Effects of seasonal precipitation regimes on microbial biomass and extracellular enzyme activity during shrub foliar litter decomposition in a subtropical forest.

Elucidating the mechanisms underlying microbial biomass and extracellular enzyme activity responses to the seasonal precipitation regime during foliar litter decomposition is highly important for understanding the material cycle of forest ecosystems in the context of global climate change; however, the specific underlying mechanisms remain unclear. Hence, a precipitation manipulation experiment involving a control (CK) and treatments with decreased precipitation in the dry season and extremely increased precipitation in the wet season (IE) and decreased precipitation in the dry season and proportionally increased precipitation in the wet season (IP) was conducted in a subtropical evergreen broad-leaved forest in China from October 2020 to October 2021. The moisture, microbial biomass, and extracellular enzyme activities of foliar litter from two dominant shrub species, Phyllostachys violascens and Alangium chinense, were measured at six stages during the dry and wet seasons. The results showed that (1) both IE and IP significantly decreased the microbial biomass carbon and microbial biomass nitrogen content and the activities of ß-1,4-glucosidase, ß-1,4-N-acetylglucosaminidase, acid phosphatase and cellulase in the dry season, while the opposite effects were observed in the wet season. (2) Compared with those of IE, the effects of IP on foliar litter microbial biomass and extracellular enzyme activity were more significant. (3) The results from the partial least squares model indicated that extracellular enzyme activity during foliar litter decomposition was strongly controlled by the foliar litter water content, microbial biomass nitrogen, the ratio of total carbon to total phosphorus, foliar litter total carbon, and foliar litter total nitrogen. These results provide an important theoretical basis for elucidating the microbial mechanisms driving litter decomposition in a subtropical forest under global climate change scenarios.

Synergistic dual sites of Zn-Mg on hierarchical porous carbon as an advanced oxygen reduction electrocatalyst for Zn-air batteries.

The development of cost-effective and high-performance non-noble metal catalysts for the oxygen reduction reaction (ORR) holds substantial promise for real-world applications. Introducing a secondary metal to design bimetallic sites enables effective modulation of a metal-nitrogen-carbon (M-N-C) catalyst's electronic structure, providing new opportunities for enhancing ORR activity and stability. Here, we successfully synthesized an innovative hierarchical porous carbon material with dual sites of Zn and Mg (Zn/Mg-N-C) using polymeric ionic liquids (PILs) as precursors and SBA-15 as a template through a bottom-up approach. The hierarchical porous structure and optimized Zn-Mg bimetallic catalytic centers enable Zn/Mg-N-C to exhibit a half-wave potential of 0.89 V, excellent stability, and good methanol tolerance in 0.1 M KOH solution. Theoretical calculations indicated that the Zn-Mg bimetallic sites in Zn/Mg-N-C effectively lowered the ORR energy barrier. Furthermore, the Zn-air batteries assembled based on Zn/Mg-N-C demonstrated an outstanding peak power density (298.7 mW cm-2) and superior cycling stability. This work provides a method for designing and synthesizing bimetallic site catalysts for advanced catalysis.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

Exploring FGFR signaling inhibition as a promising approach in breast cancer treatment.

As our grasp of cancer genomics deepens, we are steadily progressing towards the domain of precision medicine, where targeted therapy stands out as a revolutionary breakthrough in the landscape of cancer therapeutics. The fibroblast growth factor receptors (FGFR) pathway has been unveiled as a fundamental instigator in the pathophysiological mechanisms underlying breast carcinoma, paving the way for the exhilarating development of precision-targeted therapeutics. In the pursuit of exploring inhibitors that specifically target the FGFR signaling pathways, a multitude of kinase inhibitors targeting FGFR has been assiduously engineered to address the heterogeneous landscape of human malignancies. This review offers an exhaustive exploration of aberrations within the FGFR pathway and their functional implications in breast cancer. Additionally, we delve into cutting-edge therapeutic approaches for the treatment of breast cancer patients bearing FGFR alterations and the management of toxicity associated with FGFR inhibitors. Furthermore, our contemplation of the evolution of cutting-edge FGFR inhibitors foresees their potential to spearhead innovative therapeutic approaches in the ongoing combat against cancer.

Multiscale Synergistic Gecko-Inspired Adhesive for Stable Adhesion under Varying Preload and Surface Roughness.

