Li-Hong Tang alleviates dextran sodium sulfate-induced colitis by regulating NRF2/HO-1 signaling pathway and gut microbiota.

Introduction: Ulcerative colitis (UC) is marked by recurring inflammation. Existing treatments are ineffective and may have toxic side effects. Thus, new therapeutic agents are urgently needed. We studied the botanical formula "Li-Hong Tang (LHT)", which contains two main ingredients, Salvia plebeia R. Br and Rhodiola crenulata (Hook. f. et Thoms.) H. Ohba. In this study, we aimed to identify the effects of LHT on UC and explore its potential mechanism. Methods: LHT was analyzed using a mass spectrometer (MS). DSS at a dose of 2.5% was utilized to develop UC in mice. The administered groups received low, medium, and high dosages (0.32 g/kg, 0.64 g/kg, and 1.28 g/kg) of LHT and the positive medication, sulfasalazine (0.2 g/kg), respectively. Body weight, disease activity index (DAI) score, colon length, spleen index, serum myeloperoxidase (MPO), nitric oxide (NO), superoxide dismutase (SOD) and inflammatory factor concentrations were monitored. The expression of NRF2 and HO-1 in colonic tissues was evaluated by immunohistochemistry. 16S rDNA sequencing was employed to investigate alterations in the gut microbiota of the mice, aiming to elucidate the extent of LHT's impact. Results: LHT may ameliorate DSS-induced colitis in mice by lowering inflammation, reducing oxidative stress, restoring the intestinal barrier, and influencing the NRF2/HO-1 pathway. Moreover, LHT treatment exhibited a regulatory effect on the gut microbiota, characterized by elevated levels of Patescibacteria, Verrucomicrobiota, Candidatus_Saccharimonas, Lactobacillus, and Ligilactobacillus levels while decreasing Oscillibacter and Colidextribacter levels. Further study indicated that MPO, NO, and inflammatory factors were positively correlated with Oscillibacter, Colidextribacter, Escherichia-Shigella, Anaerostines, and negatively with Lactobacillus, Clostridiales_unclassified, Candidatus_Saccharimonas, and Patescibacteria. Furthermore, colony network analysis revealed that Lactobacillus was negatively associated with Oscillibacter and Colidextribacter, whereas Oscillibacter was positively related to Colidextribacter. Conclusion: LHT protects against DSS-induced mice by inhibiting the inflammatory response, oxidative stress, and mucosal injury. The protective role may involve regulating the NRF2/HO-1 signaling pathway and gut microbiota.

Non-invasive screening of bladder cancer using digital microfluidics and FLIM technology combined with deep learning.

Non-invasive screening for bladder cancer is crucial for treatment and postoperative follow-up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non-invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three-class prediction of high-risk (malignant), low-risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high-grade and low-grade bladder cancer cases. This highly accurate non-invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential.

[Bioinformatics database and production layout visual analysis platform of medicinal plants:a case study of Dao-di medicinal materials in Sichuan province].

In the new stage of trans-omics and trans-subjects for medicinal plants, it is an urgent need to integrate big data, provide interactive applications, and form a unified and multi-level research system and big data platform. Dao-di medicinal material, as an important source of medicinal plants, is a unique quality concept and comprehensive standard of tranditional Chinese medicine(TCM). Several databases have been developed in China and abroad, such as the Encyclopedia of Traditional Chinese Medicine(ETCM) and the Global Pharmacopoeia Genome Database(GPGD). Yet, most databases do not provide multi-dimensional data, including geographic data, phenotype data, compound data, and genetic data. Sichuan, known as the hometown of TCM therapies and the treasure trove of TCM, is the most representative region of medicinal plant diversity in China. According to the latest data of the fourth national survey of TCM resources, there are more than 8 000 TCM and 86 Dao-di medicinal materials in Sichuan province. Based on resource census data and relevant achievements, this study constructed the bioinformatics database of medicinal plants and the visual analysis platform of production layout by taking the Dao-di medicinal materials in Sichuan province as an example, covering geographic data, phenotype data, compound data, and genetic data. It effectively integrates multi-dimensional data of Dao-di medicinal materials and provides different levels of data interaction applications. The platform is the first large-scale multi-dimensional database and visual platform of Dao-di medicinal materials in Sichuan province, which serves as an essential resource for germplasm resources identification, decomposition of biosynthetic pathways, molecular breeding of varieties and provides medicinal plant resource information and data support for development and utilization of medicinal plants in China and abroad.

Research and implementation of multi-disease diagnosis on chest X-ray based on vision transformer.

