A retrospective study of occlusal reconstruction in patients with old jaw fractures and dentition defects.

PURPOSE: This study evaluated the methods and clinical effects of multidisciplinary collaborative treatment for occlusal reconstruction in patients with old jaw fractures and dentition defects. METHODS: Patients with old jaw fractures and dentition defects who underwent occlusal reconstruction at the Third Affiliated Hospital of Air Force Military Medical University from January 2018 to December 2022 were enrolled. Clinical treatment was classified into 3 phases. In phase I, techniques such as orthognathic surgery, microsurgery, and distraction osteogenesis were employed to reconstruct the correct three-dimensional (3D) jaw position relationship. In phase II, bone augmentation and soft tissue management techniques were utilized to address insufficient alveolar bone mass and poor gingival soft tissue conditions. In phase III, implant-supported overdentures or fixed dentures were used for occlusal reconstruction. A summary of treatment methods, clinical efficacy evaluation, comparative analysis of imageological examinations, and satisfaction questionnaire survey were utilized to evaluate the therapeutic efficacy in patients with traumatic old jaw fractures and dentition defects. All data are summarized using the arithmetic mean and standard deviation and compared using independent sample t-tests. RESULTS: In 15 patients with old jaw fractures and dentition defects (an average age of 32 years, ranging from 18 to 53 years), there were 7 cases of malocclusion of single maxillary fracture, 6 of malocclusion of single mandible fracture, and 2 of malocclusion of both maxillary and mandible fractures. There were 5 patients with single maxillary dentition defects, 2 with single mandibular dentition defects, and 8 with both maxillary and mandibular dentition defects. To reconstruct the correct 3D jaw positional relationship, 5 patients underwent Le Fort I osteotomy of the maxilla, 3 underwent bilateral sagittal split ramus osteotomy of the mandible, 4 underwent open reduction and internal fixation for old jaw fractures, 3 underwent temporomandibular joint surgery, and 4 underwent distraction osteogenesis. All patients underwent jawbone augmentation, of whom 4 patients underwent a free composite vascularized bone flap (26.66%) and the remaining patients underwent local alveolar bone augmentation. Free gingival graft and connective tissue graft were the main methods for soft tissue augmentation (73.33%). The 15 patients received 81 implants, of whom 11 patients received implant-supported fixed dentures and 4 received implant-supported removable dentures. The survival rate of all implants was 93.82%. The final imageological examination of 15 patients confirmed that the malocclusion was corrected, and the clinical treatment ultimately achieved occlusal function reconstruction. The patient satisfaction questionnaire survey showed that they were satisfied with the efficacy, phonetics, aesthetics, and comfort after treatment. CONCLUSION: Occlusal reconstruction of old jaw fractures and dentition defects requires a phased sequential comprehensive treatment, consisting of 3D spatial jaw correction, alveolar bone augmentation and soft tissue augmentation, and implant-supported occlusal reconstruction, achieving satisfactory clinical therapeutic efficacy.

M2 macrophage-derived exosomes carry miR-142-3p to restore the differentiation balance of irradiated BMMSCs by targeting TGF-ß1.

Radiotherapy is essential to cancer treatment, while it inevitably injures surrounding normal tissues, and bone tissue is one of the most common sites prone to irradiation. Bone marrow mesenchymal stem cells (BMMSCs) are sensitive to irradiation and the irradiated dysfunction of BMMSCs may be closely related to irradiation-induced bone damage. Macropahges play important role in regulating stem cell function, bone metabolic balance and irradiation response, but the effects of macrophages on irradiated BMMSCs are still unclear. This study aimed to investigate the role of macrophages and macrophage-derived exosomes in restoring irradiated BMMSCs function. The effects of macrophage conditioned medium (CM) and macrophage-derived exosomes on osteogenic and fibrogenic differentiation capacities of irradiated BMMSCs were detected. The key microribonucleic acids (miRNAs) and targeted proteins in exosomes were also determined. The results showed that irradiation significantly inhibited the proliferation of BMMSCs, and caused differentiation imbalance of BMMSCs, with decreased osteogenic differentiation and increased fibrogenic differentiation. M2 macrophage-derived exosomes (M2D-exos) inhibited the fibrogenic differentiation and promoted the osteogenic differentiation of irradiated BMMSCs. We identified that miR-142-3p was significantly overexpressed in M2D-exos and irradiated BMMSCs treated with M2D-exos. After inhibition of miR-142-3p in M2 macrophage, the effects of M2D-exos on irradiated BMMSCs differentiation were eliminated. Furthermore, transforming growth factor beta 1 (TGF-ß1), as a direct target of miR-142-3p, was significantly decreased in irradiated BMMSCs treated with M2D-exos. This study indicated that M2D-exos could carry miR-142-3p to restore the differentiation balance of irradiated BMMSCs by targeting TGF-ß1. These findings pave a new way for promising and cell-free method to treat irradiation-induced bone damage.

