Identification of lignans as selective cyclooxygenase-2 inhibitors from the extract of Acanthopanacis cortex.

Acanthopanacis cortex (the dried root bark of Acanthopanax gracilistylus W. W. Smith) has been used for the treatment of rheumatic diseases in China for over 2000 years. Four previously undescribed lignans (1-4) and 12 known lignans (5-16) were isolated from Acanthopanacis cortex. In this study, the inhibitory activities of compounds 1-16 against neutrophil elastase (NE), cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) are reported. The results show that compounds 1-16 exhibit weak inhibitory activities against NE and COX-1. However, compounds 2, 6-8 and 13-16 demonstrate better COX-2 inhibitory effects with IC50 values from 0.75 to 8.17 µΜ. These findings provide useful information for the search for natural selective COX-2 inhibitors.

Electro-optically tunable optical delay on a lithium niobate photonic chip.

An approach for continuous tuning of on-chip optical delay with a microring resonator is proposed and demonstrated. By introducing an electro-optically tunable waveguide coupler, the bus waveguide to the resonance coupling can be effectively tuned from the under-coupling regime to the over-coupling regime. The optical delay is experimentally characterized by measuring the relative phase shift between lasers and shows a large dynamic range of delay from -600 to 600 ps and an efficient tuning of delay from -430 to -180 ps and from 40 to 240 ps by only a 5 V voltage.

A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information.

Background and aims: Through a nested cohort study, we evaluated the diagnostic performance of breath-omics in differentiating between benign and malignant breast lesions, and assessed the diagnostic performance of a multi-omics approach that combines breath-omics, ultrasound radiomics, and clinic-omics in distinguishing between benign and malignant breast lesions. Materials and methods: We recruited 1,723 consecutive patients who underwent an automated breast volume scanner (ABVS) examination. Breath samples were collected and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) to obtain breath-omics features. 238 of 1,723 enrolled participants have received pathological confirmation of breast nodules finally. The breast lesions of the 238 participants were contoured manually based on ABVS images for ultrasound radiomics feature calculation. Then, single- and multi-omics models were constructed and evaluated for breast nodules diagnosis via five-fold cross-validation. Results: The area under the curve (AUC) of the breath-omics model was 0.855. In comparison, the multi-omics model demonstrated superior diagnostic performance for breast cancer, with sensitivity, specificity, and AUC of 84.1 %, 89.9 %, and 0.946, respectively. The multi-omics performance was comparable to that of the Breast Imaging Reporting and Data System (BI-RADS) classification via senior ultrasound physician evaluation. Conclusion: The multi-omics approach combining metabolites in exhaled breath, ultrasound imaging, and basic clinical information exhibits superior diagnostic performance and promises to be a non-invasive and reliable tool for breast cancer diagnosis.

Adaptive multi-source domain collaborative fine-tuning for transfer learning.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning (AMCF) to address this issue. AMCF utilizes multiple source domain models for collaborative fine-tuning, thereby improving the feature extraction capability of model in the target task. Specifically, AMCF employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models, aiming to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of AMCF, it is applied to seven public visual classification datasets commonly used in transfer learning, and compared with the most widely used single-source domain fine-tuning methods. Experimental results demonstrate that, in comparison with the existing fine-tuning methods, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, thereby significantly improving the fine-tuning performance.

Corrigendum: High-definition transcranial direct current stimulation for upper extremity rehabilitation in moderate-to-severe ischemic stroke: a pilot study.

[This corrects the article DOI: 10.3389/fnhum.2023.1286238.].

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

Objectives: To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods: MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results: The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion: In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement: Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

Investigating distinct clinical features and constructing a nomogram model for survival probability in adults with cerebellar high-grade gliomas.

BACKGROUND: The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model. METHODS: Patient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model. RESULTS: There were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p < 0.001). Multivariate Cox regression analyses revealed that race, WHO grade, surgical procedures, radiotherapy, and chemotherapy were independent prognostic factors for cHGGs. Based on these factors, a nomogram was developed to predict 1-, 3-, and 5-year survival probabilities, with AUC of 0.860, 0.837, and 0.810, respectively. The model's accuracy was validated by machine learning approaches, demonstrating consistent predictive effectiveness. CONCLUSIONS: Adult cHGGs are distinguished by distinctive clinical features different from those of sHGGs and are associated with an inferior prognosis. Based on these risk factors affecting cHGGs prognosis, the nomogram prediction model serves as a crucial tool for clinical decision-making in patient care.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.

The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis.

BACKGROUND: Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored. METHODS: We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives. RESULTS: Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines. CONCLUSION: The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

Functional variation among mesenchymal stem cells derived from different tissue sources.

Background: Mesenchymal stem cells (MSCs) are increasingly recognized for their regenerative potential. However, their clinical application is hindered by their inherent variability, which is influenced by various factors, such as the tissue source, culture conditions, and passage number. Methods: MSCs were sourced from clinically relevant tissues, including adipose tissue-derived MSCs (ADMSCs, n = 2), chorionic villi-derived MSCs (CMMSCs, n = 2), amniotic membrane-derived MSCs (AMMSCs, n = 3), and umbilical cord-derived MSCs (UCMSCs, n = 3). Passages included the umbilical cord at P0 (UCMSCP0, n = 2), P3 (UCMSCP3, n = 2), and P5 (UCMSCP5, n = 2) as well as the umbilical cord at P5 cultured under low-oxygen conditions (UCMSCP5L, n = 2). Results: We observed that MSCs from different tissue origins clustered into six distinct functional subpopulations, each with varying proportions. Notably, ADMSCs exhibited a higher proportion of subpopulations associated with vascular regeneration, suggesting that they are beneficial for applications in vascular regeneration. Additionally, CMMSCs had a high proportion of subpopulations associated with reproductive processes. UCMSCP5 and UCMSCP5L had higher proportions of subpopulations related to female reproductive function than those for earlier passages. Furthermore, UCMSCP5L, cultured under low-oxygen (hypoxic) conditions, had a high proportion of subpopulations associated with pro-angiogenic characteristics, with implications for optimizing vascular regeneration. Conclusions: This study revealed variation in the distribution of MSC subpopulations among different tissue sources, passages, and culture conditions, including differences in functions related to vascular and reproductive system regeneration. These findings hold promise for personalized regenerative medicine and may lead to more effective clinical treatments across a spectrum of medical conditions.

The functional and structural alterations in brain regions related to the fear network model in panic disorder: A resting-state fMRI and T1-weighted imaging study.

Abnormal functional connectivity (FC) within the fear network model (FNM) has been identified in panic disorder (PD) patients, but the specific local structural and functional properties, as well as effective connectivity (EC), remain poorly understood in PD. The purpose of this study was to investigate the structural and functional patterns of the FNM in PD. Magnetic resonance imaging data were collected from 33 PD patients and 35 healthy controls (HCs). Gray matter volume (GMV), degree centrality (DC), regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) were used to identify the structural and functional characteristics of brain regions within the FNM in PD. Subsequently, FC and EC of abnormal regions, based on local structural and functional features, and their correlation with clinical features were further examined. PD patients exhibited preserved GMV, ReHo, and ALFF in the brain regions of the FNM compared with HCs. However, increased DC in the bilateral amygdala was observed in PD patients. The amygdala and its subnuclei exhibited altered EC with rolandic operculum, insula, medial superior frontal gyrus, supramarginal gyrus, opercular part of inferior frontal gyrus, and superior temporal gyrus. Additionally, Hamilton Anxiety Scale score was positively correlated with EC from left lateral nuclei (dorsal portion) of amygdala to right rolandic operculum and left superior temporal gyrus. Our findings revealed a reorganized functional network in PD involving brain regions regulating exteroceptive-interoceptive signals, mood, and somatic symptoms. These results enhance our understanding of the neurobiological underpinnings of PD, suggesting potential biomarkers for diagnosis and targets for therapeutic intervention.

Development of best evidence-based practice protocols for central venous catheter placement and maintenance to reduce CLABSI.

Although evidence-based interventions can reduce the incidence of central line-associated bloodstream infection (CLABSI), there is a large gap between evidence-based interventions and the actual practice of central venous catheter (CVC) care. Evidence-based interventions are needed to reduce the incidence of CLABSI in intensive care units (ICU) in China. Professional association, guidelines, and database websites were searched for data relevant to CLABSI in the adult ICUs from inception to February 2020. Checklists were developed for both CVC placement and maintenance. Based on the Integrated Promoting Action on Research Implementation in Health Services framework, a questionnaire collected the cognition and practice of ICU nursing and medical staff on the CLABSI evidence-based prevention guidelines. From January 2018 to December 2021, ICU CLABSI rates were collected monthly. Ten clinical guidelines were included after the screening and evaluation process and used to develop the best evidence-based protocols for CVC placement and maintenance. The CLABSI rates in 2018, 2019, and 2020 were 2.98‰ (9/3021), 1.83‰ (6/3276), and 1.69‰ (4/2364), respectively. Notably, the CLABSI rate in 2021 was 0.38‰ (1/2607). In other words, the ICU CLABSI rate decreased from 1.69‰ to 0.38‰ after implementation of the new protocols. Additionally, our data suggested that the use of ultrasound-guidance for catheter insertion, chlorhexidine body wash, and the use of a checklist for CVC placement and maintenance were important measures for reducing the CLABSI rate. The evidence-based processes developed for CVC placement and maintenance were effective at reducing the CLABSI rate in the ICU.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Simultaneous Optical Imaging of Action Potentials and Calcium Transients in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Cardiovascular diseases have emerged as one of the leading causes of human mortality, but the discovery of new drugs has been hindered by the absence of suitable in vitro platforms. In recent decades, continuously refined protocols for differentiating human induced pluripotent stem cells (hiPSCs) into hiPSC-derived cardiomyocytes (hiPSC-CMs) have significantly advanced disease modeling and drug screening; however, this has led to an increasing need to monitor the function of hiPSC-CMs. The precise regulation of action potentials (APs) and intracellular calcium (Ca2+) transients is critical for proper excitation-contraction coupling and cardiomyocyte function. These important parameters are usually adversely affected in cardiovascular diseases or under cardiotoxic conditions and can be measured using optical imaging-based techniques. However, this procedure is complex and technologically challenging. We have adapted the IonOptix system to simultaneously measure APs and Ca2+ transients in hiPSC-CMs loaded with the fluorescent dyes FluoVolt and Rhod 2, respectively. This system serves as a powerful high-throughput platform to facilitate the discovery of new compounds to treat cardiovascular diseases with the cellular phenotypes of abnormal APs and Ca2+ handling. Here, we present a comprehensive protocol for hiPSC-CM preparation, device setup, optical imaging, and data analysis. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Maintenance and seeding of hiPSC-CMs Basic Protocol 2: Simultaneous detection of action potentials and Ca2+ transients in hiPSC-CMs.

Non-target discovery and risk prediction of per- and polyfluoroalkyl substances (PFAS) and transformation products in wastewater treatment systems.

Wastewater treatment plants (WWTPs) serve as the main destination of many wastes containing per- and polyfluoroalkyl substances (PFAS). Here, we investigated the occurrence and transformation of PFAS and their transformation products (TPs) in wastewater treatment systems using high-resolution mass spectrometry-based target, suspect, and non-target screening approaches. The results revealed the presence of 896 PFAS and TPs in aqueous and sludge phases, of which 687 were assigned confidence levels 1-3 (46 PFAS and 641 TPs). Cyp450 metabolism and environmental microbial degradation were found to be the primary metabolic transformation pathways for PFAS within WWTPs. An estimated 52.3 %, 89.5 %, and 13.6 % of TPs were believed to exhibit persistence, bioaccumulation, and toxicity effects, respectively, with a substantial number of TPs posing potential health risks. Notably, the length of the fluorinated carbon chain in PFAS and TPs was likely associated with increased hazard, primarily due to the influence of biodegradability. Ultimately, two high riskcompounds were identified in the effluent, including one PFAS (Perfluorobutane sulfonic acid) and one enzymatically metabolized TP (23-(Perfluorobutyl)tricosanoic acid@BTM0024_cyp450). It is noteworthy that the toxicity of some TPs exceeded that of their parent compounds. The results from this study underscores the importance of PFAS TPs and associated environmental risks.

Collagen in hepatocellular carcinoma: A novel biomarker and therapeutic target.

HCC is globally recognized as a major health threat. Despite significant progress in the development of treatment strategies for liver cancer, recurrence, metastasis, and drug resistance remain key factors leading to a poor prognosis for the majority of liver cancer patients. Thus, there is an urgent need to develop effective biomarkers and therapeutic targets for HCC. Collagen, the most abundant and diverse protein in the tumor microenvironment, is highly expressed in various solid tumors and plays a crucial role in the initiation and progression of tumors. Recent studies have shown that abnormal expression of collagen in the tumor microenvironment is closely related to the occurrence, development, invasion, metastasis, drug resistance, and treatment of liver cancer, making it a potential therapeutic target and a possible diagnostic and prognostic biomarker for HCC. This article provides a comprehensive review of the structure, classification, and origin of collagen, as well as its role in the progression and treatment of HCC and its potential clinical value, offering new insights into the diagnosis, treatment, and prognosis assessment of liver cancer.

Rescue Therapies for Steroid-Refractory Acute Severe Ulcerative Colitis: A Systemic Review and Network Meta-analysis.

BACKGROUND AND AIMS: Approximately 40% of patients with steroid-refractory acute severe ulcerative colitis (steroid-refractory (SR) ASUC) requires colectomies. Advanced therapies may reduce the short-term colectomy rates in patients with SR ASUC. However, comparative clinical studies evaluating the effectiveness of these rescue therapies are lacking. Therefore, we conducted a network meta-analysis to study the effectiveness of rescue therapies for SR ASUC. METHODS: Six randomized controlled trials and 15 cohort studies including 2,004 patients were analyzed. Rescue drugs included tofacitinib, infliximab with a 5 or 10 mg/kg induction dose at 0, 2, and 6 weeks (IFX and IFX10, respectively), IFX with an accelerated regimen of three 5 mg/kg induction doses timed according to clinical need (accelerated IFX), tacrolimus, cyclosporine (CyA), ustekinumab, and adalimumab. Treatments were compared with a placebo. RESULTS: Tofacitinib (odds ratio [OR]: 0.09 [95% confidence interval [CI]: 0.02-0.52]), accelerated IFX (OR: 0.16 [95% CI: 0.03-0.94]), IFX (OR: 0.2 [95% CI: 0.07-0.58]), and tacrolimus (OR: 0.24 [95% CI: 0.06-0.96]) significantly reduced the short-term colectomy rates compared with placebo. IFX10 and CyA tended to prevent colectomies. However, ustekinumab and adalimumab did not significantly affect the colectomy rates. CONCLUSION: This is the first network meta-analysis to investigate the efficacy of advanced therapies in reducing short-term colectomy rates in patients with SR ASUC. Tofacitinib, accelerated IFX, standard IFX, and tacrolimus significantly reduced the colectomy rates in SR ASUC patients compared with placebo. Thus, advanced therapies should be considered for rescue therapies in patients with SR ASUC.

Tumor treating fields for newly diagnosed high-grade glioma based on the criteria of 2021 WHO CNS5: A retrospective analysis of Chinese patients in a single center.

BACKGROUND AND OBJECTIVE: High-grade glioma (HGG) is known to be characterized by a high degree of malignancy and a worse prognosis. The classical treatment is safe resection supplemented by radiotherapy and chemotherapy. Tumor treating fields (TTFields), an emerging physiotherapeutic modality that targets malignant solid tumors using medium-frequency, low-intensity, alternating electric fields to interfere with cell division, have been used for the treatment of new diagnosis of glioblastoma, however, their administration in HGG requires further clinical evidence. The efficacy and safety of TTFields in Chinese patients with HGG were retrospectively evaluated by us in a single center. METHODS: We enrolled and analyzed 52 patients with newly diagnosed HGG undergoing surgery and standard chemoradiotherapy regimens from December 2019 to June 2022, and followed them until June 2023. Based on whether they used TTFields, they were divided into a TTFields group and a non-TTFields group. Progression-free survival (PFS) and overall survival (OS) were compared between the two groups. RESULTS: There were 26 cases in the TTFields group and 26 cases in the non-TTFields group. In the TTFields group, the median PFS was 14.2 months (95% CI: 9.50-18.90), the median OS was 19.7 months (95% CI: 14.95-24.25) , the median interval from surgery to the start of treatment with TTFields was 2.47 months (95% CI: 1.47-4.13), and the median duration of treatment with TTFields was 10.6 months (95% CI: 9.57-11.63). 15 (57.69%) patients experienced an adverse event and no serious adverse event was reported. In the non-TTFields group, the median PFS was 9.57 months (95% CI: 6.23-12.91) and the median OS was 16.07 months (95% CI: 12.90-19.24). There was a statistically significant difference in PFS (p = 0.005) and OS (p = 0.007) between the two groups. CONCLUSIONS: In this retrospective analysis, TTFields were observed to improve newly diagnosed HGG patients' median PFS and OS. Compliance was much higher than reported in clinical trials and safety remained good.

Discovery and engineering of ChCas12b for precise genome editing.

Many clustered regularly interspaced short palindromic repeat and CRISPR-associated protein 12b (CRISPR-Cas12b) nucleases have been computationally identified, yet their potential for genome editing remains largely unexplored. In this study, we conducted a GFP-activation assay screening 13 Cas12b nucleases for mammalian genome editing, identifying five active candidates. Candidatus hydrogenedentes Cas12b (ChCas12b) was found to recognize a straightforward WTN (W = T or A) proto-spacer adjacent motif (PAM), thereby dramatically expanding the targeting scope. Upon optimization of the single guide RNA (sgRNA) scaffold, ChCas12b exhibited activity comparable to SpCas9 across a panel of nine endogenous loci. Additionally, we identified nine mutations enhancing ChCas12b specificity. More importantly, we demonstrated that both ChCas12b and its high-fidelity variant, ChCas12b-D496A, enabled allele-specific disruption of genes harboring single nucleotide polymorphisms (SNPs). These data position ChCas12b and its high-fidelity counterparts as promising tools for both fundamental research and therapeutic applications.

Plasma Ionomic Profile and Interaction Patterns in Coronary Artery Disease Patients.

Humans are exposed to various chemical elements that have been associated with the development and progression of diseases such as coronary artery disease (CAD). Unlike previous research, we employed a multi-element approach to investigate CAD patients and those with comorbid conditions such as diabetes (CAD-DM2), high blood pressure (CAD-HBP), or high blood lipids (CAD-HBL). Plasma concentrations of 21 elements, including lithium (Li), boron (B), aluminum (Al), calcium (Ca), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), strontium (Sr), cadmium (Cd), tin (Sn), stibium (Sb), barium (Ba), and lead (Pb), were measured in CAD patients (n = 201) and healthy subjects (n = 110) using inductively coupled plasma-mass spectrometry (ICP-MS). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were utilized to analyze the ionomic profiles. Spearman correlation analysis was employed to identify the interaction patterns among individual elements. We found that levels of Ba, Li, Ni, Zn and Pb were elevated in the CAD group compared to the healthy group, while Sb, Ca, Cu, Ti, Fe, and Se were lower. Furthermore, the CAD-DM2 group exhibited higher levels of Ni and Cd, while the CAD-HBP group showed lower levels of Co and Mn. In the CAD-HBL group, Ti was increased, whereas Ba, Cr, Cu, Co, Mn, and Ni were reduced. In conclusion, ionomic profiles can be utilized to differentiate CAD patients from healthy individuals, potentially providing insights for future treatment or dietary interventions.