HucMSC-Exo Induced N2 Polarization of Neutrophils: Implications for Angiogenesis and Tissue Restoration in Wound Healing.

Background: Neutrophils rapidly accumulate in large numbers at sites of tissue damage, exhibiting not only their well-known bactericidal capabilities but also playing crucial roles in angiogenesis and tissue repair. While exosomes derived from human umbilical cord mesenchymal stem cells (HucMSC-Exo) have emerged as a promising therapeutic tool, their exact mechanisms of action remain partly elusive. We hypothesize that HucMSC-Exo treatment may modulate neutrophil phenotypes, thereby significantly influencing wound healing outcomes. Methods: HucMSC-Exo were isolated via ultracentrifugation and subsequently administered through subcutaneous injection into full-thickness cutaneous wounds in mice. To determine the impact of host neutrophils on the healing effects of HucMSC-Exo in skin injuries, strategies including neutrophil depletion and adoptive transfer were employed. Flow cytometry was used to evaluate the proportion of N2 subtype neutrophils in both normal and diabetic wounds, and the effect of HucMSC-Exo on this proportion was assessed. Furthermore, the mitochondrial metabolic reprogramming driven by HucMSC-Exo during N2 polarization was investigated through JC1 staining, ATP quantification, fatty acid uptake assays, and assessment of FAO-related genes (Cpt1b, Acadm, and Acadl). Results: Depleting host neutrophils strikingly dampened prohealing effect of HucMSC-Exo on skin injury, while adoptive transfer of bone marrow neutrophils rescued this process. During normal healing process, some neutrophils expressed N2 markers, in contrast, diabetic wounds exhibited a reduced expression of N2 markers. After treatment with HucMSC-Exo, most neutrophils increased the phosphorylation of STAT6, leading to mitochondrial metabolic reprogramming and thus acquired an N2 phenotype. These N2 neutrophils, polarized by HucMSC-Exo, boosted the release of proangiogenic factors, particularly BV8, a myeloid cell-derived proangiogenic factor, and induced angiogenesis thereby favoring tissue restoration. Conclusion: This research uniquely demonstrates the identification of N2 neutrophils in skin injury and shows that HucMSC-Exo could skew neutrophils toward N2 phenotype, enhancing our insight into how cells react to HucMSC-Exo.

Extracellular vesicles of Bifidobacterium longum reverse the acquired carboplatin resistance in ovarian cancer cells via p53 phosphorylation on Ser15.

We previously found that the relative abundance of Bifidobacterium was increased after chemotherapy; however, the role of Bifidobacterium longum in chemotherapeutic drug resistance in ovarian cancer (OVC) remains unclear. This study aimed to understand the potential effects and mechanism of B. longum extracellular vesicles (B. longum-EVs) on carboplatin (CBP) resistance in OVC. Eight normal and 11 ovarian tissues were collected and the expression of B. longum genomic DNA and its association with acquired CBP resistance in OVC patients was determined. After isolating EVs by ultracentrifugation from B. longum (ATCC 15707), CBP-resistant A2780 cells were treated with PBS, CBP, B. longum-EVs, or CBP + B. longum-EVs, and subsequently analyzed by CCK-8, Edu staining, Annexin V/PI double staining, wound healing, and Transwell assays to detect cell viability, proliferation, apoptosis, migration, and invasion, respectively. MRP1, ATP7A, ATP7B, and p53 expression as well as p53 phosphorylation were measured by western blot analysis. S15A mutation of p53 was assessed to examine the potential role of p53 Ser15 phosphorylation in CBP-resistant OVC. B. longum levels were elevated and positively associated with CBP resistance in OVC patients. Only high concentrations of B. longum-EVs attenuated A2780 cell proliferation, apoptosis, migration, and invasion. B. longum-EVs exposure significantly enhanced the sensitivity of CBP-resistant A2780 cells to CBP and decreased the expression of drug resistance-related proteins. The effect of B. longum-EVs on reversing CBP resistance was completely inhibited by S15A mutation of p53. B. longum-EVs enhanced the sensitivity of OVC cells to CBP through p53 phosphorylation on Ser15.

Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines.

