Therapeutic potential of adipose-derived stem cell extracellular vesicles: from inflammation regulation to tissue repair.

Inflammation is a key pathological feature of many diseases, disrupting normal tissue structure and resulting in irreversible damage. Despite the need for effective inflammation control, current treatments, including stem cell therapies, remain insufficient. Recently, extracellular vesicles secreted by adipose-derived stem cells (ADSC-EVs) have garnered attention for their significant anti-inflammatory properties. As carriers of bioactive substances, these vesicles have demonstrated potent capabilities in modulating inflammation and promoting tissue repair in conditions such as rheumatoid arthritis, osteoarthritis, diabetes, cardiovascular diseases, stroke, and wound healing. Consequently, ADSC-EVs are emerging as promising alternatives to conventional ADSC-based therapies, offering advantages such as reduced risk of immune rejection, enhanced stability, and ease of storage and handling. However, the specific mechanisms by which ADSC-EVs regulate inflammation under pathological conditions are not fully understood. This review discusses the role of ADSC-EVs in inflammation control, their impact on disease prognosis, and their potential to promote tissue repair. Additionally, it provides insights into future clinical research focused on ADSC-EV therapies for inflammatory diseases, which overcome some limitations associated with cell-based therapies.

Prognostic analysis of anoikis-related genes in bladder cancer: An observational study.

Anoikis is proved to play a crucial role in the development of cancers. However, the impact of anoikis on the prognosis of bladder cancer (BLCA) is currently unknown. Thus, this study aimed to find potential effect of anoikis in BLCA. The Cancer Genome Atlas (TCGA)-BLCA and GSE13507 cohorts were downloaded from TCGA and the Gene Expression Omnibus (GEO) databases, respectively. Differentially expressed genes (DEGs) were screened between BLCA and normal groups, which intersected with anoikis-related genes to yield anoikis-related DEGs (AR DEGs). Univariate COX, rbsurv, and multivariate COX analyses were adopted in order to build a prognostic risk model. The differences of risk score in the different clinical subgroups and the relevance between survival rate and clinical characteristics were explored as well. Finally, chemotherapy drug sensitivity in different risk groups was analyzed. In total, 78 AR DEGs were acquired and a prognostic signature was build based on the 6 characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1), where the patients of low-risk group had longer survival time. The survival rate of BLCA patients was significantly differential in different groups of age, stage, smoking history, pathologic-T, and pathologic-N. The IC50 of 56 drugs showed significant differences between 2 risk groups, such as imatinib, docetaxel, and dasatinib. At last, the results of real time quantitative-polymerase chain reaction (RT-qPCR) demonstrated that the expression trend of CALR, HGF, and INHBB was consistent with the result obtained previously based on public databases. Taken together, this study identified 6 anoikis-related characteristic genes (CALR, FASN, CSPG4, HGF, INHBB, SATB1) for the prognosis of BLCA patients, providing a scientific reference for further research on BLCA.

Experiences of bowel symptoms in patients with rectal cancer after sphincter-preserving surgery: a qualitative meta-synthesis.

PURPOSE: This study is to identify and synthesize the available evidence of bowel symptom experiences of patients with rectal cancer after sphincter-preserving surgery (SPS). METHODS: This qualitative meta-synthesis was conducted following the Joanna Briggs Institute (JBI) qualitative systematic review methodology and reported following the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidelines. Seven databases were searched on 22 December 2021. The selected studies were reviewed by two independent reviewers, and disagreements were resolved by discussion or with a third reviewer. RESULTS: Seven qualitative articles were included in the meta-synthesis with 192 total rectal cancer patients. The review summarized 53 qualitative findings into three synthesized findings: (a) Patients experienced bowel symptoms and triggered additional physiological problems, and they underestimated bowel symptoms; (b) patients had many negative emotions, and their daily life and social interaction were disturbed; and (c) patients adopted strategies to adapt or control their bowel symptoms. According to the ConQual evidence grading approach, the confidence of the synthesized findings was rated as moderate to low. CONCLUSIONS: The bowel symptoms of patients with rectal cancer after SPS have troubled their lives. Timely acquisition of symptom-related knowledge and enhancement of their coping abilities are important for the control and management of bowel symptoms. Healthcare professionals should clearly understand the bowel symptoms that patients may experience after SPS and provide supportive care for patients to improve patients' self-management abilities and quality of life. TRIAL REGISTRATION: PROSPERO: CRD42021242610.

