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.

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.

SLC5A3 is important for cervical cancer cell growth.

Novel molecular targets for cervical cancer must be identified. This study examined the role of SLC5A3, a myo-inositol transporter, in the pathogenesis of cervical cancer. Through boinformatics analysis, we showed that the SLC5A3 mRNA levels were upregulated in cervical cancer tissues. The upregulated SLC5A3 mRNA levels were negatively correlated with survival and progression-free interval. Genes co-expressed with SLC5A3 were enriched in multiple signaling cascades involved in cancer progression. In primary/established cervical cancer cells, SLC5A3 shRNA/knockout (KO) exerted growth-inhibitory effects and promoted cell death/apoptosis. Furthermore, SLC5A3 knockdown or KO downregulated myo-inositol levels, induced oxidative injury, and decreased Akt-mTOR activation in cervical cancer cells. In contrast, supplementation of myo-inositol or n-acetyl-L-cysteine or transduction of a constitutively active Akt1 construct mitigated SLC5A3 KO-induced cytotoxicity in cervical cancer cells. Lentiviral SLC5A3 overexpression construct transduction upregulated the cellular myo-inositol level and promoted Akt-mTOR activation, enhancing cervical cancer cell proliferation and migration. The binding of TonEBP to the SLC5A3 promoter was upregulated in cervical cancer. In vivo studies showed that intratumoral injection of SLC5A3 shRNA-expressing virus arrested cervical cancer xenograft growth in mice. SLC5A3 KO also inhibited pCCa-1 cervical cancer xenograft growth. The SLC5A3-depleted xenograft tissues exhibited myo-inositol downregulation, Akt-mTOR inactivation, and oxidative injury. Transduction of sh-TonEBP AAV construct downregulated SLC5A3 expression and inhibited pCCa-1 cervical cancer xenograft growth. Together, overexpressed SLC5A3 promotes growth of cervical cancer cells, representing as a novel therapeutic oncotarget for the devastating disease.

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.

Identification lodging degree of wheat using point cloud data and convolutional neural network.

Wheat is one of the important food crops, and it is often subjected to different stresses during its growth. Lodging is a common disaster in filling and maturity for wheat, which not only affects the quality of wheat grains, but also causes severe yield reduction. Assessing the degree of wheat lodging is of great significance for yield estimation, wheat harvesting and agricultural insurance claims. In particular, point cloud data extracted from unmanned aerial vehicle (UAV) images have provided technical support for accurately assessing the degree of wheat lodging. However, it is difficult to process point cloud data due to the cluttered distribution, which limits the wide application of point cloud data. Therefore, a classification method of wheat lodging degree based on dimensionality reduction images from point cloud data was proposed. Firstly, 2D images were obtained from the 3D point cloud data of the UAV images of wheat field, which were generated by dimensionality reduction based on Hotelling transform and point cloud interpolation method. Then three convolutional neural network (CNN) models were used to realize the classification of different lodging degrees of wheat, including AlexNet, VGG16, and MobileNetV2. Finally, the self-built wheat lodging dataset was used to evaluate the classification model, aiming to improve the universality and scalability of the lodging discrimination method. The results showed that based on MobileNetV2, the dimensionality reduction image from point cloud obtained by the method proposed in this paper has achieved good results in identifying the lodging degree of wheat. The F1-Score of the classification model was 96.7% for filling, and 94.6% for maturity. In conclusion, the point cloud dimensionality reduction method proposed in this study could meet the accurate identification of wheat lodging degree at the field scale.

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.

Preference prediction analysis based on graph model for environmental governance conflict.

A mathematical model for decision maker's preference prediction in environmental governance conflict is established based on the graph model for conflict resolution. The rapid economic development in many countries, over the past decades, has caused serious environmental pollution. Sewage companies are the main source of contamination since they are always wavering on the issue of environmental governance because of their profit-seeking nature. Environmental management departments cannot grasp the offending company preferences accurately. The problem of how to obtain decision maker's preference in environmental governance conflict is studied in this paper. The mathematical model established in this paper can obtain a preference set of one decision maker on the promise that the ideal conflict outcome and preference of the other decision makers are known. Then, preference value distribution information entropy is introduced to mine the preference information contained in the preference set, which means that it is possible to get the preference information of conflict opponent at their own ideal conflict outcome. All of these preference sets provide the possibility to choose the appropriate coping strategies and lead the conflict to the direction that some decision makers want. Finally, the effectiveness and superiority of the preference prediction analysis method is verified through a case study of "Chromium Pollution in Qujing County" which took place in China. The preference prediction analysis method in this paper can provide decision making support for the decision makers in environmental governance from strategic level.

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.

MicroRNA-196b promotes cell growth and metastasis of ovarian cancer by targeting ZMYND11.