Background: Solar lentigines (SLs), serving as a prevalent characteristic of skin photoaging, present as cutaneous aberrant pigmentation. However, the underlying pathogenesis remains unclear and there is a dearth of reliable diagnostic biomarkers. Objective: The aim of this study was to identify diagnostic biomarkers for SLs and reveal its immunological features. Methods: In this study, gene expression profiling datasets (GSE192564 and GSE192565) of SLs were obtained from the GEO database. The GSE192564 was used as the training group for screening of differentially expressed genes (DEGs) and subsequent depth analysis. Gene set enrichment analysis (GSEA) was employed to explore the biological states associated with SLs. The weighted gene co-expression network analysis (WGCNA) was employed to identify the significant modules and hub genes. Then, the feature genes were further screened by the overlapping of hub genes and up-regulated differential genes. Subsequently, an artificial neural network was constructed for identifying SLs samples. The GSE192565 was used as the test group for validation of feature genes expression level and the model's classification performance. Furthermore, we conducted immune cell infiltration analysis to reveal the immune infiltration landscape of SLs. Results: The 9 feature genes were identified as diagnostic biomarkers for SLs in this study. And an artificial neural network based on diagnostic biomarkers was successfully constructed for identification of SLs. GSEA highlighted potential role of immune system in pathogenesis of SLs. SLs samples had a higher proportion of several immune cells, including activated CD8 T cell, dendritic cell, myeloid-derived suppressor cell and so on. And diagnostic biomarkers exhibited a strong relationship with the infiltration of most immune cells. Conclusion: Our study identified diagnostic biomarkers for SLs and explored its immunological features, enhancing the comprehension of its pathogenesis.

The role of WRKY transcription factors in exogenous potassium (K+) response to NaCl stress in Tamarix ramosissima.

Introduction: Soil salinization poses a significant challenge to plant growth and vitality. Plants like Tamarix ramosissima Ledeb (T. ramosissima), which are halophytes, are often integrated into planting schemes tailored for saline environments. Yet, the role of WRKY transcription factors in T. ramosissima, especially under sodium chloride (NaCl) stress mitigated by exogenous K+ application, is not well-understood. This research endeavors to bridge this knowledge gap. Methods: Using Pfam protein domain prediction and physicochemical property analysis, we delved into the WRKY genes in T. ramosissima roots that are implicated in counteracting NaCl stress when aided by exogenous K+ applications. By observing shifts in the expression levels of WRKY genes annotated to the KEGG pathway under NaCl stress at 0, 48, and 168 h, we aimed to identify potential key WRKY genes. Results: We found that the expression of 56 WRKY genes in T. ramosissima roots responded to exogenous K+ application during NaCl stress at the indicated time points. Particularly, the expression levels of these genes were primarily upregulated within 168 h. From these, 10 WRKY genes were found to be relevant in the KEGG pathways. Moreover, six genes, namely Unigene0024962, Unigene0024963, Unigene0010090, Unigene0007135, Unigene0070215, and Unigene0077293, were annotated to the Plant-pathogen interaction pathway or the MAPK signaling pathway in plants. These genes exhibited dynamic expression regulation at 48 h with the application of exogenous K+ under NaCl stress. Discussion: Our research highlights that WRKY transcription factors can modulate the activation or inhibition of related genes during NaCl stress with the application of exogenous K+. This regulation enhances the plant's adaptability to saline environments and mitigates the damage induced by NaCl. These findings provide valuable gene resources for future salt-tolerant Tamarix breeding and expand our understanding of the molecular mechanisms of WRKY transcription factors in alleviating NaCl toxicity.

Total Synthesis of the Proposed Structure of Neaumycin B.

The total synthesis of the proposed structure of anti-glioblastoma natural product neaumycin B was achieved in 22 steps (longest linear sequence). The synthesis features HCl-mediated [6,6]-spiroketalization, a combination of Krische iridium-catalyzed crotylation, Marshall palladium-catalyzed propargylation, Fürstner nickel-catalyzed regio- and enantioselective vicinal monoprotected diol formation, Brown crotylation and asymmetric halide-aldehyde cycloaddition, so as to establish the challenging contiguous stereocenters.

Application of Machine Learning Algorithms to Predict New-Onset Postoperative Atrial Fibrillation and Identify Risk Factors Following Isolated Valve Surgery.