Background: Disease diagnosis in chest X-ray images has predominantly relied on convolutional neural networks (CNNs). However, Vision Transformer (ViT) offers several advantages over CNNs, as it excels at capturing long-term dependencies, exploring correlations, and extracting features with richer semantic information. Methods: We adapted ViT for chest X-ray image analysis by making the following three key improvements: (I) employing a sliding window approach in the image sequence feature extraction module to divide the input image into blocks to identify small and difficult-to-detect lesion areas; (II) introducing an attention region selection module in the encoder layer of the ViT model to enhance the model's ability to focus on relevant regions; and (III) constructing a parallel patient metadata feature extraction network on top of the image feature extraction network to integrate multi-modal input data, enabling the model to synergistically learn and expand image-semantic information. Results: The experimental results showed the effectiveness of our proposed model, which had an average area under the curve value of 0.831 in diagnosing 14 common chest diseases. The metadata feature network module effectively integrated patient metadata, further enhancing the model's accuracy in diagnosis. Our ViT-based model had a sensitivity of 0.863, a specificity of 0.821, and an accuracy of 0.834 in diagnosing these common chest diseases. Conclusions: Our model has good general applicability and shows promise in chest X-ray image analysis, effectively integrating patient metadata and enhancing diagnostic capabilities.

Analysis of Fluid Replacement in Two Fluidic Chambers for Oblique-Incidence Reflectivity Difference (OI-RD) Biosensor.

Optical biosensors have a significant impact on various aspects of our lives. In many applications of optical biosensors, fluidic chambers play a crucial role in facilitating controlled fluid delivery. It is essential to achieve complete liquid replacement in order to obtain accurate and reliable results. However, the configurations of fluidic chambers vary across different optical biosensors, resulting in diverse fluidic volumes and flow rates, and there are no standardized guidelines for liquid replacement. In this paper, we utilize COMSOL Multiphysics, a finite element analysis software, to investigate the optimal fluid volume required for two types of fluidic chambers in the context of the oblique-incidence reflectivity difference (OI-RD) biosensor. We found that the depth of the fluidic chamber is the most crucial factor influencing the required liquid volume, with the volume being a quadratic function of the depth. Additionally, the required fluid volume is also influenced by the positions on the substrate surface bearing samples, while the flow rate has no impact on the fluid volume.

Enhancing the photodynamic effect of curcumin through modification with TiO2 nanoparticles and cationic polymers.

Curcumin (CUR), a natural compound extracted from turmeric, has shown potential as a photosensitizer in photodynamic therapy (PDT). The aim of this work was to enhance the efficacy of CUR by modifying it using titanium dioxide (TiO2) nanoparticles and a cationic polymer called Sofast to create a nanocomposite TiO2-CUR-Sofast (TCS). Compared to unmodified CUR, TCS exhibited a broadening toward longer wavelength in the absorption wavelength within the 400-550 nm range, leading to improved CUR absorption. Cellular uptake efficiency of TCS was also enhanced, and it demonstrated nearly 4.7-fold higher reactive oxygen species (ROS) generation than CUR. Furthermore, TCS displayed the ability to attach to the cell membrane and enter cells within a 30-min incubation period. Upon irradiation, TCS exhibited remarkable cytotoxicity, resulting in a significant reduction in the viability of various cancer cells. Autofluorescence lifetime imaging of intracellular reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) enzymes indicated that cancer cells treated with TCS and irradiation undergo a metabolic pathway shift from oxidative phosphorylation to glycolysis. These findings highlight the potential of TCS as an effective PDT agent for cancer treatment.

Ojeok-san enhances platinum sensitivity in ovarian cancer by regulating adipocyte paracrine IGF1 secretion.

BACKGROUND: Platinum is a commonly used drug for ovarian cancer (OvCa) treatment, but drug resistance limits its clinical application. This study intended to delineate the effects of adipocytes on platinum resistance in OvCa. METHODS: OvCa cells were maintained in the adipocyte-conditioned medium. Cell viability and apoptosis were detected by CCK-8 and flow cytometry, separately. Proliferation and apoptosis-related protein expression were assayed by western blot. The IC50 values of cisplatin and carboplatin were determined using CCK-8. IGF1 secretion and expression were assayed via ELISA and western blot, respectively. A xenograft model was established, and pathological changes were detected by H&E staining. Proliferation and apoptosis-associated protein expression was assessed via IHC. RESULTS: Adipocytes promoted the viability and repressed cell apoptosis in OvCa, as well as enhancing platinum resistance, while the addition of IGF-1 R inhibitor reversed the effects of adipocytes on proliferation, apoptosis, and drug resistance of OvCa cells. Treatment with different concentrations of Ojeok-san (OJS) inhibited the adipocyte-induced platinum resistance in OvCa cells by suppressing IGF1. The combined treatment of OJS and cisplatin significantly inhibited tumour growth in vivo with good mouse tolerance. CONCLUSION: In summary, OJS inhibited OvCa proliferation and platinum resistance by suppressing adipocyte paracrine IGF1 secretion.