A Fast Decision Algorithm for VVC Intra-Coding Based on Texture Feature and Machine Learning.

Due to the development and application of information technology, a series of modern information technologies represented by 5G, big data, and artificial intelligence are changing rapidly, and people's requirements for video coding standards have become higher. In the High-Efficiency Video Coding (HEVC) standard, the coding block division is not flexible enough, and the prediction mode is not detailed enough. A new generation of Versatile Video Coding (VVC) standards was born. VVC inherits the hybrid coding framework adopted by HEVC, improves the original technology of each module, introduces a series of new coding technologies, and builds on this greatly improving the coding efficiency. Compared with HEVC, the block division structure of VVC has undergone great changes, retaining the quad-tree (QT) division method and increasing the multi-type tree (MTT) division method, which brings high coding complexity. To reduce the computational complexity of VVC coding block division, a fast decision algorithm for VVC intra-frame coding based on texture characteristics and machine learning is proposed. First, we analyze the characteristics of the CU partition structure decision and then use the texture complexity of the CU partition structure decision to terminate the CU partition process early; for CUs that do not meet the early termination of the partition, use the global sample information, local sample information, and context information. The three-category feature-trained tandem classifier framework predicts the division type of CU. The experimental results show that in the full intra mode, compared with the existing VTM10.0, the encoding output bit rate is increased by 1.36%, and the encoding time is saved by 52.63%.

The Quality and Clinical Applicability of Recommendations in Ostomy Guidelines: A Systematic Review.

Aim: To systematically search ostomy clinical practice guidelines, critically assess their quality and clinical applicability of recommendations, and summarize the recommendations. Design: Systematic review. Data Sources: The PubMed, ProQuest and CINAHL databases, eight guideline databases, and three ostomy institution websites were searched on September 3, 2021. Review Methods: Appraisal of Guidelines for Research and Evaluation II (AGREE II) and AGREE Recommendation EXcellence (AGREE-REX) were used to assess the guideline. Results: The initial search identified 1475 documents. Of these, 27 full-text documents were reviewed. Finally, 10 guidelines were included. Among these, the 2019 Registered Nurses' Association of Ontario (RNAO) guidelines had the highest total scores using AGREE II and AGREE-REX. The 2019 National Institute for Health and Care Excellence (NICE) and 2018 European Hernia Society (EHS) were also ranked as high-quality and evaluated as "recommended." The median of the "applicability" domain was the lowest (45%) among the six AGREE II domains. The median of the "values and preferences" domain was the lowest (38%) among the three AGREE-REX domains. In total, 172 recommendations were summarized and parastomal hernia received the most attention among the recommendations. Conclusion: The quality of the 10 clinical practice guidelines varied widely. The three identified high-quality guidelines might be appropriate first choices in daily ostomy care and management practice and can be tailored to the local context. Ostomy guidelines require further improvement in the "applicability" and "values and preferences" domains. No Patient or Public Contribution: This review only searched and evaluated relevant documents, so such details do not apply to this review.

Preoperative physical exercise strategies for patients undergoing major abdominal cancer surgery: a scoping review.

BACKGROUND: Although preoperative physical exercise has been demonstrated to be associated with clinical improvements among abdominal cancer patients, there remains heterogeneity in exercise strategies and uncertainty of what the optimal strategies should be. PURPOSE: This scoping review aimed to review and summarize the existing knowledge about preoperative exercise strategies for abdominal cancer patients. METHODS: The Joanna Briggs Institute (JBI) framework for conducting scoping reviews was adopted to investigate preoperative physical exercise strategies for patients undergoing major abdominal cancer surgery. RESULTS: A total of 1563 studies were identified and 24 were included in this review. Most of the study populations were colorectal cancer (n = 14). The most common type of exercise was a combination of aerobic and resistance exercises (n = 19). The most common form of aerobic exercise was walking (n = 13) or cycling (n = 12). Most resistance exercises were targeting at major muscle groups (n = 12). Most of the studies were performed at moderate intensity (n = 14) with frequency of 2 ~ 3 times/week (n = 14). The time of a training session was 30 ~ 60 min in half studies (n = 12). The intervention duration for most studies was less than 4 weeks (n = 11). Exercise locations included home (n = 10), facility (n = 7), and a combination of home and facility (n = 5). Phone calls were utilized by nearly half of the home-based exercise studies to supervise the exercise adherence (n = 7). CONCLUSIONS: This review provides a comprehensive summary of preoperative physical exercise strategies. It has the potential to inform clinical practice and future studies.

DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment.

Mitochondrial proteins are physiologically active in different compartments, and their abnormal location will trigger the pathogenesis of human mitochondrial pathologies. Correctly identifying submitochondrial locations can provide information for disease pathogenesis and drug design. A mitochondrion has four submitochondrial compartments, the matrix, the outer membrane, the inner membrane, and the intermembrane space, but various existing studies ignored the intermembrane space. The majority of researchers used traditional machine learning methods for predicting mitochondrial protein localization. Those predictors required expert-level knowledge of biology to be encoded as features rather than allowing the underlying predictor to extract features through a data-driven procedure. Besides, few researchers have considered the imbalance in datasets. In this paper, we propose a novel end-to-end predictor employing deep neural networks, DeepPred-SubMito, for protein submitochondrial location prediction. First, we utilize random over-sampling to decrease the influence caused by unbalanced datasets. Next, we train a multi-channel bilayer convolutional neural network for multiple subsequences to learn high-level features. Third, the prediction result is outputted through the fully connected layer. The performance of the predictor is measured by 10-fold cross-validation and 5-fold cross-validation on the SM424-18 dataset and the SubMitoPred dataset, respectively. Experimental results show that the predictor outperforms state-of-the-art predictors. In addition, the prediction of results in the M983 dataset also confirmed its effectiveness in predicting submitochondrial locations.

Efficient intra mode decision for low complexity HEVC screen content compression.

High efficiency video coding screen content coding (HEVC-SCC) extension is the latest HEVC development to improve the compression performance of screen content (SC) video. Similar to HEVC, the intra mode selection in HEVC-SCC is performed all the coding unit (CU) partitions to find the least rate distortion (RD) cost. Furthermore, additional intra tools are introduced to improve HEVC-SCC coding efficiency. However, these new tools could cause high computation complexity which restricts HEVC-SCC from ongoing applications. To solve the problem, an efficient intra mode decision for HEVC-SCC that adaptively utilizes the texture complexity of SC treeblock is proposed. The texture complexity of a SC treeblock is first analyzed according to the variation degree of the luminance value. And then, two efficient approaches are proposed based on the constructed model, which are early CU depth level determination and adaptive intra mode selection. Experimental results demonstrate that the proposed method can save 48.5% encoder runtime while keeping nearly the same coding efficiency as the HEVC-SCC encoders.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.

Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.

A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization.

Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.

Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.

