Finite element simulation and experimental validation of thermal damage to isolated porcine skin tissue by femtosecond laser welding.

The welding effect of the laser on skin tissue is reduced by thermal damage to skin tissue, and greater thermal damage to skin tissue caused by the laser is prevented by predicting thermal damage. In this paper, a finite element model is established for the temperature field of skin tissue scanned by a femtosecond laser to obtain the influence of laser process parameters and scanning path on the thermal damage parameters of skin tissue and the thermal damage area, and verified experimentally. The results show that the established finite element model is accurate and can accurately reflect the temperature distribution during the process of femtosecond laser welding of porcine skin tissues; used to predict the thermal damage parameters of the skin tissues and the thermal damage area; and provide guidance for the study of the femtosecond laser welding of the skin tissues process to obtain the optimal process parameters.

Characteristic Extraction and Assessment Methods for Transformers DC Bias Caused by Metro Stray Currents.

Metro stray currents flowing into transformer-neutral points cause the high neutral DC and a transformer to operate in the DC bias state.Because neutral DC caused by stray current varies with time, the neutral DC value cannot be used as the only characteristic indicator to evaluate the DC bias risk level. Thus, unified characteristic extraction and assessment methods are proposed to evaluate the DC bias risk of a transformer caused by stray current, considering the signals of transformer-neutral DC and vibration. In the characteristic extraction method, the primary characteristics are obtained by comparing the magnitude and frequency distributions of transformer-neutral DC and vibration with and without metro stray current invasion. By analyzing the correlation coefficients, the final characteristics are obtained by clustering the primary characteristics with high correlation. Then, the magnitude and frequency characteristics are extracted and used as indicators to evaluate the DC bias risk. Moreover, to avoid the influence of manual experience on indicator weights, the entropy weight method (EWM) is used to establish the assessment model. Finally, the proposed methods are applied based on the neutral DC and vibration test data of a certain transformer. The results show that the characteristic indicators can be extracted, and the transformer DC bias risk can be evaluated by using the proposed methods.

2D arrays of hollow carbon nanoboxes: outward contraction-induced hollowing mechanism in Fe-N-C catalysts.

Maximizing the utilization efficiency of monatomic Fe sites in Fe-N-C catalysts poses a significant challenge for their commercial applications. Herein, a structural and electronic dual-modulation is achieved on a Fe-N-C catalyst to substantially enhance its catalytic performance. We develop a facile multi-component ice-templating co-assembly (MIC) strategy to construct two-dimensional (2D) arrays of monatomic Fe-anchored hollow carbon nanoboxes (Fe-HCBA) via a novel dual-outward interfacial contraction hollowing mechanism. The pore engineering not only enlarges the physical surface area and pore volume but also doubles the electrochemically active specific surface area. Additionally, the unique 2D carbon array structure reduces interfacial resistance and promotes electron/mass transfer. Consequently, the Fe-HCBA catalysts exhibit superior oxygen reduction performance with a six-fold enhancement in both mass activity (1.84 A cm-2) and turnover frequency (0.048 e- site-1 s-1), compared to microporous Fe-N-C catalysts. Moreover, the incorporation of phosphorus further enhances the total electrocatalytic performance by three times by regulating the electron structure of Fe-N4 sites. Benefitting from these outstanding characteristics, the optimal 2D P/Fe-HCBA catalyst exhibits great applicability in rechargeable liquid- and solid-state zinc-air batteries with peak power densities of 186 and 44.5 mW cm-2, respectively.

Enhancing Effect of Fullerene Guest and Counterion on the Structural Stability and Electrical Conductivity of Octahedral Metallo-Supramolecular Cages.

Metallo-supramolecular cages have garnered tremendous attention for their diverse yet molecular-level precision structures. However, physical properties of these supramolecular ensembles, which are of potential significance in molecular electronics, remain largely unexplored. We herein constructed a series of octahedral metallo-cages and cage-fullerene complexes with notably enhanced structural stability. As such, we could systematically evaluate the electrical conductivity of these ensembles at both single-molecule level and aggregated bulk state (as well-defined films). Our findings reveal that counteranions and fullerene guests play a pivotal role in determining the electrical conductivity of aggregated state, while such effects are less significant for single-molecule conductance. Both counteranions and fullerenes effectively tune the electronic structures and packing density of metallo-supramolecular assemblies, and facilitate efficient charge transfer between the cage hosts and fullerenes, resulting in a notable one order of magnitude increase in electrical conductivity of the aggregated state.