Identification of key immune cell-related genes involved in tumorigenesis and prognosis of cervical squamous cell carcinoma.

The infiltration of immune cells can significantly affect the prognosis and immune therapy of patients with cervical squamous cell carcinoma (CSCC). This study aimed to explore key immune cell-related genes in the tumorigenesis and prognosis of CSCC. The module significantly related to immunity was screened by weighted gene co-expression network analysis (WGCNA) and ESTIMATE analysis, followed by correlation analysis with clinical traits. Key candidate genes were intersected with the protein-protein interaction (PPI) network genes for immune-related genes. The relationship between immune cell infiltration and key genes was analyzed. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) predicted the response to immunotherapy in CSCC patients. Clinically, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were manipulated for analyzing the changes in mRNA and protein expression of key genes in cancer. Western blot was conducted to assess the correlation between key genes and immune infiltration. The brown module was notably associated with the immune microenvironment of CSCC, from which three immune-related key genes (TYROBP, CCL5, and HLA-DRA) were obtained. High expression of these genes was significantly positively associated with the infiltration abundance of T cells, B cells, and other immune cells. High expression levels of three key genes were confirmed in para-cancer tissue and correlated with the abundance of immune cells. The high-expression group of key genes was more sensitive to immunotherapy. We provide a theoretical basis for searching for potential targets for effective treatment and diagnosis of CSCC and provide new ideas for developing novel immunotherapy strategies.

CD39+MDSC Facilitates Cisplatin Resistance of Ovarian Cancer Cells by Affecting Adenosine Synthesis.

BACKGROUND: Chemoresistance is a major cause of relapse or death in ovarian cancer (OC) patients. New evidence suggests the crucial role of myeloid-derived suppressor cells (MDSCs) in mediating chemoresistance of cancer cells. We aimed to dissect the way MDSCs affect the cisplatin resistance phenotype of OC and the related mechanisms. METHODS: MDSCs were isolated from the spleen of OC mice isograft. CCK-8 and colony formation assays revealed the effects of an MDSC-conditioned medium with dysregulated CD39 on the proliferation and cisplatin sensitivity of OC cells. Fluorescence assay was used to reveal the effects of MDSCs with dysregulated CD39 on adenosine triphosphate (ATP) hydrolysis and adenosine (ADO) synthesis. RESULTS: MDSCs with highly expressed CD39 could facilitate the proliferation and cisplatin resistance of OC cells, while MDSCs with downregulated CD39 caused the opposite results. In addition, MDSCs with upregulated CD39 could facilitate the hydrolysis of immunogenic ATP to immunosuppressive ADO, while the introduction of CD39 inhibitor could repress such hydrolysis of ATP and generation of ADO, thereby abating the proliferation and cisplatin resistance of OC cells. CONCLUSION: CD39+MDSC could promote the proliferation and cisplatin resistance of OC cells by generating high concentrations of ADO, which indicates that targeting CD39+MDSC might be a feasible way to improve cisplatin resistance in OC.

Evaluating Differentiation Status of Mesenchymal Stem Cells by Label-Free Microscopy System and Machine Learning.

Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.

Detection speed optimization of the OI-RD microscope for ultra-high throughput screening.

The oblique-incidence reflectivity difference (OI-RD) microscope is a label-free detection system for microarrays that has many successful applications in high throughput drug screening. The increase and optimization of the detection speed of the OI-RD microscope will enable it to be a potential ultra-high throughput screening tool. This work presents a series of optimization methods that can significantly reduce the time to scan an OI-RD image. The wait time for the lock-in amplifier was decreased by the proper selection of the time constant and development of a new electronic amplifier. In addition, the time for the software to acquire data and for translation stage movement was also minimized. As a result, the detection speed of the OI-RD microscope is 10 times faster than before, making the OI-RD microscope suitable for ultra-high throughput screening applications.

Prognosis and Efficacy of Laparoscopic Surgery on Patients with Endometrial Carcinoma: Systematic Evaluation and Meta-Analysis.

