Ionic Pumping Effect at the Tailored Mesoporous Carbon Interface for an Extra-Stable Lithium Ion Battery at Low Temperatures.

Ion transportation at the interface significantly influences the electrochemical performance of the lithium ion battery, especially at high rates and low temperatures. Here, we develop a controlled self-assembly strategy for constructing a mesoporous carbon nanolayer with a uniform pore size and varied thicknesses on the two-dimensional monolayer MXene substrate. On the basis of the excellent electron conductivity of MXene, the mesoporous carbon layer is found with a voltage-driven ion accumulation effect, acting as an "ionic pump". The thicker mesoporous layer (∼2.28 nm) has the ability to accommodate a substantial quantity of ions, demonstrating enhanced ionic conductivity, remarkable cycling stability (192.8 mAh/g after 9400 cycles at 5.0 A/g), and outstanding rate capability at ambient and sub-zero temperatures (∼601 mAh/g at 0 °C and 0.05 A/g). This work provides valuable insights and guidance for the further development of high-performance electrode materials at high rates or low temperatures.

Simultaneous detection of 5-methylcytosine and 5-hydroxymethylcytosine at specific genomic loci by engineered deaminase-assisted sequencing.

Cytosine modifications, particularly 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), play crucial roles in numerous biological processes. Current analytical methods are often constrained to the separate detection of either 5mC or 5hmC, or the combination of both modifications. The ability to simultaneously detect C, 5mC, and 5hmC at the same genomic locations with precise stoichiometry is highly desirable. Herein, we introduce a method termed engineered deaminase-assisted sequencing (EDA-seq) for the simultaneous quantification of C, 5mC, and 5hmC at the same genomic sites. EDA-seq utilizes a specially engineered protein, derived from human APOBEC3A (A3A), known as eA3A-M5. eA3A-M5 exhibits distinct deamination capabilities for C, 5mC, and 5hmC. In EDA-seq, C undergoes complete deamination and is sequenced as T. 5mC is partially deaminated resulting in a mixed readout of T and C, and 5hmC remains undeaminated and is read as C. Consequently, the proportion of T readouts (P T) reflects the collective occurrences of C and 5mC, regulated by the deamination rate of 5mC (R 5mC). By determining R 5mC and P T values, we can deduce the precise levels of C, 5mC, and 5hmC at particular genomic locations. We successfully used EDA-seq to simultaneously measure C, 5mC, and 5hmC at specific loci within human lung cancer tissue and their normal counterpart. The results from EDA-seq demonstrated a strong concordance with those obtained from the combined application of BS-seq and ACE-seq methods. EDA-seq eliminates the need for bisulfite treatment, DNA oxidation or glycosylation and uniquely enables simultaneous quantification of C, 5mC and 5hmC at the same genomic locations.

Robust, stretchable bioelectronic interfaces for cardiac pacing enabled by interfacial transfer of laser-induced graphene via water-response, nonswellable PVA gels.

Implantable cardiac pacemakers are crucial therapeutic tools for managing various cardiac conditions. For effective pacing, electrodes should exhibit flexibility, deformability, biocompatibility, and high conductivity/capacitance. Laser-induced graphene (LIG) shows promise due to its exceptional electrical and electrochemical properties. However, the fragility of LIG and the non-stretchability of polyimide substrates pose challenges when interfacing with the beating heart. Here, we present a simple method for fabricating robust, flexible, and stretchable bioelectronic interfaces by transferring LIG via water-responsive, nonswellable polyvinyl alcohol (PVA) gels. PVA solution penetrates the porous structure of LIG and solidifies into PVA xerogel as the solvent evaporates. The robust PVA xerogel enables the smooth transfer of LIG and prevents stretching of the LIG network during this process, which helps maintain its conductivity. When hydrated, the xerogel becomes a stable, nonswellable hydrogel. This gives the LIG-PVA hydrogel (LIG-PVA-H) composites with excellent conductivity (119.7 ± 4.3Ω sq-1), high stretchability (up to 420%), reliability (cyclic stretch under 15% strain, with ∼ 1-time resistance increase), and good stability in phosphate buffered saline. The LIG-PVA-H composites were used as biointerfaces for electrocardiogram signal recording and electrical pacing on rat hearts ex vivo and in vivo, using commercial setups and a custom-built implantable wireless device. This work expands the application of LIG in bioelectronic interfaces and facilitates the development of electrotherapy for cardiac diseases.

