Cryo-electron microscopy structure and translocation mechanism of the crenarchaeal ribosome.

Archaeal ribosomes have many domain-specific features; however, our understanding of these structures is limited. We present 10 cryo-electron microscopy (cryo-EM) structures of the archaeal ribosome from crenarchaeota Sulfolobus acidocaldarius (Sac) at 2.7-5.7 Å resolution. We observed unstable conformations of H68 and h44 of ribosomal RNA (rRNA) in the subunit structures, which may interfere with subunit association. These subunit structures provided models for 12 rRNA expansion segments and 3 novel r-proteins. Furthermore, the 50S-aRF1 complex structure showed the unique domain orientation of aRF1, possibly explaining P-site transfer RNA (tRNA) release after translation termination. Sac 70S complexes were captured in seven distinct steps of the tRNA translocation reaction, confirming conserved structural features during archaeal ribosome translocation. In aEF2-engaged 70S ribosome complexes, 3D classification of cryo-EM data based on 30S head domain identified two new translocation intermediates with 30S head domain tilted 5-6° enabling its disengagement from the translocated tRNA and its release post-translocation. Additionally, we observed conformational changes to aEF2 during ribosome binding and switching from three different states. Our structural and biochemical data provide new insights into archaeal translation and ribosome translocation.

Archaeal ribosomes display variations in their ribosomal proteins and ribosomal RNA (rRNA) expansion segments (ESs). Protein translation in archaea combines features in both bacterial and eukaryotic translation. In this study, we present 10 cryo-electron microscopy structures of the archaeal ribosome from crenarchaeota Sulfolobus acidocaldarius (Sac). The 50S and 30S subunit structures present 3 novel ribosomal proteins and 12 rRNA ESs. The 70S Sac ribosome structures were captured in seven distinct functional states, including pre-, intermediate- and post-translocation states. Specifically, we identified two novel translocation intermediates, in which the 30S subunit head domain tilts outward to release the translocated P-site transfer RNA. The structures of archaeal ribosomes provide insights into the archaeal translation and ribosome translocation.

Randomized phase 2 trial of tremelimumab and durvalumab in combination versus sequentially in recurrent platinum-resistant ovarian cancer.

BACKGROUND: Single-agent immune checkpoint inhibitors (ICIs) have demonstrated limited responses in recurrent ovarian cancer; however, 30%-40% of patients achieve stable disease. The primary objective was to estimate progression-free survival (PFS) after sequential versus combination cytotoxic T-lymphocyte antigen 4 and programmed death ligand 1 ICIs in patients with platinum-resistant high-grade serous ovarian cancer (HGSOC). METHODS: Patients were randomized to a sequential arm (tremelimumab followed by durvalumab on progression) or a combination arm (tremelimumab plus durvalumab, followed by durvalumab) via a Bayesian adaptive design that made it more likely for patients to be randomized to the more effective arm. The primary end point was immune-related PFS (irPFS). RESULTS: Sixty-one subjects were randomized to sequential (n = 38) or combination therapy (n = 23). Thirteen patients (34.2%) in the sequential arm received durvalumab. There was no difference in PFS in the sequential arm (1.84 months; 95% CI, 1.77-2.17 months) compared with the combination arm (1.87 months; 95% CI, 1.77-2.43 months) (p = .402). In the sequential arm, no responses were observed, although 12 patients (31.6%) demonstrated stable disease. In the combination arm, two patients (8.7%) had partial response, whereas one patient (4.4%) had stable disease. Adverse events were consistent with those previously reported for ICIs. Patient-reported outcomes were similar in both arms. CONCLUSIONS: There was no difference in irPFS for combination tremelimumab plus durvalumab compared to tremelimumab alone (administered as part of a sequential treatment strategy) in a heavily pretreated population of patients with platinum-resistant HGSOC. Response rates were comparable to prior reports, although the combination regimen did not add significant benefit, as has been previously described.

Dynamic phase-differencing profilometry with number-theoretical phase unwrapping and interleaved projection.