Objective: The prognosis and efficacy of laparoscopic surgery (LPS) and open surgery or robotic surgery (RS) on endometrial carcinoma (EC) patients were compared. Methods: Data as of May 2021 were retrieved from databases like PubMed, Embase, Cochrane Library, and Web of Science. The study involved randomized controlled trials (RCTs), cohort studies, or case-control studies for comparing the effects of LPS and open surgery or robotic surgery (RS) on EC treatment. The primary outcomes included duration of operation, blood loss, length of stay (LOS), postoperative complications, and recurrence rate. Secondary outcomes included 3-year progression-free survival (PFS) rate/disease-free survival (DFS) rate and 3-year overall survival (OS) rate. Results: A total of 24 studies were involved, and all of them were cohort studies except 1 RCT and 1 case-control study. There was no significant difference in duration of operation between LPS and open surgery (MD = -0.06, 95% CI: -0.37 to 0.25) or RS (MD = -0.15, 95% CI: -1.27 to 0.96). In comparison with the open surgery, LPS remarkably reduced blood loss (MD = -0.43, 95% CI: -0.58 to -0.29), LOS (MD = -0.71, 95% CI: -0.92 to -0.50), and the complication occurrence rate (RR = 0.83, 95% CI: 0.73 to 0.95). However, LPS and RS saw no difference in blood loss (MD = 0.01, 95% CI: -0.77 to 0.79). Besides, in comparison with RS, LPS prominently shortened the LOS (MD = 0.26, 95% CI: 0.12 to 0.40) but increased the complication occurrence rate (RR = 1.74, 95% CI: 1.57 to 1.92). In contrast to open surgery or RS, LPS saw no difference in occurrence rate (RR = 0.75, 95% CI: 0.56 to 1.01; RR = 0.97, 95% CI: 0.62 to 1.53), 3-year PFS/DFS (RR = 0.99, 95% CI: 0.90 to 1.09; RR = 1.30, 95% CI: 0.87 to 1.96), and 3-year OS (RR = 0.97, 95% CI: 0.91 to 1.04; RR = 1.21, 95% CI: 0.91 to 1.60). Conclusion: In sum, LPS was better than open surgery, which manifested in the aspects of less blood loss, shorter LOS, and fewer complications. LPS, therefore, was the most suitable option for EC patients. Nevertheless, LPS had no advantage over RS, and sufficient prospective RCTs are needed to further confirm its strengths.

Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning.

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.

Fluorescence lifetime imaging of NAD(P)H upon oxidative stress in Kluyveromyces marxianus.

K. marxianus is a promising cell factory for producing heterologous proteins. Oxidative stresses were raised during overexpression of heterologous proteins, leading to the shift of the redox state. How to measure the redox state of live K. marxianus cells without perturbing their growth remains a big challenge. Here, a fluorescence lifetime imaging (FLIM)-based method was developed in live K. marxianus cells. During the early exponential growth, K. marxianus cells exhibited an increased mean fluorescence lifetime (τ-mean) of NAD(P)H compared with Saccharomyces cerevisiae cells, which was consistent with the preference for respiration in K. marxianus cells and that for fermentation in S. cerevisiae cells. Upon oxidative stresses induced by high temperature or H2O2, K. marxianus cells exhibited an increased τ-mean in company with decreased intracellular NAD(P)H/NAD(P)+, suggesting a correlation between an increased τ-mean and a more oxidized redox state. The relationship between τ-mean and the expression level of a heterologous protein was investigated. There was no difference between the τ-means of K. marxianus strains which were not producing a heterologous protein. The τ-mean of a strain yielding a high level of a heterologous protein was higher than that of a low-yielding strain. The results suggested the potential application of FLIM in the non-invasive screen of high-yielding cells.

Visible Particle Identification Using Raman Spectroscopy and Machine Learning.

Visible particle identification is a crucial prerequisite step for process improvement and control during the manufacturing of injectable biotherapeutic drug products. Raman spectroscopy is a technology with several advantages for particle identification including high chemical sensitivity, minimal sample manipulation, and applicability to aqueous solutions. However, considerable effort and experience are required to extract and interpret Raman spectral data. In this study, we applied machine learning algorithms to analyze Raman spectral data for visible particle identification in order to minimize expert support and improve data analysis accuracy. We manually prepared ten types of particle standard solutions to simulate the particle types typically observed during manufacturing and established a Raman spectral library with accurate peak assignments for the visible particles. Five classification algorithms were trained using visible particle Raman spectral data. All models had high prediction accuracy of >98% for all types of visible particles. Our results demonstrate that the combination of Raman spectroscopy and machine learning can provide a simple and accurate data analysis approach for visible particle identification.

Application of Dual-Enhanced Surface-Enhanced Raman Scattering Probe Technology in the Diagnosis of Tumor Cells in Vitro.