BACKGROUND: The purpose of this study was to explore the mechanism by which microRNA-196b exerts a tumor promotion effect on ovarian cancer (OCa). METHODS: Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to examine the expression of microRNA-196b in 60 pairs of tumor samples and paracancerous ones collected from patients with ovarian cancer, meanwhile, microRNA-196b expression in human ovarian cancer cell lines and normal ovarian epithelial cell lines were also analyzed by qRT-PCR. Bioinformatics methods suggested that ZMYND11 was predicted the target gene of microRNA-196b and its binding relationship was verified by dual luciferase reporter gene experiment. Then cell counting kit (CCK-8) and 5-Ethynyl-2'-deoxyuridine (EdU) assay were performed to analyze the influence of microRNA-196b overexpression on OCa cell proliferation. RESULTS: QRT-PCR results showed that microRNA-196b level in OCa tissues was remarkably higher than that in normal ovarian tissues, which was closely relevant to the poor prognosis of tumors. The dual luciferase reporter gene experiments confirmed that microRNA-196b could directly bind to the 3'-UTR of ZMYND11. Overexpression of microRNA-196b remarkably enhanced the proliferation, invasiveness and migratory ability of OCa cells. Meanwhile, overexpression of microRNA-196b significantly decreased ZMYND11 mRNA and protein expression. In addition, ZMYND11 level was also significantly increased in ovarian cancer cells. Compared with the miRNA-NC group, microRNA-196b-mimics + ZMYND11-OE treatment reversed the effect of microRNA-196b-mimics on OCa cell functions. CONCLUSIONS: MicroRNA-196b was highly expressed in OCa tissues, which can promote the proliferation, invasiveness and migratory ability of OCa cells by targeting ZMYND11. In addition, the expression disorder of microRNA-196b was associated with the malignant development of OCa.

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.

X-ray computed tomography images and network data of sands under compression.

Ottawa sand and Angular sand consist of particles with distinct shapes. The x-ray computed tomography (XCT) image stacks of their in-situ confined compressive testings are provided in this paper. For each image stack, a contact network, a thermal network and a network feature - edge betweenness centrality - of each edge in the networks are also provided. The readers can use the image data to construct digital sands with applications of (1) extracting microstructural parameters such as particle size, particle shape, coordination number and more network features; (2) analysing mechanical behaviour and transport processes such as fluid flow, heat transfer and electrical conduction using either traditional simulation tools such as finite element method and discrete element method or newly network models which could be built based on the network files available here.

The Efficacy of Memantine for the Treatment of Migraine: A Meta-Analysis of Randomized Controlled Studies.

INTRODUCTION: The efficacy of memantine for migraine remains controversial. We conduct a systematic review and meta-analysis to explore the influence of memantine versus placebo on treatment in migraine patients. METHODS: We search PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through February 2020 for randomized controlled trials assessing the effect of memantine versus placebo on treatment efficacy in migraine patients. This meta-analysis is performed using the random-effect model. RESULTS: Three randomized controlled trials are included in the meta-analysis. Overall, compared with control group in migraine patients, memantine treatment is associated with substantially reduced monthly attack frequency (mean difference [MD], -2.14; 95% confidence interval [CI], -2.83 to -1.46; P < 0.00001), number of migraine days (MD, -4.17; 95% CI, -6.40 to -1.93; P = 0.0003) and Migraine Disability Assessment (MD, -5.63; 95% CI, -6.46 to -4.79; P < 0.00001), but demonstrates no obvious influence on acute pain medications (MD, -1.23; 95% CI, -4.63 to 2.17; P = 0.48). CONCLUSIONS: Memantine treatment may benefit to the control of migraine.

Exploration of nucleos(t)ide analogs cessation in chronic hepatitis B patients with hepatitis B e antigen loss.

