chain fatty acid levels. The microbiome-metabolome analysis revealed that 49 genus-specific microorganisms and 156 metabolites showed a consistent trend after exposure to both 4% and 8% HSDs. Pathway analysis identified significant alterations in key metabolites within bile acid and arachidonic acid metabolism pathways. A two-sample Mendelian randomization (MR) analysis verified the link between high dietary salt intake and hemorheology. It also suggested that some key microbes and metabolites (such as Ruminococcaceae_UCG-005, Lachnospiraceae_NK4A136, Ruminiclostridium_6, and Ruminococcaceae_UCG-010, TXB-2, 11,12-diHETrE, glycochenodeoxycholate) may involve in abnormalities in blood rheology caused by high salt intake. Collectively, our findings underscored the adverse effects of high dietary salt on hemorheological functions and provide new insight into the underlying mechanism based on microbiome-metabolome analysis.

The biosynthesis of bioactive secondary metabolites, specifically antibiotics, is of great scientific and economic importance. The control of antibiotic production typically involves different processes and molecular mechanism. Despite numerous efforts to improve antibiotic yields, joint engineering strategies for combining genetic manipulation with fermentation optimization remain finite. Lincomycin A (Lin-A), a lincosamide antibiotic, is industrially fermented by Streptomyces lincolnensis. Herein, the leucine-responsive regulatory protein (Lrp)-type regulator SLCG_4846 was confirmed to directly inhibit the lincomycin biosynthesis, whereas indirectly controlled the transcription of SLCG_2919, the first reported repressor in S. lincolnensis. Inactivation of SLCG_4846 in the high-yield S. lincolnensis LA219X (LA219XΔ4846) increases the Lin-A production and deletion of SLCG_2919 in LA219XΔ4846 exhibits superimposed yield increment. Given the effect of the double deletion on cellular primary metabolism of S. lincolnensis, Plackett-Burman design, steepest ascent and response surface methodologies were utilized and employed to optimize the seed medium of this double mutant in shake flask, and Lin-A yield using optimal seed medium was significantly increased over the control. Above strategies were performed in a 15-L fermenter. The maximal yield of Lin-A in LA219XΔ4846-2919 reached 6.56 g/L at 216 h, 55.1 % higher than that in LA219X at the parental cultivation (4.23 g/L). This study not only showcases the potential of this strategy to boost lincomycin production, but also could empower the development of high-performance actinomycetes for other antibiotics.

In this study, we investigated the effects of major royal jelly proteins (MRJPs) on the estrogen, gut microbiota, and immunological responses in mice. Mice given 250 or 500 mg/kg, not 125 mg/kg of MRJPs, enhanced the proliferation of splenocytes in response to mitogens. The splenocytes and mesenteric lymphocytes activated by T-cell mitogens (ConA and anti-CD3/CD28 antibodies) released high levels of IL-2 but low levels of IFN-γ and IL-17A. The release of IL-4 was unaffected by MRJPs. Additionally, splenocytes and mesenteric lymphocytes activated by LPS were prevented by MRJPs at the same dose as that required for producing IL-1β and IL-6, two pro-inflammatory cytokines. The production of IL-1β, IL-6, and IFN-γ was negatively associated with estrogen levels, which were higher in the MRJP-treated animals than in the control group. Analysis of the gut microbiota revealed that feeding mice 250 mg/kg of MRJPs maintained the stability of the natural intestinal microflora of mice. Additionally, the LEfSe analysis identified biomarkers in the MRJP-treated mice, including Prevotella, Bacillales, Enterobacteriales, Gammaproteobacteria, Candidatus_Arthromitus, and Shigella. Our results showed that MRJPs are important components of royal jelly that modulate host immunity and hormone levels and help maintain gut microbiota stability.

Abstract Background Spinal cord ischemia‒reperfusion injury (SCIRI) can lead to paraplegia, which leads to permanent motor function loss. It is a disastrous complication of surgery and causes tremendous socioeconomic burden. However, effective treatments for SCIRI are still lacking. PANoptosis consists of three kinds of programmed cell death, pyroptosis, apoptosis, and necroptosis, and may contribute to ischemia‒reperfusion-induced neuron death. Previous studies have demonstrated that hydrogen sulfide (H2S) exerts a neuroprotective effect in many neurodegenerative diseases. However, whether H2S is anti-PANoptosis and neuroprotective in the progression of acute SCIRI remains unclear. Thus, in this study we aimed to explore the role of H2S in SCIRI and its underlying mechanisms. Methods Measurements of lower limb function, neuronal activity, microglia/macrophage function histopathological examinations, and biochemical levels were performed to examine the efficacy of H2S and to further demonstrate the mechanism and treatment of SCIRI. Results The results showed that GYY4137 (a slow-releasing H2S donor) treatment attenuated the loss of Nissl bodies after SCIRI and improved the BBB score. Additionally, the number of TUNEL-positive and cleaved caspase-3-positive cells was decreased, and the upregulation of expression of cleaved caspase-8, cleaved caspase-3, Bax, and Bad and downregulation of Bcl-2 expression were reversed after GYY4137 administration. Meanwhile, both the expression and activation of p-MLKL, p-RIP1, and p-RIP3, along with the number of PI-positive and RIP3-positive neurons, were decreased in GYY4137-treated rats. Furthermore, GYY4137 administration reduced the expression of NLRP3, cleaved caspase-1 and cleaved GSDMD, decreased the colocalization NeuN/NLRP3 and Iba1/interleukin-1β-expressing cells, and inhibited proinflammatory factors and microglia/macrophage polarization. Conclusions H2S ameliorated spinal cord neuron loss, prevented motor dysfunction after SCIRI, and exerted a neuroprotective effect via the inhibition of PANoptosis and overactivated microglia-mediated neuroinflammation in SCIRI.

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery.

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods.