Borate bonds-containing pH-responsive chitosan hydrogel for postoperative tumor recurrence and wound infection prevention.

Chitosan has been widely used in biomedical fields due to its good antibacterial properties, excellent biocompatibility, and biodegradability. In this study, a pH-responsive and self-healing hydrogel was synthesized from 3-carboxyphenylboronic acid grafted with chitosan (CS-BA) and polyvinyl alcohol (PVA). The dynamic boronic ester bonds and intermolecular hydrogen bonds are responsible for the hydrogel formation. By changing the mass ratio of CS-BA and PVA, the tensile stress and compressive stress of hydrogel can controlled in the range of 0.61 kPa - 0.74 kPa and 295.28 kPa - 1108.1 kPa, respectively. After doping with tannic acid (TA)/iron nanocomplex (TAFe), the hydrogel successful killed tumor cells through the near infrared laser-induced photothermal conversion and the TAFe-triggered reactive oxygen species generation. Moreover, the photothermal conversion of the hydrogel and the antibacterial effect of CS and TA give the hydrogel a good antibacterial effect. The CS-BA/PVA/TAFe hydrogel exhibit good in vivo and in vitro anti-tumor recurrence and antibacterial ability, and therefore has the potential to be used as a powerful tool for the prevention of local tumor recurrence and bacterial infection after surgery.

Magnetic covalent organic frameworks as sorbents in the chromatographic analysis of environmental organic pollutants.

Covalent organic frameworks (COFs) are featured with large specific surface areas, good thermal stability, and abundant pores. These properties are exactly what the sorbents used for extraction or adsorption of interest substances are desired with. While, the low density and hydrophobicity of COFs often makes them difficult to be dispersed evenly and recovered from the aqueous solution. Magnetic covalent organic frameworks (MCOFs) inherit magnetic property of the magnetic particles and porous structure of COFs. They have improved dispersity in aqueous solution and phase separation can be rapidly achieved via external magnetic fields. This review summarized the synthesis strategies for MCOFs, and their application in trace environmental organic pollutants analysis by chromatography techniques. The selection of COFs types and modification with active groups for a certain adsorption purpose is discussed, along with the exploration of adsorption mechanisms, which is beneficial for the design and synthesis of MCOFs.

Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial.

BACKGROUND: This multicenter, double-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneurysm detection in CT angiography (CTA) and its influence on patients' short-term and long-term outcomes. METHODS: Study design: Prospective, multicenter, double-blinded RCT. SETTINGS: The model was designed for the automatic detection of intracranial aneurysms from original CTA images. PARTICIPANTS: Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm). RANDOMIZATION: Block randomization, stratified by center, gender, and age group. PRIMARY OUTCOMES: Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms. SECONDARY OUTCOMES: Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis. BLINDING: Study participants and participating radiologists will be blinded to the intervention. SAMPLE SIZE: Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm. DISCUSSION: The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients' short-term and long-term outcomes. TRIAL REGISTRATION: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.

Suppression of lysosome metabolism-meditated GARP/TGF-ß1 complexes specifically depletes regulatory T cells to inhibit breast cancer metastasis.

Regulatory T cells (Tregs) prevent autoimmunity and contribute to cancer progression. They exert contact-dependent inhibition of immune cells through the production of active transforming growth factor-ß1 (TGF-ß1). However, the absence of a specific surface marker makes inhibiting the production of active TGF-ß1 to specifically deplete human Tregs but not other cell types a challenge. TGF-ß1 in an inactive form binds to Tregs membrane protein Glycoprotein A Repetitions Predominant (GARP) and then activates it via an unknown mechanism. Here, we demonstrated that tumour necrosis factor receptor-associated factor 3 interacting protein 3 (TRAF3IP3) in the Treg lysosome is involved in this activation mechanism. Using a novel naphthalenelactam-platinum-based anticancer drug (NPt), we developed a new synergistic effect by suppressing ATP-binding cassette subfamily B member 9 (ABCB9) and TRAF3IP3-mediated divergent lysosomal metabolic programs in tumors and human Tregs to block the production of active GARP/TGF-ß1 for remodeling the tumor microenvironment. Mechanistically, NPt is stored in Treg lysosome to inhibit TRAF3IP3-meditated GARP/TGF-ß1 complex activation to specifically deplete Tregs. In addition, by promoting the expression of ABCB9 in lysosome membrane, NPt inhibits SARA/p-SMAD2/3 through CHRD-induced TGF-ß1 signaling pathway. In addition to expose a previously undefined divergent lysosomal metabolic program-meditated GARP/TGF-ß1 complex blockade by exploring the inherent metabolic plasticity, NPt may serve as a therapeutic tool to boost unrecognized Treg-based immune responses to infection or cancer via a mechanism distinct from traditional platinum drugs and currently available immune-modulatory antibodies.

Portable Mass Spectrometry for On-site Detection of Hazardous Volatile Organic Compounds via Robotic Extractive Sampling.

Various hazardous volatile organic compounds (VOCs) are frequently released into environments during accidental events that cause many hazards to ecosystems and humans. Therefore, rapid, sensitive, and on-site detection of hazardous VOCs is crucial to understand their compositions, characteristics, and distributions in complex environments. However, manual handling of hazardous VOCs remains a challenging task, because of the inaccessible environments and health risk. In this work, we designed a quadruped robotic sampler to reach different complex environments for capturing trace hazardous VOCs using a needle trap device (NTD) by remote manipulation. The captured samples were rapidly identified by portable mass spectrometry (MS) within minutes. Rapid detection of various hazardous VOCs including toxicants, chemical warfare agents, and burning materials from different environments was successfully achieved using this robot-MS system. On-site detection of 83 typical hazardous VOCs was examined. Acceptable analytical performances including low detection limits (at subng/mL level), good reproducibility (relative standard deviation (RSD) < 20%, n = 6), excellent quantitative ability (R2 > 0.99), and detection speed (within minutes) were also obtained. Our results show that the robot-MS system has excellent performance including safety, controllability, applicability, and robustness under dangerous chemical conditions.

Application of sourdough in gluten-free bakery products.

ABSTRACTSIn recent years, the demand for gluten-free (GF) bakery products has grown rapidly due to the remarkable rising number of celiac patients and the increasing health awareness of GF products. However, GF products generally suffer from defects such as poor sensorial level, low nutritional value, high prices and short shelf life. Sourdough is the important starter culture applied in bakery field, and it has been proven to be ideal for enhancing the overall quality of bakery products. This review aims to systematically reviewed the application of sourdough in GF bakery products and its improvement to GF bakery products in terms of texture, shelf life, nutrition and flavor. Its positive effects derive from the complex metabolic activities of sourdough microorganisms, such as acidification, proteolysis, production of exopolysaccharides (EPS), activation of endogenous enzymes, and production of antibacterial substances. Finally, researchers are encouraged to expand the use of sourdough in GF bakery products to increase the variety of GF products. And the technical and nutritional potential of sourdough should be developed more widely.

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique 'Callosum- like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum- like block.

Liver fibrosis automatic diagnosis utilizing dense-fusion attention contrastive learning network.

BACKGROUND: Liver fibrosis poses a significant public health challenge given its elevated incidence and associated mortality rates. Diffusion-Weighted Imaging (DWI) serves as a non-invasive diagnostic tool for supporting the identification of liver fibrosis. Deep learning, as a computer-aided diagnostic technology, can assist in recognizing the stage of liver fibrosis by extracting abstract features from DWI images. However, gathering samples is often challenging, posing a common dilemma in previous research. Moreover, previous studies frequently overlooked the cross-comparison information and latent connections among different DWI parameters. Thus, it is becoming a challenge to identify effective DWI parameters and dig potential features from multiple categories in a dataset with limited samples. PURPOSE: A self-defined Multi-view Contrastive Learning Network is developed to automatically classify multi-parameter DWI images and explore synergies between different DWI parameters. METHODS: A Dense-fusion Attention Contrastive Learning Network (DACLN) is designed and used to recognize DWI images. Concretely, a multi-view contrastive learning framework is constructed to train and extract features from raw multi-parameter DWI. Besides, a Dense-fusion module is designed to integrate feature and output predicted labels. RESULTS: We evaluated the performance of the proposed model on a set of real clinical data and analyzed the interpretability by Grad-CAM and annotation analysis, achieving average scores of 0.8825, 0.8702, 0.8933, 0.8727, and 0.8779 for accuracy, precision, recall, specificity and F-1 score. Of note, the experimental results revealed that IVIM-f, CTRW-ß, and MONO-ADC exhibited significant recognition ability and complementarity. CONCLUSION: Our method achieves competitive accuracy in liver fibrosis diagnosis using the limited multi-parameter DWI dataset and finds three types of DWI parameters with high sensitivity for diagnosing liver fibrosis, which suggests potential directions for future research.

Is wedge resection equivalent to segmentectomy in pathological stage IA (≤2 cm) non-small cell lung cancers?

BACKGROUND: Sublobar resection (wedge resection and segmentectomy) has been established as an oncologically equivalent option to lobectomy for early-stage patients with non-small cell lung cancer (NSCLC) ≤ 2 cm. However, the optimal approach of sublobar resection remains subject to debate. In the present study we aimed to compare the oncological outcomes of wedge resection and segmentectomy in these patients. METHODS: We identified patients with pT1a-bN0M0 NSCLC who underwent wedge resection and segmentectomy from the Surveillance, Epidemiology, and End Results database between 2010 and 2020. A Cox regression model and propensity-score matching (PSM) analysis were used. Overall survival (OS) and lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method. RESULTS: A total of 4190 patients met our selection criteria, including wedge resection in 3137 and segmentectomy in 1053. Patients undergoing wedge resection were less likely to have total lymph nodes resected (4 vs. 7, p < 0.001). Before PSM, patients undergoing segmentectomy had a higher 5-year OS rate (87.75% vs. 82.72%; p = 0.0023), while exhibiting a similar LCSS rate (93.45% vs. 92.73%; p = 0.32). After PSM, segmentectomy consistently demonstrated significantly better OS and there was no statistically significant difference in LCSS. Analysis of causes of death revealed that a higher incidence of deaths related to other causes among patients undergoing wedge resection compared to those undergoing segmentectomy. CONCLUSIONS: Both wedge resection and segmentectomy yield comparable oncological outcomes for patients with NSCLC ≤ 2 cm, although segmentectomy exhibits superior OS due to less death related to other causes.

Establishment of an Efficient Protoplast Isolation and Transfection Method for Eucommia ulmoides Oliver.

BACKGROUND: Eucommia ulmoides Oliver is a unique high-quality natural rubber tree species and rare medicinal tree species in China. The rapid characterization of E. ulmoides gene function has been severely hampered by the limitations of genetic transformation methods and breeding cycles. The polyethylene glycol (PEG)-mediated protoplast transformation system is a multifunctional and rapid tool for the analysis of functional genes in vivo, but it has not been established in E. ulmoides. METHODS: In this study, a large number of highly active protoplasts were isolated from the stems of E. ulmoides seedlings by enzymatic digestion, and green fluorescent protein expression was facilitated using a PEG-mediated method. RESULTS: Optimal enzymatic digestion occurred when the enzyme was digested for 10 h in an enzymatic solution containing 2.5% Cellulase R-10 (w/v), 0.6% Macerozyme R-10 (w/v), 2.5% pectinase (w/v), 0.5% hemicellulase (w/v), and 0.6 mol/L mannitol. The active protoplast yield under this condition was 1.13 × 106 protoplasts/g fresh weight, and the protoplast activity was as high as 94.84%. CONCLUSIONS: This study established the first protoplasm isolation and transient transformation system in hard rubber wood, which lays the foundation for subsequent functional studies of E. ulmoides genes to achieve high-throughput analysis, and provides a reference for future gene function studies of medicinal and woody plants.

OSTF1 knockdown mitigates IL-1ß-induced chondrocyte injury via inhibiting the NF-κB signaling pathway.

Osteoarthritis (OA) is an age-related joint disease characterized by progressive heterogeneous changes in articular cartilage and subchondral bone. Osteoclast stimulating factor 1 (OSTF1) is a small intracellular protein involved in bone formation and bone resorption. However, to our best knowledge, its role in OA is still unclear. In this study, an OA rat model was established by anterior cruciate ligament transection (ALCT). OSTF1 was increased in the cartilage tissues of OA patients and OA rats. Next, the role of OSTF1 in interleukin-1ß (IL-1ß)-induced chondrocyte apoptosis, inflammation and extracellular matrix degradation was explored through loss of function assays. Strikingly, OSTF1 knockdown relieved IL-1ß-induced chondrocyte apoptosis, with decreased cleaved caspase-3 and cleaved PARP levels. Besides, OSTF1 knockdown restrained IL-1ß-induced inflammation and degradation of extracellular matrix of chondrocytes. Subsequently, the molecular mechanism of OSTF1 was explored. Transcriptomic analysis revealed the potential gene network map regulated by OSTF1 knockdown. Some differentially expressed genes (DEGs) were involved in regulating the NF-κB signaling pathway. Furthermore, our results demonstrated that OSTF1 knockdown inhibited IL-1ß-activated the NF-κB signaling pathway. Ultimately, we analyzed the potential gene network map regulated by OSTF1 and its downstream NF-κB. Bioinformatics analysis showed that 18 DEGs in OSTF1-silenced chondrocytes overlapped with the NF-κB downstream targets. Collectively, our findings indicate that OSTF1 knockdown mitigates IL-1ß-induced chondrocyte injury via inhibiting the NF-κB signaling pathway.

