MRSL: a causal network pruning algorithm based on GWAS summary data.

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

Structure-based characterization and compound identification of the wild-type THF class-II riboswitch.

Riboswitches are conserved regulatory RNA elements participating in various metabolic pathways. Recently, a novel RNA motif known as the folE RNA motif was discovered upstream of folE genes. It specifically senses tetrahydrofolate (THF) and is therefore termed THF-II riboswitch. To unravel the ligand recognition mechanism of this newly discovered riboswitch and decipher the underlying principles governing its tertiary folding, we determined both the free-form and bound-form THF-II riboswitch in the wild-type sequences. Combining structural information and isothermal titration calorimetry (ITC) binding assays on structure-based mutants, we successfully elucidated the significant long-range interactions governing the function of THF-II riboswitch and identified additional compounds, including alternative natural metabolites and potential lead compounds for drug discovery, that interact with THF-II riboswitch. Our structural research on the ligand recognition mechanism of the THF-II riboswitch not only paves the way for identification of compounds targeting riboswitches, but also facilitates the exploration of THF analogs in diverse biological contexts or for therapeutic applications.

Allosteric regulation of Senecavirus A 3Cpro proteolytic activity by an endogenous phospholipid.

Seneca virus A (SVA) is an emerging novel picornavirus that has recently been identified as the causative agent of many cases of porcine vesicular diseases in multiple countries. In addition to cleavage of viral polyprotein, the viral 3C protease (3Cpro) plays an important role in the regulation of several physiological processes involved in cellular antiviral responses by cleaving critical cellular proteins. Through a combination of crystallography, untargeted lipidomics, and immunoblotting, we identified the association of SVA 3Cpro with an endogenous phospholipid molecule, which binds to a unique region neighboring the proteolytic site of SVA 3Cpro. Our lipid-binding assays showed that SVA 3Cpro displayed preferred binding to cardiolipin (CL), followed by phosphoinositol-4-phosphate (PI4P) and sulfatide. Importantly, we found that the proteolytic activity of SVA 3Cpro was activated in the presence of the phospholipid, and the enzymatic activity is inhibited when the phospholipid-binding capacity decreased. Interestingly, in the wild-type SVA 3Cpro-substrate peptide structure, the cleavage residue cannot form a covalent binding to the catalytic cysteine residue to form the acyl-enzyme intermediate observed in several picornaviral 3Cpro structures. We observed a decrease in infectivity titers of SVA mutants harboring mutations that impaired the lipid-binding ability of 3Cpro, indicating a positive regulation of SVA infection capacity mediated by phospholipids. Our findings reveal a mutual regulation between the proteolytic activity and phospholipid-binding capacity in SVA 3Cpro, suggesting that endogenous phospholipid may function as an allosteric activator that regulate the enzyme's proteolytic activity during infection.

An amber-encoding helper phage for more efficient phage display of noncanonical amino acids.

Using an amber suppression-based noncanonical amino acid (ncAA) mutagenesis approach, the chemical space in phage display can be significantly expanded for drug discovery. In this work, we demonstrate the development of a novel helper phage, CMa13ile40, for continuous enrichment of amber obligate phage clones and efficient production of ncAA-containing phages. CMa13ile40 was constructed by insertion of a Candidatus Methanomethylophilus alvus pyrrolysyl-tRNA synthetase/PylT gene cassette into a helper phage genome. The novel helper phage allowed for a continuous amber codon enrichment strategy for two different libraries and demonstrated a 100-fold increase in packaging selectivity. CMa13ile40 was then used to create two peptide libraries containing separate ncAAs, Nϵ-tert-butoxycarbonyl-lysine and Nϵ-allyloxycarbonyl-lysine, respectively. These libraries were used to identify peptide ligands that bind to the extracellular domain of ZNRF3. Each selection showed differential enrichment of unique sequences dependent upon the ncAA used. Peptides from both selections were confirmed to have low micromolar affinity for ZNRF3 that was dependent upon the presence of the ncAA used for selection. Our results demonstrate that ncAAs in phages provide unique interactions for identification of unique peptides. As an effective tool for phage display, we believe that CMa13ile40 can be broadly applied to a wide variety of applications.

Mapping of Long-Wavelength Phonon Contribution in the Thermal Transport of Alloyed Semiconductor Superlattices.