With the development of precision medicine, antigen/antibody-targeted therapy has brought great hope to tumor patients; however, the migration of tumor cells, especially a small number of cells flowing into blood or other tissues, remains a clinical challenge. In particular, it is difficult to use functional gold nanomaterials for targeted clinical tumor diagnosis while simultaneously obtaining stable and highly sensitive Raman signals. Therefore, we developed a detection method for functional Au Nanostars (AuNSs) with dual signal enhancement that can specifically track location and obtain high-intensity surface-enhanced Raman scattering (SERS) signals. First, AuNSs with specific optical properties were synthesized and functionalized. The Raman dye 4-mercapto-hydroxybenzoic acid and polyethylene glycol were coupled with the tumor marker, epidermal growth factor receptor, to obtain the targeted SERS probes. In addition, a detection chip was prepared for Raman detection with physical enhancement, exhibiting a 40-times higher signal intensity than that of quartz glass. This study combines physical enhancement and SERS enhancement technologies to achieve dual enhancement, enabling the detection of a highly sensitive and stable Raman signal; this has potential clinical value for antigen/antibody-targeted tumor diagnosis and treatment.

Single cell sorting of young yeast based on label-free fluorescence lifetime imaging microscopy.

Saccharomyces cerevisiae is an attractive organism used in the fermentation industry and is an important model organism for virus research. The ability to sort yeast cells is important for diverse applications. Replicative aging of Saccharomyces Cerevisiae is accompanied by metabolic changes that are related to an essential coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H). Here, a single cell sorting method based on fluorescence lifetime imaging microscopy (FLIM) and laser-induced forward transfer (LIFT) was implemented for the first time. The aging level of yeast was determined based on the FLIM by NAD(P)H, which was a label-free and noninvasive method for studying individual cells. Then, young and active yeast cells were sorted by the LIFT system at the single cell level. During the entire experiment, a sterile and humid environment was maintained to ensure the activity of cells. The high viability of sorted cells was achieved by the LIFT combining with FLIM.

Dynamic Structure of Yeast Septin by Fast Fluctuation-Enhanced Structured Illumination Microscopy.

When Saccharomyces cerevisiae divides, a structure composed of different septin proteins arranged according to a certain rule is formed at the cell division site. The structure undergoes multiple remodeling stages during the cell cycle, thus guiding the yeast cells to complete the entire division process. Although the higher-order structure of septins can be determined using electron microscopy, the septin's dynamic processes are poorly understood because of limitations in living cell super-resolution imaging technology. Herein, we describe a high lateral resolution and temporal resolution technique, known as fast fluctuation-enhanced structured illumination microscopy (fFE-SIM), which more than doubles the SIM resolution at a frame rate of 38 Hz in living cells. This allows a highly dynamic and sparse septin structure to be observed in Saccharomyces cerevisiae.

Dearomatization of 2,3-Disubstituted Indoles via 1,8-Addition of Propargylic (Aza)-para-Quinone Methides.

Dearomatization of indole is a useful strategy to access indolimines: a motif widely exists in biologically active molecules and natural products. Herein, an efficient method for the dearomatization of 2,3-disubstituted indoles to generate diverse indolimines with tetrasubstituted allenes is described. This work accomplishes dearomatization of 2,3-disubstituted indoles through 1,8-addition of (aza)-para-quinone methides, which are generated in situ from propargylic alcohols. A series of synthetically useful indolimines containing quaternary carbon centers and tetrasubstituted allenes can be accessed in good yields (up to 99%). Additionally, the separability of product isomers, diversified product transformations, and easy scale-up of the reaction demonstrate the potential application of this method.

Reversing the Polarity of MoS2 with PTFE.

Pristine monolayer molybdenum disulfide (MoS2) demonstrates predominant and persistent n-type semiconducting polarity due to the natural sulfur vacancy, which hinders its electronic and optoelectronic applications in the rich bipolarity area of semiconductors. Current doping strategies in single-layer MoS2 are either too mild to reverse the heavily n-doped polarity or too volatile to create a robust electronic device meeting the requirements of both a long lifetime and compatibility for mass production. Herein, we demonstrate that MoS2 can be transferred onto polytetrafluoroethylene (PTFE), one of the most electronegative substrates. After transfer, the MoS2 photoluminescence exhibits an obvious blueshift from 1.83 to 1.89 eV and a prolonged lifetime, from 0.13 to 3.19 ns. The Fermi level of MoS2 experiences a remarkable 510 meV decrease, transforming its electronic structure into that of a hole-rich p-type semiconductor. Our work reveals a strong p-doping effect and charge transfer between MoS2 and PTFE, shedding light on a new nonvolatile strategy to fabricate p-type MoS2 devices.