Machine Learning Accelerates Precise Excited-State Potential Energy Surface Calculations on a Quantum Computer.

Electronically excited-state problems represent a crucial research field in quantum chemistry, closely related to numerous practical applications in photophysics and photochemistry. The emerging of quantum computing provides a promising computational paradigm to solve the Schrödinger equation for predicting potential energy surfaces (PESs). Here, we present a deep neural network model to predict parameters of the quantum circuits within the framework of variational quantum deflation and subspace search variational quantum eigensolver, which are two popular excited-state algorithms to implement on a quantum computer. The new machine learning-assisted algorithm is employed to study the excited-state PESs of small molecules, achieving highly accurate predictions. We then apply this algorithm to study the excited-state properties of the ArF system, which is essential to a gas laser. Through this study, we believe that with future advancements in hardware capabilities, quantum computing could be harnessed to solve excited-state problems for a broad range of systems.

Genome-wide selection reveals candidate genes associated with multiple teats in Hu sheep.

Increasing the number of teats in sheep helps to improve the survival rate of sheep lambs after birth. In order to analyze the candidate genes related to the formation of multiple teats in Hu sheep, the present study was conducted to investigate the genetic pattern of multiple teats in Hu sheep. In this study, based on genome-wide data from 157 Hu sheep, Fst, xp-EHH, Pi and iHS signaling were performed, and the top 5% signal regions of each analyzed result were annotated based on the Oar_v4.0 for sheep. The results show that a total of 142 SNP loci were selected. We found that PTPRG, TMEM117 and LRP1B genes were closely associated with polypodium formation in Hu sheep, in addition, among the candidate genes related to polypodium we found genes such as TMEM117, SLC25A21 and NCKAP5 related to milk traits. The present study screened out candidate genes for the formation of multiple teats at the genomic level in Hu sheep.

Identification of a novel apoptosis-related genes signature to improve gastric cancer prognosis prediction.

Dysregulation of apoptosis occurs in different types of malignant tumors and is likely to influence the tumor evolution, as well as clinical prognosis. However, the limited number of studies investigating the predictive power of apoptosis-related genes (ARGs) in gastric cancer indicates a gap in the current research. 174 ARGs who differentially expressed were screened using public databases, including the Gene Expression Omnibus and the Molecular Signatures Database. Univariate and LASSO regression analyses were rigorous approaches to recognize the 12 optimal genes (CTHRC1, PDGFRL, VCAN, GJA1, LOX, UPP1, ANGPT2, CRIM1, HIF1A, APOD, RNase1, and ID1) that make up the prognostic risk model. Molecular mutations, related signaling pathways, and immune system characteristics in different subgroups defined by the risk model were analyzed using different R packages. Moreover, based on the database of Genomics of Drug Sensitivity in Cancer, chemotherapy sensitivity was predicted among the risk subgroups. As a result, there were differences in mutation profiles, signaling pathways, and infiltrated immune cells between patients in various risk groups. Moreover, the low-risk group displayed greater sensitivity to chemotherapy than the high-risk group. Risk model provided a better prognostic value than the T, N, and M stages, according to the receiver operating characteristic curve. Finally, in a nomogram, the risk model and clinical factors were combined to visualize the survival rates of patients with GC. In response to the differential expression of apoptosis-related genes, a novel model for predicting the prognosis of GC patients was developed. This model may be highly valuable for guiding doctors to deliver treatment plans tailored to the need of patients with GC.

Phase separation of RNF214 promotes the progression of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors, and the expression and function of an uncharacterized protein RNF214 in HCC are still unknown. Phase separation has recently been observed to participate in the progression of HCC. In this study, we investigated the expression, function, and phase separation of RNF214 in HCC. We found that RNF214 was highly expressed in HCC and associated with poor prognosis. RNF214 functioned as an oncogene to promote the proliferation, migration, and metastasis of HCC. Mechanically, RNF214 underwent phase separation, and the coiled-coil (CC) domain of RNF214 mediated its phase separation. Furthermore, the CC domain was necessary for the oncogenic function of RNF214 in HCC. Taken together, our data favored that phase separation of RNF214 promoted the progression of HCC. RNF214 may be a potential biomarker and therapeutic target for HCC.

In-Silico and in vitro Studies Revealed that Rosmarinic Acid Inhibited Methanogenesis via Regulating Composition and Function of Rumen Microbiota.