Inspired by geckos, fibrillar microstructures hold great promise as controllable and reversible adhesives in the engineering field. However, enhancing the adhesion strength and stability of gecko-inspired adhesives (GIAs) under complex real-world contact conditions, such as rough surfaces and varying force fields, is crucial for its commercialization, yet further research is lacking. Here, we propose a hierarchically designed GIA, which features a silicone foam (SF) backing layer and a film-terminated fibrillar microstructure under a subtle multiscale design. This structure has been proven to have a "multiscale synergistic effect", allowing the material to maintain strong and stable adhesion to surfaces with changing normal pressures and roughness. Specifically, under a high load, the adhesive strength is 2 times more than that of conventional GIA, and it is 1.5 times stronger on rough surfaces compared to conventional GIA. Under high pressure and high surface roughness simultaneously, the adhesive strength is 3.3 times higher compared to conventional GIA. Our research demonstrates that the synergistic effect of multiscale biomimetic adhesion structures is highly effective in enhancing the adhesive strength of GIA under some harsh contact conditions.

Identification of naphthalimide-derivatives as novel PBD-targeted polo-like kinase 1 inhibitors with efficacy in drug-resistant lung cancer cells.

Targeting polo-box domain (PBD) small molecule for polo-like kinase 1 (PLK1) inhibition is a viable alternative to target kinase domain (KD), which could avoid pan-selectivity and dose-limiting toxicity of ATP-competitive inhibitors. However, their efficacy in these settings is still low and inaccessible to clinical requirement. Herein, we utilized a structure-based high-throughput virtual screen to find novel chemical scaffold capable of inhibiting PLK1 via targeting PBD and identified an initial hit molecule compound 1a. Based on the lead compound 1a, a structural optimization approach was carried out and several series of derivatives with naphthalimide structural motif were synthesized. Compound 4Bb was identified as a new potent PLK1 inhibitor with a KD value of 0.29 µM. 4Bb could target PLK1 PBD to inhibit PLK1 activity and subsequently suppress the interaction of PLK1 with protein regulator of cytokinesis 1 (PRC1), finally leading to mitotic catastrophe in drug-resistant lung cancer cells. Furthermore, 4Bb could undergo nucleophilic substitution with the thiol group of glutathione (GSH) to disturb the redox homeostasis through exhausting GSH. By regulating cell cycle machinery and increasing cellular oxidative stress, 4Bb exhibited potent cytotoxicity to multiple cancer cells and drug-resistant cancer cells. Subcutaneous and oral administration of 4Bb could effectively inhibit the growth of drug-resistant tumors in vivo, doubling the survival time of tumor bearing mice without side effects in normal tissues. Thus, our study offers an orally-available, structurally-novel PLK1 inhibitor for drug-resistant lung cancer therapy.

Compilation of a self-management assessment scale for postoperative patients with aortic dissection.

Objective: The aim of this research was to compile a self-management assessment scale for patients with aortic dissection (AD). The questionnaire is useful in making the patient aware of the need for post-operative care in order to contribute to improving the outcome and quality of life. Methods: The initial version of the "postoperative self-management assessment scale for patients with aortic dissection" was developed using the Delphi expert consultation method based on qualitative research results, consultation of self-management-related literature, reference to the existing self-management scale, and self-efficacy theory, combined with the disease characteristics of AD. By using the convenience sampling method, a total of 201 patients with AD who had undergone surgery were selected as the research participants. The initial version of the scale was used for follow-up investigation, and the scale entries were evaluated and exploratory factor analysis carried out to form the formal version of the "postoperative self-management assessment scale for patients with aortic dissection." A total of 214 patients with AD after surgery were selected as the research participants. The formal version of the scale was used for follow-up investigation, and its reliability and validity were evaluated. Results: The formal version of the scale had 6 dimensions and 35 entries. The Cronbach's α coefficient for the total scale was 0.908, the split-half reliability was 0.790, and the test-retest reliability after 2 weeks was 0.471. The content validity index of the total scale was 0.963. Exploratory factor analysis yielded six common factors, and the cumulative contribution rate of variance was 66.303%. Confirmatory factor analysis showed that except for the incremental fit index, Tucker-Lewis index, and comparative fit index >0.85, slightly lower than 0.90, χ 2/df <3, root mean square of approximation <0.08, parsimonious goodness-of-fit index, and parsimonious normed fit index >0.50; all other model fitting requirements were satisfied, indicating that the model fitting was acceptable. Conclusion: We compiled the postoperative self-management assessment scale for patients with AD, which has demonstrated excellent reliability and validity and can be used as a tool to evaluate the postoperative self-management level in patients with aortic dissection.

