VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.

Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.

Systematic Reviews and Meta-analyses of the Procedure-specific Risks of Thrombosis and Bleeding in General Abdominal, Colorectal, Upper Gastrointestinal, and Hepatopancreatobiliary Surgery.

OBJECTIVE: To provide procedure-specific estimates of symptomatic venous thromboembolism (VTE) and major bleeding after abdominal surgery. BACKGROUND: The use of pharmacological thromboprophylaxis represents a trade-off that depends on VTE and bleeding risks that vary between procedures; their magnitude remains uncertain. METHODS: We identified observational studies reporting procedure-specific risks of symptomatic VTE or major bleeding after abdominal surgery, adjusted the reported estimates for thromboprophylaxis and length of follow-up, and estimated cumulative incidence at 4 weeks postsurgery, stratified by VTE risk groups, and rated evidence certainty. RESULTS: After eligibility screening, 285 studies (8,048,635 patients) reporting on 40 general abdominal, 36 colorectal, 15 upper gastrointestinal, and 24 hepatopancreatobiliary surgery procedures proved eligible. Evidence certainty proved generally moderate or low for VTE and low or very low for bleeding requiring reintervention. The risk of VTE varied substantially among procedures: in general abdominal surgery from a median of <0.1% in laparoscopic cholecystectomy to a median of 3.7% in open small bowel resection, in colorectal from 0.3% in minimally invasive sigmoid colectomy to 10.0% in emergency open total proctocolectomy, and in upper gastrointestinal/hepatopancreatobiliary from 0.2% in laparoscopic sleeve gastrectomy to 6.8% in open distal pancreatectomy for cancer. CONCLUSIONS: VTE thromboprophylaxis provides net benefit through VTE reduction with a small increase in bleeding in some procedures (eg, open colectomy and open pancreaticoduodenectomy), whereas the opposite is true in others (eg, laparoscopic cholecystectomy and elective groin hernia repairs). In many procedures, thromboembolism and bleeding risks are similar, and decisions depend on individual risk prediction and values and preferences regarding VTE and bleeding.

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.

csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames.

Short open reading frames (sORFs) refer to the small nucleic fragments no longer than 303 nt in length that probably encode small peptides. To date, translatable sORFs have been found in both untranslated regions of messenger ribonucleic acids (RNAs; mRNAs) and long non-coding RNAs (lncRNAs), playing vital roles in a myriad of biological processes. As not all sORFs are translated or essentially translatable, it is important to develop a highly accurate computational tool for characterizing the coding potential of sORFs, thereby facilitating discovery of novel functional peptides. In light of this, we designed a series of ensemble models by integrating Efficient-CapsNet and LightGBM, collectively termed csORF-finder, to differentiate the coding sORFs (csORFs) from non-coding sORFs in Homo sapiens, Mus musculus and Drosophila melanogaster, respectively. To improve the performance of csORF-finder, we introduced a novel feature encoding scheme named trinucleotide deviation from expected mean (TDE) and computed all types of in-frame sequence-based features, such as i-framed-3mer, i-framed-CKSNAP and i-framed-TDE. Benchmarking results showed that these features could significantly boost the performance compared to the original 3-mer, CKSNAP and TDE features. Our performance comparisons showed that csORF-finder achieved a superior performance than the state-of-the-art methods for csORF prediction on multi-species and non-ATG initiation independent test datasets. Furthermore, we applied csORF-finder to screen the lncRNA datasets for identifying potential csORFs. The resulting data serve as an important computational repository for further experimental validation. We hope that csORF-finder can be exploited as a powerful platform for high-throughput identification of csORFs and functional characterization of these csORFs encoded peptides.

Risk of thrombosis and bleeding in gynecologic cancer surgery: systematic review and meta-analysis.

OBJECTIVE: This study aimed to provide procedure-specific estimates of the risk of symptomatic venous thromboembolism and major bleeding in the absence of thromboprophylaxis, following gynecologic cancer surgery. DATA SOURCES: We conducted comprehensive searches on Embase, MEDLINE, Web of Science, and Google Scholar for observational studies. We also reviewed reference lists of eligible studies and review articles. We performed separate searches for randomized trials addressing effects of thromboprophylaxis and conducted a web-based survey on thromboprophylaxis practice. STUDY ELIGIBILITY CRITERIA: Observational studies enrolling ≥50 adult patients undergoing gynecologic cancer surgery procedures reporting absolute incidence for at least 1 of the following were included: symptomatic pulmonary embolism, symptomatic deep vein thrombosis, symptomatic venous thromboembolism, bleeding requiring reintervention (including reexploration and angioembolization), bleeding leading to transfusion, or postoperative hemoglobin <70 g/L. METHODS: Two reviewers independently assessed eligibility, performed data extraction, and evaluated risk of bias of eligible articles. We adjusted the reported estimates for thromboprophylaxis and length of follow-up and used the median value from studies to determine cumulative incidence at 4 weeks postsurgery stratified by patient venous thromboembolism risk factors. The GRADE approach was applied to rate evidence certainty. RESULTS: We included 188 studies (398,167 patients) reporting on 37 gynecologic cancer surgery procedures. The evidence certainty was generally low to very low. Median symptomatic venous thromboembolism risk (in the absence of prophylaxis) was <1% in 13 of 37 (35%) procedures, 1% to 2% in 11 of 37 (30%), and >2.0% in 13 of 37 (35%). The risks of venous thromboembolism varied from 0.1% in low venous thromboembolism risk patients undergoing cervical conization to 33.5% in high venous thromboembolism risk patients undergoing pelvic exenteration. Estimates of bleeding requiring reintervention varied from <0.1% to 1.3%. Median risks of bleeding requiring reintervention were <1% in 22 of 29 (76%) and 1% to 2% in 7 of 29 (24%) procedures. CONCLUSION: Venous thromboembolism reduction with thromboprophylaxis likely outweighs the increase in bleeding requiring reintervention in many gynecologic cancer procedures (eg, open surgery for ovarian cancer and pelvic exenteration). In some procedures (eg, laparoscopic total hysterectomy without lymphadenectomy), thromboembolism and bleeding risks are similar, and decisions depend on individual risk prediction and values and preferences regarding venous thromboembolism and bleeding.

Risk of thrombosis and bleeding in gynecologic noncancer surgery: systematic review and meta-analysis.

OBJECTIVE: This study aimed to provide procedure-specific estimates of the risk for symptomatic venous thromboembolism and major bleeding in noncancer gynecologic surgeries. DATA SOURCES: We conducted comprehensive searches on Embase, MEDLINE, Web of Science, and Google Scholar. Furthermore, we performed separate searches for randomized trials that addressed the effects of thromboprophylaxis. STUDY ELIGIBILITY CRITERIA: Eligible studies were observational studies that enrolled ≥50 adult patients who underwent noncancer gynecologic surgery procedures and that reported the absolute incidence of at least 1 of the following: symptomatic pulmonary embolism, symptomatic deep vein thrombosis, symptomatic venous thromboembolism, bleeding that required reintervention (including re-exploration and angioembolization), bleeding that led to transfusion, or postoperative hemoglobin level <70 g/L. METHODS: A teams of 2 reviewers independently assessed eligibility, performed data extraction, and evaluated the risk of bias of the eligible articles. We adjusted the reported estimates for thromboprophylaxis and length of follow-up and used the median value from studies to determine the cumulative incidence at 4 weeks postsurgery stratified by patient venous thromboembolism risk factors and used the Grading of Recommendations Assessment, Development and Evaluation approach to rate the evidence certainty. RESULTS: We included 131 studies (1,741,519 patients) that reported venous thromboembolism risk estimates for 50 gynecologic noncancer procedures and bleeding requiring reintervention estimates for 35 procedures. The evidence certainty was generally moderate or low for venous thromboembolism and low or very low for bleeding requiring reintervention. The risk for symptomatic venous thromboembolism varied from a median of <0.1% for several procedures (eg, transvaginal oocyte retrieval) to 1.5% for others (eg, minimally invasive sacrocolpopexy with hysterectomy, 1.2%-4.6% across patient venous thromboembolism risk groups). Venous thromboembolism risk was <0.5% for 30 (60%) of the procedures; 0.5% to 1.0% for 10 (20%) procedures; and >1.0% for 10 (20%) procedures. The risk for bleeding the require reintervention varied from <0.1% (transvaginal oocyte retrieval) to 4.0% (open myomectomy). The bleeding requiring reintervention risk was <0.5% in 17 (49%) procedures, 0.5% to 1.0% for 12 (34%) procedures, and >1.0% in 6 (17%) procedures. CONCLUSION: The risk for venous thromboembolism in gynecologic noncancer surgery varied between procedures and patients. Venous thromboembolism risks exceeded the bleeding risks only among selected patients and procedures. Although most of the evidence is of low certainty, the results nevertheless provide a compelling rationale for restricting pharmacologic thromboprophylaxis to a minority of patients who undergo gynecologic noncancer procedures.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework.

