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Background In recent years, unilateral biportal endoscopic (UBE) surgery has become one of the most popular minimally invasive spine surgeries. Unlike traditional open surgery, UBE surgery is performed in isotonic saline solution. Therefore, comprehending the water dynamics involved in UBE surgery is crucial. Methods This prospective study involved 29 patients with single-level lumbar instability or degenerative disk disease who underwent UBE surgery between April 2021 and March 2022. Water flow pressure was measured using a disposable pressure transducer. Multifidus muscle MRI images were analyzed by ImageJ software at intervertebral disc levels. Perioperative blood loss was estimated by the Gross formula. The obtained data were then analyzed with independent t tests, chi-squared tests, and Pearson’s correlation. Results Height and weight were risk factors for increased water flow pressure during UBE surgery ( r  = 0.424, P  = 0.022, r  = 0.384, P  = 0.040). The phenomenon of low water flow pressure led to escalations in perioperative total blood loss, hematocrit loss and hemoglobin loss ( r  = -0.369, P  = 0.049, r  = -0.424, P  = 0.022, r  = -0.405, P  = 0.029). An excessive water flow pressure can worsen postoperative multifidus swelling and elevate the patient's leg pain visual analogue scale (VAS) score at 1 week ( r  = 0.442, P  = 0.016, r  = 0.394, P  = 0.034). Registration Trial registration Chinese Clinical Trial Registry, registration number ChiCTR2300078497, date of registration: 11/12/2023. Conclusion Both low and high water flow pressures can have deleterious effects. The water flow pressure should be controlled within a reasonable range during UBE surgery.

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions and solvent accessibility.

Summary The Orchidaceae is of economic and ecological importance and constitutes ˜10% of all seed plant species. Here, we report a genome physical map for Cymbidium sinense, a well‐known species belonging to genus Cymbidium that has thousands of natural variation varieties of flower organs, flower and leaf colours and also referred as the King of Fragrance, which make it arose into a unique cultural symbol in China. The high‐quality chromosome‐scale genome assembly was 3.52 Gb in size, 29 638 protein‐coding genes were predicted, and evidence for whole‐genome duplication shared with other orchids was provided. Marked amplification of cytochrome‐ and photosystem‐related genes was observed, which was consistent with the shade tolerance and dark green leaves of C. sinense. Extensive duplication of MADS‐box genes, and the resulting subfunctional and expressional differentiation, was associated with regulation of species‐specific flower traits, including wild‐type and mutant‐type floral patterning, seasonal flowering and ecological adaption. CsSEP4 was originally found to positively regulate gynostemium development. The CsSVP genes and their interaction proteins CsAP1 and CsSOC1 were significantly expanded and involved in the regulation of low‐temperature‐dependent flowering. Important genetic clues to the colourful leaf traits, purple‐black flowers and volatile trait in C. sinense were also found. The results provide new insights into the molecular mechanisms of important phenotypic traits of Cymbidium and its evolution and serve as a powerful platform for future evolutionary studies and molecular breeding of orchids.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Immune infiltration of tumors is closely associated with clinical outcome in renal cell carcinoma (RCC). Tumor‐infiltrating immune cells (TIICs) regulate cancer progression and are appealing therapeutic targets. The purpose of this study was to determine the composition of TIICs in RCC and further reveal the independent prognostic values of TIICs. CIBERSORT, an established algorithm, was applied to estimate the proportions of 22 immune cell types based on gene expression profiles of 891 tumors. Cox regression was used to evaluate the association of TIICs and immune checkpoint modulators with overall survival (OS). We found that CD8+ T cells were associated with prolonged OS (hazard ratio [HR] = 0.09, 95% confidence interval [CI].01‐.53; P = 0.03) in chromophobe carcinoma (KICH). A higher proportion of regulatory T cells was associated with a worse outcome (HR = 1.59, 95% CI 1.23‐.06; P < 0.01) in renal clear cell carcinoma (KIRC). In renal papillary cell carcinoma (KIRP), M1 macrophages were associated with a favorable outcome (HR = .43, 95% CI .25‐.72; P < 0.01), while M2 macrophages indicated a worse outcome (HR = 2.55, 95% CI 1.45‐4.47; P < 0.01). Moreover, the immunomodulator molecules CTLA4 and LAG3 were associated with a poor prognosis in KIRC, and IDO1 and PD‐L2 were associated with a poor prognosis in KIRP. This study indicates TIICs are important determinants of prognosis in RCC meanwhile reveals potential targets and biomarkers for immunotherapy development. We described the immune landscape in detail, revealing the distinct immune infiltration patterns of different subtypes and stages of RCC. We further revealed relationships between TIIC and molecular subtypes, tumor stages, recurrent genomic alterations and survival in RCC. Our work advances the understanding of immune response meanwhile reveals potential targets and biomarkers for immunotherapy development.

A bstract The dynamical studies on the non-leptonic weak decays of charmed baryons are always challenging, due to the large non-perturbative contributions at the charm scale. In this work, we develop the final-state rescattering mechanism to study the two-body non-leptonic decays of charmed baryons. The final-state interaction is a physical picture of long-distance effects. Instead of using the Cutkosky rule to calculate the hadronic triangle diagrams which can only provide the imaginary part of decay amplitudes, we point out that the loop integral is more appropriate, as both the real parts and the imaginary parts of amplitudes can be calculated completely. In this way, it can be obtained for the non-trivial strong phases which are essential to calculate CP violations. With the physical picture of long-distance effects and the reasonable method of calculations, it is amazingly achieved that all the nine existing experimental data of branching fractions for the Λ c + decays into an octet light baryon and a vector meson can be explained by only one parameter of the model. Besides, the decay asymmetries and CP violations are not sensitive to the model parameter, since the dependence on the parameter is mainly cancelled in the ratios, so that the theoretical uncertainties on these observables are lowered down.