Development and validation of a nomogram to predict the risk of residual low back pain after tubular microdiskectomy of lumbar disk herniation.

OBJECTIVE: Tubular microdiskectomy (tMD) is one of the most commonly used for treating lumbar disk herniation. However, there still patients still complain of persistent postoperative residual low back pain (rLBP) postoperatively. This study attempts to develop a nomogram to predict the risk of rLBP after tMD. METHODS: The patients were divided into non-rLBP (LBP VAS score < 2) and rLBP (LBP VAS score ≥ 2) group. The correlation between rLBP and these factors were analyzed by multivariate logistic analysis. Then, a nomogram prediction model of rLBP was developed based on the risk factors screened by multivariate analysis. The samples in the model are randomly divided into training and validation sets in a 7:3 ratio. The Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diskrimination, calibration and clinical value of the model, respectively. RESULTS: A total of 14.3% (47/329) of patients have persistent rLBP. The multivariate analysis suggests that higher preoperative LBP visual analog scale (VAS) score, lower facet orientation (FO), grade 2-3 facet joint degeneration (FJD) and moderate-severe multifidus fat atrophy (MFA) are risk factors for postoperative rLBP. In the training and validation sets, the ROC curves, calibration curves, and DCAs suggested the good diskrimination, predictive accuracy between the predicted probability and actual probability, and clinical value of the model, respectively. CONCLUSION: This nomogram including preoperative LBP VAS score, FO, FJD and MFA can serve a promising prediction model, which will provide a reference for clinicians to predict the rLBP after tMD.

Monolayer Vacancy-Induced MXene Memory for Write-Verify-Free Programming.

The fundamental logic states of 1 and 0 in Complementary Metal-Oxide-Semiconductor (CMOS) are essential for modern high-speed non-volatile solid-state memories. However, the accumulated storage signal in conventional physical components often leads to data distortion after multiple write operations. This necessitates a write-verify operation to ensure proper values within the 0/1 threshold ranges. In this work, a non-gradual switching memory with two distinct stable resistance levels is introduced, enabled by the asymmetric vertical structure of monolayer vacancy-induced oxidized Ti3C2Tx MXene for efficient carrier trapping and releasing. This non-cumulative resistance effect allows non-volatile memories to attain valid 0/1 logic levels through direct reprogramming, eliminating the need for a write-verify operation. The device exhibits superior performance characteristics, including short write/erase times (100 ns), a large switching ratio (≈3 × 104), long cyclic endurance (>104 cycles), extended retention (>4 × 106 s), and highly resistive stability (>104 continuous write operations). These findings present promising avenues for next-generation resistive memories, offering faster programming speed, exceptional write performance, and streamlined algorithms.

[Opening experiments: synthesis, purification, and characterization of polyethyleneimine-modified carbon dots].

Open experiments are an effective means of cultivating top-notch innovative talents. Based on student interests, research hotspots and our laboratory conditions, an experimental scheme was designed. In this experiment, polyethyleneimine modified carbon dots (PEI-CDs) were prepared via a one-step hydrothermal method using citric acid (CA) as the carbon source and PEI as the surface passivator. First, CA and PEI were completely dissolved in 0.1 mol/L HCl and transferred into an autoclave. The autoclave was heated to 130 ℃ for 2 h. PEI-CDs solution was obtained. After cooling to room temperature, the solution was concentrated to 2 mL by rotary evaporation. Finally, the PEI-CDs were precipitated, washed with ethanol, and dried under vacuum at 70 ℃ for 12 h. The obtained PEI-CDs were characterized by fluorescence spectrophotometry, absorption spectrophotometry, infrared spectrometry, and transmission electron microscopy. The results indicated that anhydrous-ethanol precipitation is a simple, rapid, economical, and green purification method. The as-prepared PEI-CDs had unique properties, such as good water solubility, high luminescence, uniform particle sizes, and good stability. Through this open experiment, students can not only master the operation of large-scale instruments but also enhance their interest in scientific research.