Inhibition of methyl-coenzyme M reductase can suppress the activity of ruminal methanogens, thereby reducing enteric methane emissions of ruminants. However, developing specific and environmentally friendly inhibitors is a challenging endeavor. To identify a natural and effective methane inhibitor that specifically targets methyl-coenzyme M reductase, molecular docking technology was employed to screen a library of phytogenic compounds. A total of 52 candidate compounds were obtained through molecular docking technique. Rosmarinic acid (RA) was one of the compounds that could traverse a narrow channel and bind to the active sites of methyl-coenzyme M reductase, with a calculated binding free energy of -9.355 kcal/mol. Furthermore, the effects of rosmarinic acid supplementation on methane production, rumen fermentation, and the microorganism's community in dairy cows were investigated through in vitro rumen fermentation simulations according to a random design. Supplementation of RA resulted in a 15% decrease in methane production compared with the control. In addition, RA increased the molar proportion of acetate and propionate, whereas the sum of acetate and butyrate divided by propionate was decreased. At the bacterial level, the relative abundance of Rikenellaceae RC9 gut group, Christensenellaceae R7 group, Candidatus Saccharimonas, Desulfovibrio, and Lachnospiraceae FE2018 group decreased with RA supplementation. Conversely, the addition of RA significantly increased the relative abundance of DNF00809 (a genus from Eggerthellaceae), Denitrobacterium, an unclassified genus from Eggerthellaceae, an unclassified genus from Bacteroidales, and an unclassified genus from Atopobiaceae. At the archaeal level, the relative abundance of Methanobrevibacter decreased, while that of Methanosphaera increased with the RA supplementation. These findings suggested that RA has the potential to be used as a novel natural additive for inhibiting ruminal methane production.

Complimentary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome.

Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments, neurobehavioral phenotypes including autism and attention deficit hyperactivity disorder (ADHD), and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts to GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss of function that will pave the way for future molecular studies into DESSH. These studies begin to uncover some biological underpinnings of DESSH syndrome and elucidate the biology of Wac, with advantages in each model.

Ultra-pH-sensitive nanoparticle of gambogenic acid for tumor targeting therapy via anti-vascular strategy plus immunotherapy.

Although the combination of anti-vascular strategy plus immunotherapy has emerged as the optimal first-line treatment of hepatocellular carcinoma, lack of tumor targeting leads to low antitumor efficacy and serious side effect. Here, we report an ultra-pH-sensitive nanoparticle of gambogenic acid (GNA) encapsulated by poly(ethylene glycol)-poly(2-azepane ethyl methacrylate) (PEG-PAEMA) for tumor-targeting combined therapy of anti-vascular strategy plus immunotherapy. PEG-PAEMA-GNA nanoparticle was quite stable at pH 7.4 for 30 d. In contrast, it exerted size shrinkage, charge reversal and the release of GNA at pH 6.7 within 24 h. Moreover, PEG-PAEMA-GNA significantly enhanced the anti-vascular activity, membrane-disruptive capability and pro-apoptosis when pH changed from 7.4 to 6.7. Western blot analysis exhibits that PEG-PAEMA and its GNA nanoparticle facilitated the phosphorylation of STING protein. In vivo assays show that PEG-PAEMA-GNA not only displayed much higher tumor inhibition of 92 % than 37 % of free GNA, but also inhibited tumor vasculature, promoted the maturation of dendritic cells and recruited more cytotoxic t-lymphocytes for sufficient anti-vascular therapy and immunotherapy. All these results demonstrate that PEG-PAEMA-GNA displayed tumor-targeting combined treatment of anti-vascular therapy and immunotherapy. This study offers a simple and novel method for the combination of anti-vascular therapy and immunotherapy with high selectivity towards tumor.

Synthesis of All-Benzene Multi-Macrocyclic Nanocarbons by Post-Functionalization of meta-Cycloparaphenylenes.

Expanding the diversity of multi-macrocyclic nanocarbons, particularly those with all-benzene scaffolds, represents intriguing yet challenging synthetic tasks. Complementary to the existing synthetic approaches, here we report an efficient and modular post-functionalization strategy that employs iridium-catalyzed C-H borylation of the highly strained meta-cycloparaphenylenes (mCPPs) and an mCPP-derived catenane. Based on the functionalized macrocyclic synthons, a number of novel all-benzene topological structures including linear and cyclic chains, polycatenane, and pretzelane have been successfully prepared and characterized, thereby showcasing the synthetic utility and potential of the post-functionalization strategy.

The Oncolytic virus VT1092M and an Anti-PD-L1 antibody synergize to induce systemic antitumor immunity in a murine bilateral tumor model.

This study investigated the synergistic potential of an oncolytic herpes simplex virus armed with interleukin 12 (VT1092M) in combination with immune checkpoint inhibitors for enhancing antitumor responses. The potential of this combination treatment to induce systemic antitumor immunity was assessed using bilateral subcutaneous tumor and tumor re-challenge mouse models. The antitumor efficacy of various OV and ICI treatment combinations and the underlying mechanisms were explored through diverse analytical techniques, including flow cytometry and RNA sequencing. Using VT1092M, either alone or in combination with an anti-PD-L1 antibody, significantly reduced the sizes of both the injected and untreated abscopal tumors in a bilateral tumor mouse model. The combination therapy demonstrated superior antitumor efficacy to the other treatment conditions tested, which was accompanied by an increase in T cell numbers and CD8+T cell activation. Results from the survival and tumor re-challenge experiments showed that the combination therapy elicited long-term, tumor-specific immune responses, which were associated with tumor clearance and prolonged survival. Immune cell depletion assays identified CD8+T cells as the crucial mediators of systemic antitumor immunity during combination therapy. In conclusion, the combination of VT1092M and PD-L1 blockade emerged as a potent inducer of antitumor immune responses, surpassing the efficacy of each monotherapy. This synergistic approach holds promise for achieving robust and sustained antitumor immunity, with potential implications for preventing tumor metastasis in patients with cancer.

Dorsomorphin attenuates ABCG2-mediated multidrug resistance in colorectal cancer.

Colorectal cancer is a common malignant tumor with high mortality, for which chemotherapy resistance is one of the main reasons. The high expression of ABCG2 in the cancer cells and expulsion of anticancer drugs directly cause multidrug resistance (MDR). Therefore, the development of new ABCG2 inhibitors that block the active causes of MDR may provide a strategy for the treatment of colorectal cancer. In this study, we find that dorsomorphin (also known as compound C or BML-275) potently inhibits the transporter activity of ABCG2, thereby preserving the chemotherapeutic agents mitoxantrone and doxorubicin to antagonize MDR in ABCG2-overexpressing colorectal cancer cells. Additionally, dorsomorphin does not alter ABCG2 protein expression. The results of molecular docking studies show that dorsomorphin is bound stably to the ABCG2-binding pocket, suggesting that dorsomorphin is a potent ABCG2 inhibitor that attenuates ABCG2-mediated MDR in colorectal cancer.

Enhanced wave absorption due to local flexibility on wide beams using mass-spring-damper scatterersa).

It is known that total absorption of flexural waves in a thin beam is possible through the use of monopole-dipole scatterers. In this study, we introduce a pair of identical monopole scatterers for near-total absorption of flexural waves in a thin and wide beam. Despite the two scatterers being both of the monopole type, the resonant modes of the scatterer pair exhibit monopole and dipole properties. By selecting the proper width for the beam, the two resonant modes degenerate, which leads to the total absorption. Although the beam is considerably wide, the frequency range of interest remains below the cut-on frequency of the n = 1 propagating mode, ensuring one-dimensional flexural wave propagation. Further simulations and theoretical analysis revealed that the degeneracy of the monopole and dipole modes results from their interaction with a higher-order localized flexural mode. The simulation results demonstrate absorption exceeding 99%, complemented by experimental data showing approximately 90% absorption.

Localized, highly efficient secretion of signaling proteins by migrasomes.

Migrasomes, enriched with signaling molecules such as chemokines, cytokines and angiogenic factors, play a pivotal role in the spatially defined delivery of these molecules, influencing critical physiological processes including organ morphogenesis and angiogenesis. The mechanism governing the accumulation of signaling molecules in migrasomes has been elusive. In this study, we show that secretory proteins, including signaling proteins, are transported into migrasomes by secretory carriers via both the constitutive and regulated secretion pathways. During cell migration, a substantial portion of these carriers is redirected to the rear of the cell and actively transported into migrasomes, driven by the actin-dependent motor protein Myosin-5a. Once at the migrasomes, these carriers fuse with the migrasome membrane through SNARE-mediated mechanisms. Inhibiting migrasome formation significantly reduces secretion, suggesting migrasomes as a principal secretion route in migrating cells. Our findings reveal a specialized, highly localized secretion paradigm in migrating cells, conceptually paralleling the targeted neurotransmitter release observed in neuronal systems.