Simulating multiple variability in spatially resolved transcriptomics with scCube.

A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

Nongenetic Precise Neuromodulation and Spatiotemporal Neuroprotection for Epilepsy Therapy via Rationally Designed Multifunctional Nanotransducer.

The precise modulation of electrical activity in specific neuronal populations is paramount for rectifying abnormal neurological functions and is a critical element in the therapeutic arsenal for neurological disorders. However, achieving a balance between minimal invasiveness and robust neuroprotection poses a considerable challenge. Herein, we present a nanoneuromodulation strategy integrating neuroprotective features to effectively address epilepsy with minimal invasiveness and enable wireless functionality. Strategically engineered nanotransducer, adorned with platinum (Pt) decoration with titanium disulfide (TiS2) (TiS2/Pt), enables precise modulation of neuronal electrical activity in vitro and in vivo, ensuring exceptional temporal fidelity under millisecond-precision near-infrared (NIR) light pulses irradiation. Concurrently, TiS2/Pt showcase a pronounced enhancement in enzyme-mimicking activity, offering a robust defense against oxidative neurological injury in vitro. Nanotransducer-enabled wireless neuromodulation with biocatalytic neuroprotective capacity is highly effective in alleviating epileptic high-frequency neural activity and diminishing oxidative stress levels, thereby restoring redox equilibrium. This integrated therapeutic approach reduces the severity of epilepsy, demonstrating minimal invasiveness and obviating the requirements for genetic manipulation and optical fiber implantation, while providing an alternative avenue for neurological disorder treatment.

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.

PURPOSE: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. METHODS: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. RESULTS: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. CONCLUSION: This study aimed to improve clinicians' ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (Dmap) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the Dmap, T1-CE, and planning target volume (PTV) segmentation (PTVseg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in Dmap. A spherical projection was implemented to transform planar image content onto a spherical surface using multiple projection centers, and the resultant T1-CE/Dmap/PTVseg projections were stacked as a 3-channel variable. Four Visual Geometry Group (VGG-19) deep encoders were used in an ensemble design, with each submodel using a different spherical projection formula as input for BM outcome prediction. In each submodel, clinical features after positional encoding were fused with VGG-19 deep features to generate logit results. The ensemble's outcome was synthesized from the 4 submodel results via logistic regression. In total, 10 model versions with random validation sample assignments were trained to study model robustness. Performance was compared with (1) a single VGG-19 encoder, (2) an ensemble with a T1-CE MRI as the sole image input after projections, and (3) an ensemble with the same image input design without clinical feature inclusion. RESULTS: The ensemble model achieved an excellent area under the receiver operating characteristic curve (AUCROC: 0.89 ± 0.02) with high sensitivity (0.82 ± 0.05), specificity (0.84 ± 0.11), and accuracy (0.84 ± 0.08) results. This outperformed the MRI-only VGG-19 encoder (sensitivity: 0.35 ± 0.01, AUCROC: 0.64 ± 0.08), the MRI-only deep ensemble (sensitivity: 0.60 ± 0.09, AUCROC: 0.68 ± 0.06), and the 3-channel ensemble without clinical feature fusion (sensitivity: 0.78 ± 0.08, AUCROC: 0.84 ± 0.03). CONCLUSIONS: Facilitated by the spherical image projection method, a deep ensemble model incorporating Dmap and clinical variables demonstrated excellent performance in predicting BM post-SRS local failure. Our novel approach could improve other radiation therapy outcome models and warrants further evaluation.

Automated and ultrasensitive point-of-care glycoprotein detection using boronate-affinity enhanced organic electrochemical transistor patch.

Quantifying trace glycoproteins in biofluids requires ultrasensitive components, but feedback is not available in the current portable platforms of point-of-care (POC) diagnosis technologies. A compact and ultrasensitive bioelectrochemical patch was based on boronate-affinity amplified organic electrochemical transistors (BAAOECTs) for POC use was developed to overcome this dilemma. Benefit from the cascading signal enhancement deriving from boronate-affinity targeting multiple regions of glycoprotein and OECTs' inherent signal amplification capability, the BAAOECTs achieved a detection limit of 300 aM within 25 min, displaying about 3 orders of magnitude improvement in sensitivity compared with the commercial electrochemical luminescence (ECL) kit. By using a microfluidic chip, a microcontroller module, and a wireless sensing system, the testing workflows of the above patch was automated, allowing for running the sample-to-answer pipeline even in a resource-limited environment. The reliability of such portable biosensing platform is well recognized in clinical diagnostic applications of heart failure. Overall, the remarkable enhanced sensitivity and automated workflow of BAAOECTs biosensing platform provide a prospective and generalized design policy for expanding the POC diagnosis capabilities of glycoproteins.