Genome-wide identification of Aux/IAA gene family in white clover (Trifolium repens L.) and functional verification of TrIAA18 under different abiotic stress.

BACKGROUND: White clover (Trifolium repens L.) is an excellent leguminous cool-season forage with a high protein content and strong nitrogen-fixing ability. Despite these advantages, its growth and development are markedly sensitive to environmental factors. Indole-3-acetic acid (IAA) is the major growth hormone in plants, regulating plant growth, development, and response to adversity. Nevertheless, the specific regulatory functions of Aux/IAA genes in response to abiotic stresses in white clover remain largely unexplored. RESULTS: In this study, we identified 47 Aux/IAA genes in the white clover genome, which were categorized into five groups based on phylogenetic analysis. The TrIAAs promoter region co-existed with different cis-regulatory elements involved in developmental and hormonal regulation, and stress responses, which may be closely related to their diverse regulatory roles. Collinearity analysis showed that the amplification of the TrIAA gene family was mainly carried out by segmental duplication. White clover Aux/IAA genes showed different expression patterns in different tissues and under different stress treatments. In addition, we performed a yeast two-hybrid analysis to investigate the interaction between white clover Aux/IAA and ARF proteins. Heterologous expression indicated that TrIAA18 could enhance stress tolerance in both yeast and transgenic Arabidopsis thaliana. CONCLUSION: These findings provide new scientific insights into the molecular mechanisms of growth hormone signaling in white clover and its functional characteristics in response to environmental stress.

Nrf2 expression, mitochondrial fission, and neuronal apoptosis in the prefrontal cortex of methamphetamine abusers and rats.

Methamphetamine (MA), a representative amphetamine-type stimulant, is one of the most abused drugs worldwide. Studies have shown that MA-induced neurotoxicity is strongly associated with oxidative stress and apoptosis. While nuclear factor E2-related factor 2 (Nrf2), an antioxidant transcription factor, is known to exert neuroprotective effects, its role in MA-induced dopaminergic neuronal apoptosis remains incompletely understood. In the present study, we explored the effects of MA on the expression levels of Nrf2, dynamin-related protein 1 (Drp1), mitofusin 1 (Mfn1), cytochrome c oxidase (Cyt-c), and cysteine aspartate-specific protease 3 (Caspase 3), as well as the correlations between Nrf2 and mitochondrial dynamics and apoptosis. Brain tissue from MA abusers was collected during autopsy procedures. An MA-dependent rat model was also established by intraperitoneal administration of MA (10 mg/kg daily) for 28 consecutive days, followed by conditioned place preference (CPP) testing. Based on immunohistochemical staining and western blot analysis, the protein expression levels of Nrf2 and Mfn1 showed a decreasing trend, while levels of Drp1, Cyt-c, and Caspase 3 showed an increasing trend in the cerebral prefrontal cortex of both MA abusers and MA-dependent rats. Notably, the expression of Nrf2 was positively associated with the expression of Mfn1, but negatively associated with the expression levels of Drp1, Cyt-c, and Caspase 3. These findings suggest that oxidative stress and mitochondrial fission contribute to neuronal apoptosis, with Nrf2 potentially playing a critical role in MA-induced neurotoxicity.

Identification of Panax notoginseng origin using terahertz precision spectroscopy and neural network algorithm.

Panax notoginseng (P. notoginseng), a Chinese herb containing various saponins, benefits immune system in medicines development, which from Wenshan (authentic cultivation) is often counterfeited by others for large demand and limited supply. Here, we proposed a method for identifying P. notoginseng origin combining terahertz (THz) precision spectroscopy and neural network. Based on the comparative analysis of four qualitative identification methods, we chose high-performance liquid chromatography (HPLC) and THz spectroscopy to detect 252 samples from five origins. After classifications using Convolutional Neural Networks (CNNs) model, we found that the performance of THz spectra was superior to that of HPLC. The underlying mechanism is that there are clear nonlinear relations among the THz spectra and the origins due to the wide spectra and multi-parameter characteristics, which makes the accuracy of five-classification origin identification up to 97.62%. This study realizes the rapid, non-destructive and accurate identification of P. notoginseng origin, providing a practical reference for herbal medicine.

Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions.