High-speed 3D measurement is receiving increasing attention. However, simultaneously achieving high computational efficiency, algorithmic robustness, and reconstructing ratio is challenging. Therefore, a dynamic phase-differencing profilometry (DPDP) is proposed. By capturing the minimum three phase-shifting sinusoidal deformed patterns and establishing a brand-new model, the phase difference between the object on the reference plane and the reference plane is directly resolved to effectively improve computational efficiency. Although it is wrapped, by using only two auxiliary complementary gratings with a purposely designed lower frequency, a DPDP-based number-theoretical temporal phase unwrapping (NT-TPU) algorithm is also proposed to unwrap the wrapped phase difference rather than the phase itself with high robustness. Furthermore, compared to existing PSP-based NT-TPU, the proposed NT-TPU can normally work under more relaxed restrictions. In order to accomplish a high reconstructing ratio, a pentabasic interleaved projection (PIP) strategy based on time division multiplexing is proposed. It can improve the reconstructing ratio from one reconstruction per every five patterns to an equivalent of one reconstruction per every 1.67 patterns. Experimental results demonstrate that the proposed method achieves high computational efficiency, high algorithmic robustness, and high reconstructing ratio simultaneously and has prospective application in high-speed 3D measurement.

Insights into the Synergistic Catalytic Effect of TiO2 Supported V-W Oxides for Selective C-H Bond Oxidation of Cyclohexane to KA Oil.

It is urgent to develop an efficient and stable non-noble metal catalyst for selective C-H bond oxidation of cyclohexane. Herein, a series of V-W oxides supported on TiO2 catalysts (V-W/TiO2) were fabricated. The V-W/TiO2 catalysts exhibited much higher catalytic activity for the selective oxidation of cyclohexane to KA oil, compared to that of V/TiO2 and W/TiO2 catalysts. The good distribution of active metals and the synergistic effect were responsible for the enhanced catalytic activity. H2-TPR results disclosed that the presence of V in V-W/TiO2 affected the reducibility of W6+ species, and XPS verified that an electronic interaction was formed between them. Such results led to good catalytic reusability of V-W/TiO2 catalyst during the reactions, and no obvious activity loss was found after six runs. The reaction mechanism was investigated, and the results verified that hydroxyl radicals generated from H2O2 homolysis were the main active oxidative species. Theoretical study revealed that V dopant could regulate electronic structure of adjacent O atom, facilitating the adsorption of cyclohexane, and lower energy was needed for the rate-limiting step over V-W/TiO2 during the whole oxidation reaction. This work developed an efficient V-W/TiO2 catalyst for the selective oxidation of cyclohexane via a synergistic effect.

Connection of social anxiety to impaired pattern of cognitive control and underlying motivational deficiencies: Evidence from event-related potentials.

Numerous studies have established a correlation between social anxiety and poor cognitive control. However, little is known about the cognitive control pattern of individuals with high social anxiety (HSAs) and the underlying mechanisms. Based on the Dual Mechanisms of Control framework and the Expected Value of Control theory, this study explored whether HSAs have an impaired cognitive control pattern (Experiment 1) and whether motivational deficiencies underlie the impaired control pattern (Experiment 2). In Experiment 1, 21 individuals with low social anxiety (LSAs) and 21 HSAs completed an AX-Continuous Performance Task. Results showed that HSAs had a smaller P3b amplitude than LSAs, indicating their weakened proactive control in the cue processing stage, but a larger contingent negative variation (CNV) on cue B as compensation for the negative effects of anxiety in the response preparation stage. No group difference was found in N2 and P3a amplitude on probes, suggesting that reactive control in HSAs was not affected compared to LSAs. In Experiment 2, 21 LSAs and 21 HSAs completed a cued-flanker task, where the likelihood of proactive control engagement was manipulated. The results revealed that HSAs exhibited motivation deficiencies in engaging in proactive control, as evidenced by P3b, CNV amplitude, and response times. These findings shed light on the impaired cognitive control pattern of HSAs and suggest that motivational deficiencies may be the crucial underlying factor.

Development of a PROMIS multidimensional cancer-related fatigue (mCRF) form using modern psychometric techniques.