The mapping of long-wavelength phonons is important to understand and manipulate the thermal transport in multilayered structures, but it remains a long-standing challenge due to the collective behaviors of phonons. In this study, an experimental demonstration of mapping the long-wavelength phonons in an alloyed Al0.1Ga0.9As/Al0.9Ga0.1As superlattice system is reported. Multiple strategies to filter out the short- to mid-wavelength phonons are used. The phonon mean-free-path-dependent thermal transport properties directly demonstrate both the suppression effect of the ErAs nanoislands and the contribution of long-wavelength phonons. The contribution from phonons with mean free path longer than 1 µm is clearly demonstrated. A model based on the Boltzmann transport equation is proposed to calculate and describe the thermal transport properties, which depicts a clear physical picture of the transport mechanisms. This method can be extended to map different wavelength phonons and become a universal strategy to explore their thermal transport in various application scenarios.

Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.

BACKGROUND: Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH. METHODS: Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model's performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform. RESULTS: A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705-0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model's variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients. CONCLUSION: We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.

Antiretroviral Treatment Gaps and Adherence Among People with HIV in the U.S. Medicare Program.

Approximately one-quarter of people with HIV (PWH) in the U.S. receive coverage through the Medicare program; however, no prior real-world study has examined antiretroviral therapy (ART) gaps and adherence and associated factors in this population. This retrospective cohort analysis used 2013-2018 national Medicare fee-for-service claims data to identify all PWH initiated on a new ART regimen including protease inhibitors [PI], non-nucleoside reverse transcriptase inhibitors [NNRTIs], or integrase strand transfer inhibitors [INSTIs] between 1/1/2014 and 12/31/2017. Study outcomes included ART adherence (based on proportion of days covered [PDC]), continuous treatment gaps ranging from 1 to 6 days to ≥ 180 days, and discontinuation (continuous gap ≥ 90 days) in the 12-month follow-up period. Multivariable regressions were used to assess factors associated with ART adherence and discontinuation. The final sample included 48,627 PWH (mean age: 54.5 years, 74.4% male, 47.5% White, 89.8% disabled). Approximately 53.0% of PWH had a PDC ≥ 0.95, 30.2% had a PDC between 0.70 and < 0.95, and 16.8% had PDC < 0.70. Treatment gaps of at least ≥ 7-days (55.2%) and ≥ 30-days (26.2%) were common and 10.1% PWH discontinued treatment. Younger age, female sex, Black race, higher comorbidity score, mental health conditions, and substance use disorder were associated with higher odds of lower adherence and discontinuation (all p-values < 0.05). In conclusion, suboptimal adherence and treatment gaps in ART use were commonly observed among PWH in Medicare. Interventions and policies to mitigate barriers to adherence are urgently needed in this population to both improve their survival and increase the potential for ending the HIV epidemic in the US.

Admission Left-Arm Systolic Blood Pressure and In-Hospital Mortality After Acute Type A Aortic Dissection Repair.

AIM: Admission systolic blood pressure is a significant predictor of in-hospital mortality in patients with acute type A aortic dissection (ATAAD). While previous studies have focussed on recording the highest blood pressure value from both arms, this study aimed to evaluate the associations between blood pressure in bilateral arms and in-hospital mortality. METHODS: Data were analysed from 262 patients with ATAAD treated at a single centre. The relationship between bilateral arm blood pressure upon admission and in-hospital mortality was assessed in a logistic regression model. To comprehensively evaluate potential non-linear relationships, the association between admission bilateral systolic blood pressure (SBP) and the risk of in-hospital mortality was analysed using restricted cubic splines on a continuous scale. RESULTS: Mean age was 53.6±12.5 years and 194 (74.0%) were male. Baseline and operative data showed that ages, body mass index, smoking, left-arm SBP, left-arm diastolic blood pressure (DBP), right-arm SBP, right-arm DBP, syncope, cerebral/cardiac ischaemia, retrograde brain perfusion, Bentall procedure, coronary artery bypass grafting, and aortic valve replacement significantly differed among the left-arm SBP tertiles. In-hospital mortality was 17.6% (46 of 262). Restricted cubic splines demonstrated that the relationship between presenting left-arm SBP and in-hospital mortality followed a U-shaped curve, whereas non-linearity was not detected in the right arm. CONCLUSION: This study found a U-shaped association between admission left-arm SBP and in-hospital mortality in ATAAD surgery patients, whereas a non-linearity relationship was not detected for right-arm SBP. Low left-arm SBP independently correlated with increased in-hospital mortality, underscoring the significance of bilateral blood pressure differences in ATAAD prognosis.

Effects of Antarctic Krill Meal in Diet on Reproductive Performance and Embryo Quality of Eriocheir sinensis.

A 60-day feeding trial was conducted to evaluate the impact of dietary Antarctic krill meal on the reproductive performance and embryo quality of the Chinese mitten crab, Eriocheir sinensis. Three diets were formulated, incorporating varying levels of Antarctic krill meal at 0% (Diet K0), 10% (Diet K10), and 20% (Diet K20), with a control group fed razor clam Sinonovacula constricta. Each diet was randomly assigned to three replicate tanks, each stocked with 5 males and 10 females. Male and female weights were 145.38 ± 8.01 and 102.57 ± 9.73 g, respectively. The results revealed no significant differences in weight gain rate, specific growth rate, and survival rate. However, the hepatopancreatic weight and hepatopancreas index of female crabs in each group decreased, while gonadal weight and gonadosomatic index increased significantly after 60 days, with Diet K20 showing the highest values. Egg production and fecundity of female crabs reached their peak in Diet K20, with no significant differences in reproductive indices among all groups. The phospholipid content in Diet K20 was significantly higher than in the other groups (P < 0.05). Cholesterol contents in Diet K0 and the control group were significantly higher than in Diet K10 and K20 (P < 0.05). No significant differences were observed in egg diameter, egg weight, moisture, crude protein, and crude fat between the groups. The content of C20 : 2 and C20 : 4n6 was highest in Diet K0, with a significant difference compared to Diet K10 (P < 0.05). However, no significant differences were found in the total content of saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids among all groups. Based on the research findings, it is recommended that the optimal level of Antarctic krill meal in diets is 20%.

Serum IgG N-glycans enable early detection and early relapse prediction of colorectal cancer.

Colorectal cancer (CRC) develops mainly from colorectal advanced adenomas (AA), which are considered precancerous lesions. Novel early diagnostic biomarkers are urgently needed to distinguish CRC and AA from healthy control (HC). Alternative glycosylation of serum IgG has been shown to be closely associated with CRC. We aimed to explore the potential of IgG N-glycan as biomarkers in the early differential diagnosis of CRC. The study population was strictly matched to the exclusion criteria process. Serum IgG N-glycan profiles were analyzed by a robust and reliable relative quantitative method based on ultra-performance liquid chromatography (UPLC). Relative quantification and classification performance of IgG N-glycans were evaluated by Mann-Whitney U tests and ROC curve based on directly detected and derived glycan traits, respectively. Six and 14 directly detected glycan traits were significantly changed in AA and CRC, respectively, compared with HC. GP1 and GP3 were able to accurately distinguish AA from HC for early precancerous lesions screening. GP4 and GP14 provided a high value in discriminating CRC from HC. A novel combined index named GlycoF, including GP1, GP3, GP4, GP14 and CEA was developed to provide a potential early diagnostic biomarker in discriminating simultaneously AA (AUC = 0.847) and CRC (AUC = 0.844) from HC. GlycoF also demonstrated a superior CRC detection rate across CRC all stages and conspicuous prediction ability of risk of relapse. Serum IgG N-glycans analysis provided powerful early screening biomarkers that can efficiently differentiate CRC and AA from HC.

Tweezer PCR: A Highly Specific Method for Accurate Identification of Low-Abundance Mutations.

Somatic mutation is a valuable biomarker for tracking tumor progression and migration due to its distinctive feature in various tumors and its wide distribution throughout body fluids. However, accurately detecting somatic mutations from the abundant DNA of noncancerous origins remains a practical challenge in the clinic. Herein, we developed an ultraspecific method, called tweezer PCR, for detecting low-abundance mutations inspired by the design of DNA origami. The high specificity of tweezer PCR relies on a tweezer-shaped primer containing six basic functional units: a primer, a hairpin, a linker, a blocker, a spacer, and a toehold. After optimizing the structure of the tweezer-shaped primer and enhancing its specificity by adding additional Mg2+ and Na+, tweezer PCR distinguished as low as 20 copies of mutations from 2 million copies of wild-type templates per test. By testing synthesized plasmids and plasma samples gathered from nonsmall-cell lung cancer patients, tweezer PCR showed higher specificity and robustness for detecting low-copy-number mutations in contrast with digital droplet PCR. Additionally, the need for conventional instruments makes tweezer PCR a practically accessible method for testing low-abundance mutations. Because of its numerous advantages, we believe that tweezer PCR offers a precise, robust, and pragmatic tool for cancer screening, prognosis, and genotyping in the clinic.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Subgroup analysis using Bernoulli-gated hierarchical mixtures of experts models.

When it is suspected that the treatment effect may only be strong for certain subpopulations, identifying the baseline covariate profiles of subgroups who benefit from such a treatment is of key importance. In this paper, we propose an approach for subgroup analysis by firstly introducing Bernoulli-gated hierarchical mixtures of experts (BHME), a binary-tree structured model to explore heterogeneity of the underlying distribution. We show identifiability of the BHME model and develop an EM-based maximum likelihood method for optimization. The algorithm automatically determines a partition structure with optimal prediction but possibly suboptimal in identifying treatment effect heterogeneity. We then suggest a testing-based postscreening step to further capture effect heterogeneity. Simulation results show that our approach outperforms competing methods on discovery of differential treatment effects and other related metrics. We finally apply the proposed approach to a real dataset from the Tennessee's Student/Teacher Achievement Ratio project.

Structure of the full-length glucagon class B G-protein-coupled receptor.

The human glucagon receptor, GCGR, belongs to the class B G-protein-coupled receptor family and plays a key role in glucose homeostasis and the pathophysiology of type 2 diabetes. Here we report the 3.0 Å crystal structure of full-length GCGR containing both the extracellular domain and transmembrane domain in an inactive conformation. The two domains are connected by a 12-residue segment termed the stalk, which adopts a ß-strand conformation, instead of forming an α-helix as observed in the previously solved structure of the GCGR transmembrane domain. The first extracellular loop exhibits a ß-hairpin conformation and interacts with the stalk to form a compact ß-sheet structure. Hydrogen-deuterium exchange, disulfide crosslinking and molecular dynamics studies suggest that the stalk and the first extracellular loop have critical roles in modulating peptide ligand binding and receptor activation. These insights into the full-length GCGR structure deepen our understanding of the signalling mechanisms of class B G-protein-coupled receptors.

Synergistic interactions of microbial fuel cell and microbially induced carbonate precipitation technology with molasses as the substrate.

The application of microbially induced carbonate precipitation (MICP) technology is critical, but many challenges remain. In this paper, a microbial fuel cell (MFC) is used to treat molasses wastewater, and the effluent is used as the substrate to promote the growth of urease-producing bacteria. The results showed that the maximum voltage of MFC was 500 mV, and the maximum power density was 169.86 mW/m2. The mineralization rate reached 100% on the 15th day, and the mineralized product was calcite CaCO3. According to the microbial community analysis, the unclassified_Comamondaceae, Arcobacter, and Aeromonas, which could improve the OH-, signal molecular transmission and small molecular nutrients to promote the urease activity of urease-producing bacteria. The above conclusions provide a new way to reuse molasses wastewater efficiently and to apply MICP technology in dust suppression.

Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit.

BACKGROUND: A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients. METHODS: As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA). RESULTS: Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838-0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems. CONCLUSIONS: We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.

Addition of preoperative transversus abdominis plane block to multimodal analgesia in open gynecological surgery: a randomized controlled trial.

BACKGROUND: Transversus abdominis plane (TAP) block can provide effective analgesia for abdominal surgery. However, it was questionable whether TAP had additional effect in the context of multimodal analgesia (MMA). Therefore, this study aimed to assess the additional analgesic effect of preoperative TAP block when added to MMA protocol in open gynecological surgery. METHODS: In this prospective, randomized-controlled trial, 64 patients scheduled for open gynecological surgery were randomized to receive preoperative TAP block (Study group, n = 32) or placebo (Control group, n = 32) in addition to MMA protocol comprising dexamethasone, acetaminophen, flurbiprofen and celecoxib, and rescued morphine analgesia. The primary outcome was rescued morphine within 24 h after surgery. Secondary outcomes included pain scores, adverse effects, quality of recovery measured by 40-item quality of recovery questionnaire score (QoR-40) at 24 h, and quality of life measured with short-form health survey (SF - 36) on postoperative day (POD) 30. RESULTS: The Study group had less rescued morphine than the control group within 24 h [5 (2-9) vs. 8.5 (5-12.8) mg, P = 0.013]. The Study group had lower pain scores at 1 h [3 (2-4) vs. 4 (3-5), P = 0.007], 2 h [3 (2-4) vs. 3.5 (3-5), P = 0.010] and 6 h [3 (2-3) vs. 3 (2.3-4), P = 0.028], lower incidence of nausea at 48 h (25.8% vs. 50%, P = 0.039), and higher satisfaction score [10 (10-10) vs. 10 (8-10), P = 0.041]. The SF-36 bodily pain score on POD 30 was higher in the Study group (59 ± 13 vs. 49 ± 16, P = 0.023). CONCLUSIONS: Preoperative TAP block had additional analgesic effect for open gynecological surgery when used as part of multimodal analgesia. Rescued morphine within 24 h was significantly reduced and the SF-36 bodily pain dimension at 30 days after surgery was significantly improved. TRIAL REGISTRATION: www.chictr.org.cn (ChiCTR2000040343, on Nov 28 2020).

An ultra-sensitive and specific nanoplasmonic-enhanced isothermal amplification platform for the ultrafast point-of-care testing of SARS-CoV-2.

The novel mutations attributed by the high mutagenicity of the SARS-CoV-2 makes its prevention and treatment challenging. Developing an ultra-fast, point-of-care-test (POCT) protocol is critical for responding to large-scale spread of SARS-CoV-2 in public places and in resource-poor remote areas. Here, we developed a nanoplasmonic enhanced isothermal amplification (NanoPEIA) strategy that combines a nanoplasmonic sensor with isothermal amplification. The novel strategy provides an ideal easy-to operate detection platform for obtaining accurate, ultra-fast and high-throughput (96 samples can be tested together) data. For clinical samples with viral detection at Ct value <25, the entire process (including sample preparation, virus lysis, detection, and data analysis) can be completed within six minutes. The method is also appropriate for detection of SARS-CoV-2 γ-coronavirus mutants. The NanoPEIA method was validated using clinical samples from 21 patients with SARS-CoV-2 infection and 31 healthy individuals. The detection result on the 52 clinical samples for SARS-CoV-2 showed that the NanoPEIA platform had a 100% sensitivity for N and orf1ab genes, which was higher than those obtained using RT-qPCR (88.9% and 90.0%, respectively). The specificities of 31 clinical negative samples were 92.3% and 91.7% for the N gene and the orf1ab gene, respectively. The limits of detection (LoD) of the clinical samples were 28.3 copies/mL and 23.3 copies/mL for the N gene and the orf1ab gene, respectively. The efficient NanoPEIA detection strategy facilitates real-time detection and visualization within ultrashort durations and can be applied for POCT diagnosis in resource-poor and highly populated areas.

Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein.

Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.

Performance optimization and mechanism analysis of applied Enteromorpha-based composite dust suppressant.

In order to solve the problem of environmental pollution caused by the escape of coal dust in open-pit coal mines, a composite dust suppressant was prepared from Enteromorpha, and the preparation factors (water-soluble polymer, temperature, solid content and surfactant) were optimized. The mechanism of dust suppression and the possibility of large-scale field application were discussed. The research results on the related properties of dust suppressants showed that the performance of Enteromorpha-based dust suppressants prepared by this method was excellent compared with similar studies. Among them, polyacrylamide (PAM) Enteromorpha-based dust suppressant had the best performance, with viscosity of 25.1 mPa s and surface tension of 27.05 mN/m. Moreover, PAM Enteromorpha-based dust suppressant had the best effect, with the mass loss of 2.94% under the wind speed of 10 m/s and the coal dust loss rate of 4.6% after rain erosion, and it had strong water retention performance. Through the discussion of dust suppression mechanism, it was found that the mechanical entangled network structure with hydrogen bonds as nodes was formed after the graft copolymerization of PAM and Enteromorpha. It had high permeability and good adhesion. After quickly wetting coal dust, it formed a dense package for coal dust. The field experiment also showed that the use of Enteromorpha-based dust suppressant can effectively inhibit the escape of coal dust. From the point of view of economy and efficiency, Enteromorpha can save 30% of the material cost and the dust suppression efficiency can reach 89-94%. Therefore, the Enteromorpha-based dust suppressant may stably suppress coal dust on the basis of reducing the cost.