BACKGROUND: New-onset postoperative atrial fibrillation (POAF) is the most common complication after valvular surgery, but its etiology and risk factors are incompletely understood. This study investigates the benefits of machine learning methods in risk prediction and in identifying relative perioperative variables for POAF after valve surgery. METHODS: This retrospective study involved 847 patients, who underwent isolated valve surgery from January 2018 to September 2021 in our institution. We used machine learning algorithms to predict new-onset postoperative atrial fibrillation and to select relatively important variables from a set of 123 preoperative characteristics and intraoperative information. RESULTS: The support vector machine (SVM) model demonstrated the best area under the receiver operating characteristic (AUC) value of 0.786, followed by logistic regression (AUC = 0.745) and the Complement Naive Bayes (CNB) model (AUC = 0.672). Left atrium diameter, age, estimated glomerular filtration rate (eGFR), duration of cardiopulmonary bypass, New York Heart Association (NYHA) class III-IV, and preoperative hemoglobin were high-ranked variables. CONCLUSIONS: Risk models based on machine learning algorithms may be superior to traditional models, which were primarily based on logistic algorithms to predict the occurrence of POAF after valve surgery. Further prospective multicenter studies are needed to confirm the performance of SVM in predicting POAF.

Average capacity of an underwater wireless communication link with the quasi-Airy hypergeometric-Gaussian vortex beam based on a modified channel model.

Prompted by alleviating the random perturbation of underwater channel and enhancing the performance for the orbital angular momentum (OAM) -based underwater wireless optical communication (UWOC), the quasi-Airy Hypergeometric-Gaussian (QAHyGG) vortex beam is first proposed and demonstrated. Moreover, an underwater channel model is first modified for more accurate simulated results of the propagation property of various beams. Based on the modified model, the transmission and communication performance of three different OAM-carrying beams (the Gauss vortex (GV) beam, the Hypergeometric-Gaussian (HyGG) vortex beam, and the QAHyGG vortex beam) are comparatively studied. In addition, the parameters optimization of the QAHyGG vortex beam is made for further enhancing the average capacity. The results show that the QAHyGG vortex beam exhibits higher received power and lower crosstalk probability under different channel conditions. The average capacity of the QAHyGG vortex beam has enhanced by ∼8% and ∼27% compared with the HyGG vortex beam and the GV beam at 100m, respectively. The QAHyGG vortex beam is more suitable in an OAM-based UWOC system with a limited-size receiving aperture or lower transmit power. Besides, the average capacity will improve effectively at longer distances with the optimized beam parameters. These research results can provide advances in designing the practical OAM-based UWOC system.

Oscillatory-Flow PCR Microfluidic Chip Driven by Low Speed Biaxial Centrifugation.

PCR is indispensable in basic science and biotechnology for in-orbit life science research. However, manpower and resources are limited in space. To address the constraints of in-orbit PCR, we proposed an oscillatory-flow PCR technique based on biaxial centrifugation. Oscillatory-flow PCR remarkably reduces the power requirements of the PCR process and has a relatively high ramp rate. A microfluidic chip that could perform dispensing, volume correction, and oscillatory-flow PCR of four samples simultaneously using biaxial centrifugation was designed. An automatic biaxial centrifugation device was designed and assembled to validate the biaxial centrifugation oscillatory-flow PCR. Simulation analysis and experimental tests indicated that the device could perform fully automated PCR amplification of four samples in one hour, with a ramp rate of 4.4 ∘C/s and average power consumption of less than 30 W. The PCR results were consistent with those obtained using conventional PCR equipment. Air bubbles generated during amplification were removed by oscillation. The chip and device realized a low-power, miniaturized, and fast PCR method under microgravity conditions, indicating good space application prospects and potential for higher throughput and extension to qPCR.

Antifungal Activity and Potential Action Mechanism of Allicin against Trichosporon asahii.

Trichosporon asahii is an emerging opportunistic pathogen that causes potentially fatal disseminated trichosporonosis. The global prevalence of coronavirus disease 2019 (COVID-19) poses an increasing fungal infection burden caused by T. asahii. Allicin is the main biologically active component with broad-spectrum antimicrobial activity in garlic. In this study, we performed an in-depth analysis of the antifungal characteristics of allicin against T. asahii based on physiological, cytological, and transcriptomic assessments. In vitro, allicin inhibited the growth of T. asahii planktonic cells and biofilm cells significantly. In vivo, allicin improved the mean survival time of mice with systemic trichosporonosis and reduced tissue fungal burden. Electron microscopy observations clearly demonstrated damage to T. asahii cell morphology and ultrastructure caused by allicin. Furthermore, allicin increased intracellular reactive oxygen species (ROS) accumulation, leading to oxidative stress damage in T. asahii cells. Transcriptome analysis showed that allicin treatment disturbed the biosynthesis of cell membrane and cell wall, glucose catabolism, and oxidative stress. The overexpression of multiple antioxidant enzymes and transporters may also place an additional burden on cells, causing them to collapse. Our findings shed new light on the potential of allicin as an alternative treatment strategy for trichosporonosis. IMPORTANCE Systemic infection caused by T. asahii has recently been recognized as an important cause of mortality in hospitalized COVID-19 patients. Invasive trichosporonosis remains a significant challenge for clinicians, due to the limited therapeutic options. The present work suggests that allicin holds great potential as a therapeutic candidate for T. asahii infection. Allicin demonstrated potent in vitro antifungal activity and potential in vivo protective effects. In addition, transcriptome sequencing provided valuable insights into the antifungal effects of allicin.

Prediction of new onset postoperative atrial fibrillation using a simple Nomogram.

BACKGROUND: New onset postoperative atrial fibrillation (POAF) is the most common complication of cardiac surgery, with an incidence ranging from 15 to 50%. This study aimed to develop a new nomogram to predict POAF using preoperative and intraoperative risk factors. METHODS: We retrospectively analyzed the data of 2108 consecutive adult patients (> 18 years old) who underwent cardiac surgery at our medical institution. The types of surgery included isolated coronary artery bypass grafting, valve surgery, combined valve and coronary artery bypass grafting (CABG), or aortic surgery. Logistic regression or machine learning methods were applied to predict POAF incidence from a subset of 123 parameters. We also developed a simple nomogram based on the strength of the results and compared its predictive ability with that of the CHA2DS2-VASc and POAF scores currently used in clinical practice. RESULTS: POAF was observed in 414 hospitalized patients. Logistic regression provided the highest area under the receiver operating characteristic curve (ROC) in the validation cohort. A simple bedside tool comprising three variables (age, left atrial diameter, and surgery type) was established, which had a discriminative ability with a ROC of 0.726 (95% CI 0.693-0.759) and 0.727 (95% CI 0.676-0.778) in derivation and validation subsets respectively. The calibration curve of the new model was relatively well-fit (p = 0.502). CONCLUSIONS: Logistic regression performed better than machine learning in predicting POAF. We developed a nomogram that may assist clinicians in identifying individuals who are prone to POAF.

Rapid automatic nucleic acid purification system based on gas-liquid immiscible phase.

The continuous expansion of nucleic acid detection applications has resulted in constant developments in rapid, low-consumption, and highly automated nucleic acid extraction methods. Nucleic acid extraction using magnetic beads across an immiscible phase interface offers significant simplification and parallelization potential. The gas-liquid immiscible phase valve eliminates the requirement for complicated cassettes and is suitable for automation applications. By analyzing the process of magnetic beads crossing the gas-liquid interface, we utilized a low magnetic field strength to drive large magnetic bead packages to cross the gas-liquid interface, providing a solution of high magnetic bead recovery rate for solid-phase extraction with a low-surfactant system based on gas-liquid immiscible phase valve. The recovery rate of magnetic beads was further improved to 90%-95% and the carryover of the reagents was below 1%. Consequently, a chip and an automatic system were developed to verify the applicability of this method for nucleic acid extraction. The Hepatitis B virus serum standard was used for the extraction test. The extraction of four samples was performed within 7 minutes, with nucleic acid recovery maintained above 80% and good purity. Thus, through analysis and experiments, a fast, highly automated, and low-consumption nucleic acid recovery method was proposed in this study.

Machine learning-based dynamic prediction of lateral lymph node metastasis in patients with papillary thyroid cancer.