PURPOSE: To develop a PRO assessment of multidimensional cancer-related fatigue based on the PROMIS fatigue assessments. METHOD: Cancer patients reporting fatigue were recruited from a comprehensive cancer care center and completed a survey including 39 items from the PROMIS Cancer Item Bank-Fatigue. Component and factor structures of the fatigue items were explored with Monte Carlo parallel factor and Mokken analyses, respectively. Psychometric properties were determined using item response theory, ensuring unidimensionality, scalability, and item independence. RESULTS: Fatigue scores from a sample of 333 fatigued cancer patients (mean age = 59.50, SD = 11.62, 67% women) were used in all scale development analyses. Psychometric analyses yielded 3 dimensions: motivational fatigue (15 items), cognitive fatigue (9 items), and physical fatigue (9 items). The subscales showed strong unidimensionality, were scalable, and were free of differential item function. Confirmatory factor analyses in a new sample of 182 patients confirmed the findings. CONCLUSION: The resulting 33-item PROMIS multidimensional cancer-related fatigue (mCRF) form provides a novel measure for the assessment of the different dimensions of cancer-related fatigue. It is the only multidimensional scale specific for cancer patients that has been developed using modern psychometric approaches. With its 3 dimensions (motivational, cognitive, and physical fatigue), this scale accurately captures the fatigue experienced by cancer patients, allowing clinicians to optimize fatigue management and improve patient care. The scale could also advance research on the nature and experience of cancer-related fatigue.

Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004).

BACKGROUND: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data. PATIENTS AND METHODS: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains. We employed ten-fold cross-validation to train and test the algorithms, and then externally validated them using the 11th site's data. We considered area-under-the-receiver-operating-characteristics-curve (AUC) as the primary metric to evaluate performance. RESULTS: Overall, between 1454 and 1538 patients completed 2 year follow-up with data for physical, sexual, and psychosocial well-being. In the hold-out validation set, our ML algorithms were able to predict clinically significant changes in physical well-being (chest and upper body) (worsened: AUC range 0.69-0.70; improved: AUC range 0.81-0.82), sexual well-being (worsened: AUC range 0.76-0.77; improved: AUC range 0.74-0.76), and psychosocial well-being (worsened: AUC range 0.64-0.66; improved: AUC range 0.66-0.66). Baseline patient-reported outcome (PRO) variables showed the largest influence on model predictions. CONCLUSIONS: Machine learning can predict long-term individual PROs of patients undergoing postmastectomy breast reconstruction with acceptable accuracy. This may better help patients and clinicians make informed decisions regarding expected long-term effect of treatment, facilitate patient-centered care, and ultimately improve postoperative health-related quality of life.

Evaluation of the clinical efficacy of vaginal treatment options for persistent high-risk human papillomavirus infection after excisional treatment of cervical high-grade squamous intraepithelial lesions: a systematic review and Bayesian network meta-analysis.