Heart Sound Abnormality Detection From Multi-Institutional Collaboration: Introducing a Federated Learning Framework.

OBJECTIVE: Early diagnosis of cardiovascular diseases is a crucial task in medical practice. With the application of computer audition in the healthcare field, artificial intelligence (AI) has been applied to clinical non-invasive intelligent auscultation of heart sounds to provide rapid and effective pre-screening. However, AI models generally require large amounts of data which may cause privacy issues. Unfortunately, it is difficult to collect large amounts of healthcare data from a single centre. METHODS: In this study, we propose federated learning (FL) optimisation strategies for the practical application in multi-centre institutional heart sound databases. The horizontal FL is mainly employed to tackle the privacy problem by aligning the feature spaces of FL participating institutions without information leakage. In addition, techniques based on deep learning have poor interpretability due to their "black-box" property, which limits the feasibility of AI in real medical data. To this end, vertical FL is utilised to address the issues of model interpretability and data scarcity. CONCLUSION: Experimental results demonstrate that, the proposed FL framework can achieve good performance for heart sound abnormality detection by taking the personal privacy protection into account. Moreover, using the federated feature space is beneficial to balance the interpretability of the vertical FL and the privacy of the data. SIGNIFICANCE: This work realises the potential of FL from research to clinical practice, and is expected to have extensive application in the federated smart medical system.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis.

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and individualized information of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

METHANOL STEAM REFORMING FOR HYDROGEN PRODUCTION OVER Ni-BASED CATALYSTS: STATE-OF-THE-ART REVIEW AND FUTURE PROSPECTS.

With increasing global emphasis on environmental sustainability, the reliance on traditional energy sources such as coal, natural gas, and oil is encountering significant challenges. H2, known for its high energy content and pollution-free usage, emerges as a promising alternative. However, despite the great potential of H2, approximately 95% of hydrogen production still depends on non-renewable resources. Hence, the shift towards producing H2 from renewable sources, particularly through methods like steam reforming of methanol - a renewable resource - represents a beacon of hope for advancing sustainable energy practices. This review comprehensively examines recent advancements in efficient H2 production using Ni-based catalysts in methanol steam reforming (MSR) and proposes the future prospects. Firstly, the fundamental principles of MSR technology and the significance in clean energy generation are elucidated. Subsequently, the design, synthesis techniques, and optimization strategies for enhancing the catalytic performance of Ni-based catalysts are discussed. Through the analysis of various catalyst compositions, structural adjustments, surface active sites, and modification methods, the review uncovers effective approaches for boosting the activity and durability of MSR reactions. By offering a comprehensive critical analysis, this review serves as a valuable reference to enhance MSR hydrogen production efficiency and catalyst performance.

Acetylation- and ubiquitination-regulated SFMBT2 acts as a tumor suppressor in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (RCC) is the most common kidney tumor. The analysis from medical database showed that Scm-like with four MBT domains protein 2 (SFMBT2) was decreased in advanced clear cell RCC cases, and its downregulation was associated with the poor prognosis. This study aims to investigate the role of SFMBT2 in clear cell RCC. METHODS: The expression of SFMBT2 in clear cell RCC specimens were determined by immunohistochemistry staining and western blot. The overexpression and knockdown of SFMBT2 was realized by infection of lentivirus loaded with SFMBT2 coding sequence or silencing fragment in 786-O and 769-P cells, and its effects on proliferation and metastasis were assessed by MTT, colony formation, flow cytometry, wound healing, transwell assay, xenograft and metastasis experiments in nude mice. The interaction of SFMBT2 with histone deacetylase 3 (HDAC3) and seven in absentia homolog 1 (SIAH1) was confirmed by co-immunoprecipitation. RESULTS: In our study, SFMBT2 exhibited lower expression in clear cell RCC specimens with advanced stages than those with early stages. Overexpression of SFMBT2 inhibited the growth and metastasis of clear cell RCC cells, 786-O and 769-P, in vitro and in vivo, and its silencing displayed opposites effects. HDAC3 led to deacetylation of SFMBT2, and the HDAC3 inhibitor-induced acetylation prevented SFMBT2 from SIAH1-mediated ubiquitination modification and proteasome degradation. K687 in SFMBT2 protein molecule may be the key site for acetylation and ubiquitination. CONCLUSIONS: SFMBT2 exerted an anti-tumor role in clear cell RCC cells, and HDAC3-mediated deacetylation promoted SIAH1-controlled ubiquitination of SFMBT2. SFMBT2 may be considered as a novel clinical diagnostic marker and/or therapeutic target of clear cell RCC, and crosstalk between its post-translational modifications may provide novel insights for agent development.