Objective: To develop a web-based machine learning server to predict lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC) patients. Methods: Clinical data for PTC patients who underwent primary thyroidectomy at our hospital between January 2015 and December 2020, with pathologically confirmed presence or absence of any LLNM finding, were retrospectively reviewed. We built all models from a training set (80%) and assessed them in a test set (20%), using algorithms including decision tree, XGBoost, random forest, support vector machine, neural network, and K-nearest neighbor algorithm. Their performance was measured against a previously established nomogram using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), precision, recall, accuracy, F1 score, specificity, and sensitivity. Interpretable machine learning was used for identifying potential relationships between variables and LLNM, and a web-based tool was created for use by clinicians. Results: A total of 1135 (62.53%) out of 1815 PTC patients enrolled in this study experienced LLNM episodes. In predicting LLNM, the best algorithm was random forest. In determining feature importance, the AUC reached 0.80, with an accuracy of 0.74, sensitivity of 0.89, and F1 score of 0.81. In addition, DCA showed that random forest held a higher clinical net benefit. Random forest identified tumor size, lymph node microcalcification, age, lymph node size, and tumor location as the most influentials in predicting LLNM. And the website tool is freely accessible at http://43.138.62.202/. Conclusion: The results showed that machine learning can be used to enable accurate prediction for LLNM in PTC patients, and that the web tool allowed for LLNM risk assessment at the individual level.

Development of machine learning models for mortality risk prediction after cardiac surgery.

Background: We developed machine learning models that combine preoperative and intraoperative risk factors to predict mortality after cardiac surgery. Methods: Machine learning involving random forest, neural network, support vector machine, and gradient boosting machine was developed and compared with the risk scores of EuroSCORE I and II, Society of Thoracic Surgeons (STS), as well as a logistic regression model. Clinical data were collected from patients undergoing adult cardiac surgery at the First Medical Centre of Chinese PLA General Hospital between December 2008 and December 2017. The primary outcome was post-operative mortality. Model performance was estimated using several metrics, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The visualization algorithm was implemented using Shapley's additive explanations. Results: A total of 5,443 patients were enrolled during the study period. The mean EuroSCORE II score was 3.7%, and the actual in-hospital mortality rate was 2.7%. For predicting operative mortality after cardiac surgery, the AUC scores were 0.87, 0.79, 0.81, and 0.82 for random forest, neural network, support vector machine, and gradient boosting machine, compared with 0.70, 0.73, 0.71, and 0.74 for EuroSCORE I and II, STS, and logistic regression model. Shapley's additive explanations analysis of random forest yielded the top-20 predictors and individual-level explanations for each prediction. Conclusions: Machine learning models based on available clinical data may be superior to clinical scoring tools in predicting postoperative mortality in patients following cardiac surgery. Explanatory models show the potential to provide personalized risk profiles for individuals by accounting for the contribution of influencing factors. Additional prospective multicenter studies are warranted to confirm the clinical benefit of these machine learning-driven models.

Learning Curve of Aortic Arch Replacement Surgery in Chinese Mainland with Stanford Type A Aortic Dissection.

BACKGROUND: Stanford type A aortic dissection (TAAD) is the most common cause of death caused by aortic disease in the Chinese mainland. Patients suffering TAAD need immediate surgical treatment [Pompilio 2001; Di Eusanio 2003; Ueda 2003; Li 2013; Afifi 2016; Zhou 2019; Zhou 2021]. Emergency aortic arch replacement is difficult and risky. The prognosis following surgery varies depending on the different surgical approaches [Pompilio 2001; Kazui 2002; Di Eusanio 2003; Ueda 2003; Moon 2009; Li 2013; Afifi 2016; Zhou 2019; Zhou 2021]. Aortic arch replacement includes total-arch replacement (Sun's operation) and hemi-arch replacement. The comparative analysis of learning curves between the two procedures has not been systematically studied. In this study, we studied and analyzed the learning curves of total-arch replacement and hemi-arch replacement using cumulative sum (CUSUM) analysis. METHODS: From January 2013 to December 2019, a total of 139 Stanford TAAD operations were performed by the same surgeon and two assistants, including 61 cases of hemi-arch replacement and 78 cases of total-arch replacement. Baseline information, including preoperative conditions, intraoperative related data and postoperative prognosis, were collected. Descriptive statistics and CUSUM were used to analyze the total operation time, cardiopulmonary bypass (CPB) time, aortic clamping (AC) time, operative mortality, incidence of postoperative complications, postoperative intensive care unit (ICU) time, hospital stay, and postoperative drainage volume. RESULTS: A total of 139 patients with TAAD (age 48.8 ± 12.3, male, 107, female, 32) underwent emergency aortic arch replacement. A total of 61 patients (43.9%) underwent hemi-arch replacement, and 78 patients (56.1%) underwent total-arch replacement. The total time, cardiopulmonary bypass (CPB) time, and aortic clamping (AC) time of hemi-arch operation were 434.2 ± 137.0 minutes, 243.3 ± 87.2 minutes, and 157.0 ± 60.2 minutes. The total, CPB, and AC times of total-arch operation were 747.8 ± 164.3 minutes, 476.4 ± 121.6 minutes, and 238.5 ± 67.6 minutes. The mortality of hemi-arch operation was 3.3%, and that of total-arch operation was 6.4%. The incidence of complications after hemi-arch operation was 11.3%, and that after total-arch operation was 46.2%. The ICU time and hospital stay after hemi-arch surgery were 7.3 ± 4.4 days and 27.2 ± 16.2 days, respectively, and the ICU time and total hospital stay after total-arch surgery were 7.2 ± 5.9 days and 24.0 ± 10.3 days, respectively. The total drainage volume after hemi-arch operation was 2182.4 ± 1236.4 ml, and that after total-arch operation was 2467.3 ± 1385.7 ml. According to CUSUM analysis, the same cardiovascular surgery team seems to have different learning curves in the time of two operations. CUSUM analysis of intraoperative and postoperative indicators shows that after a certain period of professional and systematic cardiovascular surgery training, aortic hemi-arch replacement has the characteristics of short learning cycle and easy to master for surgeons, while total-arch replacement requires a longer learning cycle. CONCLUSIONS: Although the emergency operation of TAAD is difficult and risky, according to results the of CUSUM analysis, cardiovascular surgeons can achieve better learning results in hemi-arch replacement than total-arch replacement.

Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis.

BACKGROUND: Aortic valve stenosis (AS) disease is the most common valvular disease in developed countries. The pathology of AS is complex, and its main processes include calcification of the valve stroma and involve genetic factors, lipoprotein deposition and oxidation, chronic inflammation, osteogenic transition of cardiac valve interstitial cells, and active valve calcification. The aim of this study was to identify potential genes associated with AS. METHODS: Three original gene expression profiles (GSE153555, GSE12644, and GSE51472) were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R tool or 'limma' in R to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. STRING was utilized for the Protein-Protein Interaction (PPI) Network construct, and tissue-specific gene expression were identified using BioGPS database. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in Targetscan, miWalk, miRDB, Hoctar, and TarBase. RESULTS: A total of 58 upregulated genes and 20 downregulated genes were screened out, which were mostly enriched in matrix remodeling and the immune system process. A module was thus clustered into by PPI network analysis, which mainly involved in Fc gamma R-mediated phagocytosis, Osteoclast differentiation. Ten genes (IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1) with the highest degree scores were subsequently identified as the hub genes for AS by applying the CytoHubba plugin. And hsa-miR-1276 was finally identified as potential miRNA and miRNA-gene regulatory network was constructed using NetworkAnalyst. CONCLUSIONS: Our analysis suggested that IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1 might be hub genes associated with AS, and hsa-miR-1276 was potential miRNA. This result could provide novel insight into pathology and therapy of AS in the future.

1.7 µm gain-switched and mode-locked hybrid Tm-Ho codoped fiber laser signal generation and optimization.

We propose and experimentally demonstrate 1.7 µm gain-switched and mode-locked hybrid laser signal generation using a modulated pump and the nonlinear polarization rotation (NPR) effect. In the laser scheme, a 1.55 µm amplified modulated optical signal was used as a homemade pump. A bidirectional pumping configuration was adopted by splitting the homemade pump. A 1 m long thulium-holmium (Tm-Ho) codoped fiber was used as the gain medium. A fiber Bragg grating was employed as a spectral filter. The mode-locked laser pulse was obtained with a central wavelength of 1724 nm. The repetition rate was 11.81 MHz and the pulse width was 65.27 ps. Additionally, the gain-switched pulse sequences with a repetition rate from 50 kHz to 200 kHz were obtained by the modulated pump. Moreover, the mode-locked pulse train was filtered and modulated by the shape of the gain-switched pulse, and the hybrid pulse train was then obtained. Furthermore, the hybrid laser signals were analyzed and optimized by applying different waveforms of the modulated pump. The experimental results showed that the generated laser pulse driven by the sinusoidal signal has a better SNR (49.39 dB).