Efficacy of autologous platelet concentrate products for alveolar preservation: A meta-analysis.

PURPOSE: The purpose of the study was to systematically evaluate the efficacy of autologous platelet concentrate products in the preservation of the alveolar ridge after tooth extraction through meta-analysis and provide a theoretical basis for the clinical application of autologous platelet concentrates to reduce alveolar bone resorption. METHODS: This study conducted a meta-analysis of clinical trials between 2013 and 2023, focusing on autologous platelet concentrate products (e.g., PRP, PRF, CGF, and PRCF) used for alveolar ridge preservation after tooth extraction. The analysis included 122 articles and 371 extraction sockets. All statistical analyses were performed using Review Manager version 5.4. RESULTS: Results indicate that these platelet concentrates effectively reduced changes in horizontal width 1 mm below the alveolar crest and vertical socket height. They also promoted a higher percentage of new bone formation in extraction sockets compared with control groups. However, they did not significantly prevent horizontal bone resorption at 3 and 5 mm below the alveolar crest. CONCLUSION: In conclusion, autologous platelet concentrates are useful for alveolar ridge preservation, but larger clinical studies are needed to confirm these findings due to the relatively small sample size in this study.

Spatiotemporal correlation between HIF-1α and bone regeneration.

Hypoxia-inducible factors (HIFs) are core regulators of the hypoxia response. HIF signaling is activated in the local physiological and pathological hypoxic environment, acting on downstream target genes to synthesize the corresponding proteins and regulate the hypoxic stress response. HIFs belong to the hypoxia-activated transcription family and contain two heterodimeric transcription factors, HIF-α and HIF-ß. Under hypoxia, the dimer formed by HIF-α binding to HIF-ß translocates into the nucleus and binds to the hypoxia response element (HRE) to induce transcription of a series of genes. HIF-1α plays an important role in innate bone development and acquired bone regeneration. HIF-1α promotes bone regeneration mainly through the following two pathways: (1) By regulating angiogenesis-osteoblast coupling to promote bone regeneration; and (2) by inducing metabolic reprogramming in osteoblasts, promoting cellular anaerobic glycolysis, ensuring the energy supply of osteoblasts under hypoxic conditions, and further promoting bone regeneration and repair. This article reviews recent basic research on HIF-1α and its role in promoting osteogenesis, discusses the possible molecular mechanisms, introduces the hypoxia-independent role of HIF-1α and reviews the application prospects of HIF-1α in tissue engineering.

An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery.

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness.

Nucleoside/nucleotide analog consolidation therapy in hepatitis B e-antigen positive chronic hepatitis B patients: Three years should be preferred.

AIM: To assess the role of consolidation therapy after cessation of nucleoside/nucleotide analogs (NAs) for off-treatment outcome and to explore the optimal consolidation period in hepatitis B e-antigen (HBeAg) positive chronic hepatitis B (CHB) patients. METHODS: We undertook a prospective, observational cohort study including HBeAg positive CHB patients discontinuing NAs. Virological relapse was the primary endpoint of the current study and was defined as a serum hepatitis B virus DNA more than 104 copies/ml (another assay was carried out for confirmation at least 2 weeks later). Propensity score matching analysis was used to balance the baseline characteristics between HBeAg positive CHB patients with different consolidation periods. RESULTS: A total of 190 HBeAg positive CHB patients were included. Fifty-four patients relapsed during follow-up and the cumulative relapse rates at years 1, 3, 5, and 10 were 19.5%, 24.1%, 27.2%, and 32.1%, respectively. Most patients (61.1%) relapsed in the first 6 months. After propensity score matching, significantly lower cumulative relapse rates were observed in patients with consolidation period ≥36 months compared with patients with consolidation period more than 36 months (29.3% vs. 52.8% at year 10; p = 0.012). CONCLUSIONS: Cessation of NAs after HBeAg seroconversion and long-term consolidation is generally safe and feasible in HBeAg positive CHB patients. Three years of consolidation should be preferred in clinical practice.

Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model.

The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has low accuracy and poor real-time performance. To overcome this gap, first, four-channel fusion images, including RGB and DSM (digital surface model), as well as RGB and ExG (excess green), were constructed based on the RGB image acquired from unmanned aerial vehicle (UAV). Second, a Mobile U-Net model that combined a lightweight neural network with a depthwise separable convolution and U-Net model was proposed. Finally, three data sets (RGB, RGB + DSM and RGB + ExG) were used to train, verify, test and evaluate the proposed model. The results of the experiment showed that the overall accuracy of lodging recognition based on RGB + DSM reached 88.99%, which is 11.8% higher than that of original RGB and 6.2% higher than that of RGB + ExG. In addition, our proposed model was superior to typical deep learning frameworks in terms of model parameters, processing speed and segmentation accuracy. The optimized Mobile U-Net model reached 9.49 million parameters, which was 27.3% and 33.3% faster than the FCN and U-Net models, respectively. Furthermore, for RGB + DSM wheat lodging extraction, the overall accuracy of Mobile U-Net was improved by 24.3% and 15.3% compared with FCN and U-Net, respectively. Therefore, the Mobile U-Net model using RGB + DSM could extract wheat lodging with higher accuracy, fewer parameters and stronger robustness.

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery.

Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

LINC00958 facilitates cervical cancer cell proliferation and metastasis by sponging miR-625-5p to upregulate LRRC8E expression.

Accepted as a malignant tumor worldwide, cervical cancer (CC) has attracted much attention for its high incidence and mortality rates. Previous studies have elucidated the critical regulatory function that long noncoding RNAs (lncRNAs) exert on the tumorigenesis and progression of diverse tumors. Although multiple investigations have depicted that LINC00958 has a great impact on the complex biological process of many cancers, knowledge concerning the regulatory role of LINC00958 in CC remains limited and needs to be further explored. In our study, LINC00958 expression was evidently overexpressed in CC tissues and cells. Besides this, LINC00958 negatively regulated miR-625-5p expression and was verified to bind with miR-625-5p in CC. Subsequently, it was testified by a series of experiments that LINC00958 promotes CC cell proliferation and metastasis by sponging miR-625-5p. Furthermore, the leucine-rich repeat containing the eight family member E (LRRC8E) could bind with miR-625-5p, and its expression was negatively modulated by miR-625-5p, whereas positively regulated by LINC00958 in CC. Final rescue assays verified the effects of LINC0095/LRRC8E interaction and miR-625-5p/LRRC8E interaction on CC cell proliferation and metastasis. Collectively, LINC00958 facilitates CC cell proliferation and metastasis via the miR-625-5p/LRRC8E axis.

Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery.

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder-random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network.

Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to replace the fully connected layer to reduce the parameters and a batch normalization layer is added to improve the convergence speed. A transfer learning strategy is used to avoid a long training time. The experimental results show that the overall accuracy of apple leaf classification based on the proposed model can reach 99.01%. Compared with the classical VGG16, the model parameters are reduced by 89%, the recognition accuracy is improved by 6.3%, and the training time is reduced to 0.56% of that of the original model. Therefore, the deep convolutional neural network model proposed in this work provides a better solution for the identification of apple leaf diseases with higher accuracy and a faster convergence speed.

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery.

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles.

Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

Effect of Palliative Care for Patients with Heart Failure.

Palliative care might be beneficial to heart failure. However, the results remain controversial. We conducted a systematic review and meta-analysis to explore the effect of palliative care on heart failure.PubMed, Embase, Web of Science, EBSCO, and Cochrane library databases were systematically searched. Randomized controlled trials (RCTs) assessing the effect of palliative care versus usual care on heart failure were included. Two investigators independently searched articles, extracted data, and assessed the quality of included studies. The primary outcome was readmission. Meta-analysis was performed using random-effect model.Five RCTs involving 545 patients were included in the meta-analysis. Overall, compared with control intervention, palliative care intervention was found to significantly reduce the readmission [Std. mean difference = 0.79; 95% confidence intervals (CI) = 0.23 to 1.35; P = 0.006], Edmonton Symptom Assessment Scale (ESAS) (Std. mean difference = -2.5; 95% CI = -4.39 to -0.62; P = 0.009), and PHQ-9 (Std. mean difference = -1.16; 95% CI = -1.73 to -0.58; P < 0.005), as well as improve heart failure questionnaire (Std. mean difference = 4.46; 95% CI = 3.44 to 5.47; P < 0.005), but had no influence on mortality (RR = 1.54; 95% CI = 0.80 to 2.96; P = 0.19) and quality of life questionnaire (Std. mean difference = 1.81; 95% CI = -0.14 to 3.77; P = 0.07).Compared with control intervention, palliative care intervention was found to significantly reduce readmission, ESAS, PHQ-9, and improve heart failure questionnaire, but showed no influence on mortality and quality of life questionnaire in patients with heart failure.

Efficacy and safety of sofosbuvir-based interferon-free therapies for hepatitis C in liver transplant recipients.

BACKGROUND AND AIM: The aim of this study is to assess the efficacy and safety of sofosbuvir-based interferon-free therapies in liver transplantation recipients with hepatitis C virus infection recurrence. METHODS: A systematic literature search was conducted in PubMed, EMBASE, Web of Science, and CENTRAL on the Cochrane Library without time or language limitation. The search strategy used was "sofosbuvir AND transplantation." Sustained virologic response at 12 weeks after the end of treatment (SVR12) rate, incidence of serious adverse events (SAEs) and/or adverse events, discontinuation rate with 95% confidence intervals (CI) were pooled with random-effects model. RESULTS: Twenty-two studies (1730 patients) were included for our meta-analysis. The pooled SVR12 rate was 90.1% (95% CI 86.4-93.4%, I2 = 81.6%). SVR12 rate was higher in patients with mild fibrosis than in patients with advanced fibrosis and cirrhosis (RR = 1.072, 95% CI 1.031-1.115, I2 = 3.6%). For patients with hepatitis C virus genotype 1, the pooled SVR12 rate was 91.9% (95% CI 89.2-94.2%, I2 = 53.3%). The pooled SAEs incidence was 8.3% (95% CI 5.6-11.5%, I2 = 78.4%). The pooled discontinuation rate because of adverse events or SAEs was 3.3% (95% CI 1.8-5.2%). CONCLUSIONS: Sofosbuvir-based interferon-free therapy is an effective and well-tolerated treatment strategy for patients with hepatitis C virus infection recurrence after liver transplantation.

Evaluation of large esophageal varices in cirrhotic patients by transient elastography: a meta-analysis.

BACKGROUND AND PURPOSE: Transient elastography (TE) has been shown to be a valuable tool for the prediction of large esophageal varices. However, the conclusions have not been always consistent throughout the different studies. Therefore, we performed a further meta-analysis in order to evaluate the diagnostic accuracy of transient elastography for the prediction of large esophageal varices. METHODS: We performed a systematic literature search in PubMed, EMBASE, Web of Science, and CENTRAL in The Cochrane Library without time restriction. The strategy we used was "(fibroscan OR transient elastography OR stiffness) AND esophageal varices". Accuracy measures such as pooled sensitivity, specificity, among others, were calculated using Meta-DiSc statistical software. RESULTS: Twenty studies (2,994 patients) were included in our meta-analysis. The values of pooled sensitivity, specificity, positive and negative likelihood ratios and diagnostic odds ratio were as follows: 0.81 (95% CI, 0.79-0.84), 0.71 (95% CI, 0.69-0.73), 2.63 (95% CI, 2.15-3.23), 0.27 (95% CI, 0.22-0.34) and 10.30 (95% CI, 7.33-14.47). The area under the receiver operating characteristics curve was 0.83. The Spearman correlation coefficient was 0.246 with a p-value of 0.296, indicating the absence of any significant threshold effects. In our subgroup analysis, the heterogeneity could be partially explained by the geographical origin of the study or etiology; or it could be partially explained blindingly, through the appropriate interval and cut-off value of the liver stiffness (LS). CONCLUSIONS: Transient elastography could be used as a valuable non-invasive screening tool for the prediction of large esophageal varices. However, since LS cut-off values vary throughout the different studies and significant heterogeneity also exists among them, we need more reasonable approaches or flow diagram in order to improve the operability of this technology.

Metabolism of diosbulbin B in vitro and in vivo in rats: formation of reactive metabolites and human enzymes involved.