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion.

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.

Enantioselective Nickel-Catalyzed Denitrogenative Transannulation En Route to N-N Atropisomers.

Nickel-catalyzed transannulation reactions triggered by the extrusion of small gaseous molecules have emerged as a powerful strategy for the efficient construction of heterocyclic compounds. However, their use in asymmetric synthesis remains challenging because of the difficulty in controlling stereo- and regioselectivity. Herein, we report the first nickel-catalyzed asymmetric synthesis of N-N atropisomers by the denitrogenative transannulation of benzotriazones with alkynes. A broad range of N-N atropisomers was obtained with excellent regio- and enantioselectivity under mild conditions. Moreover, density functional theory (DFT) calculations provided insights into the nickel-catalyzed reaction mechanism and enantioselectivity control.

Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.

X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew's correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.

Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.

PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations.

An essential step in engineering proteins and understanding disease-causing missense mutations is to accurately model protein stability changes when such mutations occur. Here, we developed a new sequence-based predictor for the protein stability (PROST) change (Gibb's free energy change, ΔΔG) upon a single-point missense mutation. PROST extracts multiple descriptors from the most promising sequence-based predictors, such as BoostDDG, SAAFEC-SEQ, and DDGun. RPOST also extracts descriptors from iFeature and AlphaFold2. The extracted descriptors include sequence-based features, physicochemical properties, evolutionary information, evolutionary-based physicochemical properties, and predicted structural features. The PROST predictor is a weighted average ensemble model based on extreme gradient boosting (XGBoost) decision trees and an extra-trees regressor; PROST is trained on both direct and hypothetical reverse mutations using the S5294 (S2647 direct mutations + S2647 inverse mutations). The parameters for the PROST model are optimized using grid searching with 5-fold cross-validation, and feature importance analysis unveils the most relevant features. The performance of PROST is evaluated in a blinded manner, employing nine distinct data sets and existing state-of-the-art sequence-based and structure-based predictors. This method consistently performs well on frataxin, S217, S349, Ssym, S669, Myoglobin, and CAGI5 data sets in blind tests and similarly to the state-of-the-art predictors for p53 and S276 data sets. When the performance of PROST is compared with the latest predictors such as BoostDDG, SAAFEC-SEQ, ACDC-NN-seq, and DDGun, PROST dominates these predictors. A case study of mutation scanning of the frataxin protein for nine wild-type residues demonstrates the utility of PROST. Taken together, these findings indicate that PROST is a well-suited predictor when no protein structural information is available. The source code of PROST, data sets, examples, and pretrained models along with how to use PROST are available at https://github.com/ShahidIqb/PROST and https://prost.erc.monash.edu/seq.

Effect of different disinfection treatments on the adhesion and separation of biofilm on stainless steel surface.

Attachment and separation of sulfate-reducing bacteria (SRB) biofilm on stainless steel (SS) in simulated cooling water with and without different sterilization treatments was investigated by calculation of surface energy, theoretical work of adhesion and analysis of Scanning Electron Microscope/Energy Dispersive Spectrometer. Two types of biocides, glutaraldehyde and Polyhexamethylene guanidine (PHMG), and electromagnetic treatment were used in this paper. The results show that PHMG had the best bactericidal performance, followed by glutaraldehyde, and electromagnetic treatment was the lowest one. The theoretical work of adhesion was used to quantitatively evaluate the adhesion of biofilm on the surface of the metal. Theoretical work of adhesion between biofilm and SS in simulated cooling water increased with time. The theoretical adhesion work and adhesive capacity of biofilm to SS surface increased after treating with glutaraldehyde while decreasing after treating with PHMG and electromagnetic field. As the theoretical adhesion work decreased, the biofilm was gradually removed from the stainless steel surface. On the contrary, the biofilm adhered more firmly. The results of SEM were also consistent with the calculation results of theoretical adhesion work. The results obtained indicated that electromagnetic treatment had the lowest effect in sterilization but the best in biofilm separation.

SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network.

There has been a significant improvement in protein residue contact prediction in recent years. Nevertheless, state-of-the-art methods still show deficiencies in the contact prediction of proteins with low-homology information. These top methods depend largely on statistical features that derived from homologous sequences, but previous studies, along with our analyses, show that they are insufficient for inferencing an accurate contact map for nonhomology protein targets. To compensate, we proposed a brand new single-sequence-based contact predictor (SSCpred) that performs prediction through the deep fully convolutional network (Deep FCN) with only the target sequence itself, i.e., without additional homology information. The proposed pipeline makes good use of the target sequence by utilizing the pair-wise encoding technique and Deep FCN. Experimental results demonstrated that SSCpred can produce accurate predictions based on the efficient pipeline. Compared with several most recent methods, SSCpred achieves completive performance on nonhomology targets. Overall, we explored the possibilities of single-sequence-based contact prediction and designed a novel pipeline without using a complex and redundant feature set. The proposed SSCpred can compensate for current methods' disadvantages and achieves better performance on the nonhomology targets. The web server of SSCpred is freely available at http://csbio.njust.edu.cn/bioinf/sscpred/.

Decellularized Extracellular Matrices for Tissue Engineering and Regeneration.

Decellularized extracellular matrices (dECMs) from mammalian tissues and organs are particularly interesting as scaffolds for tissue engineering and regeneration when considering their ability to retain chemical compositions and three-dimensional (3D) microstructures that are similar to native ECMs. This review discusses the advantages and disadvantages of different decellularization methods that use various agents, such as ionic and nonionic detergents and biological enzymes. The applications of dECMs as scaffolds or hydrogels for tissue engineering of specific tissues including heart valves, blood vessels, and skin, as well as their performance in vitro and in vivo, are also discussed. In addition, whole organ regeneration (i.e., the heart, kidney, liver) using dECM scaffolds has been explored, which are able to recapitulate partial functions of native organs.

Effect of low dose naloxone on the immune system function of a patient undergoing video-assisted thoracoscopic resection of lung cancer with sufentanil controlled analgesia - a randomized controlled trial.

BACKGROUND: Perioperative immune function plays an important role in the prognosis of patients. Several studies have indicated that low-dose opioid receptor blockers can improve immune function. METHODS: Sixty-nine patients undergoing video-assisted thoracoscopic resection of the lung cancer were randomly assigned to either the naloxone group (n = 35) or the non-naloxone group (n = 34) for postoperative analgesia during the first 48 h after the operation. Both groups received sufentanil and palonosetron via postoperative analgesia pump, while 0.05 µg·kg- 1·h- 1 naloxone was added in naloxone group. The primary outcomes were the level of opioid growth factor (OGF) and immune function assessed by natural killer cells and CD4+/CD8+ T-cell ratio. Second outcomes were assessed by the intensity of postoperative pain, postoperative rescue analgesia dose, postoperative nausea and vomiting (PONV). RESULTS: The level of OGF in the naloxone group increased significantly at 24 h (p<0.001) and 48 h after the operation (P < 0.01). The natural killer cells (P < 0.05) and CD4+/CD8+ T-cell ratio (P < 0.01) in the naloxone group increased significantly at 48 h after the operation. The rest VAS scores were better with naloxone at 12 and 24 h after operation(P < 0.05), and the coughing VAS scores were better with naloxone at 48 h after the operation(P < 0.05). The consumption of postoperative rescue analgesics in the naloxone group was lower (0.00(0.00-0.00) vs 25.00(0.00-62.50)), P < 0.05). Postoperative nausea scores at 24 h after operation decreased in naloxone group(0.00 (0.00-0.00) vs 1.00 (0.00-2.00), P < 0.01). CONCLUSION: Infusion of 0.05 µg·kg- 1·h- 1 naloxone for patients undergoing sufentanil-controlled analgesia for postoperative pain can significantly increase the level of OGF, natural killer cells, and CD4+/CD8+ T-cell ratio compared with non-naloxone group, and postoperative pain intensity, request for rescue analgesics, and opioid-related side effects can also be reduced. TRIAL REGISTRATION: The trial was registered at the Chinese Clinical Trial Registry on January 26, 2019 (ChiCTR1900021043).

[Preliminary study on antibacterial activity of artemisinin and its derivatives].

In this study,in order to detect the antimicrobial activity of artemisinin and its derivatives artesunate and dihydroartemisinin,two methods including broth dilution and plate punching method were used to detect the antibacterial activity against gram-negative bacteria(Escherichia coli)and gram-positive bacteria(Staphylococcus aureus)of artemisinin,dihydroartemisinin and artesunate at various concentrations within 5 mmol·L~(-1)and at four time points(8,16,24,32 h).Two antibacterial positive drugs,streptomycin against E.coli and penicillin against S.aureus,were used as positive controls.Plate punching method showed that,unlike the results of 5 mmol·L~(-1)dihydroartemisinin or artesunate,no inhibition zone was detected at the same concentration of artemisinin after 24 h-treatment against E.coli.Broth dilution method showed that,the antibacterial activity of dihydroartemisinin against E.coli.was stronger than those of both artesunate and artemisinin;IC_(50)at24 h-treatment was 155.9µmol·L~(-1)for dihydroartemisinin,370.0µmol·L~(-1)for artesunate and none for artemisinin.Interestingly,dihydroartemisinin and artesunate showed the strongest antibacterial activity between 16-24 h,while artemisinin showed relatively stronger antibacterial activity between 8-16 h.Dihydroartermisinin showed no antibacterial activity against S.aureus.Above all,the antibacterial activity of artemisinins against E.coli is dihydroartemisinin>artesunate>artemisinin.Artemisinin and its derivatives have showed different antibacterial kinetics,and no antibacterial activity against S.aureus.has been detected with dihydroartemisinin.

[Study and application of multidirectional differentiation potential of dental pulp stem cells].

Dental pulp stem cells(DPSCs) are adult stem cells with strong proliferative ability, self-renewal ability and multidirectional differentiation potential. DPSCs have abundant source are easy to obtain, and do not have ethical problems. As seed cells, they played an important role and showed great potential in tissue engineering and regenerative medicine, making them potential ideal seed cells for repairation and regeneration of tissue and organ. Clinical application of DPSCs in bone regeneration has already been achieved, and studies on differentiation of DPSCs into other tissues are still at different levels of basic stage. In this paper, the research and application of directional differentiation potential such as tooth formation, osteogenesis, and nerve formation are reviewed in order to provide clues and ideas for further study on DPSCs in the field of tissue engineering and regenerative medicine.

A clinical application of dynamic contrast-enhanced CT (DCE-CT) in patients with variously differentiated breast ductal carcinoma.

PURPOSE: To explore the hemodynamic characteristics of variously differentiated breast ductal carcinoma (BDC) using the dynamic contrast-enhanced CT (DCE-CT) based CT perfusion imaging (CTPI), including the specific perfusion parameter values, and to identify potential clinical applications in the cell differentiation degree of BDC. MATERIALS AND METHODS: Forty patients with breast ductal carcinoma confirmed by needle puncture biopsy were studied prospectively using CTPI on a 64-slice spiral CT scanner. The acquired volume data were used for calculations, mapping, and analysis by using a tumor perfusion protocol in the CT perfusion software package to measure 4 parameters namely, blood flow (BF), blood volume (BV), mean transit time (MTT), and the permeability surface (PS) area product. The different differentiated BDC with CT perfusion parameters were divided into 3 groups of high, moderate and poor differentiation. The comparison among these groups were then made using statistical data analysis software. RESULTS: The patients were categorized into three groups of 12, 13, and 15 highly, moderately and poorly differentiated ductal carcinoma cases, respectively. Comparing the perfusion parameters values of the three groups, BF, BV, and PS values increased from highly to poorly differentiated BDC cases. Differences between the highly and moderately or poorly differentiated groups were all statistically significant for BF, BV, and PS values (p < 0.05), while MTT value showed no statistical difference among the three groups (p > 0.05). CONCLUSION: CTPI is a functional imaging technology from the perspective of hemodynamics with potential clinical applications. Three parameters of BF, BV and PS values have potential to serve as indicators of the cell differentiation degree of the breast ductal carcinoma.