Surface ozone pollution, as a pressing environmental concern, has garnered widespread attention across China. Due to air mass transport, effective control of ozone pollution is highly dependent on collaborative efforts across neighboring regions. However, specific regions with strong internal interactions of ozone pollution are not yet well identified. Here, we introduced the Geospatial SHapley Additive exPlanation (GeoSHAP) approach, which primarily involves machine learning and geostatistical algorithms. Based on extensive atmospheric environmental monitoring data from 2017 to 2021, machine learning models were employed to train and predict ozone concentrations at the target location. The R2 values on the test sets of different scale regions all reached 0.98 in the overall condition, indicating that the core model has good accuracy and generalization ability. The results highlight key regions with high ozone geospatial relationship (OGR) index, predominantly located in the Northern District (ND), spanning the Fen-Wei Plain, the Loess Plateau, and the North China Plain, as well as within portions of the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). Further investigation indicated that high geospatial relationships stem from a synergy between anthropogenic and natural factors, with anthropogenic factors serving as a pivotal element. This study revealed key regions with the most urgent need for joint control of anthropogenic sources to mitigate ozone pollution.

Menarche-a journey into womanhood: age at menarche and health-related outcomes in East Asians.

STUDY QUESTION: Are there associations of age at menarche (AAM) with health-related outcomes in East Asians? SUMMARY ANSWER: AAM is associated with osteoporosis, Type 2 diabetes (T2D), glaucoma, and uterine fibroids, as demonstrated through observational studies, polygenic risk scores, genetic correlations, and Mendelian randomization (MR), with additional findings indicating a causal effect of BMI and T2D on earlier AAM. WHAT IS KNOWN ALREADY: Puberty timing is linked to adult disease risk, but research predominantly focuses on European populations, with limited studies in other groups. STUDY DESIGN, SIZE, DURATION: We performed an AAM genome-wide association study (GWAS) with 57 890 Han Taiwanese females and examined the association between AAM and 154 disease outcomes using the Taiwanese database. Additionally, we examined genetic correlations between AAM and 113 diseases and 67 phenotypes using Japanese GWAS summary statistics. PARTICIPANTS/MATERIALS, SETTING, METHODS: We performed AAM GWAS and gene-based GWAS studies to obtain summary statistics and identify potential AAM-related genes. We applied phenotype, polygenic risk scores, and genetic correlation analyses of AAM to explore health-related outcomes, using multivariate regression and linkage disequilibrium score regression analyses. We also explored potential bidirectional causal relationships between AAM and related outcomes through univariable and multivariable MR analyses. MAIN RESULTS AND THE ROLE OF CHANCE: Fifteen lead single-nucleotide polymorphisms and 24 distinct genes were associated with AAM in Taiwan. AAM was genetically associated with later menarche and menopause, greater height, increased osteoporosis risk, but lower BMI, and reduced risks of T2D, glaucoma, and uterine fibroids in East Asians. Bidirectional MR analyses indicated that higher BMI/T2D causally leads to earlier AAM. LIMITATIONS, REASONS FOR CAUTION: Our findings were specific to Han Taiwanese individuals, with genetic correlation analyses conducted in East Asians. Further research in other ethnic groups is necessary. WIDER IMPLICATIONS OF THE FINDINGS: Our study provides insights into the genetic architecture of AAM and its health-related outcomes in East Asians, highlighting causal links between BMI/T2D and earlier AAM, which may suggest potential prevention strategies for early puberty. STUDY FUNDING/COMPETING INTEREST(S): The work was supported by China Medical University, Taiwan (CMU110-S-17, CMU110-S-24, CMU110-MF-49, CMU111-SR-158, CMU111-MF-105, CMU111-MF-21, CMU111-S-35, CMU112-SR-30, and CMU112-MF-101), the China Medical University Hospital, Taiwan (DMR-111-062, DMR-111-153, DMR-112-042, DMR-113-038, and DMR-113-103), and the Ministry of Science and Technology, Taiwan (MOST 111-2314-B-039-063-MY3, MOST 111-2314-B-039-064-MY3, MOST 111-2410-H-039-002-MY3, and NSTC 112-2813-C-039-036-B). The funders had no influence on the data collection, analyses, or conclusions of the study. No conflict of interests to declare. TRIAL REGISTRATION NUMBER: N/A.