Electrochemical CO 2 reduction in acidic electrolytes is a promising strategy to achieve high utilization efficiency of CO 2 . Although alkali cations in acidic electrolytes play a vital role in suppressing hydrogen evolution and promoting CO 2 reduction, they also cause precipitation of bicarbonate on the gas diffusion electrode (GDE), flooding of electrolyte through the GDE, and drift of the electrolyte pH. In this work, we realize the electroreduction of CO 2 in a metal cation-free acidic electrolyte by covering the catalyst with cross-linked poly-diallyldimethylammonium chloride. This polyelectrolyte provides a high density of cationic sites immobilized on the surface of the catalyst, which suppresses the mass transport of H + and modulates the interfacial field strength. By adopting this strategy, the Faradaic efficiency (FE) of CO reaches 95 ± 3% with the Ag catalyst and the FE of formic acid reaches 76 ± 3% with the In catalyst in a 1.0 pH electrolyte in a flow cell. More importantly, with the metal cation-free acidic electrolyte the amount of electrolyte flooding through the GDE is decreased to 2.5 ± 0.6% of that with alkali cation-containing acidic electrolyte, and the FE of CO maintains above 80% over 36 h of operation at −200 mA·cm −2 . Alkali bicarbonate precipitation hinders electrochemical CO 2 reduction in acidic electrolytes. Here, the authors report CO 2 reduction in a metal cation-free acidic electrolyte by covering the catalyst with crosslinked polyelectrolyte, achieving 36-hour stability in a flow cell.

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI .

Hepatocellular carcinoma (HCC) is one of the most common malignancies and the fourth leading cause of cancer‐related death worldwide. Our previous study showed that EYA4 functioned by suppressing growth of HCC tumor cells, but its molecular mechanism is still not elucidated. Based on the results of gene microassay, EYA4 was inversely correlated with MYCBP and was verified in human HCC tissues by immunohistochemistry and western blot. Overexpressed and KO EYA4 in human HCC cell lines confirmed the negative correlation between EYA4 and MYCBP by qRT‐PCR and western blot. Transfected siRNA of MYCBP in EYA4 overexpressed cells and overexpressed MYCBP in EYA4 KO cells could efficiently rescue the proliferation and G2/M arrest effects of EYA4 on HCC cells. Mechanistically, armed with serine/threonine‐specific protein phosphatase activity, EYA4 reduced nuclear translocation of β‐catenin by dephosphorylating β‐catenin at Ser552, thereby suppressing the transcription of MYCBP which was induced by β‐catenin/LEF1 binding to the promoter of MYCBP. Clinically, HCC patients with highly expressed EYA4 and poorly expressed MYCBP had significantly longer disease‐free survival and overall survival than HCC patients with poorly expressed EYA4 and highly expressed MYCBP. In conclusion, EYA4 suppressed HCC tumor cell growth by repressing MYCBP by dephosphorylating β‐catenin S552. EYA4 combined with MYCBP could be potential prognostic biomarkers in HCC. Our previous studies showed that EYA4 suppressed hepatocellular carcinoma (HCC) tumor cells growth. Nevertheless, the functional involvement of EYA4 in HCC is not clearly understood. In the present study, we reported that EYA4 suppressed HCC tumor cell growth by reducing the nuclear translocation of β‐catenin by dephosphorylating β‐catenin at Ser552, thereby suppressing the transcription of MYCBP which was induced by β‐catenin/LEF1 binding to the promoter of MYCBP. EYA4 combined with MYCBP could be potential prognostic biomarkers in HCC.

Normal frequency jet ventilation (NFJV) is commonly used during rigid bronchoscopy; however, airway opening does not reliably measure or predict tidal volume (V T ) or peak inspiratory pressure (PIP). This study aimed to investigate the comprehensive effects on V T and PIP concerning key variables, including driving pressure (DP pipeline ), frequency (F jet ), needle inner diameter (ID needle ), and lung dynamic compliance (C dyn ). Three jet needles (N 1 , N 2 , and N 3 ) with different internal diameters (1.2–1.9 mm) were used to deliver jet ventilation via a rigid bronchoscope at DPs of 0.6–1.6 bar and frequencies of 10–60 min⁻¹. Airway pressure (Paw) was measured near the tracheal prominence of the simulated lung. Expiratory gas flow was diverted into a dedicated collection chamber fitted with a solenoid valve over a one-minute period, and the collected volume was measured as minute volume (MV). ①. Statistical analysis of the two lung models revealed consistent findings: both V T and PIP values in N 2 and N 3 demonstrated statistically significant differences compared to N 1 ( P  < 0.05). Additionally, significant differences in the V T and PIP values were observed between N 2 and N 3 ( P  < 0.05). ②. Multiple linear regression analyses indicated that DP pipeline , F jet , and ID needle had statistically significant effects on V T and PIP ( P  < 0.05). Conversely, C dyn significantly affected V T ( P  < 0.05) but did not have a significant impact on PIP ( P  > 0.05). The primary variables exerting a significant influence on V T and PIP were DP pipeline , F jet , and ID needle . Furthermore, C dyn significantly affected V T but not PIP. V T and PIP can be accurately predicted using regression equations.