CB1 receptor mediates anesthetic drug ketamine­induced neuroprotection against glutamate in HT22 cells.

The anesthetic drug, ketamine (KTM) has been shown to induce therapeutic effects against major depressive disorder (MDD), however the related underlying mechanisms remain unclear. In the present study, HT22 neuronal cells were treated with glutamate to imitate oxidative stress injury in MDD, and it was hypothesized that the cannabinoid type 1 (CB1) receptor mediates KTM-induced neuroprotection via ameliorating mitochondrial function in glutamate-treated neuronal cells. Compared with the control, glutamate decreased cell viability and intracellular antioxidants, including glutathione (GSH), catalase and superoxide dismutase 2 levels, and inhibited mitochondrial function simultaneously. Moreover, glutamate increased lactate dehydrogenase release, cellular apoptosis level, cleaved caspase-3 expression and intracellular oxidants, such as reactive oxygen species, oxidized GSH and mitochondrial superoxide in the cells. The presence of KTM, however, significantly decreased the glutamate-induced oxidative stress injury, ameliorated the antioxidant/oxidant levels in the cells, enhanced mitochondrial function and upregulated CB1 receptor expression (P<0.05). Co-administration of the CB1 receptor antagonist AM251 markedly abolished the KTM-induced cytoprotective effects and ameliorations of antioxidant/oxidant levels and mitochondrial function, and also reversed CB1 upregulation (P<0.05). These observations indicated that KTM decreases the oxidative stress injury caused by glutamate in HT22 neuronal cells, and the neuroprotective effects may be mediated by the CB1 receptor.

Revealing the Effect of α' Decomposition on Microstructure Evolution and Mechanical Properties in Ti80 Alloy.

Three types of solution treatment and aging were designed to reveal the α' decomposition and its effect on the mechanical properties of near-α Ti-80 alloy, as follows: solution at 970 °C then quenching (ST), ST + aging at 600 °C for 5 h (STA-1), and ST + aging 600 °C for 24 h (STA-2). The results show that the microstructures of the ST samples were mainly composed of equiaxed αp and acicular α', with a large number of dislocations confirmed by the KAM results. After subsequent aging for 5 h, α' decomposed into acicular fine αs and nano-ß (intergranular ß, intragranular ß) in the STA-1 specimen, which obstructed dislocation motion during deformation, resulting in the STA-1 specimen exhibiting the most excellent yield strength (1012 MPa) and maintaining sufficient elongation (8.1%) compared with the ST (898 MPa) and STA-2 (871 MPa) samples. By further extending the aging time to 24 h, the size of acicular αs and nano-ß gradually increased while the density of dislocations decreased, which resulted in a decrease in strength and an increase in plasticity. Based on this, a microstructures-properties correlation model was proposed. This study provides a new method for strength-plasticity matching of near-α titanium alloys through α' decomposition to acicular αs+nano-ß.

Comprehensive preimplantation genetic testing for balanced insertional translocation carriers.

BACKGROUND: Balanced insertional translocations (BITs) can increase the risk of infertility, recurrent miscarriages or neonatal birth defects due to chromosomal imbalances in gametes. However, studies on preimplantation genetic testing (PGT) for patients carrying BITs are inadequate. METHODS: A preimplantation genetic genotyping and haplotype analysis approach was developed and implemented in this study. Genome-wide SNP genotyping was performed, followed by core family-based haplotype analysis. The balanced insertion segments in euploid embryos were inferred from the haplotypes inherited from the carrier parent. RESULTS: A total of 10 BIT carrier couples were enrolled in our study. 15 in vitro fertilisation cycles were conducted, resulting in 73 blastocysts biopsied and subjected to PGT analysis. Among these, 20 blastocysts displayed rearrangement-related imbalances, 13 exhibited de novo aneuploidies, 15 presented a complex anomaly involving both imbalances and additional aneuploidies, while 25 were euploid. Within the euploid embryos, 12 were balanced carrier embryos and 13 were non-carrier embryos. To date, eight non-carrier and one carrier embryos have been transferred, resulting in seven clinical pregnancies. All pregnancies were recommended to perform prenatal diagnosis, our date revealed complete concordance between fetal genetic testing results and PGT results. Presently, five infants have been born from these pregnancies, and two pregnancies are still ongoing. CONCLUSION: The proposed method facilitates comprehensive chromosome screening and the concurrent identification of balanced insertions or normal karyotypes in embryos. This study offers an effective and universally applicable strategy for BIT carriers to achieve a healthy pregnancy and prevent the transmission of BITs to their offspring.

A Sinh-Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems.

The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh-Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm.