Role of per- and polyfluoroalkyl substances in the cardiorenal system: Unraveling crosstalk from the network of pollutants and phenotypes.

Although per- and polyfluoroalkyl substances (PFAS) have been frequently linked to cardiovascular and renal disease separately, evidence remains scarce regarding their systematic effect. Therefore, we recruited 546 newly diagnosed acute coronary syndrome (ACS) patients and detected seven myocardial enzymes and six kidney function biomarkers. Twelve PFAS were also assessed with ultra-high-performance liquid chromatography-tandem mass spectrometry. Generalized linear model and restricted cubic spline model were applied to single pollutant analysis. Quantile g-computation was used for mixture analysis. Network model was utilized to identify central and bridge nodes of pollutants and phenotypes. In the present study, perfluorohexane sulfonic acid was positively associated with uric acid (UA) (ß= 0.04, 95% confidence interval (CI): 0.01, 0.07), and perfluorobutanoic acid was negatively associated with estimated glomerular filtration rate (ß= -0.04, 95% CI: -0.07, -0.01) but positively associated with UA (ß= 0.03, 95% CI: 0.01, 0.06). In mixture analysis, each quantile increase in the PFAS mixture was significantly associated with UA (ß= 0.08, 95% CI: 0.04, 0.11). Network analysis revealed that perfluorooctanoate, UA, and myoglobin were denoted as bridge nodes, and the first principal component of lactate dehydrogenase and creatine kinase- myocardial band was identified as the node with the highest strength and expected influence. This study investigates the systematic impact of PFAS exposure through cardiorenal interaction network, which highlights that PFAS may serve as an upstream approach in UA-modulated cardiorenal network to affect cardiorenal system comprehensively.

Different surgical methods for FIGO stage IVB cervical cancer patients receiving chemotherapy: a population-based study.

OBJECTIVE: To assess survival differences between non-extensive surgery (NES) and extensive surgery (ES) in International Federation of Gynecology and Obstetrics (FIGO) stage IVB cervical cancer patients receiving chemotherapy from a population-based database, the Surveillance, Epidemiology and End Results. METHODS: Propensity matching was conducted to minimize heterogeneity. Survival analysis was performed by the Kaplan-Meier method, log-rank test, and Cox proportional hazards model. RESULTS: A total of 154 patients met screening criteria, among whom 84 patients (84/154) underwent NES while 70 patients (70/154) underwent ES. After matching, no survival advantage was observed in ES group compared with NES group (p=0.066; hazard ratio [HR]=1.54; 95% confidence interval [CI]=0.97-2.42). Stratified analyses suggested ES prolonged overall survival in patients with histology other than squamous cell carcinoma and adenocarcinoma (p=0.028; HR=0.36; 95% CI=0.15-0.89) and American Joint Committee on Cancer (AJCC) T stage T1 (p=0.009; HR=0.18; 95% CI=0.05-0.66). Despite no survival benefit after regional lymph node surgery (p=0.629; HR=0.88; 95% CI=0.53-1.47), subgroup analyses demonstrated that patients younger than 50 (p=0.006; HR=0.21; 95% CI=0.07-0.64), with AJCC T stage T1 (p=0.002; HR=0.09; 95% CI=0.02-0.42), T3 (p=0.001; HR=0.02; 95% CI=0.00-0.21), hematogenous metastasis (p=0.036; HR=0.27; 95% CI=0.08-0.92) and without surgery of other sites (p=0.040; HR=0.01; 95% CI=0.00-0.79) might achieve longer survival after regional lymph node surgery. CONCLUSION: In conclusion, ES or regional lymph node surgery may provide survival advantage for certain subgroup of FIGO IVB cervical cancer patients receiving chemotherapy. However, it deserves large scale prospective clinical trials to confirm.

Oxidative Cleavage of Aromatic C-O Linkages by Oxoammonium Salts.

Oxidative cleavage of aromatic C(sp2)-O bond is important to the conversion of biomass and plastic wastes into value-added chemicals. Here we put forward the oxidative cleavage of para-C-O bonds in phenolic compounds in use of oxoammonium salts as oxidant and water as the oxygen source. The mechanism is that oxoammonium cation activates water to form hydroxy-oxoammonium adduct and thus realizes the ipso-substitution of 4-alkoxyphenol, which is proved by substituent effect, isotope labelling experiments, and kinetic analysis. Furthermore, this protocol is successfully applied into the depolymerization of both lignin model compounds with α-O-5 and 4-O-5 linkages and polyphenylene oxide (PPO).

Key Roles of Free Cations in Modulating the Reaction Rate in Imidazolium Ionic Liquid-Dimethyl Sulfoxide Mixtures.

Imidazolium ionic liquids (ILs) are widely utilized in various fields due to their distinctive properties. However, their high viscosity limits their application in specific reactions, and mixing ILs with organic components is a way to solve this problem. While previous studies mainly focused on the structural changes of ILs after adding organic molecules, no studies elucidated the influence of their existing species on chemical reactions. In this study, aerobic α-hydroxylation of 2-methylcyclohexanone was chosen as a model reaction, and the reaction rate was found to be adjusted by varying imidazolium concentration in its mixtures with dimethyl sulfoxide. To elucidate the mechanism, the distribution of species in an IL solution and its change with concentration were studied by molecular dynamics simulations, and the results revealed the significant impact of the concentration of free cations on the reaction rate. The interaction between the ionic species and reaction intermediate, as calculated by density functional theory, highlighted the crucial role of free cations in this reaction. This study demonstrates the feasibility of tuning the concentration of free cations by varying the concentration of the IL solution, establishing the relationship between its microstructure and chemical reaction efficiency, thus providing vital information for the design and application of ILs.

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

Electrochemical Decarboxylation Coupling Reactions.

Carboxylic acids are attractive synthetic feedstocks with stable, non-toxic, and inexpensive properties that can be easily obtained from natural sources or through synthesis. Carboxylic acids have long been considered environmentally friendly coupling agents in various organic transformations. In recent years, electrochemically mediated decarboxylation reactions of decarboxylic acids and their derivatives (NHPI) have emerged as effective new methods for constructing carbon-carbon or carbon-heterocarbon chemical bonds. Compared with transition metal and photochemistry-mediated catalytic reactions, which do not require the addition of oxidants and strong bases, electrochemically-mediated decarboxylative transformations are considered a sustainable strategy. In addition, various functional groups tolerate the electrochemical decarboxylation conversion strategy well. Here, we summarize the recent electrochemical decarboxylation reactions to better elucidate the advantages of electrochemical decarboxylation reactions.

Fixation behavior of oceanic manganese nodule-sodium alginate composite microspheres for heavy metal release during manganese nodule mining.

Research on the environmental impact of deep-sea mining is crucial, particularly for fragile deep-sea ecosystems. This research focuses on the issue of heavy metal release during mining activities. Through simulation experiments, we investigated the release of Cu2+, Co2+, and Ni2+ from sediments under disturbance conditions and the fixation behavior during the deployment of ocean manganese nodule-sodium alginate composite microspheres (OMN@SA). The experimental results revealed that mining disturbances cause the release of 0.291% of Cu2+, 7.34% of Co2+, and 4.13% of Ni2+ from sediments into the water, primarily in the form of exchangeable metals. Compared with the bottom adsorption, OMN@SA has a faster adsorption rate in the slow settling process. The removal rates of Cu2+, Co2+ and Ni2+ reached 54.0%, 78.3% and 61.8% for 5 h adsorption, and the bottom adsorption removal rates reached 96.4%, 97.8% and 95.1% for 30 d adsorption, which has a good removal effect. In addition, OMN@SA can effectively block the diffusion of Cu2+, Co2+, and Ni2+ from interstitial water to overlying water, and reduce the influence of interstitial water on overlying water. SEM-EDS, FTIR, and XRD analyses revealed that OMN@SA adsorbs heavy metal ions through its abundant -OH groups and incorporates Cu2+, Co2+, and Ni2+ into the crystal lattices of vernadite and todorokite via substitution or intercalation. This study provides guidance for the remediation of heavy metal release from deep-sea mining using adsorption methods and demonstrates the promising application prospects of OMN@SA.

Monitoring the Biochemical Activity of Single Anammox Granules with Microbarometers.

Granule-based anaerobic ammonium oxidation (Anammox) is a promising biotechnology for wastewater treatments with extraordinary performance in nitrogen removal. However, traditional analytical methods often delivered an average activity of a bulk sample consisting of millions and even billions of Anammox granules with distinct sizes and components. Here, we developed a novel technique to monitor the biochemical activity of individual Anammox granules in real-time by recording the production rate of nitrogen gas with a microbarometer in a sealed chamber containing only one granule. It was found that the specific activity of a single Anammox granule not only varied by tens of folds among different individuals with similar sizes (activity heterogeneity) but also revealed significant breath-like dynamics over time (temporal fluctuation). Statistical analysis on tens of individuals further revealed two subpopulations with distinct color and specific activity, which were subsequently attributed to the different expression levels of heme c content and hydrazine dehydrogenase activity. This study not only provides a general methodology for various kinds of gas-producing microbial processes but also establishes a bottom-up strategy for exploring the structural-activity relationship at a single sludge granule level, with implications for developing a better Anammox process.

Construction of prognosis prediction model and visualization system of acute paraquat poisoning based on improved machine learning model.

Objective: This study aims to develop a prognosis prediction model and visualization system for acute paraquat poisoning based on an improved machine learning model. Methods: 101 patients with acute paraquat poisoning admitted to 6 hospitals from March 2020 to March 2022 were selected for this study. After expiry of the treatment period (one year of follow-up for survivors and up to the time of death for deceased patients) and they were categorized into the survival group (n = 37) and death group (n = 64). The biochemical indexes of the patients were analyzed, and a prognosis prediction model was constructed using HHO-XGBoost, an improved machine-learning algorithm. Multivariate logistic analysis was used to verify the value of the self-screening features in the model. Results: Seven features were selected in the HHO-XGBoost model, including oral dose, serum creatinine, alanine aminotransferase (ALT), white blood cell (WBC) count, neutrophil count, urea nitrogen level, and thrombin time. Univariate analysis showed statistically significant differences between these features' survival and death groups (P < 0.05). Multivariate logistic analysis identified four features significantly associated with prognosis- serum creatinine level, oral dose, ALT level, and WBC count - indicating their critical significance in predicting outcomes. Conclusion: The HHO-XGBoost model based on machine learning is precious in constructing a prognosis prediction model and visualization system for acute paraquat poisoning, which can help clinical prognosis prediction of patients with paraquat poisoning.

Comprehensive screening of industrially relevant components at genome scale using a high-quality gene overexpression collection of Corynebacterium glutamicum.

Development of efficient microbial strains for biomanufacturing requires deep understanding of the biology and functional components responsible for the synthesis, transport, and tolerance of the target compounds. A high-quality controllable gene overexpression strain collection was constructed for the industrial workhorse Corynebacterium glutamicum covering 99.7% of its genes. The collection was then used for comprehensive screening of components relevant to biomanufacturing features. In total, 15 components endowing cells with improved hyperosmotic tolerance and l-lysine productivity were identified, including novel transcriptional factors and DNA repair proteins. Systematic interrogation of a subset of the collection revealed efficient and specific exporters functioning in both C. glutamicum and Escherichia coli. Application of the new exporters was showcased to construct a strain with the highest l-threonine production level reported for C. glutamicum (75.1 g/l and 1.5 g/l·h) thus far. The genome-scale gene overexpression collection will serve as a valuable resource for fundamental biological studies and for developing industrial microorganisms for producing amino acids and other biochemicals.

A Nomogram Based on a Non-Invasive Method to Distinguish Between Gram-Positive and Gram-Negative Bacterial Infections of Liver Abscess.

Purpose: The diagnosis of liver abscess (LA) caused by Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB) depends on ultrasonography, but it is difficult to distinguish the overlapping features. Valuable ultrasonic (US) features were extracted to distinguish GPB-LA and GNB-LA and establish the relevant prediction model. Materials and Methods: We retrospectively analyzed seven clinical features, three laboratory indicators and 11 US features of consecutive patients with LA from April 2013 to December 2023. Patients with LA were randomly divided into training group (n=262) and validation group (n=174) according to a ratio of 6:4. Univariate logistic regression and LASSO regression were used to establish prediction models. The performance of the model was evaluated using area under the curve(AUC), calibration curves, and decision curve analysis (DCA), and subsequently validated in the validation group. Results: A total of 436 participants (median age: 55 years; range: 42-68 years; 144 women) were evaluated, including 369 participants with GNB-LA and 67 with GPB-LA, respectively. A total of 11 predictors by LASSO regression analysis, which included gender, age, the liver background, internal gas bubble, echogenic debris, wall thickening, whether the inner wall is worm-eaten, temperature, diabetes mellitus, hepatobiliary surgery and neutrophil(NEUT). The performance of the Nomogram prediction model distinguished between GNB-LA and GPB-LA was 0.80, 95% confidence interval [CI] (0.73-0.87). In the validation group, the AUC of GNB was 0.79, 95% CI (0.69-0.89). Conclusion: A model for predicting the risk of GPB-LA was established to help diagnose pathogenic organism of LA earlier, which could help select sensitive antibiotics before the results of drug-sensitive culture available, thereby shorten the treatment time of patients.

Association between dietary copper intake and bone mineral density in children and adolescents aged 8-19 years: A cross-sectional study.

PURPOSE: Some studies showed the possible role of copper intake on bone mineral density (BMD) in adults or the elderly, but the association remained uncertain in children and adolescents. Our research explored the association between copper intake and BMD in individuals aged 8-19 years from the National Health and Nutrition Examination Survey (NHANES) 2011-2016. METHODS: In the present study, 6,965 individuals aged 8-19 (mean age 13.18 ± 3.38 years) were enrolled from the NHANES 2011-2016. Copper intake was evaluated by averaging two 24-hour copper dietary intake recalls. Multivariate linear regression analyses were used to explore the association between copper intake and total BMD, subtotal BMD, and total spine BMD in children and adolescents. Stratified analyses and interaction tests were performed by age, gender, and race. RESULTS: Participants of the higher quartile of copper intake were more likely to be older, men, Non-Hispanic White, and Other Hispanic. They have higher values of poverty income ratio (PIR), serum phosphorus, blood urea nitrogen, serum vitamin D, and BMD and lower values of body mass index (BMI), cholesterol, total protein, and serum cotinine. In the fully adjusted model, we found positive associations between copper intake and total BMD (ß = 0.013, 95CI: 0.006, 0.019)), subtotal BMD (ß = 0.020, 95CI: 0.015, 0.024), and total spine BMD (ß = 0.014, 95CI: 0.009, 0.019). Stratified analyses showed that the association was stronger in men, individuals aged 14-19, Non-Hispanic White, and Other Hispanic. CONCLUSIONS: Our study suggests that copper intake is positively associated with BMD in U.S. children and adolescents. The study emphasizes the role of copper intake on bone health in the early stages of life. However, more investigations are needed to verify our findings and their underlying mechanisms.

Solute carrier family 4 member 4 (SLC4A4) is associated with cell proliferation, migration and immune cell infiltration in colon cancer.

BACKGROUND: Solute Carrier Family 4 Member 4 (SLC4A4) is a membrane protein-coding gene for a Na+/HCO3- cotransporter and plays a crucial role in regulating pH, bicarbonate secretion and homeostasis. However, the prognostic and immunological role of SLC4A4 in colon cancer remains unknown. METHOD: In this study, expression profiles of SLC4A4 were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, to which a variety of bioinformatic analyses were performed. Sangerbox, Xiantao, ESTIMATE and TIMER online tools were used to delve into the relationship between SLC4A4 expression and immune cell infiltration. The role of SLC4A4 in the proliferation and migration of colon cancer cells was verified by CCK8, EdU and wound healing assays. The related molecules and pathways that SLC4A4 may affect were validated by bioinformatic prediction and western blotting analysis. RESULTS: The expression levels of SLC4A4 were significantly lower in colon cancer tissues than in normal tissues and its low expression was positively correlated with poor prognosis. TIMER and ESTIMATE showed that SLC4A4 broadly influenced immune cell infiltration. Experiments in vitro demonstrated that SLC4A4 inhibited partial epithelial-mesenchymal transition (EMT) phenotypes. CONCLUSIONS: To conclude, our study revealed that SLC4A4 is lowly expressed in colon cancer tissues, and SLC4A4 may inhibit the progression of colon cancer via regulating partial EMT phenotypes and immune cell infiltration, which may provide new perspectives for the development of more precise and personalized immune anti-tumor therapies.

Chemical Synthesis, Herbicidal Activity, Crop Safety, and Molecular Basis of ortho-Fluoroalkoxy Substituted Sulfonylureas as Novel Acetohydroxyacid Synthase Inhibitors.

In the face of increasing resistance to the currently used commercial herbicides and the lack of success in identifying new herbicide targets, alternative herbicides need to be developed to control unwanted monocotyledon grasses in food crops. Here, a panel of 29 novel sulfonylurea-based compounds with ortho-fluoroalkoxy substitutions at the phenyl ring were designed and synthesized. Pot assays demonstrated that two of these compounds, 6d and 6u, have strong herbicidal activities against Echinochloa crus-galli, Eleusine indica, Alopecurus aequalis, and Alopecurus japonicus Steudel at a dosage of 15 g ha-1. Furthermore, these two compounds exhibited <5% inhibition against wheat at a dosage of 30 g ha-1 under post-emergence conditions. 6u also exhibited <5% inhibition against rice at a dosage of 30 g ha-1 under both post-emergence and pre-emergence conditions. A kinetics study demonstrated that 6d and 6u are potent inhibitors of Arabidopsis thaliana acetohydroxyacid synthase (AHAS; EC 2.2.1.6) with potent Ki values of 18 ± 1.1 and 11.9 ± 4.0 nM, respectively. The crystal structure of 6u in complex with A. thaliana (At)AHAS has also been determined at 2.7 Å resolution. These new compounds represent new alternative herbicide choices to protect wheat or rice from invading grasses.

Metric learning guided sinogram denoising for cone beam CT enhancement.

BACKGROUND: Cone beam computed tomography (CBCT) is a widely available modality, but its clinical utility has been limited by low detail conspicuity and quantitative accuracy. Convenient post-reconstruction denoising is subject to back projected patterned residual, but joint denoise-reconstruction is typically computationally expensive and complex. PURPOSE: In this study, we develop and evaluate a novel Metric-learning guided wavelet transform reconstruction (MEGATRON) approach to enhance image domain quality with projection-domain processing. METHODS: Projection domain based processing has the benefit of being simple, efficient, and compatible with various reconstruction toolkit and vendor platforms. However, they also typically show inferior performance in the final reconstructed image, because the denoising goals in projection and image domains do not necessarily align. Motivated by these observations, this work aims to translate the demand for quality enhancement from the quantitative image domain to the more easily operable projection domain. Specifically, the proposed paradigm consists of a metric learning module and a denoising network module. Via metric learning, enhancement objectives on the wavelet encoded sinogram domain data are defined to reflect post-reconstruction image discrepancy. The denoising network maps measured cone-beam projection to its enhanced version, driven by the learnt objective. In doing so, the denoiser operates in the convenient sinogram to sinogram fashion but reflects improvement in reconstructed image as the final goal. Implementation-wise, metric learning was formalized as optimizing the weighted fitting of wavelet subbands, and a res-Unet, which is a Unet structure with residual blocks, was used for denoising. To access quantitative reference, cone-beam projections were simulated using the X-ray based Cancer Imaging Simulation Toolkit (XCIST). In both learning modules, a data set of 123 human thoraxes, which was from Open-Source Imaging Consortium (OSIC) Pulmonary Fibrosis Progression challenge, was used. Reconstructed CBCT thoracic images were compared against ground truth FB and performance was assessed in root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). RESULTS: MEGATRON achieved RMSE in HU value, PSNR, and SSIM were 30.97 ± 4.25, 37.45 ± 1.78, and 93.23 ± 1.62, respectively. These values are on par with reported results from sophisticated physics-driven CBCT enhancement, demonstrating promise and utility of the proposed MEGATRON method. CONCLUSION: We have demonstrated that incorporating the proposed metric learning into sinogram denoising introduces awareness of reconstruction goal and improves final quantitative performance. The proposed approach is compatible with a wide range of denoiser network structures and reconstruction modules, to suit customized need or further improve performance.

Asymmetric magnetic nanosnowman loaded with AgPd nanocage toward NIR-enhanced catalytic activity.

Although bimetallic noble nanostructures often possess high activity in nanocatalysis, their controllable fabrication, tunable catalytic activity, and easy separation remain significant challenges. In this study, an Fe3O4@AgPd/Polydopamine (Fe3O4@AgPd/PDA) nanosnowman loaded with an AgPd nanocage was designed by a one-step template-disposition-redox polymerization method. The AgPd nanocage endowed the product with high catalytic activity for the reduction of organic pollutants (4-NP, MO, MB). Interestingly, under near-infrared (NIR) light, the catalytic kinetics of the Fe3O4@AgPd/PDA nanosnowman on catalytic reduction of organic pollutants increased by 2.6, 1.57, and 5.45 times, respectively. The asymmetric nanostructure facilitated the separation of electron-hole pairs, promoted electron transfer, and accelerated the catalytic activity. Density functional theory (DFT) analysis indicated that the electron transfer between the AgPd alloy and the Fe3O4 nanosphere played a critical role on the high catalytic activity. Moreover, Fe3O4@AgPd/PDA also demonstrated excellent catalytic activity in the Heck carbon-carbon coupling reaction with a >95% conversion rate and >99% selectivity. Owing to the well-encapsulated PDA shell and outstanding magnetic properties, the Fe3O4@AgPd/PDA nanosnowman exhibited good cyclic catalytic activity. With its multi-mode catalysis, NIR-enhanced catalytic activity, and easy separation, the Fe3O4@AgPd/PDA nanosnowman exhibits great application potential in nanocatalysis.

Hierarchical deep learning for autonomous multi-label arrhythmia detection and classification on real-world wearable electrocardiogram data.

Objective: Arrhythmia detection and classification are challenging because of the imbalanced ratio of normal heartbeats to arrhythmia heartbeats and the complicated combinations of arrhythmia types. Arrhythmia classification on wearable electrocardiogram monitoring devices poses a further unique challenge: unlike clinically used electrocardiogram monitoring devices, the environments in which wearable devices are deployed are drastically different from the carefully controlled clinical environment, leading to significantly more noise, thus making arrhythmia classification more difficult. Methods: We propose a novel hierarchical model based on CNN+BiLSTM with Attention to arrhythmia detection, consisting of a binary classification module between normal and arrhythmia heartbeats and a multi-label classification module for classifying arrhythmia events across combinations of beat and rhythm arrhythmia types. We evaluate our method on our proprietary dataset and compare it with various baselines, including CNN+BiGRU with Attention, ConViT, EfficientNet, and ResNet, as well as previous state-of-the-art frameworks. Results: Our model outperforms existing baselines on the proprietary dataset, resulting in an average accuracy, F1-score, and AUC score of 95%, 0.838, 0.906 for binary classification, and 88%, 0.736, 0.875 for multi-label classification. Conclusions: Our results validate the ability of our model to detect and classify real-world arrhythmia. Our framework could revolutionize arrhythmia diagnosis by reducing the burden on cardiologists, providing more personalized treatment, and achieving emergency intervention of patients by allowing real-time monitoring of arrhythmia occurrence.

Harnessing noncanonical trans-cleavage characteristics of Cas12 and Cas13a to enhance CRISPR-based diagnostics.

Cas12 and Cas13 are extensively utilized in molecular diagnostics for their trans-cleavage activities, yet their activation characteristics remain partially understood. Here, we conduct an in-depth investigation of Cas12a, Cas12f1, and Cas13a, uncovering the characteristics of their trans-DNase and trans-RNase activities with noncanonical activators. Our findings reveal that DNA can serve as a direct target for CRISPR-Cas13a, markedly increasing the detection sensitivity for single-base mismatches. Moreover, the trans-cleavage activities of Cas12a and Cas13a can be activated by diverse RNA:DNA and RNA:RNA duplexes, respectively, indicating that the presence of stem-loop structures in crRNAs is not essential for their activation. Notably, Cas12f1, unlike Cas12a, exhibits intrinsic RNase activity independently of activation. Leveraging these insights, we have improved the accuracy of a dual-gene target detection approach that employs the CRISPR-Cas12f1 and Cas13a systems. Our research advances the understanding of the noncanonical activation characteristics of Cas12 and Cas13a, contributing to the field of CRISPR-based diagnostics.

Effectiveness of ChatGPT 4.0 in Telemedicine-Based Management of Metastatic Prostate Carcinoma.

The recent rise in telemedicine, notably during the COVID-19 pandemic, highlights the potential of integrating artificial intelligence tools in healthcare. This study assessed the effectiveness of ChatGPT versus medical oncologists in the telemedicine-based management of metastatic prostate cancer. In this retrospective study, 102 patients who met inclusion criteria were analyzed to compare the competencies of ChatGPT and oncologists in telemedicine consultations. ChatGPT's role in pre-charting and determining the need for in-person consultations was evaluated. The primary outcome was the concordance between ChatGPT and oncologists in treatment decisions. Results showed a moderate concordance (Cohen's Kappa = 0.43, p < 0.001). The number of diagnoses made by both parties was not significantly different (median number of diagnoses: 5 vs. 5, p = 0.12). In conclusion, ChatGPT exhibited moderate agreement with oncologists in management via telemedicine, indicating the need for further research to explore its healthcare applications.