BpGRP1 acts downstream of BpmiR396c/BpGRF3 to confer salt tolerance in Betula platyphylla.

Glycine-rich RNA-binding proteins (GRPs) have been implicated in the responses of plants to environmental stresses, but the function of GRP genes involved in salt stress and the underlying mechanism remain unclear. In this study, we identified BpGRP1 (glycine-rich RNA-binding protein), a Betula platyphylla gene that is induced under salt stress. The physiological and molecular responses to salt tolerance were investigated in both BpGRP1-overexpressing and suppressed conditions. BpGRF3 (growth-regulating factor 3) was identified as a regulatory factor upstream of BpGRP1. We demonstrated that overexpression of BpGRF3 significantly increased the salt tolerance of birch, whereas the grf3-1 mutant exhibited the opposite effect. Further analysis revealed that BpGRF3 and its interaction partner, BpSHMT, function upstream of BpGRP1. We demonstrated that BpmiR396c, as an upstream regulator of BpGRF3, could negatively regulate salt tolerance in birch. Furthermore, we uncovered evidence showing that the BpmiR396c/BpGRF3 regulatory module functions in mediating the salt response by regulating the associated physiological pathways. Our results indicate that BpmiR396c regulates the expression of BpGRF3, which plays a role in salt tolerance by targeting BpGRP1.

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications.

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis.

Modern management of MS targets No Evidence of Disease Activity (NEDA): no clinical relapses, no magnetic resonance imaging (MRI) disease activity and no disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent MS disease activity and, where appropriate, escalating treatment, standard radiology reports are qualitative and may be insensitive to the development of new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate clinical validation. In 397 multi-center MRI scan pairs acquired in routine practice, we demonstrate superior case-level sensitivity of a clinically integrated AI-based tool over standard radiology reports (93.3% vs 58.3%), relative to a consensus ground truth, with minimal loss of specificity. We also demonstrate equivalence of the AI-tool with a core clinical trial imaging lab for lesion activity and quantitative brain volumetric measures, including percentage brain volume loss (PBVC), an accepted biomarker of neurodegeneration in MS (mean PBVC -0.32% vs -0.36%, respectively), whereas even severe atrophy (>0.8% loss) was not appreciated in radiology reports. Finally, the AI-tool additionally embeds a clinically meaningful, experiential comparator that returns a relevant MS patient centile for lesion burden, revealing, in our cohort, inconsistencies in qualitative descriptors used in radiology reports. AI-based image quantitation enhances the accuracy of, and value-adds to, qualitative radiology reporting. Scaled deployment of these tools will open a path to precision management for patients with MS.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions.

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

ZXDC enhances cervical cancer metastasis through IGF2BP3-mediated activation of RhoA/ROCK signaling.

Metastasis in cervical cancer (CC) has a significant negative impact on patient survival, highlighting the urgent need for investigation in this area. In this study, we identified significant overexpression of zinc finger, X-linked, duplicated family member C (ZXDC) in CC tissue with metastasis, which correlates with poor outcomes for CC patients. We observed that overexpression of ZXDC promotes, while silencing of ZXDC inhibits the metastasis of CC cells both in vitro and in vivo. Additionally, our research demonstrated that ZXDC activated RhoA/ROCK signaling pathway, leading to enhanced cytoskeleton remodeling in CC cells. Besides, we found that IGF2BP3 plays an essential role in the activation of ZXDC on the RhoA/ROCK signaling pathway by stabilizing RhoA mRNA. These findings reveal a mechanism whereby ZXDC promotes the cervical cancer metastasis by targeting IGF2BP3/RhoA/ROCK pathway.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.

Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.