Experimental investigation of the influence of mine water on mechanical properties of resin grout.

Resin grout is widely used in geotechnical and underground engineering, and is often affected by different mine water solutions. This study considered the effects of different mine water solutions and soaking times on resin grout. Soaking tests and uniaxial compression tests were conducted to investigate the changes in the solution pH, relative specimen mass, and uniaxial compressive strength (UCS), and the deterioration of the resin grout's mechanical properties caused by the mine water solution was analyzed. The corrosion mechanism of resin grout under the action of different mine water solutions was investigated through scanning electron microscopy tests. The results reveal that the pH value of the solution and the relative mass of the specimen gradually stabilized as the soaking time was extended, and the final solution was weakly alkaline. The increase in the acidity and alkalinity of the solution and the extension of the soaking time led to a gradual decrease in the UCS and elastic modulus of resin grout under the action of mine water. As the soaking was prolonged, the resin grout properties deteriorated to different degrees and Poisson's ratio increased. Moreover, owing to the different types and degrees of mine water action on resin grout in different mine water environments, the changes in the resin grout microstructure were also different. The defined damage parameters can express the damage process of the resin grout's UCS quantitatively under the action of mine water solution. Finally, beneficial engineering application countermeasures are proposed for different resin grout types used in roadway support applications in coal mines.

Gut Microbiota as Predictive Biomarker for Chronic Heart Failure in Patients with Different Nutritional Risk.

To examine the relationship between gut microbiota and disease development in chronic heart failure patients with different nutritional risk. The study analyzed stool samples from 62 CHF patients and 21 healthy peoples using 16S rRNA gene sequencing. CHF patients were separated into risk (n = 30) and non-risk group (n = 32) based on NRS2002 scores. Analysis methods used were LEfSe, random forest regression model, ROC curves, BugBase, PICRUSt2, metagenomeSeq. Risk group includes 11 cases of HFrEF, 6 cases of HFpEF, and 13 cases of HFmrEF. LefSe analysis confirmed that the risk group had higher levels of Enterobacter and Escherichia-Shigella. Correlation analysis revealed a negative correlation between prealbumin and Escherichia-Shigella. The presence of Enterobacter and Escherichia-Shigella worsens intestinal inflammation in CHF patients, impacting lysine metabolism by influencing its degradation metabolic function. This interference further disrupts albumin and prealbumin synthesis, leading to malnutrition in CHF patients and ultimately worsening the disease.

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

Investigation of collagen reconstruction mechanism in skin wound through dual-beam laser welding: Insights from multi-spectroscopy, molecular dynamics simulation, and finite element multiphysics simulation.

Since the mechanism underlying real-time acquisition of mechanical strength during laser-induced skin wound fusion remains unclear, and collagen is the primary constituent of skin tissue, this study investigates the structural and mechanical alterations in collagen at temperatures ranging from 40 °C to 60 °C using various spectroscopic techniques and molecular dynamics calculations. The COMSOL Multiphysics coupling is employed to simulate the three-dimensional temperature field, stress-strain relationship, and light intensity distribution in the laser thermal affected zone of skin wounds during dual-beam laser welding process. Raman spectroscopy, synchronous fluorescence spectroscopy and circular dichroism measurement results confirm that laser energy activates biological activity in residues, leading to a transformation in the originally fractured structure of collagen protein for enhanced mechanical strength. Molecular dynamics simulations reveal that stable hydrogen bonds form at amino acid residues within the central region of collagen protein when the overall temperature peak around the wound reaches 60 °C, thereby providing stability to previously fractured skin incisions and imparting instantaneous strength. However, under a 55 °C system, Type I collagen ensures macrostructural stability while activating biological properties at amino acid bases to promote wound healing function; this finding aligns with experimental analysis results. The COMSOL simulation outcomes also correspond well with macroscopic morphology after laser welding samples, confirming that by maintaining temperatures between 55 °C-60 °C during laser welding of skin incisions not only can certain instantaneous mechanical strength be achieved but irreversible thermal damage can also be effectively controlled. It is anticipated that these findings will provide valuable insights into understanding the healing mechanism for laser-welded skin wounds.