Diosbulbin B (DB), a major constituent of the furano-norditerpenes in Dioscorea bulbifera Linn, exhibits potential antineoplasmic activity and hepatotoxicity. The metabolism and reactive metabolites of DB in vitro (with human and animal liver microsomes) and in vivo in rats were investigated. The human enzymes involved in DB metabolism were identified. DB was first catalyzed into reactive metabolites of 2-butene-1,4-dial derivatives dependent on NADPH and then trapped by Tris base or oxidized to hemiacetal lactones (M12 and M13) in microsomal incubations. Tris base was used as buffer constituent and as a trapping agent for aldehyde. Methoxylamine and glutathione (GSH) were also used as trapping agents. DB metabolism in vivo in rats after oral administration was consistent with that in vitro. The structures of M12 and M13, as well as mono-GSH conjugates of DB (M31), were confirmed by nuclear magnetic resonance spectroscopy of the chemically synthesized products. The bioactivation enzymes of DB were identified as CYP3A4/5, 2C9, and 2C19. CYP3A4 was found to be the primary enzyme using human recombinant cytochrome P450 enzymes, specific inhibitory studies, and a relative activity factor approach for pooled human liver microsomes. Michaelis-Menten constants K(m) and V(max) were determined by the formation of M31. The reactive metabolites may be related to the hepatotoxicity of DB. The gender difference in CYP3A expression in mice and rats contributed to the gender-related liver injury and pharmacokinetics in mice and rats, respectively.

A caveolae-targeted L-type Ca²+ channel antagonist inhibits hypertrophic signaling without reducing cardiac contractility.

RATIONALE: The source of Ca(2+) to activate pathological cardiac hypertrophy is not clearly defined. Ca(2+) influx through the L-type Ca(2+) channels (LTCCs) determines "contractile" Ca(2+), which is not thought to be the source of "hypertrophic" Ca(2+). However, some LTCCs are housed in caveolin-3 (Cav-3)-enriched signaling microdomains and are not directly involved in contraction. The function of these LTCCs is unknown. OBJECTIVE: To test the idea that LTCCs in Cav-3-containing signaling domains are a source of Ca(2+) to activate the calcineurin-nuclear factor of activated T-cell signaling cascade that promotes pathological hypertrophy. METHODS AND RESULTS: We developed reagents that targeted Ca(2+) channel-blocking Rem proteins to Cav-3-containing membranes, which house a small fraction of cardiac LTCCs. Blocking LTCCs within this Cav-3 membrane domain eliminated a small fraction of the LTCC current and almost all of the Ca(2+) influx-induced NFAT nuclear translocation, but it did not reduce myocyte contractility. CONCLUSIONS: We provide proof of concept that Ca(2+) influx through LTCCs within caveolae signaling domains can activate "hypertrophic" signaling, and this Ca(2+) influx can be selectively blocked without reducing cardiac contractility.

Genetic variation of the CYP17 and susceptibility to endometrial cancer: a meta-analysis.

Excessive estrogenic influence is known to be associated with initiation/promotion of endometrial cancer (EC). Common variants among genes coding for enzymes in sex steroid biosynthetic pathways may influence the risk of EC. Cytochrome P450c17α (CYP17), a gene that codes for a key enzyme (cytochrome P450c17α) in a rate-limiting step of estrogen biosynthesis has attracted considerable attention as a candidate gene for EC. The relationship between CYP17 and EC has been reported in various ethnic groups; however, these studies have yielded contradictory results. To investigate this inconsistency, we performed a meta-analysis of 13 studies involving a total of 3,258 cases and 4,614 controls for -34T>C (rs743572) polymorphism of the CYP17 gene to evaluate the effect of CYP17 on genetic susceptibility for EC. An overall random effects odds ratio of 0.71 (95% confidence interval 0.58-0.88, P=0.001) was found under recessive genetic model. Stratified analysis based on ethnicity, sample size and Hardy-Weinberg equilibrium status was conducted to explore potential heterogeneity. This meta-analysis demonstrated that the C allele of -34T>C in CYP17 is a protective factor associated with decreased EC susceptibility, but these associations vary in different ethnic populations.

Predictive Value of HBsAg for Off-treatment Response in HBeAg-negative Chronic Hepatitis B Patients.

OBJECTIVE: To investigate the virological relapse (VR) rate and prognostic value of the HBsAg level at treatment completion for predicting sustained off-treatment response in HBeAg-negative patients after neucleos(t)ide analog (NA) therapy. STUDY DESIGN: Prospective observational cohort study. PLACE AND DURATION OF STUDY: The Second Hospital of Shandong University, Ji'nan, China, between December 2001 and January 2020. METHODOLOGY: Eighty-one HBeAg-negative chronic hepatitis B patients who stopped NA treatment were included. Factors associated with the VR were identified using univariate and multivariate Cox regression models. RESULTS: Of the 81 patients, 42 had sustained off-treatment response with a median follow-up of 60.0 months (interquartile range [IQR] 33.0-111.0 months). Thirty-nine patients relapsed and 32 relapsed within the first year. The cumulative VR rates were 34.6%, 41.0%, 42.5%, 48.1%, and 55.8% at 1, 2, 3, 5, and 10 years off-therapy, respectively. For patients with end-of-treatment (EOT) HBsAg <250 IU/mL, the 10-year cumulative VR rate was 26.0%. Time to HBV DNA negativity (median, 2 months [IQR 1.0-3.0 months]) and age at EOT were also independent predictors of sustained off-treatment response. CONCLUSION: Discontinuing long-term NA treatment is a feasible option for HBeAg-negative chronic hepatitis B patients whose HBsAg levels are low, and HBsAg <250 IU/mL may be an acceptable cut-off value. Younger age at EOT and shorter time to HBV DNA negativity are also independent factors associated with sustained off-treatment response. KEY WORDS: Hepatitis B surface antigen, Nucleos(t)ide analogs, Cessation, Relapse.

Lipoaspirate fluid derived factors and extracellular vesicles accelerate wound healing in a rat burn model.

Background: The regenerative capabilities of derivatives derived from the fat layer of lipoaspirate have been demonstrated. However, the large volume of lipoaspirate fluid has not attracted extensive attention in clinical applications. In this study, we aimed to isolate the factors and extracellular vesicles from human lipoaspirate fluid and evaluate their potential therapeutic efficacy. Methods: Lipoaspirate fluid derived factors and extracellular vesicles (LF-FVs) were prepared from human lipoaspirate and characterized by nanoparticle tracking analysis, size-exclusion chromatography and adipokine antibody arrays. The therapeutic potential of LF-FVs was evaluated on fibroblasts in vitro and rat burn model in vivo. Wound healing process was recorded on days 2, 4, 8, 10, 12 and 16 post-treatment. The scar formation was analyzed by histology, immunofluorescent staining and scar-related gene expression at day 35 post-treatment. Results: The results of nanoparticle tracking analysis and size-exclusion chromatography indicated that LF-FVs were enriched with proteins and extracellular vesicles. Specific adipokines (adiponectin and IGF-1) were detected in LF-FVs. In vitro, LF-FVs augmented the proliferation and migration of fibroblasts in a dose-dependent manner. In vivo, the results showed that LF-FVs significantly accelerated burn wound healing. Moreover, LF-FVs improved the quality of wound healing, including regenerating cutaneous appendages (hair follicles and sebaceous glands) and decreasing scar formation in the healed skin. Conclusion: LF-FVs were successfully prepared from lipoaspirate liquid, which were cell-free and enriched with extracellular vesicles. Additionally, they were found to improve wound healing in a rat burn model, suggesting that LF-FVs could be potentially used for wound regeneration in clinical settings.

Maxillofacial Fractures: A Four-Year Retrospective Study of 1828 Cases in West China.

OBJECTIVE: To analyze the epidemiological and clinical characteristics of maxillofacial fracture cases in a stomatological center in southwest China. METHODS: This study includes 1828 cases of maxillofacial fractures treated in our hospital from January 2018 to December 2021. We analyzed the gender, age, causes of injury, fracture sites, concomitant injuries, treatment, and postoperative infection of these cases. Our data are also compared with those from similar domestic studies. RESULTS: Among the 1828 cases, the male-to-female ratio was 2.48:1 with an average age of 34.55 ± 16.36 years. The highest incidence of fracture was 21-50 years old, and the most common cause of injury was falls (38.95%). There was a statistically significant difference in the composition of injury causes among different age groups(P<0.05). Mandible (37.56%) was the most easily fractured site, and limb injury (17.89%) was the most common concomitant body injury. In all cases, 85.23% of patients were treated with open reduction and internal fixation.  Conclusions: Maxillofacial fractures often occur in the mandible of young and middle-aged men. Falls and traffic accidents are the main causes of injury, often accompanied by limb and brain injuries. Open reduction and internal fixation is still the most commonly used treatment. There are some differences in the results reported by different domestic hospitals.