Background and introduction: Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's applications remain suboptimal in neuroimage analysis tasks such as lesion segmentation in multiple sclerosis (MS), due to variance in lesion characteristics imparted by different scanners and acquisition parameters. Methods: In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training. Results: The proposed method has been validated on two FL MS segmentation scenarios using public and clinical datasets. Specifically, the case-wise and voxel-wise Dice score of the proposed method under the first public dataset is 65.20 and 74.30, respectively. On the second in-house dataset, the case-wise and voxel-wise Dice score is 53.66, and 62.31, respectively. Discussions and conclusions: The Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by significantly outperforming other FL methods. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can achieve comparable performance to that from centralized training with all the raw data.

Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer.

High-grade serous ovarian cancer (HGSOC) is a heterogeneous cancer characterized by high relapse rate. Approximately 80% of women are diagnosed with late-stage disease, and 15-25% of patients experience primary treatment resistance. Ovarian cancer brings tremendous suffering and is the most malignant type in all gynecologic malignancies. Metabolic reprogramming in tumor microenvironment (TME), especially fatty acid metabolism, has been identified to play a crucial role in cancer prognosis. Yet, the underlying mechanism of fatty acid metabolism on ovarian cancer progression is severely understudied. Recently, studies have demonstrated the role of fatty acid metabolism reprogramming in immune cells, but their roles on cancer cell metastasis and cancer immunotherapy response are poorly characterized. Here, we reported that the fatty acid-related genes are aberrantly varied between ovarian cancer and normal samples. Using samples in publicly databases and bio-informatic analyses with fatty acid-related genes, we disentangled that cancer cases can be classified into high- and low-risk groups related with prognosis. Furthermore, the nomogram model was constructed to predict the overall survival. Additionally, we reported that different immune cells infiltration was presented between groups, and immunotherapy response differed in two groups. Results showed that our signature may have good prediction value on immunotherapy efficacy, especially for anti-PD-1 and anti-CTLA-4. Our study systematically marked the critical association between cancer immunity in TME and fatty acid metabolism, and bridged immune phenotype and metabolism programming in tumors, thereby constructed the metabolic-related prognostic model and help to understand the underlying mechanism of immunotherapy response.

Enabling a high-performance saltwater Al-air battery via ultrasonically driven electrolyte flow.

As an emerging battery technology, the Al-air flow battery (AAFB) exhibits high energy density due to the recycling of electrolytes, thus showing great potential as a type of clean and sustainable energy storage system. Conventionally, it employs an external mechanical pump to recycle the electrolyte. In this work, the saltwater AAFB in which the electrolyte is recycled by the ultrasonic capillary effect (rather than a mechanical pump) and the reaction chamber is agitated by ultrasonic vibration, is proposed and investigated. Our numerical simulations show that a travelling ultrasonic wave in the electrolyte flow system causes the capillary flow and agitation. The experimental results show that the percentage increase of the peak power density (relative to that with static electrolyte) can be up to about 7.5 times of that with the electrolyte flow driven by a mechanical pump, under the same electrolyte flow rate and concentration (3.3 ml min-1 and 3 M NaCl). The optimal peak power density, which can be achieved by optimizing the reaction chamber thickness, electrolyte concentration and ultrasonic vibration velocity, is 43.88 mW cm-2. This work illustrates that the acoustofluidic method can not only improve the discharge performance of the saltwater AAFB effectively, but also greatly decrease the energy consumption, weight and volume of the electrolyte driving unit of the AAFB. In addition, analyses based on experimental results show that the energy gain of a series/parallel battery system formed by multiple identical cells can be larger than one, if the number of cells in the system is large enough.

Prognostic nomograms to predict overall survival and cause specific death in vulvar squamous cell carcinoma.

OBJECTIVE: The incidence of vulvar squamous cell carcinoma has been rising in recent decades. The prognosis of patients with vulvar squamous cell carcinoma was explored, and nomograms were constructed to predict survival rates. METHODS: Vulvar squamous cell carcinoma patient data were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training dataset and testing dataset. Univariable and multivariable Cox regression were used to identify risk factors affecting vulvar squamous cell carcinoma overall survival in the training dataset. Cumulative incidence function and Fine-Gray regression were used to analyze cancer specific death in the training dataset. Overall survival and cancer specific death nomograms were constructed and validated in the testing and whole datasets. Receiver operating characteristic and calibration were used to verify the predictive value and clinical applicability of the models. RESULTS: Age ≥60 years, grade 3, American Joint Committee on Cancer stages III and IV, TNM (tumor, nodes, metastasis) stages T2, T3, N1, and M1 had a negative effect on overall survival in vulvar cancer patients. Surgery (hazard ratio (HR)=0.416, 95% confidence interval (CI) 0.349 to 0.496, p<0.001) and chemotherapy (HR=0.637, 95% CI 0.544 to 0.746, p<0.001) may improve overall survival. Age, tumor grade, American Joint Committee on Cancer stage, T stage, N stage, M stage, surgery, and chemotherapy significantly affected vulvar cancer specific death. For area under the receiver operating characteristic curve, the predictive ability of the nomograms for overall survival and cancer specific death for 1 year (area under the curve (AUC)=0.862), 3 years (AUC=0.832), and 5 years (AUC=0.808) were all >0.800. CONCLUSION: The nomograms established in our study had an excellent predictive ability for overall survival and cancer specific death in vulvar cancer patients.

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications.

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.

A Case Study of Grassroots Water Conservancy Services Evaluation and Obstacle Factors Diagnosis Based on Gray Correlation-TOPSIS Model in Hunan Province, China.

Based on the evaluation model of the gray correlation-TOPSIS method, this paper examines the index system of grass-roots water conservancy services in Hunan Province, China. This paper aims at the present situation of grassroots water conservancy in Hunan province, which assisted it in developing grassroots water conservancy services. The evaluation indicators include five criteria levels (institutional staffing, personnel quality, management level, public policy and service capacity) and twenty-four indicator levels. In this paper, the weight calculation method combined with an analytic hierarchy process and an entropy weight method, as well as quantitative and qualitative methods, was used to conduct an empirical study on the basic water conservancy service level of Hunan Province in 2020. The results classify grassroots water services in Hunan Province into three levels. By fitting the GDP of cities and prefectures with the comprehensive closeness, we conclude that there is considerable convergence between the grassroots water conservancy service level of Hunan Province and its local economic level. The more developed the economy, the higher the grassroots water conservancy service level. In addition, through obstacle factor analysis, the main constraints of grassroots water conservancy in various cities and prefectures are obtained. Therefore, the grassroots water conservancy service's ability can be comprehensively improved from three aspects: serviceability, capital investment, and talent construction. This indicator system can promote the overall governance capacity of grassroots water conservancy in the future development of cities and prefectures, and it can also provide Hunan with experience and case examples for the implementation of rural revitalization.

A potential association between Toxoplasma gondii infection and schizophrenia in mouse models.

Schizophrenia is a serious neuropsychiatric disease of uncertain etiology, which causes human mental disorder and affects about 1% of the population. In recently years, some studies showed that some cases of schizophrenia may be associated with Toxoplasma gondii infection. In order to investigate a potential association between Toxoplasma infection and schizophrenia, we investigated the relative clinical symptom of schizophrenia such as learning and memory capability, depression and stereotypy to find some useful information by behavioral test in mouse models. Our results demonstrated that mice from Toxoplasma infection and MK-801 administration (as the model of schizophrenia) were impaired in learning and memory capability, and they had more serious depression and stereotypy compared with the control mice, especially the mice from congenital Toxoplasma infection. In addition, our results clearly showed that the number of cysts in brain tissue of congenital Toxoplasma infection mice was significantly low than in acquired Toxoplasma infected mice. Collectively, these results suggested a potential association between Toxoplasma infection and schizophrenia.

[Construction and identification of recombinant adenovirus of muramidase-released protein gene fragment from Streptococcus suis type 2].

OBJECTIVE: To construct and identify the recombinant adenovirus of muramidase-released protein (MRP) gene fragment from Streptococcus suis type 2 (SS2). METHODS: The specific primers were designed based on the sequence of MRP gene fragment. The MRP gene fragment (467-1351 bp) was amplified by PCR method with genomic DNA of SS2 as a template. PCR products were cloned in pMD18-T vector. Then MRP gene fragment was linked into the adenovirus shuttle plasmid (pShuttle-CMV) to construct recombinant shuttle plasmid (pShuttle-CMV-MRP). After PmeI digestion, it was transformed into BJ5183-AD-1 competent cells containing adenoviral backbone plasmid pAdEasy-1 to construct homogeneous recombinant adenovirus plasmid (pAdeno-CMV-MRP). Then the recombinant adenovirus plasmid was linearized by PmeI and then transfected into AD-293 cells for viral packaging. Finally, the virus liquid was tested by PCR and Western blotting. RESULTS: Cytopathic effect (CPE) was observed at 8 d after transfection of linear pAdeno-CMV-MRP in AD-293 cells. MRP gene fragment and protein expression were also detected in the virus liquid. CONCLUSION: The recombinant adenovirus of MRP gene fragment (rAdeno-MRP) from SS2 was constructed successfully.

[Research progress on the molecular pathogenesis of Trichomonas vaginalis].

Trichomonas vaginalis is one of the most common human sexually transmitted pathogens that colonize the urogenital mucosa. This paper reviews those factors in the molecular pathogenesis of the parasite, including cell adhesin, interaction with fibronectin and laminin, G-proteins, pore-forming protein and proteinases.

[In vitro anti-Trichomonas vaginalis effects of a mixture of dihydroartemisinin and metronidazole].

OBJECTIVE: To observe the effect of a mixture of dihydroartemisinin and metronidazole on ultrastructure of Trichomonas vaginalis trophozoites in vitro for exploring trichomonacidal mechanism of the drug mixture. METHODS: The trophozoites were cultivated with liver extract solution medium that contained 2.5 x 10(6) parasites/ml. There were dihydroartemisinin 0.5 mg/ml and metronidazole 0.002 mg/ml in the experimental tubes of the drug mixture group. Groups of control (without drug), dihydroartemisinin (1 mg/ml) and metronidazole (5 mg/ml) were established and performed in the same experimental conditions. The parasites were observed by scanning and transmission electron microscopes after having treated with the drugs at 37 degrees for 3.5-5 h. RESULTS: Under scanning electron microscope, the cell membrane of T. vaginalis treated only with dihydroartemisinin for 35 h was damaged, part of pellicle peeled off. Although the surface of the trophozoites treated only with metronidazole for 5 h showed many small bubbles and hollows, the cell membrane looked integral. However, surface of the parasite exposed to the drug mixture for 3.5-4.2 h showed deep folds and cracks, the cell membrane was damaged and even peeled off. When the cell ruptured, the nucleus, axostyle, pelta and hydrogenosomes were exposed, and the cytoplasm spilled out. Transmission electron microscopy showed that the membrane system of the trophozoites treated only with dihydroartemisinin for 3.5 h was damaged considerably. The cytoplasm of damaged parasite spilled out. The cytoplasm of the parasite treated only with metronidazole for 3.5-5 h was damaged seriously. Vacuoles and crevices were visible in the cytoplasm. The cell membrane and the content of the parasites treated with the drug mixture for 3.5-4.5 h were damaged seriously. There were some vacuoles and crevices, dilated endoplasmic reticulum, injured and deformed hydrogenosomes in the cytoplasm. The cell organelles mostly disappeared. Crevices also existed in the nucleus. The nuclear membrane fractured and even disappeared. CONCLUSION: The acting targets of dihydroartemisinin and metronidazole to T. vaginalis trophozoite were different, and a combination of the two drugs shows stronger effect in killing the parasites.

[Effect of dihydroartemisinin on the ultrastructure of Trichomonas vaginalis trophozoites in vitro].

OBJECTIVE: To observe the effect of dihydroartemisinin (DHA) on the ultrastructure of Trichomonas vaginalis cultured in vitro. METHODS: The trophozoites of T. vaginalis were cultivated with liver extract solution medium containing 1 mg/ml dihydroartemisinin, and were then observed by scanning and transmission electron microscopes after the treatment for 2.5-4.0 h. RESULTS: The cell membranes of the trophozoites treated with DHA were damaged considerably. The surface of T. vaginalis showed deepening folds, hollow, and cracks. The nuclear membrane appeared fractures. There were a few of crevices in the nucleus and cytoplasm. Disordered and dilated endoplasmic reticulum, injured and deformed hydrogenosomes were found. Cytoplasm of the damaged parasites spilled over from torn place. After the cell membrane was peeled off, the nuclei, hydrogenosomes and pelta were exposed, which finally resulted in the death of the denatured parasites. CONCLUSION: Dihydroartemisinin can destroy membrane structure and organelles of Trichomonas vaginalis trophozoites, which leads to decomposition and necrosis of the parasites.