Apoptosis proteins play a key role in maintaining the stability of organism; the functions of apoptosis proteins are related to their subcellular locations which are used to understand the mechanism of programmed cell death. In this paper, we utilize GO annotation information of apoptosis proteins and their homologous proteins retrieved from GOA database to formulate feature vectors and then combine the distance weighted KNN classification algorithm with them to solve the data imbalance problem existing in CL317 data set to predict subcellular locations of apoptosis proteins. It is found that the number of homologous proteins can affect the overall prediction accuracy. Under the optimal number of homologous proteins, the overall prediction accuracy of our method on CL317 data set reaches 96.8% by Jackknife test. Compared with other existing methods, it shows that our proposed method is very effective and better than others for predicting subcellular localization of apoptosis proteins.

Map-based cloning and expression analysis of BMR-6 in sorghum.

Brown midrib mutants in sorghum are associated with reduced lignin content and increased cell wall digestibility. In this study, we characterized a bmr-6 sorghum mutant, which shows reddish pigment in the midrib and stem after the fifth-leaf stage. Compared to wild type, Kalson lignin content of bmr-6 is decreased significantly. We used histological analysis to determine that the mutant exhibited a modified pattern of lignin staining and found an increased polysaccharide content. We cloned BMR-6 gene, a gene encoded a cinnamyl alcohol dehydrogenase (CAD), using a map-based cloning approach. Genetic complementation confirmed that CAD is responsible for the BMR-6 phenotype. BMR-6 gene was expressed in all tested sorghum tissues, with the highest being in midrib and stem. Transient expression assays in Nicotiana benthamiana leaves demonstrated cytomplasmic localization of BMR-6. We found that the expression level of bmr-6 was significantly decreased in the mutant but expression of SbCAD3 and SbCAD5 were significantly increased. Our results indicate that BMR-6 not only affects the distribution of lignin but also the biosynthesis of lignin in sorghum.

MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier.

MOTIVATION: Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can predict only one of all subchloroplast locations of this kind of multilabel proteins. RESULTS: To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multilabel classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reaches 55.52%, which is quite high to be able to become a promising tool for further studies. AVAILABILITY AND IMPLEMENTATION: An online web server for our proposed algorithm named MultiP-SChlo was developed, which are freely accessible at http://biomed.zzuli.edu.cn/bioinfo/multip-schlo/. CONTACT: pandaxiaoxi@gmail.com or gzli@tongji.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Low complexity mode decision for 3D-HEVC.

High efficiency video coding- (HEVC-) based 3D video coding (3D-HEVC) developed by joint collaborative team on 3D video coding (JCT-3V) for multiview video and depth map is an extension of HEVC standard. In the test model of 3D-HEVC, variable coding unit (CU) size decision and disparity estimation (DE) are introduced to achieve the highest coding efficiency with the cost of very high computational complexity. In this paper, a fast mode decision algorithm based on variable size CU and DE is proposed to reduce 3D-HEVC computational complexity. The basic idea of the method is to utilize the correlations between depth map and motion activity in prediction mode where variable size CU and DE are needed, and only in these regions variable size CU and DE are enabled. Experimental results show that the proposed algorithm can save about 43% average computational complexity of 3D-HEVC while maintaining almost the same rate-distortion (RD) performance.

Fast mode decision for 3D-HEVC depth intracoding.

The emerging international standard of high efficiency video coding based 3D video coding (3D-HEVC) is a successor to multiview video coding (MVC). In 3D-HEVC depth intracoding, depth modeling mode (DMM) and high efficiency video coding (HEVC) intraprediction mode are both employed to select the best coding mode for each coding unit (CU). This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs the 3D-HEVC from practical application. In this paper, a fast mode decision algorithm based on the correlation between texture video and depth map is proposed to reduce 3D-HEVC depth intracoding computational complexity. Since the texture video and its associated depth map represent the same scene, there is a high correlation among the prediction mode from texture video and depth map. Therefore, we can skip some specific depth intraprediction modes rarely used in related texture CU. Experimental results show that the proposed algorithm can significantly reduce computational complexity of 3D-HEVC depth intracoding while maintaining coding efficiency.

Automatic recognition of seismic intensity based on RS and GIS: a case study in Wenchuan Ms8.0 earthquake of China.

In recent years, earthquakes have frequently occurred all over the world, which caused huge casualties and economic losses. It is very necessary and urgent to obtain the seismic intensity map timely so as to master the distribution of the disaster and provide supports for quick earthquake relief. Compared with traditional methods of drawing seismic intensity map, which require many investigations in the field of earthquake area or are too dependent on the empirical formulas, spatial information technologies such as Remote Sensing (RS) and Geographical Information System (GIS) can provide fast and economical way to automatically recognize the seismic intensity. With the integrated application of RS and GIS, this paper proposes a RS/GIS-based approach for automatic recognition of seismic intensity, in which RS is used to retrieve and extract the information on damages caused by earthquake, and GIS is applied to manage and display the data of seismic intensity. The case study in Wenchuan Ms8.0 earthquake in China shows that the information on seismic intensity can be automatically extracted from remotely sensed images as quickly as possible after earthquake occurrence, and the Digital Intensity Model (DIM) can be used to visually query and display the distribution of seismic intensity.

Risk assessment of groundwater contamination: a multilevel fuzzy comprehensive evaluation approach based on DRASTIC model.

Groundwater contamination is a serious threat to water supply. Risk assessment of groundwater contamination is an effective way to protect the safety of groundwater resource. Groundwater is a complex and fuzzy system with many uncertainties, which is impacted by different geological and hydrological factors. In order to deal with the uncertainty in the risk assessment of groundwater contamination, we propose an approach with analysis hierarchy process and fuzzy comprehensive evaluation integrated together. Firstly, the risk factors of groundwater contamination are identified by the sources-pathway-receptor-consequence method, and a corresponding index system of risk assessment based on DRASTIC model is established. Due to the complexity in the process of transitions between the possible pollution risks and the uncertainties of factors, the method of analysis hierarchy process is applied to determine the weights of each factor, and the fuzzy sets theory is adopted to calculate the membership degrees of each factor. Finally, a case study is presented to illustrate and test this methodology. It is concluded that the proposed approach integrates the advantages of both analysis hierarchy process and fuzzy comprehensive evaluation, which provides a more flexible and reliable way to deal with the linguistic uncertainty and mechanism uncertainty in groundwater contamination without losing important information.

[Sero-epidemiological study on the human T-cell leukaemia virus type I/II infection in the east coastal areas of Fujian province].

OBJECTIVE: To study the seroprevalence of human T-cell leukaemia virus type I/II (HTLV-I/II) infection in adult population in the east coastal areas of Fujian and to explore the possible risk factors of HTLV-I/II. METHODS: A total number of 3259 blood samples from drug users, sexually transmitted disease (STD) patients, prostitutes and blood donors for serologic assays during 1999 to 2002, were collected. All samples were screened for HTLV-I/II antibody, using enzyme linked immunosorbent assay (ELISA) kits. All of the positive samples were confirmed by western blot (WB) kits. Statistical analysis was done by Epi software, and chi(2) test by Fisher's exact test. P value < 0.05 was considered statistically significant. RESULTS: The overall seroprevalence rate of HTLV-I/II in healthy populations was 0.06% including, 0.32% in drug users, 0.58% in STD patients and prostitutes respectively. HTLV-II had not been found. The seropositive rates for HTLV-I in STD patients and prostitutes were significantly higher than the findings among healthy populations (P < 0.05). There were no different seroprevalence rates between drug users and healthy populations (P > 0.05). No significant changes in HTLV-I prevalence rates were found in the different age groups as well as in Fuzhou and Linde cities (P > 0.05). CONCLUSION: The result suggested that in the east coastal areas of Fujian province, HTLV-I was the main prevalent virus. The seroprevalence of HTLV-I was very low, with no HTLV-II. Neither age nor gender seemed to be HTLV-I risk factor in the east coastal areas of Fujian province, but the increase of exposure to sex might be one.