BACKGROUND: To evaluate the clinical efficacy of different vaginal administration on cervical persistent high-risk human papillomavirus (HR-HPV) infection after excisional treatment for high-grade squamous intraepithelial lesions (HSIL). METHODS: Six databases (PubMed, EmBase, Cochrane Central, China Knowledge Network database, China Biomedical Literature Service, and WanFang database) were searched to collect randomized controlled trials (RCTs) of various types of vaginal administration compared to no treatment on persistent HR-HPV infection after HSIL excisional treatment, and comprehensive analysis of the clearance of different drugs on HR-HPV was performed using Bayesian reticulation meta-analysis. RESULTS: The study analyzed the efficacy of eight interventions, including Interferon, Baofukang, Paiteling, Bletilla striata Sanhuang Powder, Lactobacilli vaginal capsules, Fuanning + Interferon, Interferon + Lactobacilli vaginal capsules, and Interferon + Baofukang, on the clearance of HR-HPV after excisional treatment through pooling and analyzing data from 52 RCTs. The results of the study demonstrated that Interferon + Lactobacilli vaginal capsules [OR 16.0 (95% CIs 8.1-32.0)], Interferon + Fuanning [OR 16.0 (95% CIs 1.1-52.0)], and Interferon + Baofukang [OR 14.0 (95% CIs 6.8-28.0)] were all found to significantly improve postoperative HR-HPV clearance rates when compared to no treatment. Furthermore, when studies with high-risk bias were excluded, Interferon + Lactobacilli vaginal capsules [OR 8.6 (95% CIs 4.7-19.0)] and Interferon + Baofukang [OR 22.0 (95% CIs 8.7-59.0)] were still found to be positively associated with increased postoperative HR-HPV clearance rate. Additionally, the study´s results also indicate that Interferon + Baofukang was effective in enhancing the postoperative HR-HPV clearance rates, mainly when the studies were restricted to a follow-up period of at least 12 months [OR 9.6 (95% CIs 2.9-34.0)]. However, it is important to note that the majority of the trials (29 out of 52, 51.6%) were rated as moderate to high risk of bias, and the certainty of the evidence was moderate to very low. CONCLUSION: The application of various forms of vaginal administration, except for individual use of Lactobacilli vaginal capsules, is more efficacious than no treatment in patients with cervical persistent HR-HPV infection after excisional treatment. However, all of the estimates of the effect size for change in the efficiency of HR-HPV clearance are uncertain. Our confidence in effect estimates and ranking of treatments is low, which needs larger, more rigorous, and longer follow-up RCTs to resolve.

Surface-enhanced Raman spectroscopy as a powerful method for the analysis of Chinese herbal medicines.

Chinese herbal medicines (CHMs) derived from nature have received increasing attention and become more popular. Due to their diverse production processes, complex ingredients, and different storage conditions, it is highly desirable to develop simple, rapid, efficient and trace detection methods to ensure the drug quality. Surface-enhanced Raman spectroscopy has the advantages of being time-saving, non-destructive, usable in aqueous environments, and highly compatible with various biomolecular samples, providing a promising analytical method for CHM. In this review, we outline the major advances in the application of SERS to the identification of raw materials, detection of bioactive constituents, characterization of adulterants, and detection of contaminants. This clearly shows that SERS has strong potential in the quality control of CHM, which greatly promotes the modernization of CHM.

Systematic investigation of metal dopants and mechanism for the SnO2 electron transport layer in perovskite solar cells.

SnO2, the most promising alternative to TiO2 as the electron transport layer (ETL), has attracted great attention for perovskite solar cells (PSCs) due to its high bulk electron mobility, good band energy at the ETL/perovskite interface, and high chemical stability. To enable more efficient carrier transfer and extraction, elemental doping with different metal cations has been studied in SnO2 ETLs. However, the systematic investigation of the doping mechanism lag far behind their efficiency promotion. In this paper, elements of the same main group (Li, Na, K) and period (K, Ca, Ga) have been selected for doping in SnO2. The results showed that among the properties of the dopants, the electronegativity has the greatest influence. The smaller the electronegativity of the doping species, the more conducive it is to carrier transmission and separation. The corresponding mechanism was proposed and discussed. At last, an efficiency of 20.92% of PSCs based on SnO2-K was achieved. In addition, the doped SnO2 is more beneficial for the growth of perovskite crystals, thus reducing grain boundaries and enhancing the stability of the device.

Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data.

PURPOSE: The objective of the current study was to develop and test the performances of different ML algorithms which were trained using patient-reported symptom severity data to predict mortality within 180 days for patients with advanced cancer. METHODS: We randomly selected 630 of 689 patients with advanced cancer at our institution who completed symptom PRO measures as part of routine care between 2009 and 2020. Using clinical, demographic, and PRO data, we trained and tested four ML algorithms: generalized regression with elastic net regularization (GLM), extreme gradient boosting (XGBoost) trees, support vector machines (SVM), and a single hidden layer neural network (NNET). We assessed the performance of algorithms individually as well as part of an unweighted voting ensemble on the hold-out testing sample. Performance was assessed using area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The starting cohort of 630 patients was randomly partitioned into training (n = 504) and testing (n = 126) samples. Of the four ML models, the XGBoost algorithm demonstrated the best performance for 180-day mortality prediction in testing data (AUROC = 0.69, sensitivity = 0.68, specificity = 0.62, PPV = 0.66, NPV = 0.64). Ensemble of all algorithms performed worst (AUROC = 0.65, sensitivity = 0.65, specificity = 0.62, PPV = 0.65, NPV = 0.62). Of individual PRO symptoms, shortness of breath emerged as the variable of highest impact on the XGBoost 180-mortality prediction (1-AUROC = 0.30). CONCLUSION: Our findings support ML models driven by patient-reported symptom severity as accurate predictors of short-term mortality in patients with advanced cancer, highlighting the opportunity to integrate these models prospectively into future studies of goal-concordant care.

A preoperative nomogram predicting risk of lymph node metastasis for early-stage cervical cancer.

OBJECTIVE: This study aimed to develop a preoperative nomogram based on clinical and pathological characteristics to provide a more individualized and accurate estimation of lymph node metastasis (LNM) in patients with early-stage cervical cancer. METHODS: A total of 7,349 early-stage cervical cancer patients with pathologically confirmed between 1988 and 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n = 5,500) and validation (n = 1,849) cohorts randomly. A cohort of 455 patients from multicenter was used for the external validation. We established a multivariate logistic regression model based on preoperative clinicopathological data, from which a nomogram was developed and validated. A predicted probability of LNM < 5% was defined as low risk. RESULTS: From multivariate logistic regression analysis, age at diagnosis, histologic subtype, tumor grade, tumor size and FIGO stage were identified as preoperative independent risk factors of LNM. The nomogram incorporating these factors demonstrated good discrimination and calibration (concordance index = 0.723; 95% confidence interval (CI), 0.707-0.738). In the validation cohort, the discrimination accuracy was 0.745 (95% CI, 0.720-0.770) and 0.747 (95% CI, 0.690-0.804), respectively. The nomogram was well calibrated with a high concordance probability. We also established an R-enabled Internet browser for LNM risk assessment, which tool may be convenient for physicians. CONCLUSIONS: We developed an effective preoperative nomogram based on clinical and pathological characteristics to predict LNM for early-stage cervical cancer. This model could improve clinical trial design and help physicians to decide whether to perform lymphadenectomy or not.

First Principles Study of the Photoelectric Properties of Alkaline Earth Metal (Be/Mg/Ca/Sr/Ba)-Doped Monolayers of MoS2.

The energy band structure, density of states, and optical properties of monolayers of MoS2 doped with alkaline earth metals (Be/Mg/Ca/Sr/Ba) are systematically studied based on first principles. The results indicate that all the doped systems have a great potential to be formed and structurally stable. In comparison to monolayer MoS2, doping alkaline earth metals results in lattice distortions in the doped system. Therefore, the recombination of photogenerated hole-electron pairs is suppressed effectively. Simultaneously, the introduction of dopants reduces the band gap of the systems while creating impurity levels. Hence, the likelihood of electron transfer from the valence to the conduction band is enhanced, which means a reduction in the energy required for such a transfer. Moreover, doping monolayer MoS2 with alkaline earth metals increases the static dielectric constant and enhances its polarizability. Notably, the Sr-MoS2 system exhibits the highest value of static permittivity, demonstrating the strongest polarization capability. The doped systems exhibit a red-shifted absorption spectrum in the low-energy region. Consequently, the Be/Mg/Ca-MoS2 systems demonstrate superior visible absorption properties and a favorable band gap, indicating their potential as photo-catalysts for water splitting.

The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis.

OBJECTIVES: AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms. METHODS: Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC). RESULTS: Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05). CONCLUSIONS: The performance of humans and AI-based algorithms improves with multi-modal information. KEY POINTS: • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses.

Efficient and precise Ultra-QuickDASH scale measuring lymphedema impact developed using computerized adaptive testing.