BACKGROUND: Nucleos(t)ide analogs (NAs) cessation in chronic hepatitis B (CHB) patients remains a matter of debate in clinical practice. Current guidelines recommend that patients with hepatitis B e antigen (HBeAg) seroconversion discontinue NAs after relatively long-term consolidation therapy. However, many patients fail to achieve HBeAg seroconversion after the long-term loss of HBeAg, even if hepatitis B surface antigen (HBsAg) loss occurs. It remains unclear whether NAs can be discontinued in this subset of patients. AIM: To investigate the outcomes and factors associated with HBeAg-positive CHB patients with HBeAg loss (without hepatitis B e antibody) after cessation of NAs. METHODS: We studied patients who discontinued NAs after achieving HBeAg loss. The Cox proportional hazards model was used to identify predictors for virological relapse after cessation of NAs. The cut-off value of the consolidation period was confirmed using receiver operating characteristic curves; we confirmed the cut-off value of HBsAg according to a previous study. The log-rank test was used to compare cumulative relapse rates among groups. We also studied patients with CHB who achieved HBeAg seroconversion and compared their cumulative relapse rates. Propensity score matching analysis (PSM) was used to balance baseline characteristics between the groups. RESULTS: We included 83 patients with HBeAg loss. The mean age of these patients was 32.1 ± 9.5 years, and the majority was male (67.5%). Thirty-eight patients relapsed, and the cumulative relapse rate at months 3, 6, 12, 24, 36, 60, 120, and 180 were 22.9%, 36.1%, 41.0%, 43.5%, 45.0%, 45.0%, 45.0%, and 52.8%, respectively. Twenty-six (68.4%) patients relapsed in the first 3 mo after NAs cessation, and 35 patients (92.1%) relapsed in the first year after NAs cessation. Consolidation period (≥ 24 mo vs < 24 mo) (HR 0.506, P = 0.043) and HBsAg at cessation (≥ 100 IU/mL vs < 100 IU/mL) (HR 14.869, P = 0.008) were significant predictors in multivariate Cox regression. In the PSM cohort, which included 144 patients, there were lower cumulative relapse rates in patients with HBeAg seroconversion (P = 0.036). CONCLUSION: HBeAg-positive CHB patients with HBeAg loss may be able to discontinue NAs therapy after long-term consolidation, especially in patients with HBsAg at cessation < 100 IU/mL. Careful monitoring, especially in the early stages after cessation, may ensure a favorable outcome.

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.

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.

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.

Abnormal liver-related biomarkers in COVID-19 patients and the role of prealbumin.

Background/Aims: We aimed to evaluate the distribution of abnormal liver-related biomarkers in patients with coronavirus disease (COVID-19) and explore the prognostic value of elevated liver enzymes and abnormal liver synthetic capacity with regards to patient mortality. Patients and Methods: This retrospective observational study included 80 laboratory-confirmed COVID-19 cases. Data were collected from the electronic medical record system by a trained team of physicians. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TB), albumin, and prealbumin levels at admission and on day 7 after admission were collected. The primary outcome of the current study was patient mortality. Results: Abnormal ALT, AST, TB, albumin, and prealbumin levels were observed in 11 (13.8%), 15 (18.8%), 5 (6.3%), 22 (27.5%), and 31 (38.8%) patients, respectively. Male gender correlated with elevated ALT and AST levels (p = 0.027 and 0.036, respectively). Higher levels of AST and lower levels of albumin and prealbumin were associated with patient mortality (p = 0.009, 0.002, and 0.003, respectively). Multivariate Cox regression analysis identified patient age (p = 0.013, HR 1.108) and prealbumin levels (p = 0.015, HR 0.986) as independent predictors for patient mortality. However, changes in liver-related biomarkers were not associated with poor outcome in multivariate analysis (p > 0.05). Conclusions: Abnormalities in albumin and prealbumin levels are common among COVID-19 patients and hypoprealbuminemia independently predicts adverse outcome and should be carefully considered in clinical practice. Moreover, changes in liver-related biomarkers is not a salient feature of COVID-19.

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.

LncRNA BBOX1-AS1 upregulates HOXC6 expression through miR-361-3p and HuR to drive cervical cancer progression.

OBJECTIVES: Over the past years, growing attention has been paid to deciphering the pivotal role of long non-coding RNAs (lncRNAs) in regulating the occurrence and development of human malignancies, cervical cancer (CC) included. Nonetheless, the regulatory role of lncRNA BBOX1 antisense RNA 1 (BBOX1-AS1) has not been explored as yet. MATERIAL AND METHODS: The expression of BBOX1-AS1 was detected by reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR). Cell Counting Kit-8 (CCK-8), colony formation, TUNEL, Western blot, transwell and immunofluorescence assays testified the critical role of BBOX1-AS1 in CC. The relationship between RNAs (BBOX1-AS1, miR-361-3p, HOXC6 and HuR) was analysed by luciferase reporter, RNA Immunoprecipitation (RIP) and RNA pull-down assays. RESULTS: BBOX1 antisense RNA 1 antisense RNA 1 was revealed to be highly expressed in CC. Decreased expression of BBOX1-AS1 had suppressive effects on CC cell growth and migration. Molecular mechanism assays verified that BBOX1-AS1 had negative interaction with miR-361-3p in CC. Additionally, homeobox C6 (HOXC6) was validated to be a downstream target of miR-361-3p in CC. Furthermore, ELAV-like RNA-binding protein 1, also known as HuR, was uncovered to be capable of regulating the mRNA stability of HOXC6 in CC. More importantly, rescue assays delineated that knockdown of HuR after overexpressing miR-361-3p could reverse BBOX1-AS1 upregulation-mediated effect on CC progression. Similarly, the function induced by BBOX1-AS1 upregulation on CC progression could be countervailed by HOXC6 depletion. CONCLUSIONS: BBOX1 antisense RNA 1 facilitates CC progression by upregulating HOXC6 expression via miR-361-3p and HuR.