Bletilla striata Polysaccharide-/Chitosan-Based Self-Healing Hydrogel with Enhanced Photothermal Effect for Rapid Healing of Diabetic Infected Wounds via the Regulation of Microenvironment.

The management of diabetic ulcers poses a significant challenge worldwide, and persistent hyperglycemia makes patients susceptible to bacterial infections. Unfortunately, the overuse of antibiotics may lead to drug resistance and prolonged infections, contributing to chronic inflammation and hindering the healing process. To address these issues, a photothermal therapy technique was incorporated in the preparation of wound dressings. This innovative solution involved the formulation of a self-healing and injectable hydrogel matrix based on the Schiff base structure formed between the oxidized Bletilla striata polysaccharide (BSP) and hydroxypropyltrimethylammonium chloride chitosan. Furthermore, the introduction of CuO nanoparticles encapsulated in polydopamine imparted excellent photothermal properties to the hydrogel, which promoted the release of berberine (BER) loaded on the nanoparticles and boosted the antibacterial performance. In addition to providing a reliable physical protection to the wound, the developed hydrogel, which integrated the herbal components of BSP and BER, effectively accelerated wound closure via microenvironment regulation, including alleviated inflammatory reaction, stimulated re-epithelialization, and reduced oxidative stress based on the promising results from cell and animal experiments. These impressive outcomes highlighted their clinical potential in safeguarding the wound against bacterial intrusion and managing diabetic ulcers.

Advancing materials science through next-generation machine learning.

For over a decade, machine learning (ML) models have been making strides in computer vision and natural language processing (NLP), demonstrating high proficiency in specialized tasks. The emergence of large-scale language and generative image models, such as ChatGPT and Stable Diffusion, has significantly broadened the accessibility and application scope of these technologies. Traditional predictive models are typically constrained to mapping input data to numerical values or predefined categories, limiting their usefulness beyond their designated tasks. In contrast, contemporary models employ representation learning and generative modeling, enabling them to extract and encode key insights from a wide variety of data sources and decode them to create novel responses for desired goals. They can interpret queries phrased in natural language to deduce the intended output. In parallel, the application of ML techniques in materials science has advanced considerably, particularly in areas like inverse design, material prediction, and atomic modeling. Despite these advancements, the current models are overly specialized, hindering their potential to supplant established industrial processes. Materials science, therefore, necessitates the creation of a comprehensive, versatile model capable of interpreting human-readable inputs, intuiting a wide range of possible search directions, and delivering precise solutions. To realize such a model, the field must adopt cutting-edge representation, generative, and foundation model techniques tailored to materials science. A pivotal component in this endeavor is the establishment of an extensive, centralized dataset encompassing a broad spectrum of research topics. This dataset could be assembled by crowdsourcing global research contributions and developing models to extract data from existing literature and represent them in a homogenous format. A massive dataset can be used to train a central model that learns the underlying physics of the target areas, which can then be connected to a variety of specialized downstream tasks. Ultimately, the envisioned model would empower users to intuitively pose queries for a wide array of desired outcomes. It would facilitate the search for existing data that closely matches the sought-after solutions and leverage its understanding of physics and material-behavior relationships to innovate new solutions when pre-existing ones fall short.

Macrophage microvesicle-derived circ_YTHDF2 in methamphetamine-induced chronic lung injury.

Long-term abuse of methamphetamine (MA) can cause lung toxicity. Intercellular communication between macrophages and alveolar epithelial cells (AECs) is critical for maintaining lung homeostasis. Microvesicles (MVs) are an important medium of intercellular communication. However, the mechanism of macrophage MVs (MMVs) in MA-induced chronic lung injury remains unclear. This study aimed to investigate if MA can augment the activity of MMVs and if circ_YTHDF2 is a key factor in MMV-mediated macrophage-AEC communication, and to explore the mechanism of MMV-derived circ_YTHDF2 in MA-induced chronic lung injury. MA elevated peak velocity of the pulmonary artery and pulmonary artery accelerate time, reduced the number of alveolar sacs, thickened the alveolar septum, and accelerated the release of MMVs and the uptake of MMVs by AECs. Circ_YTHDF2 was downregulated in lung and MMVs induced by MA. The immune factors in MMVs were increased by si-circ_YTHDF. Circ_YTHDF2 knockdown in MMVs induced inflammation and remodelling in the internalised AECs by MMVs, which was reversed by circ_YTHDF2 overexpression in MMVs. Circ_YTHDF2 bound specifically to and sponged miRNA-145-5p. Runt-related transcription factor 3 (RUNX3) was identified as potential target of miR-145-5p. RUNX3 targeted zinc finger E-box-binding homeobox 1 (ZEB1)-related inflammation and EMT of AECs. In vivo, circ_YTHDF2 overexpression-MMVs attenuated MA-induced lung inflammation and remodelling by the circ_YTHDF2-miRNA-145-5p-RUNX3 axis. Therefore, MA abuse can induce pulmonary dysfunction and alveolus injury. The immunoactivity of MMVs is regulated by circ_YTHDF2. Circ_YTHDF2 in MMVs is the key to communication between macrophages and AECs. Circ_YTHDF2 sponges miR-145-5p targeting RUNX3 to participate in ZEB1-related inflammation and remodelling of AECs. MMV-derived circ_YTHDF2 would be an important therapeutic target for MA-induced chronic lung injury. KEY POINTS: Methamphetamine (MA) abuse induces pulmonary dysfunction and alveoli injury. The immunoactivity of macrophage microvesicles (MMVs) is regulated by circ_YTHDF2. Circ_YTHDF2 in MMVs is the key to MMV-mediated intercellular communication between macrophages and alveolar epithelial cells. Circ_YTHDF2 sponges miR-145-5p targeting runt-related transcription factor 3 (RUNX3) to participate in zinc finger E-box-binding homeobox 1 (ZEB1)-related inflammation and remodelling. MMV-derived circ_YTHDF2 would be an important therapeutic target for MA-induced chronic lung injury.

Improved ellipse-fitting phase demodulation technique to suppress the effect of light source intensity noise in interferometric system.

An improved ellipse-fitting algorithm phase demodulation (EFAPD) technique is proposed to reduce the influence of light source intensity noise on a system. In the original EFAPD, the sum of the intensities of coherent light (ICLS) is an important part of the interference signal noise, which makes the demodulation results suffer. The improved EFAPD corrects the ICLS and fringe contrast quantity of the interference signal by an ellipse-fitting algorithm, and then calculates the ICLS based on the structure of pull-cone 3 × 3 coupler, so as to remove it in the algorithm. Experimental results show that the noise of the improved EFAPD system is significantly reduced compared with that of the original EFAPD, with a maximum reduction of 35.57 dB. The improved EFAPD makes up for the deficiency of the original EFAPD in suppressing light source intensity noise, and promotes the application and popularization of EFAPD.

A Berberine-Loaded Bletilla striata Polysaccharide Hydrogel as a New Medical Dressing for Diabetic Wound Healing.

The healing process of a diabetic wound (DW) is often impeded by a series of interrelated factors, including severe infection, persistent inflammation, and excessive oxidative stress. Therefore, it is particularly crucial to develop a medical dressing that can address these issues simultaneously. To this end, different ratios of Bletilla striata polysaccharide (BSP) and berberine (BER) were physically blended with Carbomer 940 (CBM940) to develop a composite hydrogel as a medical dressing. The BSP/BER hydrogel was characterized using SEM, FTIR, rheological testing and other techniques. The anti-inflammatory, antioxidant, and antibacterial properties of the hydrogel were evaluated using cell and bacterial models in vitro. A DW model of ICR mice was established to evaluate the effect of the hydrogel on DW healing in vivo. The hydrogel exhibited excellent biocompatibility and remarkable antibacterial, anti-inflammatory, and antioxidant properties. In addition, animal experiments showed that the BSP/BER hydrogel significantly accelerated wound healing in DW mice. Among the different formulations, the LBSP/BER hydrogel (2% BSP, mBER:mBSP = 1:40) demonstrated the most remarkable efficacy. In conclusion, the BSP/BER hydrogel developed exhibited immense properties and great potential as a medical dressing for the repair of DW, addressing a crucial need in clinical practice.

ZEB1: New advances in fibrosis and cancer.

Zinc finger E-box binding homeobox 1 (ZEB1) is an important transcription factor in epithelial mesenchymal transition (EMT) which participates in the numerous life processes, such as embryonic development, fibrosis and tumor progression. ZEB1 has multiple functions in human body and plays a crucial part in some life processes. ZEB1 is vital for the formation and development of the organs in the embryonic period. The abnormal expression of ZEB1 is a predictor for the poor prognosis or the poor survival in several cancers. ZEB1 contributes to the occurrence of fibrosis, cancer and even chemoresistance. Some research is indicated that fibrosis is finally developed into the cancers. Therefore, ZEB1 is probably taken as a biomarker in fibrosis or cancer. In this review, it is predicted of the structure of ZEB1 and the protein binding sites of ZEB1 with some protein, and it is discussed about the roles of ZEB1 in fibrosis and cancer progression to elaborate the potential applications of ZEB1 in clinic.

Non-coding RNA: insights into the mechanism of methamphetamine neurotoxicity.

Chronic exposure of the methamphetamine has been shown to lead to neurotoxicity in rodents and humans. The manifestations of methamphetamine neurotoxicity include methamphetamine use disorder, methamphetamine abuse, methamphetamine addiction and methamphetamine behavioral sensitization. Repeated use of methamphetamine can cause methamphetamine use disorder. The abuse and addiction of methamphetamine are growing epidemic worldwide. Repeated intermittent exposure to methamphetamine can cause behavioral sensitization. In addition, many studies have shown that changes in the expression of non-coding RNA in the ventral tegmental area and nucleus accumbens will affect the behavioral effects of methamphetamine. Non-coding RNA plays an important role in the behavioral effects of methamphetamine. Therefore, it is important to study the relationship between methamphetamine and non-coding RNA. The purpose of this review is to study the non-coding RNA associated with methamphetamine neurotoxicity to search for the possible therapeutic target of the methamphetamine neurotoxicity.

Calcium-sensing receptor in the development and treatment of pulmonary hypertension.

Calcium-sensing receptor (CaSR) is widely involved in the cell proliferation, differentiation, migration, adhesion and apoptosis, which can affect the vascular remodeling in the humanbody. The main ligand of CaSR is extracellular Ca2+. CaSR has the physiological significance in Ca2+ homeostasis. Pulmonary vascular remodeling is one of the main histopathological changes of pulmonary hypertension (PH). The abnormal proliferation of pulmonary artery smooth muscle cells (PASMCs) results in the pulmonary vascular remodeling. CaSR is an important regulator of [Ca2+]i. [Ca2+]i is the main cause of the excessive pulmonary vascular remodeling in patients with PH. In this review, it was conclued that the structure of CaSR was prone to explore the devolopment or the treatment of PH. It was found that the regulation of CaSR with some miRNA could inhibit the proliferation of PASMCs, and that CaSR could affect the occurrence of autophagy in PH. Therefore, CaSR would become a new therapeutic target to PH.

Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. RESULTS: In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. CONCLUSION: Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference.

Bayesian negative binomial regression for differential expression with confounding factors.

Motivation: Rapid adoption of high-throughput sequencing technologies has enabled better understanding of genome-wide molecular profile changes associated with phenotypic differences in biomedical studies. Often, these changes are due to multiple interacting factors. Existing methods are mostly considering differential expression across two conditions studying one main factor without considering other confounding factors. In addition, they are often coupled with essential sophisticated ad-hoc pre-processing steps such as normalization, restricting their adaptability to general experimental setups. Complex multi-factor experimental design to accurately decipher genotype-phenotype relationships signifies the need for developing effective statistical tools for genome-scale sequencing data profiled under multi-factor conditions. Results: We have developed a novel Bayesian negative binomial regression (BNB-R) method for the analysis of RNA sequencing (RNA-seq) count data. In particular, the natural model parameterization removes the needs for the normalization step, while the method is capable of tackling complex experimental design involving multi-variate dependence structures. Efficient Bayesian inference of model parameters is obtained by exploiting conditional conjugacy via novel data augmentation techniques. Comprehensive studies on both synthetic and real-world RNA-seq data demonstrate the superior performance of BNB-R in terms of the areas under both the receiver operating characteristic and precision-recall curves. Availability and implementation: BNB-R is implemented in R language and is available at https://github.com/siamakz/BNBR. Supplementary information: Supplementary data are available at Bioinformatics online.

Covariate-dependent negative binomial factor analysis of RNA sequencing data.

Motivation: High-throughput sequencing technologies, in particular RNA sequencing (RNA-seq), have become the basic practice for genomic studies in biomedical research. In addition to studying genes individually, for example, through differential expression analysis, investigating co-ordinated expression variations of genes may help reveal the underlying cellular mechanisms to derive better understanding and more effective prognosis and intervention strategies. Although there exists a variety of co-expression network based methods to analyze microarray data for this purpose, instead of blindly extending these methods for microarray data that may introduce unnecessary bias, it is crucial to develop methods well adapted to RNA-seq data to identify the functional modules of genes with similar expression patterns. Results: We have developed a fully Bayesian covariate-dependent negative binomial factor analysis (dNBFA) method-dNBFA-for RNA-seq count data, to capture coordinated gene expression changes, while considering effects from covariates reflecting different influencing factors. Unlike existing co-expression network based methods, our proposed model does not require multiple ad-hoc choices on data processing, transformation, as well as co-expression measures and can be directly applied to RNA-seq data. Furthermore, being capable of incorporating covariate information, the proposed method can tackle setups with complex confounding factors in different experiment designs. Finally, the natural model parameterization removes the need for a normalization preprocessing step, as commonly adopted to compensate for the effect of sequencing-depth variations. Efficient Bayesian inference of model parameters is derived by exploiting conditional conjugacy via novel data augmentation techniques. Experimental results on several real-world RNA-seq datasets on complex diseases suggest dNBFA as a powerful tool for discovering the gene modules with significant differential expression and meaningful biological insight. Availability and implementation: dNBFA is implemented in R language and is available at https://github.com/siamakz/dNBFA.

Hybrid sensing face detection and registration for low-light and unconstrained conditions.

The capability to track, detect, and identify human targets in highly cluttered scenes under extreme conditions, such as in complete darkness or on the battlefield, has been one of the primary tactical advantages in military operations. In this paper, we propose a new collaborative, multi-spectrum sensing method to achieve face detection and registration under low-light and unconstrained conditions. We design and prototype a novel type of hybrid sensor by combining a pair of near-infrared (NIR) cameras and a thermal camera (a long-wave infrared camera). We strategically surround each NIR sensor with a ring of LED IR flashes to capture the "red-eye," or more precisely, the "bright-eye" effect of the target. The "bright-eyes" are used to localize the 3D position of eyes and face. The recovered 3D information is further used to warp the thermal face imagery to a frontal-parallel pose so that additional tasks, such as face recognition, can be reliably conducted, especially with the assistance of accurate eye locations. Experiments on real face images are provided to demonstrate the merit of our method.

Effect of Worm Predation on Changes in Waste Activated Sludge Properties.

This study explored the effects of worm predation on changes in waste activated sludge properties. Results showed that the rate by which worm predation reduced mixed liquor volatile suspended solids (MLVSS) was approximately 23.7% ± 3.1%. Particle size distribution and extracellular polymeric substance (EPS) analyses indicated that the reduction of fine particles and EPS content in sludge predated by worms mainly increased dewaterability and reduced the ratio of MLVSS/mixed liquor suspended solids. Moreover, both mean particle size and protein/carbohydrate ratio increased. The results of three-dimensional excitation-emission matrix and gel filtration chromatogram analyses demonstrated the varied properties of soluble microbial products and EPS were attributed to the worms' selective predation of low molecular-weight organic matter, which facilitated the hydrolysis of macromolecular organic matter.

Predicting the Elastic Modulus of Recycled Concrete Considering Material Nonuniformity: Mesoscale Numerical Method.

The evaluation of the elastic modulus of recycled concrete is one of the focuses of civil engineering and structural engineering, which is not only related to the stability of building structures but also related to the resource utilization of concrete. Therefore, based on the IRSM method in mesoscale, a novel model for predicting the elastic modulus of recycled concrete is proposed which has the advantages of being low-cost and high-precision, amongst others, compared to theoretical and experimental methods. Then, the influence of coarse aggregate, contact surface, gelling material, and air bubbles on the elastic modulus of recycled concrete is studied. The IRSM model includes four processes: Identification, Reconstruction, Simulation, and Monte Carlo, which can accurately reconstruct the geometric characteristics of coarse aggregate, efficiently reconstruct the coarse aggregate accumulation model, and quickly analyze the elastic modulus of concrete, as well as fully consider the nonuniform characteristics of coarse aggregate distribution and shape. Compared with the experimental results, the error is less than 5%, which verifies the rationality of the IRSM method. The results of the parametric analysis show that the influence of each factor on the elastic modulus of concrete in descending order is elastic modulus of cement, elastic modulus of coarse aggregate, content of coarse aggregate, content of air voids, elastic modulus of contacting surface, and thickness of contacting surface, and the corresponding Pearson's Coefficients are 0.688, 0.427, 0.412, -0.269, 0.188, and -0.061, respectively, in which the content of air voids and thickness of contact surface have a negative effect on the elastic modulus of concrete. These influences mainly affect the deformation resistance (elastic modulus) of concrete through "force chain" adjustment, including the force transfer effect, number of paths, and integrity.

Polarimetric Helmholtz Stereopsis.

Helmholtz stereopsis (HS) exploits the reciprocity principle of light propagation (i.e., the Helmholtz reciprocity) for 3D reconstruction of surfaces with arbitrary reflectance. In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical HS by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We derive the reciprocity relationship of Mueller matrix and formulate new reciprocity constraint that takes polarization state into account. We also utilize polarimetric constraints and extend them to the case of perspective projection. For the recovery of surface depths and normals, we incorporate reciprocity constraint with diffuse/specular polarimetric constraints in a unified optimization framework. For depth estimation, we further propose to utilize the consistency of diffuse angle of polarization. For normal estimation, we develop a normal refinement strategy based on degree of linear polarization. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.

Bletilla striata polysaccharides protect against ARDS by modulating the NLRP3/caspase1/GSDMD and HMGB1/TLR4 signaling pathways to improve pulmonary alveolar macrophage pyroptosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Bletilla striata polysaccharides (BSP) extracted from the B. striata tuber, have been demonstrated to possess anti-inflammatory properties. However, their potential protective effect against ARDS and their role in regulating cell pyroptosis remained unexplored. AIM OF THE STUDY: The aim of this study was to investigate the therapeutic effect of BSP in the alleviation of lipopolysaccharide (LPS)-induced ARDS, and to explore its mechanism of action. METHODS: The effect of BSP was assessed by LPS injection into the intraperitoneal cavity in vivo; pathological changes of ARDS mice were gauged by immunohistochemical, hematoxylin and eosin staining, and immunofluorescence assays. MH-S cells were used to model the pyroptosis in vitro. Finally, the pyroptosis of alveolar macrophage was detected by western blots, qPCR, and flow cytometry for NLRP3/caspase1/GSDMD and HMGB1/TLR4 pathway-associated proteins and mRNA. RESULTS: BSP could significantly increase the weight and survival rate of mice with ARDS, alleviate the cytokine storm in the lungs, and reduce lung damage in vivo. BSP inhibited the inflammation caused by LPS/Nigericin significantly in vitro. Compared with the control group, there was a remarkable surge in the incidence of pyroptosis observed in ARDS lung tissue and alveolar macrophages, whereas BSP significantly diminished the pyroptosis ratio. Besides, BSP reduced NLRP3/caspase1/GSDMD and HMGB1/TLR4 levels in ARDS lung tissue and MH-S cells. CONCLUSIONS: These findings proved that BSP could improve LPS-induced ARDS via inhibiting pyroptosis, and this effect was mediated by NLRP3/caspase1/GSDMD and HMGB1/TLR4, suggesting a therapeutic potential of BSP as an anti-inflammatory agent for ARDS treatment.

Structure of a polysaccharide MDP2-1 from Melastoma dodecandrum Lour. and its anti-inflammatory effects.

The anti-inflammatory activity of polysaccharides derived from Melastoma dodecandrum Lour. was evaluated in pyretic mice and HEK-Blue™ hTLR4 cells. The testing led to the identification of MDP2-1, which was then investigated for its structural characteristics and anti-inflammatory effects. Results showed that MDP2-1 had a molecular weight of 29.234 kDa and primarily consisted of galactose, arabinose, rhamnose, glucose, glucuronic acid, and galacturonic acid. Its main backbone was composed of →4)-α-D-GalpA-(1→, →2)-α-L-Rhap-(1→, →3,4)-α-D-GalpA-(1→, →2,4)-α-D-GlcpA-(1→, and its side chains were connected by →4)-α-D-Galp-(1→, α-D-Galp-(1→, →4)-ß-D-Glcp-(1→, and α-L-Araf-(1→. In vivo experiments on mice demonstrated that MDP2-1 attenuated LPS-induced acute lung injury, and in vitro experiments on RAW264.7 cells showed that MDP2-1 reduced the levels of inflammatory mediators and mitigated LPS-induced inflammatory damage by inhibiting the activation of the TLR4 downstream NF-κB/MAPK pathway. These findings suggest that MDP2-1 is a novel anti-inflammatory agent for therapeutic interventions.

Tetrastigma hemsleyanum polysaccharide ameliorates cytokine storm syndrome via the IFN-γ-JAK2/STAT pathway.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an disease characterized by pulmonary edema and widespread inflammation, leading to a notably high mortality rate. The dysregulation of both pro-inflammatory and anti-inflammatory systems, results in cytokine storm (CS), is intricately associated with the development of ALI/ARDS. Tetrastigma hemsleyanum polysaccharide (THP) exerts remarkable anti-inflammatory and immunomodulatory effects against the disease, although its precise role in pathogenesis remains unclear. In the present study, an ALI/ARDS model was established using bacterial lipopolysaccharides. THP administration via aerosol inhalation significantly mitigated lung injury, reduced the number of inflammatory cells, and ameliorated glycerophospholipid metabolism. Furthermore, specific CS-related pathways were investigated by examining the synergy between tumor necrosis factor-α and interferon-γ used to establish CS models. The results indicated that THP effectively decreased inflammatory damage and cell death. The RNA sequencing revealed the involvement of the Janus kinase (JAK) 2-signal transducers and activators of transcription (STAT) signaling pathway in exerting the mentioned effects. Additionally, THP inhibited the activation of the JAK-STAT pathway, thereby alleviating the CS both in vivo and in vitro. Overall, THP exhibited marked therapeutic potential against ALI/ARDS and CS, primarily by targeting the IFN-γ-JAK2/STAT signaling pathway.

Tetrastigma hemsleyanum polysaccharides alleviate imiquimod-induced psoriasis-like skin lesions in mice by modulating the JAK/STAT3 signaling pathway.

BACKGROUND: The pathogenesis of psoriasis involves the interaction between keratinocytes and immune cells, leading to immune imbalance. While most current clinical treatment regimens offer rapid symptom relief, they often come with significant side effects. Tetrastigma hemsleyanum polysaccharides (THP), which are naturally nontoxic, possess remarkable immunomodulatory and anti-inflammatory properties. METHODS: In this study, we utilized an imiquimod (IMQ)-induced psoriasis mouse model and a LPS/IL-6-stimulated HaCaT model. The potential and mechanism of action of THP in psoriasis treatment were assessed through methods including Psoriasis Area Severity Index (PASI) scoring, histopathology, flow cytometry, immunoblotting, and reverse transcription-polymerase chain reaction (RT-PCR). RESULTS: Percutaneous administration of THP significantly alleviated symptoms and manifestations in IMQ-induced psoriatic mice, including improvements in psoriatic skin appearance (erythema, folds, scales), histopathological changes, decreased PASI scores, and spleen index. Additionally, THP suppressed abnormal proliferation of Th17 cells and excessive proliferation and inflammation of keratinocytes. Furthermore, THP exhibited the ability to regulate the JAK/STAT3 signaling pathway. CONCLUSION: Findings from in vivo and in vitro studies suggest that THP can inhibit abnormal cell proliferation and excessive inflammation in lesional skin, balance Th17 immune cells, and disrupt the interaction between keratinocytes and Th17 cells. This mechanism of action may involve the modulation of the JAK/STAT3 signaling pathway, offering potential implications for psoriasis treatment.