PURPOSE: This study aimed to evaluate and improve the accuracy and efficiency of the QuickDASH for use in assessment of limb function in patients with upper extremity lymphedema using modern psychometric techniques. METHOD: We conducted confirmative factor analysis (CFA) and Mokken analysis to examine the assumption of unidimensionality for IRT model on data from 285 patients who completed the QuickDASH, and then fit the data to Samejima's graded response model (GRM) and assessed the assumption of local independence of items and calibrated the item responses for CAT simulation. RESULTS: Initial CFA and Mokken analyses demonstrated good scalability of items and unidimensionality. However, the local independence of items assumption was violated between items 9 (severity of pain) and 11 (sleeping difficulty due to pain) (Yen's Q3 = 0.46) and disordered thresholds were evident for item 5 (cutting food). After addressing these breaches of assumptions, the re-analyzed GRM with the remaining 10 items achieved an improved fit. Simulation of CAT administration demonstrated a high correlation between scores on the CAT and the QuickDash (r = 0.98). Items 2 (doing heavy chores) and 8 (limiting work or daily activities) were the most frequently used. The correlation among factor scores derived from the QuickDASH version with 11 items and the Ultra-QuickDASH version with items 2 and 8 was as high as 0.91. CONCLUSION: By administering just these two best performing QuickDash items we can obtain estimates that are very similar to those obtained from the full-length QuickDash without the need for CAT technology.

Nonlinear error full-field compensation method for phase measuring profilometry.

Phase measuring profilometry (PMP) has the highest measuring accuracy among structured light projection-based three-dimensional (3D) sensing methods. Due to their low-cost and high-resolution features, commercial projectors are extensively used in PMP, but they are all designed with a gamma effect purpose that considers the characteristics of human vision. Affected by the gamma effect, a set of phase-shifting sinusoidal deformed patterns captured in PMP may contain high-order harmonics which lead to nonlinear phase errors. Then, a novel nonlinear error full-field compensation method is proposed. First, the unwrapped phases modulated by the reference plane are measured several times, and their average phase is taken as the measured phase modulated by the reference plane to eliminate random errors as much as possible. Second, an expected phase plane is fitted from this average phase with the least-squares method. Third, the nonlinear phase error can be detected by subtracting the fitted expected phase from this average phase. Finally, the full-field look-up table (LUT) can be established between the nonlinear phase error and the measured phase. When an object is measured, the unwrapped phase modulated by the object is taken as the measured phase of the LUT, so the corresponding nonlinear phase error can be directly searched in the LUT. In this way, the full-field nonlinear phase error can be efficiently compensated. Experimental results show the feasibility and validity of the proposed method. The mean absolute error (MAE) can be improved from 0.48 mm to 0.06 mm, and the root mean square error (RMSE) can be improved from 0.55 mm to 0.07 mm.

Vagus nerve stimulated by microbiota-derived hydrogen sulfide mediates the regulation of berberine on microglia in transient middle cerebral artery occlusion rats.

Amelioration of neuroinflammation via modulating microglia is a promising approach for cerebral ischemia therapy. The aim of the present study was to explore gut-brain axis signals in berberine-modulating microglia polarization following cerebral ischemia. The potential pathway was determined through analyzing the activation of the vagus nerve, hydrogen sulfide (H2 S) metabolism, and cysteine persulfides of transient receptor potential vanilloid 1 (TRPV1) receptor. The cerebral microenvironment feature was explored with a metabolomics assay. The data indicated that berberine ameliorated behavioral deficiency in transient middle cerebral artery occlusion rats through modulating microglia polarization and neuroinflammation depending on microbiota. Enhanced vagus nerve activity following berberine treatment was blocked by antibiotic cocktails, capsazepine, or sodium molybdate, respectively. Berberine-induced H2 S production was responsible for vagus nerve stimulation achieved through assimilatory and dissimilatory sulfate reduction with increased synthetic enzymes. Sulfation of the TRPV1 receptor resulted in vagus nerve activation and promoted the c-fos and ChAT in the nucleus tractus solitaries with berberine. Sphingolipid metabolism is the primary metabolic characteristic with berberine in the cerebral cortex, hippocampus, and cerebral spinal fluid disrupted by antibiotics. Berberine, in conclusion, modulates microglia polarization in a microbiota-dependent manner. H2 S stimulates the vagus nerve through TRPV1 is responsible for the berberine-induced gut-brain axis signal transmission. Sphingolipid metabolism might mediate the neuroinflammation amelioration following vagus afferent fiber activation.

Clinical prognostic role of bioimpedance phase angle in diabetic and non-diabetic hemodialysis patients.

BACKGROUND AND OBJECTIVES: Bioelectrical-impedance analysis (BIA) is frequently used to estimate dry weight in hemodialysis (HD) patients. However, the clinical prognostic significance of BIA indicators is unclear. As a nutritional index, low phase angle (PA) might be an independent risk factor for predicting death in multiple chronic diseases. We performed this study to find relative influence factors of PA and other clinical prognostic significance. METHODS AND STUDY DESIGN: The study involved 87 HD patients, 33 of whom were diabetic and 54 of whom were not. We measured body composition index, body water index and nutritional indicators and collected biochemical criteria. Then, we statistically analyzed the associations of these indices. After 1 year of follow- up, we recorded death, heart failure, hospitalization, cerebrovascular and cardiovascular events and other clinical outcomes. RESULTS: We found a significant difference between the two groups in visceral-fat area, extracellular water/total body water (ECW/TBW) ratio and PA value. Two factors were negatively associated with PA: ECW/TBW ratio and HCO3- before HD. At 1 year, we noted that PA was associated with events such as heart failure or hospitalization. By further stratification and multivariate analysis adjusting for age, sex and months of dialysis, we found that low PA was an independent predictor of heart failure for diabetic HD patients. CONCLUSIONS: PA value was lower in Diabetic nephropathy (DN) HD patients, than that in non-DN HD patients. PA was mainly negatively associated with ECW/TBW ratio. It is a useful index for predicting heart failure in diabetic HD patients.

Spatial-temporal phase unwrapping algorithm for fringe projection profilometry.

In this paper, a generalized spatial-temporal phase unwrapping algorithm (STPUA) is proposed for extracting the absolute phase of the isolated objects with intricate surfaces. This proposed algorithm can eliminate thoroughly the order jumps of various temporal phase unwrapping algorithms (TPUAs), while inheriting the high measuring accuracy of quality-guided phase unwrapping algorithms (QGPUAs). Differing from the traditional phase unwrapping algorithms, wrapped phase is first divided into several regional wrapped phases, which can be extracted successively according to its areas and unwrapped individually by QGPUAs. Meanwhile, a series of reliable points from the fringe order map obtained from the code deformed patterns are selected to map the corresponding regional unwrapped phases into an absolute phase. The radii of selecting reliable points can provide the high measuring robustness compared with the classical point-to-point TPUAs for the complex surfaces and the motion blur, while keeping the same number of patterns. Therefore, the proposed STPUA combining SPUAs and TPUAs also can be employed in real-time three-dimensional (3D) reconstruction. Theoretical analysis and experimental results are performed to verify the effectiveness and capability of the proposed algorithm.

Ultrafast spatial phase unwrapping algorithm with accurately correcting transient phase error.

In fringe projection profilometry, the wrapped phase is easily polluted by many factors such as noise, shadow, and so on. In this Letter, we propose an ultrafast bi-staggered spatial phase unwrapping (BSPU) method. By constructing another staggered phase, the fringe order jump (FOJ) and local transient phase error (LTPE) can be accurately and quickly located at the same time owing to a simple difference operation. For the first time, to the best of our knowledge, a pioneering threshold separation model is established to precisely distinguish FOJ and LTPE. Based on the continuity assumption, LTPE is effectively corrected by introducing the concept of "non-integer fringe order." The range of measurable discontinuity height is improved owing to the distinction between real phase jump and random error in the spatial phase unwrapping. In addition, it is thousands of times faster than the traditional path-dependent algorithm and even has higher measurement accuracy. Experimental results show the effectiveness and robustness of the proposed method in various complex measurement environments.