MonoAux: Fully Exploiting Auxiliary Information and Uncertainty for Monocular 3D Object Detection.

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information. Most existing methods in this domain fall short of harnessing the limited information available in monocular 3D detection tasks. They typically provide only a single detection outcome, omitting essential uncertainty analysis and result post-processing during model inference, thus limiting overall model performance. In this paper, we propose a comprehensive framework that maximizes information extraction from monocular images while encompassing diverse depth estimation and incorporating uncertainty analysis. Specifically, we mine additional information intrinsic to the monocular 3D detection task to augment supervision, thereby addressing the information scarcity challenge. Moreover, our framework handles depth estimation by recovering multiple sets of depth values from calculated visual heights. The final depth estimate and 3D confidence are determined through an uncertainty fusion process, effectively reducing inference errors. Furthermore, to address task weight allocation in multi-task training, we present a versatile training strategy tailored to monocular 3D detection. This approach leverages measurement indicators to monitor task progress, adaptively adjusting loss weights for different tasks. Experimental results on the KITTI and Waymo dataset confirm the effectiveness of our approach. The proposed method consistently provides enhanced performance across various difficulty levels compared to the original framework while maintaining real-time efficiency.

Leave It to Large Language Models! Correction and Planning with Memory Integration.

As humans, we can naturally break down a task into individual steps in our daily lives and we are able to provide feedback or dynamically adjust the plan when encountering obstacles. Similarly, our aim is to facilitate agents in comprehending and carrying out natural language instructions in a more efficient and cost-effective manner. For example, in Vision-Language Navigation (VLN) tasks, the agent needs to understand instructions such as "go to the table by the fridge". This understanding allows the agent to navigate to the table and infer that the destination is likely to be in the kitchen. The traditional VLN approach mainly involves training models using a large number of labeled datasets for task planning in unseen environments. However, manual labeling incurs a high cost for this approach. Considering that large language models (LLMs) already possess extensive commonsense knowledge during pre-training, some researchers have started using LLMs as decision modules in embodied tasks, although this approach shows the LLMs' reasoning ability to plan a logical sequence of subtasks based on global information. However, executing subtasks often encounters issues, such as obstacles that hinder progress and alterations in the state of the target object. Even one mistake can cause the subsequent tasks to fail, which makes it challenging to complete the instructions through a single plan. Therefore, we propose a new approach-C (Correction) and P (Planning) with M (Memory) I (Integration)-that centered on an LLM for embodied tasks. In more detail, the auxiliary modules of the CPMI facilitate dynamic planning by the LLM-centric planner. These modules provide the agent with memory and generalized experience mechanisms to fully utilize the LLM capabilities, allowing it to improve its performance during execution. Finally, the experimental results on public datasets demonstrate that we achieve the best performance in the few-shot scenario, improving the efficiency of the successive task while increasing the success rate.

Effect of Humanistic Pain Management in Postpartum Women after Cesarean Delivery Based on Active Pain Assessment and Visual Analog Scale.

Objective: To explore the effect of humanistic pain management based on active pain assessment and the visual analog scale in postpartum women after cesarean delivery. Methods: We selected 100 postpartum women who underwent cesarean delivery in Xuzhou Maternity and Child Health Care Hospital from April to December 2021 and divided the postpartum women into a management group and a conventional group, with 50 cases in each group. The conventional group was given routine pain management, while the management group was given humanistic pain management based on active pain assessment and visual analog scale score. The quality of pain management, sleep quality, unhealthy emotion, maternal comfort, breastfeeding rates, and patient compliance in the 2 groups were compared. Results: The most severe degree of pain, the least degree of pain, the frequency of moderate and severe pain, and the influence of pain on sleep were lower in the management group than in the conventional group. The Pittsburgh Sleep Quality Index score was lower and the Self-Rating Anxiety Scale and the Self-Rating Depression Scale scores were higher in the management group than in the conventional group. In addition, the comfort scores for the second day and the third day after delivery were higher in the management group than in the conventional group. The breastfeeding rate and patient compliance were higher in the management group than in the conventional group. Conclusion: Humanistic pain management based on active pain assessment and the visual analog scale can improve the quality of maternal pain management, the quality of sleep, and maternal comfort, ameliorate maternal adverse emotions, and promote breastfeeding and patient compliance.