Graph Convolutional Network with Self-supervised Learning for Brain Disease Classification.

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, graph convolutional networks (GCNs) can be naturally used in the identification of BFN, and can generally achieve an encouraging performance if given large amounts of training data. In practice, however, it is very difficult to obtain sufficient brain functional data, especially from subjects with brain disorders. As a result, GCNs usually fail to learn a reliable feature representation from limited BFNs, leading to overfitting issues. In this paper, we propose an improved GCN method to classify brain diseases by introducing a self-supervised learning (SSL) module for assisting the graph feature representation. We conduct experiments to classify subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) respectively from normal controls (NCs). Experimental results on two benchmark databases demonstrate that our proposed scheme tends to obtain higher classification accuracy than the baseline methods.

A Potential Role for MAGI-1 in the Bi-Directional Relationship Between Major Depressive Disorder and Cardiovascular Disease.

PURPOSE OF REVIEW: Major Depressive Disorder (MDD) is characterized by persistent symptoms such as fatigue, loss of interest in activities, feelings of sadness and worthlessness. MDD often coexist with cardiovascular disease (CVD), yet the precise link between these conditions remains unclear. This review explores factors underlying the development of MDD and CVD, including genetic, epigenetic, platelet activation, inflammation, hypothalamic-pituitary-adrenal (HPA) axis activation, endothelial cell (EC) dysfunction, and blood-brain barrier (BBB) disruption. RECENT FINDINGS: Single nucleotide polymorphisms (SNPs) in the membrane-associated guanylate kinase WW and PDZ domain-containing protein 1 (MAGI-1) are associated with neuroticism and psychiatric disorders including MDD. SNPs in MAGI-1 are also linked to chronic inflammatory disorders such as spontaneous glomerulosclerosis, celiac disease, ulcerative colitis, and Crohn's disease. Increased MAGI-1 expression has been observed in colonic epithelial samples from Crohn's disease and ulcerative colitis patients. MAGI-1 also plays a role in regulating EC activation and atherogenesis in mice and is essential for Influenza A virus (IAV) infection, endoplasmic reticulum stress-induced EC apoptosis, and thrombin-induced EC permeability. Despite being understudied in human disease; evidence suggests that MAGI-1 may play a role in linking CVD and MDD. Therefore, further investigation of MAG-1 could be warranted to elucidate its potential involvement in these conditions.

Cardiorespiratory motion characteristics and their dosimetric impact on cardiac stereotactic body radiotherapy.

BACKGROUND: Cardiac stereotactic body radiotherapy (CSBRT) is an emerging and promising noninvasive technique for treating refractory arrhythmias utilizing highly precise, single or limited-fraction high-dose irradiations. This method promises to revolutionize the treatment of cardiac conditions by delivering targeted therapy with minimal exposure to surrounding healthy tissues. However, the dynamic nature of cardiorespiratory motion poses significant challenges to the precise delivery of dose in CSBRT, introducing potential variabilities that can impact treatment efficacy. The complexities of the influence of cardiorespiratory motion on dose distribution are compounded by interplay and blurring effects, introducing additional layers of dose uncertainty. These effects, critical to the understanding and improvement of the accuracy of CSBRT, remain unexplored, presenting a gap in current clinical literature. PURPOSE: To investigate the cardiorespiratory motion characteristics in arrhythmia patients and the dosimetric impact of interplay and blurring effects induced by cardiorespiratory motion on CSBRT plan quality. METHODS: The position and volume variations in the substrate target and cardiac substructures were evaluated in 12 arrhythmia patients using displacement maximum (DMX) and volume metrics. Moreover, a four-dimensional (4D) dose reconstruction approach was employed to examine the dose uncertainty of the cardiorespiratory motion. RESULTS: Cardiac pulsation induced lower DMX than respiratory motion but increased the coefficient of variation and relative range in cardiac substructure volumes. The mean DMX of the substrate target was 0.52 cm (range: 0.26-0.80 cm) for cardiac pulsation and 0.82 cm (range: 0.32-2.05 cm) for respiratory motion. The mean DMX of the cardiac structure ranged from 0.15 to 1.56 cm during cardiac pulsation and from 0.35 to 1.89 cm during respiratory motion. Cardiac pulsation resulted in an average deviation of -0.73% (range: -4.01%-4.47%) in V25 between the 3D and 4D doses. The mean deviations in the homogeneity index (HI) and gradient index (GI) were 1.70% (range: -3.10%-4.36%) and 0.03 (range: -0.14-0.11), respectively. For cardiac substructures, the deviations in D50 due to cardiac pulsation ranged from -1.88% to 1.44%, whereas the deviations in Dmax ranged from -2.96% to 0.88% of the prescription dose. By contrast, the respiratory motion led to a mean deviation of -1.50% (range: -10.73%-4.23%) in V25. The mean deviations in HI and GI due to respiratory motion were 4.43% (range: -3.89%-13.98%) and 0.18 (range: -0.01-0.47) (p < 0.05), respectively. Furthermore, the deviations in D50 and Dmax in cardiac substructures for the respiratory motion ranged from -0.28% to 4.24% and -4.12% to 1.16%, respectively. CONCLUSIONS: Cardiorespiratory motion characteristics vary among patients, with the respiratory motion being more significant. The intricate cardiorespiratory motion characteristics and CSBRT plan complexity can induce substantial dose uncertainty. Therefore, assessing individual motion characteristics and 4D dose reconstruction techniques is critical for implementing CSBRT without compromising efficacy and safety.

Envafolimab Inhibits the Growth of Gastric Cancer Cells with Low PD-L1 Expression through the DDX20/NF-κB/TNF-α Signaling Pathway.

BACKGROUND: The mechanism of action of envafolimab (also known as KN035), a programmed death ligand 1 (PD-L1) inhibitor, in gastric adenocarcinoma patients with low PD-L1 expression is not well understood. AIMS: This study aimed to observe the efficacy of envafolimab in gastric adenocarcinoma with low PD-L1 expression and explore the underlying mechanisms. OBJECTIVE: The objective of this study was to explore the underlying mechanism of envafolimab in gastric cancer with low PD-L1 expression. METHOD: Cytotoxicity and proliferation were evaluated by a CCK8 assay. Transwell assays were used to detect the migration and invasion ability of gastric cancer cells. The effect of envafolimab on the apoptosis of gastric cancer cells was detected by flow cytometry. The effect of envafolimab on gastric cancer cells with low PD-L1 expression was investigated via proteomics and bioinformatics analysis. RESULT: A total of 19 patients with advanced gastric adenocarcinoma who received envafolimab monotherapy or combination therapy were reviewed. Among them, 4 patients had low PD-L1 expression, the objective response rate (ORR) was 75% (3/4), and the disease control rate (DCR) was 100% (4/4). In vitro experiments showed that envafolimab inhibited the proliferation, invasion, and migration of gastric cancer cells with low expression of PD-L1 and induced cell apoptosis. DDX20 may be the target of envafolimab in gastric cancer cells, and it is related to the NF-κB signaling pathway. Western blot results showed that the protein expressions of DDX20, NF-κB p65, and TNF-α in gastric cancer cells were decreased after adding envafolimab. Furthermore, the DDX20 gene was silenced by small interfering RNA to further study the effect of DDX20 on PDL1 low expression in gastric cancer cells. CONCLUSION: This study confirmed that envafolimab could inhibit the growth of gastric cancer cells with low PD-L1 expression by down-regulating DDX20 expression and regulating the NFκB/TNF-α signaling pathway.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

Quantifying dose uncertainties resulting from cardiorespiratory motion in intensity-modulated proton therapy for cardiac stereotactic body radiotherapy.

Background: Cardiac stereotactic body radiotherapy (CSBRT) with photons efficaciously and safely treats cardiovascular arrhythmias. Proton therapy, with its unique physical and radiobiological properties, can offer advantages over traditional photon-based therapies in certain clinical scenarios, particularly pediatric tumors and those in anatomically challenging areas. However, dose uncertainties induced by cardiorespiratory motion are unknown. Objective: This study investigated the effect of cardiorespiratory motion on intensity-modulated proton therapy (IMPT) and the effectiveness of motion-encompassing methods. Methods: We retrospectively included 12 patients with refractory arrhythmia who underwent CSBRT with four-dimensional computed tomography (4DCT) and 4D cardiac CT (4DcCT). Proton plans were simulated using an IBA accelerator based on the 4D average CT. The prescription was 25 Gy in a single fraction, with all plans normalized to ensure that 95% of the target volume received the prescribed dose. 4D dose reconstruction was performed to generate 4D accumulated and dynamic doses. Furthermore, dose uncertainties due to the interplay effect of the substrate target and organs at risk (OARs) were assessed. The differences between internal organs at risk volume (IRV) and OARreal (manually contoured on average CT) were compared. In 4D dynamic dose, meeting prescription requirements entails V25 and D95 reaching 95% and 25 Gy, respectively. Results: The 4D dynamic dose significantly differed from the 3D static dose. The mean V25 and D95 were 89.23% and 24.69 Gy, respectively, in 4DCT and 94.35% and 24.99 Gy, respectively, in 4DcCT. Eleven patients in 4DCT and six in 4DcCT failed to meet the prescription requirements. Critical organs showed varying dose increases. All metrics, except for Dmean and D50, significantly changed in 4DCT; in 4DcCT, only D50 remained unchanged with regards to the target dose uncertainties induced by the interplay effect. The interplay effect was only significant for the Dmax values of several OARs. Generally, respiratory motion caused a more pronounced interplay effect than cardiac pulsation. Neither IRV nor OARreal effectively evaluated the dose discrepancies of the OARs. Conclusions: Complex cardiorespiratory motion can introduce dose uncertainties during IMPT. Motion-encompassing techniques may mitigate but cannot entirely compensate for the dose discrepancies. Individualized 4D dose assessments are recommended to verify the effectiveness and safety of CSBRT.

Genetic Basis Identification of a NLR Gene, TaRGA5-like, That Confers Partial Powdery Mildew Resistance in Wheat SJ106.

Wheat powdery mildew is an important fungal disease that seriously jeopardizes wheat production, which poses a serious threat to food safety. SJ106 is a high-quality, disease-resistant spring wheat variety; this disease resistance is derived from Wheat-wheatgrass 33. In this study, the powdery mildew resistance genes in SJ106 were located at the end of chromosome 6DS, a new disease resistance locus tentatively named PmSJ106 locus. This interval was composed of a nucleotide-binding leucine-rich repeat (NLR) gene cluster containing 19 NLR genes. Five NLRs were tandem duplicated genes, and one of them (a coiled coil domain-nucleotide binding site-leucine-rich repeat (CC-NBS-LRR; CNL) type gene, TaRGA5-like) expressed 69-836-fold in SJ106 compared with the susceptible control. The genome DNA and cDNA sequences of TaRGA5-like were amplified from SJ106, which contain several nucleotide polymorphisms in LRR regions compared with susceptible individuals and Chinese Spring. Overexpression of TaRGA5-like significantly increased resistance to powdery mildew in susceptible receptor wheat Jinqiang5. However, Virus induced gene silence (VIGS) of TaRGA5-like resulted in only a small decrease of SJ106 in disease resistance, presumably compensated by other NLR duplicated genes. The results suggested that TaRGA5-like confers partial powdery mildew resistance in SJ106. As a member of the PmSJ106 locus, TaRGA5-like functioned together with other NLR duplicated genes to improve wheat resistance to powdery mildew. Wheat variety SJ106 would become a novel and potentially valuable germplasm for powdery mildew resistance.

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species.

Introduction: This study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade. Methods: We integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383-982 nm), NIR (982-2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance. Results: With optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power. Discussion: These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

Daily Online Adaptive Radiation Therapy of Postoperative Endometrial and Cervical Cancer With PTV Margin Reduction to 5 mm: Dosimetric Outcomes, Acute Toxicity, and First Clinical Experience.

Purpose: This study evaluated the first clinical implementation of daily iterative cone beam computed tomography (iCBCT)-guided online adaptive radiation therapy (oART) in the postoperative treatment of endometrial and cervical cancer. Methods and Materials: Seventeen consecutive patients treated with daily iCBCT-guided oART were enrolled in this prospective study, with a reduced uniform 3-dimensional PTV margin of 5 mm. Treatment plans were designed to deliver 45 or 50.4 Gy in 1.8 Gy daily fractions to PTV. Pre- and posttreatment ultrasound and iCBCT scans were performed to record intrafractional bladder and rectal volume changes. The accuracy of contouring, oART procedure time, dosimetric outcomes, and acute toxicity were evaluated. Results: The average time from first iCBCT acquisition to completion of treatment was 22 minutes and 26 seconds. During this period, bladder volume increased by 44 cm3 using iCBCT contouring, whereas rectal volume remained stable (62.9 cm3 pretreatment vs 61.9 cm3 posttreatment). A total of 91.6% of influencers and 88.1% of CTVs required no or minor edits. The adapted plan was selected in all (434) fractions and significantly improved the dosimetry coverage for CTV and PTV, especially the vaginal PTV coverage by nearly 7% (P < .05). The adapted bladder Dmean was 104.61 cGy, and the rectum Dmean was 123.67 cGy, significantly lower than the scheduled plan of 108.24 and 128.19 cGy, respectively. The bone marrow and femur head left and right dosimetry were also improved with adaptation. Grade 2 acute gastrointestinal and genitourinary toxicities were 24% and 0, respectively. There was a grade 3 acute toxicity of decreased white blood cell count in 1 patient. Conclusions: Daily oART was associated with favorable dosimetry improvement and low acute toxicity, supporting its safety and efficacy for postoperative treatment of endometrial and cervical cancer. These results need to be validated in a larger prospective randomized controlled cohort.

Survive, revive, and thrive: The impact of COVID-19 on global park visitation.

The COVID-19 pandemic has had detrimental effects on both the physical and psychological well-being of individuals. Throughout the pandemic and in response to various policies, such as lockdowns, movement restrictions and social distancing measures, parks and greenspaces received renewed attention as people used them to help cope with the adverse effects of the pandemic. This study explored the factors influencing park and greenspace visitation at different stages of the pandemic in 2020, 2021, and 2022, from both global and regional perspectives. Data were collected primarily from Our World in Data, Google's Community Mobility Reports and the Oxford Coronavirus Government Response Tracker, and a total of 125,422 park visits were processed. Stay-at-home mandates, vaccination availability, and school closures were the most influential factors globally affecting park and greenspace visitation in 2020, 2021, and 2022, respectively. Post-2021, vaccination-related policies began to play a significantly positive role in the increase in park and greenspace visits. Following a global analysis, countries were categorized into five clusters based on social, economic, and cultural indices. The analysis revealed varying patterns of factors influencing park visitation across these clusters. Notably, income support policies were positively correlated with higher park visitation, particularly in low-income countries. Recognizing the significance of parks and green spaces as essential green infrastructure, this study suggests how the use of parks might have better coped with the COVID-19 pandemic and how future health crises might be addressed. At the same time, it considers different social, economic, and cultural contexts. Additionally, this work provides insights and suggestions as to how parks and greenspaces might be used to reduce the social inequalities exacerbated during the pandemic, especially in low-income developing countries.

Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

Network pharmacology combined with an experimental validation study to reveal the effect and mechanism of Lonicera japonica Thunb. extracts against immunomodulation.

Lonicera japonica Thunb. (LJT) is known for its valuable medicinal properties that highlight its potential application in the pharmaceutical and health food industry. We predict that LJT polyphenols by network pharmacology may be involved in immunomodulation, and the study of LJT polyphenols regulating immunity is still insufficient; therefore, we experimentally found that LJT enhances immunity by promoting the proliferation and phagocytic activity of RAW246.7 cells. A model of an immunosuppressed mouse was constructed using cyclophosphamide-induced, and LJT was extracted for the intervention. We found that LJT restored immune homeostasis in immune deficiency mice by inhibiting the abnormal apoptosis in lymphocytes, enhancing natural killer cell cytotoxicity, promoting T lymphocyte proliferation, and increasing the CD4+ and CD8+ T lymphocytes in quantity. Moreover, LJT treatment modulates immunity by significantly downregulating lipopolysaccharide-induced inflammation and oxidative stress levels. We verified the immunomodulatory function of LJT through both cell and animal experiments. The combination of potential-protein interactions and molecular docking later revealed that LJT polyphenols were associated with immunomodulatory effects on MAPK1; together, LJT intervention significantly modulates the immune, with the activation of MAPK1 as the underlying mechanism of action, which provided evidence for the utilization of LJT as a nutraceutical in immune function.

Assessment of Key Factors Impacting Variability in AAV Vector Genome Titration by Digital PCR.

Recombinant adeno-associated virus (rAAV) has emerged as a prominent vector for in vivo gene therapy, owing to its distinct advantages. Accurate determination of the rAAV genome titer is crucial for ensuring the safe and effective administration of clinical doses. The evolution of the rAAV genome titer assay from quantitative PCR (qPCR) to digital PCR (dPCR) has enhanced accuracy and precision, yet practical challenges persist. This study systematically investigated the impact of various operational factors on genome titration in a single-factor manner, aiming to address potential sources of variability in the quantitative determination process. Our findings revealed that a pretreatment procedure without genome extraction exhibits superior precision compared with titration with genome extraction. Additionally, notable variations in titration results across different brands of dPCR instruments were documented, with relative standard deviation (RSD) reaching 23.47% for AAV5 and 11.57% for AAV8. Notably, optimal operations about DNase I digestion were identified; we thought treatment time exceeding 30 min was necessary, and there was no need for thermal inactivation after digestion. And we highlighted that thermal capsid disruption before serial dilution substantially affected AAV genome titers, causing a greater than ten-fold decrease. Conversely, this study found that additive components of dilution buffer are not significant contributors to titration variations. Furthermore, we found that repeated freeze-thaw cycles significantly compromised AAV genome titers. In conclusion, a comprehensive dPCR titration protocol, incorporating insights from these impact factors, was proposed and successfully tested across multiple serotypes of AAV. The results demonstrate acceptable variations, with the RSD consistently below 5.00% for all tested AAV samples. This study provides valuable insights to reduce variability and improve the reproducibility of AAV genome titration using dPCR.

Cold unfolding of heat-responsive TRPV3.

The homotetrameric thermosensitive transient receptor potential vanilloid 1-4 (TRPV1-4) channels in sensory neurons are strongly responsive to heat stimuli. However, their cold activations have not been reported in line with the nonzero heat capacity difference during heat or cold unfolding transitions. Here, along with the experimental examinations of the predicted ring size changes in different domains against the central pore during channel gating at various temperatures, the K169A mutant of reduced human TRPV3 was first found to be activated and inactivated by cold below 42°C. Further thermoring analyses revealed distinct heat and cold unfolding pathways, which resulted in different protein thermostabilities. Thus, both cold and heat unfolding transitions of thermosensitive TRPV1-4 channels may exist once a mutation destabilizes the closed state.

Ultrasound-Assisted Extraction of Phenolic Compounds from Celtuce (Lactuca sativa var. augustana) Leaves Using Natural Deep Eutectic Solvents (NADES): Process Optimization and Extraction Mechanism Research.

Natural deep eutectic solvents (NADESs), as emerging green solvents, can efficiently extract natural products from natural resources. However, studies on the extraction of phenolic compounds from celtuce (Lactuca sativa var. augustana) leaves (CLs) by NADESs are still lacking. This study screened the NADES L-proline-lactic acid (Pr-LA), combined it with ultrasound-assisted extraction (UAE) to extract phenolic compounds from CLs, and conducted a comparative study on the extraction effect with traditional extraction solvents. Both SEM and FT-IR confirmed that Pr-LA can enhance the degree of fragmentation of cell structures and improve the extraction rate of phenolic compounds. Molecular dynamics simulation results show that Pr-LA can improve the solubility of phenolic compounds and has stronger hydrogen bonds and van der Waals interactions with phenolic compounds. Single-factor and Box-Behnken experiments optimized the process parameters for the extraction of phenolic compounds from CLs. The second-order kinetic model describes the extraction process of phenolic compounds from CLs under optimal process parameters and provides theoretical guidance for actual industrial production. This study not only provides an efficient and green method for extracting phenolic compounds from CLs but also clarifies the mechanism of improved extraction efficiency, which provides a basis for research on the NADES extraction mechanism.

Extracorporeal closed-loop respiratory regulation for patients with respiratory difficulty using a soft bionic robot.

OBJECTIVE: Respiratory regulation is critical for patients with respiratory dysfunction. Clinically used ventilators can lead to long-term dependence and injury. Extracorporeal assistance approaches such as iron-lung devices provide a noninvasive alternative, however, artificial actuator counterparts have not achieved marvelous biomimetic ventilation as human respiratory muscles. Here, we propose a bionic soft exoskeleton robot that can achieve extracorporeal closed-loop respiratory regulation by emulating natural human breath. METHODS: For inspiration, a soft vacuum chamber is actuated to produce negative thoracic pressure and thus expand lung volume by pulling the rib cage up and outward through use of external negative pressure. For expiration, a soft origami array under positive pressure pushes the abdominal muscles inward and the diaphragm upward. To achieve in vitro measurement of respiratory profile, we describe a wireless respiratory monitoring device to measure respiratory profiles with high accuracy, validated by quantitative comparisons with spirometer as gold-standard reference. By constructing a human-robot coupled respiratory mechanical model, a model-based proportional controller is designed for continuous tracking of the target respiratory profile. RESULTS: In experiments with ten healthy participants and ten patients with respiratory difficulty, the robot can adjust its assistive forces in real time and drive human-robot coupling respiratory system to track the target profile. CONCLUSION: The biomimetic robot can achieve extracorporeal closed-loop respiratory regulation for a diverse population. SIGNIFICANCE: The soft robot has important potential to assist respiration for people with respiratory difficulty, whether in a hospital or a home setting.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

County land use carbon emission and scenario prediction in Mianyang Science and Technology City New District, Sichuan Province, China.

The role of carbon emissions resulting from land use change in the compilation of national greenhouse gas emission inventories is of paramount significance. This study is centered on the Mianyang Science and Technology City New Area located in Sichuan Province, China. We used the CLUE-S model and Sentinel-2A remote sensing data from 2017 to simulate and validate land use changes in 2022. Based on this validation, we established three simulation scenarios: a baseline scenario, an agricultural development scenario, and a construction development scenario. Using remote sensing data from 2022, we projected the land use for 2030. We also used CO2 concentration data collected in 2022 and 2023, processed the data using ArcGIS and Python, and conducted a quantitative analysis of carbon emissions under each scenario. Ultimately, the accuracy of both measured and predicted CO2 values for 2023 was juxtaposed and authenticated, thus concluding the investigative cycle of this study. Key findings include: (1) The accuracy of the CLUE-S model in the study area was assessed using overall accuracy, quantity disagreement and allocation disagreement indexes. In 2022, the overall accuracy is 98.19%, the quantity disagreement is 1.7%, and the allocation disagreement is 2.2%. (2) Distinct land resource utilization characteristics in scenarios, highlighting potential impacts on economic development and pollution. (3) Increased carbon emissions across scenarios, with construction development showing the highest rise (4.170%) and agricultural development the lowest (0.766%). (4) The predictive accuracy of the validation group's CO2 concentration values can reach 99.5%. This study proposes precise CO2 prediction at the county level, thus laying the groundwork for future research endeavors. Such findings are indispensable for informing carbon policy formulation and promoting low-carbon development strategies.