Average capacity of an underwater wireless communication link with the quasi-Airy hypergeometric-Gaussian vortex beam based on a modified channel model.

Prompted by alleviating the random perturbation of underwater channel and enhancing the performance for the orbital angular momentum (OAM) -based underwater wireless optical communication (UWOC), the quasi-Airy Hypergeometric-Gaussian (QAHyGG) vortex beam is first proposed and demonstrated. Moreover, an underwater channel model is first modified for more accurate simulated results of the propagation property of various beams. Based on the modified model, the transmission and communication performance of three different OAM-carrying beams (the Gauss vortex (GV) beam, the Hypergeometric-Gaussian (HyGG) vortex beam, and the QAHyGG vortex beam) are comparatively studied. In addition, the parameters optimization of the QAHyGG vortex beam is made for further enhancing the average capacity. The results show that the QAHyGG vortex beam exhibits higher received power and lower crosstalk probability under different channel conditions. The average capacity of the QAHyGG vortex beam has enhanced by ∼8% and ∼27% compared with the HyGG vortex beam and the GV beam at 100m, respectively. The QAHyGG vortex beam is more suitable in an OAM-based UWOC system with a limited-size receiving aperture or lower transmit power. Besides, the average capacity will improve effectively at longer distances with the optimized beam parameters. These research results can provide advances in designing the practical OAM-based UWOC system.

Development, External Validation, and Visualization of Machine Learning Models for Predicting Occurrence of Acute Kidney Injury after Cardiac Surgery.

Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in short- and long-term mortality among patients. Here, we adopted machine learning algorithms to build prediction models with the overarching goal of identifying patients who are at a high risk of such unfavorable kidney outcomes. Methods: A total of 1686 patients (development cohort) and 422 patients (validation cohort), with 126 pre- and intra-operative variables, were recruited from the First Medical Centre and the Sixth Medical Centre of Chinese PLA General Hospital in Beijing, China, respectively. Analyses were performed using six machine learning techniques, namely K-nearest neighbor, logistic regression, decision tree, random forest (RF), support vector machine, and neural network, and the APPROACH score, a previously established risk score for CSA-AKI. For model tuning, optimal hyperparameter was achieved by using GridSearch with 5-fold cross-validation from the scikit-learn library. Model performance was externally assessed via the receiver operating characteristic (ROC) and decision curve analysis (DCA). Explainable machine learning was performed using the Python SHapley Additive exPlanation (SHAP) package and Seaborn library, which allow the calculation of marginal contributory SHAP value. Results: 637 patients (30.2%) developed CSA-AKI within seven days after surgery. In the external validation, the RF classifier exhibited the best performance among the six machine learning techniques, as shown by the ROC curve and DCA, while the traditional APPROACH risk score showed a relatively poor performance. Further analysis found no specific causative factor contributing to the development of CSA-AKI; rather, the development of CSA-AKI appeared to be a complex process resulting from a complex interplay of multiple risk factors. The SHAP summary plot illustrated the positive or negative contribution of RF-top 20 variables and extrapolated risk of developing CSA-AKI at individual levels. The Seaborn library showed the effect of each single feature on the model output of the RF prediction. Conclusions: Efficient machine learning approaches were successfully established to predict patients with a high probability of developing acute kidney injury after cardiac surgery. These findings are expected to help clinicians to optimize treatment strategies and minimize postoperative complications. Clinical Trial Registration: The study protocol was registered at the ClinicalTrials Registration System (https://www.clinicaltrials.gov/, #NCT04966598) on July 26, 2021.

Rapid automatic nucleic acid purification system based on gas-liquid immiscible phase.

The continuous expansion of nucleic acid detection applications has resulted in constant developments in rapid, low-consumption, and highly automated nucleic acid extraction methods. Nucleic acid extraction using magnetic beads across an immiscible phase interface offers significant simplification and parallelization potential. The gas-liquid immiscible phase valve eliminates the requirement for complicated cassettes and is suitable for automation applications. By analyzing the process of magnetic beads crossing the gas-liquid interface, we utilized a low magnetic field strength to drive large magnetic bead packages to cross the gas-liquid interface, providing a solution of high magnetic bead recovery rate for solid-phase extraction with a low-surfactant system based on gas-liquid immiscible phase valve. The recovery rate of magnetic beads was further improved to 90%-95% and the carryover of the reagents was below 1%. Consequently, a chip and an automatic system were developed to verify the applicability of this method for nucleic acid extraction. The Hepatitis B virus serum standard was used for the extraction test. The extraction of four samples was performed within 7 minutes, with nucleic acid recovery maintained above 80% and good purity. Thus, through analysis and experiments, a fast, highly automated, and low-consumption nucleic acid recovery method was proposed in this study.

Application of Machine Learning Algorithms to Predict New-Onset Postoperative Atrial Fibrillation and Identify Risk Factors Following Isolated Valve Surgery.

BACKGROUND: New-onset postoperative atrial fibrillation (POAF) is the most common complication after valvular surgery, but its etiology and risk factors are incompletely understood. This study investigates the benefits of machine learning methods in risk prediction and in identifying relative perioperative variables for POAF after valve surgery. METHODS: This retrospective study involved 847 patients, who underwent isolated valve surgery from January 2018 to September 2021 in our institution. We used machine learning algorithms to predict new-onset postoperative atrial fibrillation and to select relatively important variables from a set of 123 preoperative characteristics and intraoperative information. RESULTS: The support vector machine (SVM) model demonstrated the best area under the receiver operating characteristic (AUC) value of 0.786, followed by logistic regression (AUC = 0.745) and the Complement Naive Bayes (CNB) model (AUC = 0.672). Left atrium diameter, age, estimated glomerular filtration rate (eGFR), duration of cardiopulmonary bypass, New York Heart Association (NYHA) class III-IV, and preoperative hemoglobin were high-ranked variables. CONCLUSIONS: Risk models based on machine learning algorithms may be superior to traditional models, which were primarily based on logistic algorithms to predict the occurrence of POAF after valve surgery. Further prospective multicenter studies are needed to confirm the performance of SVM in predicting POAF.

Total Synthesis of the Proposed Structure of Neaumycin B.

The total synthesis of the proposed structure of anti-glioblastoma natural product neaumycin B was achieved in 22 steps (longest linear sequence). The synthesis features HCl-mediated [6,6]-spiroketalization, a combination of Krische iridium-catalyzed crotylation, Marshall palladium-catalyzed propargylation, Fürstner nickel-catalyzed regio- and enantioselective vicinal monoprotected diol formation, Brown crotylation and asymmetric halide-aldehyde cycloaddition, so as to establish the challenging contiguous stereocenters.

1.7 µm gain-switched and mode-locked hybrid Tm-Ho codoped fiber laser signal generation and optimization.

We propose and experimentally demonstrate 1.7 µm gain-switched and mode-locked hybrid laser signal generation using a modulated pump and the nonlinear polarization rotation (NPR) effect. In the laser scheme, a 1.55 µm amplified modulated optical signal was used as a homemade pump. A bidirectional pumping configuration was adopted by splitting the homemade pump. A 1 m long thulium-holmium (Tm-Ho) codoped fiber was used as the gain medium. A fiber Bragg grating was employed as a spectral filter. The mode-locked laser pulse was obtained with a central wavelength of 1724 nm. The repetition rate was 11.81 MHz and the pulse width was 65.27 ps. Additionally, the gain-switched pulse sequences with a repetition rate from 50 kHz to 200 kHz were obtained by the modulated pump. Moreover, the mode-locked pulse train was filtered and modulated by the shape of the gain-switched pulse, and the hybrid pulse train was then obtained. Furthermore, the hybrid laser signals were analyzed and optimized by applying different waveforms of the modulated pump. The experimental results showed that the generated laser pulse driven by the sinusoidal signal has a better SNR (49.39 dB).

Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis.

BACKGROUND: Aortic valve stenosis (AS) disease is the most common valvular disease in developed countries. The pathology of AS is complex, and its main processes include calcification of the valve stroma and involve genetic factors, lipoprotein deposition and oxidation, chronic inflammation, osteogenic transition of cardiac valve interstitial cells, and active valve calcification. The aim of this study was to identify potential genes associated with AS. METHODS: Three original gene expression profiles (GSE153555, GSE12644, and GSE51472) were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R tool or 'limma' in R to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. STRING was utilized for the Protein-Protein Interaction (PPI) Network construct, and tissue-specific gene expression were identified using BioGPS database. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in Targetscan, miWalk, miRDB, Hoctar, and TarBase. RESULTS: A total of 58 upregulated genes and 20 downregulated genes were screened out, which were mostly enriched in matrix remodeling and the immune system process. A module was thus clustered into by PPI network analysis, which mainly involved in Fc gamma R-mediated phagocytosis, Osteoclast differentiation. Ten genes (IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1) with the highest degree scores were subsequently identified as the hub genes for AS by applying the CytoHubba plugin. And hsa-miR-1276 was finally identified as potential miRNA and miRNA-gene regulatory network was constructed using NetworkAnalyst. CONCLUSIONS: Our analysis suggested that IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1 might be hub genes associated with AS, and hsa-miR-1276 was potential miRNA. This result could provide novel insight into pathology and therapy of AS in the future.

Learning Curve of Aortic Arch Replacement Surgery in Chinese Mainland with Stanford Type A Aortic Dissection.

BACKGROUND: Stanford type A aortic dissection (TAAD) is the most common cause of death caused by aortic disease in the Chinese mainland. Patients suffering TAAD need immediate surgical treatment [Pompilio 2001; Di Eusanio 2003; Ueda 2003; Li 2013; Afifi 2016; Zhou 2019; Zhou 2021]. Emergency aortic arch replacement is difficult and risky. The prognosis following surgery varies depending on the different surgical approaches [Pompilio 2001; Kazui 2002; Di Eusanio 2003; Ueda 2003; Moon 2009; Li 2013; Afifi 2016; Zhou 2019; Zhou 2021]. Aortic arch replacement includes total-arch replacement (Sun's operation) and hemi-arch replacement. The comparative analysis of learning curves between the two procedures has not been systematically studied. In this study, we studied and analyzed the learning curves of total-arch replacement and hemi-arch replacement using cumulative sum (CUSUM) analysis. METHODS: From January 2013 to December 2019, a total of 139 Stanford TAAD operations were performed by the same surgeon and two assistants, including 61 cases of hemi-arch replacement and 78 cases of total-arch replacement. Baseline information, including preoperative conditions, intraoperative related data and postoperative prognosis, were collected. Descriptive statistics and CUSUM were used to analyze the total operation time, cardiopulmonary bypass (CPB) time, aortic clamping (AC) time, operative mortality, incidence of postoperative complications, postoperative intensive care unit (ICU) time, hospital stay, and postoperative drainage volume. RESULTS: A total of 139 patients with TAAD (age 48.8 ± 12.3, male, 107, female, 32) underwent emergency aortic arch replacement. A total of 61 patients (43.9%) underwent hemi-arch replacement, and 78 patients (56.1%) underwent total-arch replacement. The total time, cardiopulmonary bypass (CPB) time, and aortic clamping (AC) time of hemi-arch operation were 434.2 ± 137.0 minutes, 243.3 ± 87.2 minutes, and 157.0 ± 60.2 minutes. The total, CPB, and AC times of total-arch operation were 747.8 ± 164.3 minutes, 476.4 ± 121.6 minutes, and 238.5 ± 67.6 minutes. The mortality of hemi-arch operation was 3.3%, and that of total-arch operation was 6.4%. The incidence of complications after hemi-arch operation was 11.3%, and that after total-arch operation was 46.2%. The ICU time and hospital stay after hemi-arch surgery were 7.3 ± 4.4 days and 27.2 ± 16.2 days, respectively, and the ICU time and total hospital stay after total-arch surgery were 7.2 ± 5.9 days and 24.0 ± 10.3 days, respectively. The total drainage volume after hemi-arch operation was 2182.4 ± 1236.4 ml, and that after total-arch operation was 2467.3 ± 1385.7 ml. According to CUSUM analysis, the same cardiovascular surgery team seems to have different learning curves in the time of two operations. CUSUM analysis of intraoperative and postoperative indicators shows that after a certain period of professional and systematic cardiovascular surgery training, aortic hemi-arch replacement has the characteristics of short learning cycle and easy to master for surgeons, while total-arch replacement requires a longer learning cycle. CONCLUSIONS: Although the emergency operation of TAAD is difficult and risky, according to results the of CUSUM analysis, cardiovascular surgeons can achieve better learning results in hemi-arch replacement than total-arch replacement.

Chronic Inflammatory Pain Impairs mGluR5-Mediated Depolarization-Induced Suppression of Excitation in the Anterior Cingulate Cortex.

The anterior cingulate cortex (ACC) is a critical hub for nociceptive perception and pain-related anxiety. Long-term synaptic plasticity in ACC was found to be important for chronic inflammatory pain and pain-related anxiety. As short-term synaptic plasticity, depolarization-induced suppression of excitation (DSE) is involved in several conditions, such as chronic stress, epilepsy, and autism. However, it is still unknown whether DSE in the ACC is involved in the central sensitization of pain and anxiety. Using a whole-cell patch clamp, calcium imaging, western blot, and behavioral testing, we found that DSE was induced by a 2 s depolarization in postsynaptic pyramidal cells in ACC. DSE was mediated by endocannabinoid signaling and modulated by metabotropic glutamate receptor 5 (mGluR5). DSE was impaired by decreasing expression and dysfunction of mGluR5 in a mouse model of inflammatory pain induced by complete Freund's adjuvant. CDPPB, an mGluR5-positive allosteric modulator, could rescue hypersensitivity and anxiety-like behavior in this pain model. Our results demonstrated that mGluR5-mediated short-term plasticity in ACC may be a critical mechanism for chronic pain, and mGluR5 may potentially serve as a target of pain therapy, including treatments for hyperalgesia and anxiety.

From autophagy to mitophagy: the roles of P62 in neurodegenerative diseases.

P62, also called sequestosome1 (SQSTM1), is the selective cargo receptor for autophagy to degenerate misfolded proteins. It has also been found to assist and connect parkin in pink1/parkin mitophagy pathway. Previous studies showed that p62 was in association with neurodegenerative diseases, and one of the diseases pathogenesis is P62 induced autophagy and mitophagy dysfunction. Autophagy is an important process to eliminate misfolded proteins. Intracellular aggregation including α-synuclein, Huntingtin, tau protein and ß-amyloid (Aß) protein are the misfolded proteins found in PD, HD and AD, respectively. P62 induced autophagy failure significantly accelerates misfolded protein aggregation. Mitophagy is the special autophagy, functions as the selective scavenger towards the impaired mitochondria. Mitochondrial dysfunction was confirmed greatly contribute to the occurrence of neurodegenerative diseases. Through assistance and connection with parkin, P62 is vital for regulating mitophagy, thus, aberrant P62 could influence the balance of mitophagy, and further disturb mitochondrial quality control. Therefore, accumulation of misfolded proteins leads to the aberrant P62 expression, aberrant P62 influence the balance of mitophagy, forming a vicious circle afterwards. In this review, we summarize the observations on the function of P62 relevant to autophagy and mitophagy in neurodegenerative diseases, hoping to give some clear and objective opinions to further study.

Hyperthermia inhibited the migration of tongue squamous cell carcinoma through TWIST2.

BACKGROUND: Hyperthermia has been shown promising in the treatment of head and neck squamous cell carcinoma (HNSCC); however, the mechanism underlying hyperthermia reducing tumor metastasis is poorly elucidated. TWIST2, an important transcription factor of epithelial-mesenchymal transition (EMT), plays a critical role in the tumor progression and metastasis. The role of TWIST2 in tongue squamous cell carcinoma (TSCC) and its association with hyperthermia still have not been reported. METHOD: The correlations between TWIST2 expression and the clinical-pathologic characteristics of 89 patients with TSCC were evaluated by immunohistochemical staining. TSCC cell lines transfected with siRNA against TWIST2 were heated for 40 min at 42.5°C, and the migration capability of cells was examined by migration assay. Xenograft tumors in nude mice were treated by hyperthermia, and TWIST2 expression was measured. RESULTS: Our data showed that TWIST2 expression was associated with the metastasis of human TSCC. In Tca8113 and Cal-27 cells, TWIST2-siRNA treatment can reduce cell migration ability and has no effect on the cell proliferation and apoptosis. Hyperthermia can decrease the level of TWIST2 in TSCC and inhibit the migration of cells. CONCLUSIONS: This demonstrated that hyperthermia might decrease the migration of Tca8113 and Cal-27 cells by reducing TWIST2 expression. Altogether, these findings suggest an as yet undescribed link between TWIST2 and hyperthermia in TSCC.

A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: A experimental study.

Deep learning models have emerged as rapid, accurate, and effective approaches for clinical decisions. Through a combination of drug screening and deep learning models, drugs that may benefit patients before and after surgery can be discovered to reduce the risk of complications or speed recovery. However, most existing drug prediction methods have high data requirements and lack interpretability, which has a limited role in adjuvant surgical treatment. To address these limitations, we propose the attention-based convolution transpositional interfusion network (ACTIN) for flexible and efficient drug discovery. ACTIN leverages the graph convolution and the transformer mechanism, utilizing drug and transcriptome data to assess the impact of chemical pharmacophores containing certain elements on gene expression. Remarkably, just with only 393 training instances, only one-tenth of the other models, ACTIN achieves state-of-the-art performance, demonstrating its effectiveness even with limited data. By incorporating chemical element embedding disparity and attention mechanism-based parameter analysis, it identifies the possible pharmacophore containing certain elements that could interfere with specific cell lines, which is particularly valuable for screening useful pharmacophores for new drugs tailored to adjuvant surgical treatment. To validate its reliability, we conducted comprehensive examinations by utilizing transcriptome data from the lung tissue of fatal COVID-19 patients as additional input for ACTIN, we generated novel lead chemicals that align with clinical evidence. In summary, ACTIN offers insights into the perturbation biases of elements within pharmacophore on gene expression, which holds the potential for guiding the development of new drugs that benefit surgical treatment.