Injectable Mesh-Like Conductive Hydrogel Patch for Elimination of Atrial Fibrillation.

Irregular electrical impulses in atrium are the leading cause of atrial fibrillation (AF), resulting in fatal arrhythmia and sudden cardiac death. Traditional medication and physical therapies are widely used, but generally suffer problems in serious physical damage and high surgical risks. Flexible and soft implants have great potential to be a novel approach for heart diseases therapy. A conductive hydrogel-based mesh cardiac patch is developed for application in AF elimination. The designed mesh patch with rhombic-shaped structure exhibits excellent flexibility, surface conformability, and deformation compliance, making it fit well with heart surface and accommodate to the deformation during heart beating. Moreover, the mechanical elastic and shape-memory properties of the mesh patch enable a minimally invasive injection of the patch into living animals. The mesh patch is implanted on the atrium surface for one month, indicating good biocompatibility and stability. Furthermore, the conductive patch can effectively eliminate AF owing to the conductivity and high charge storage capability (CSC) of the hydrogel. The proposed scheme of cardiac bioelectric signal modulation using conductive hydrogel brings new possibility for the treatment of arrhythmia diseases.

Independent Monte Carlo dose calculation identifies single isocenter multi-target radiosurgery targets most likely to fail pre-treatment measurement.

PURPOSE: For individual targets of single isocenter multi-target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre-treatment Quality Assurance (QA) measurement. METHODS: Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with > 5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy. RESULTS: Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2 cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1 mm / 10% threshold gamma pass rate (GPR) = 99.4 ± 1.9% vs. 93.1 ± 13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1 mm / 10% threshold) exceeding 90% (71.5% ± 16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5% ± 3.3%, p = 0.01). CONCLUSIONS: Independent MC complements pre-treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre-treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre-treatment QA measurement.

The Future of Molecular Studies through the Lens of Large Language Models.

The rapid advancement of large language models is reshaping research across various fields, offering a novel approach to the complex realm of molecular studies. Our evaluation of GPT-4 and GPT-3.5, focusing on their performance in generating and optimizing molecular structures, highlights GPT-4's strengths in certain aspects of molecular optimization. However, it also revealed challenges in accurately creating complex molecules. Addressing these issues, we propose possible directions for future molecular science research. These suggestions aim to forge new paths for exploring the intricacies of molecular structures, potentially bringing new efficiencies and innovations in the field.

Black Phosphorus Flake-Enabled Wireless Neuromodulation for Epilepsy Treatment.

Epilepsy is a prevalent and severe neurological disorder and generally requires prolonged electrode implantation and tether brain stimulation in refractory cases. However, implants may cause potential chronic immune inflammation and permanent tissue damage due to material property mismatches with soft brain tissue. Here, we demonstrated a nanomaterial-enabled near-infrared (NIR) neuromodulation approach to provide nongenetic and nonimplantable therapeutic benefits in epilepsy mouse models. Our study showed that crystal-exfoliated photothermal black phosphorus (BP) flakes could enhance neural activity by altering the membrane capacitive currents in hippocampus neurons through NIR photothermal neuromodulation. Optical stimulation facilitated by BP flakes in hippocampal slices evoked action potentials with a high spatiotemporal resolution. Furthermore, BP flake-enabled NIR neuromodulation of hippocampus neural circuits can suppress epileptic signals in epilepsy model mice with minimal invasiveness and high biocompatibility. Consequently, nanomaterial-enabled NIR neuromodulation may open up opportunities for nonimplantable optical therapy of epilepsy in nontransgenic organisms.

Cytotoxic depsidones and xanthones from Garcinia esculenta Y. H. Li.

Six new compounds, including two depsidones garciculendepsidones A and B (1 and 2), one prenylated xanthone garciculenxanthone (3) and three dimeric xanthones bigarciculenxanthones A-C (4-6), were isolated from the twigs and leaves of Garcinia esculenta Y. H. Li. Their structures were elucidated based on comprehensive analyses of spectral data, including HRESIMS, 1D and 2D NMR, and ECD calculation. All the isolates were tested for their cytotoxicity against five human cancer cell lines (myeloid leukemia HL-60, lung cancer A-549 cells, hepatocellular carcinoma SMMC-7721, breast cancer MDA-MB-231 and colon cancer SW480), among them, compounds 3-5 displayed cytotoxic potential, especially garciculenxanthone (3) had the lowest IC50 value of 8.2 µm for lung cancer A-549 cells.

Effects of sevoflurane on left ventricular function by speckle-tracking echocardiography in coronary bypass patients: A randomized trial.

The present study aimed to dynamically observe the segmental and global myocardial movements of the left ventricle during coronary artery bypass grafting by transesophageal speckle-tracking echocardiography, and to assess the effect of sevoflurane on cardiac function. Sixty-four patients scheduled for the off-pump coronary artery bypass grafting were randomly divided into a sevoflurane-based anesthesia (AS) group and a propofol-based total intravenous anesthesia (AA) group. The AS group demonstrated a higher absolute value of left ventricular global longitudinal strain than that of the AA group at both T 1 (after harvesting all grafts and before coronary anastomosis) and T 2 (30 min after completing all coronary anastomoses) ( P < 0.05). Moreover, strain improvement in the segment with the highest preoperative strain was significantly reduced in the AS group, compared with the AA group at both T 1 and T 2 ( P < 0.01). The flow of the left internal mammary artery-left anterior descending artery graft was superior, and the postoperative concentration of troponin T decreased rapidly in the AS group, compared with the AA group ( P < 0.05). Compared with total intravenous anesthesia, sevoflurane resulted in a significantly higher global longitudinal strain, stroke volume, and cardiac output. Sevoflurane also led to an amelioration in the condition of the arterial graft. Furthermore, sevoflurane significantly reduced strain improvement in the segmental myocardium with a high preoperative strain value. The findings need to be replicated in larger studies.

Direct prediction of gas adsorption via spatial atom interaction learning.

Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials.

Adopting duplex sequencing technology for genetic toxicity testing: A proof-of-concept mutagenesis experiment with N-ethyl-N-nitrosourea (ENU)-exposed rats.

Duplex sequencing (DS) is an error-corrected next-generation sequencing method in which molecular barcodes informatically link PCR-copies back to their source DNA strands, enabling computational removal of errors in consensus sequences. The resulting background of less than one artifactual mutation per 107 nucleotides allows for direct detection of somatic mutations. TwinStrand Biosciences, Inc. has developed a DS-based mutagenesis assay to sample the rat genome, which can be applied to genetic toxicity testing. To evaluate this assay for early detection of mutagenesis, a time-course study was conducted using male Hsd:Sprague Dawley SD rats (3 per group) administered a single dose of 40 mg/kg N-ethyl-N-nitrosourea (ENU) via gavage, with mutation frequency (MF) and spectrum analyzed in stomach, bone marrow, blood, and liver tissues at 3 h, 24 h, 7 d, and 28 d post-exposure. Significant increases in MF were observed in ENU-exposed rats as early as 24 h for stomach (site of contact) and bone marrow (a highly proliferative tissue) and at 7 d for liver and blood. The canonical, mutational signature of ENU was established by 7 d post-exposure in all four tissues. Interlaboratory analysis of a subset of samples from different tissues and time points demonstrated remarkable reproducibility for both MF and spectrum. These results demonstrate that MF and spectrum can be evaluated successfully by directly sequencing targeted regions of DNA obtained from various tissues⁠, a considerable advancement compared to currently used in vivo gene mutation assays.

Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte.

BACKGROUND: Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa. METHODS: We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm. RESULTS: 197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573-0.853), specificity of 0.869 (95% CI 0.764-0.974), F1 of 0.699 (95% CI 0.557-0.841), and AUC of 0.864 (95% CI 0.794-0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved. CONCLUSION: Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

Clinical Efficacy of Laparoscopic Cholecystectomy via Cystic Plate Approach for Gallstone Patients with Chronic Cholecystitis.

Introduction: Chronic cholecystitis has evolved into one of the digestive system diseases that negatively affect the quality of life of patients. This study was conducted to explore the clinical efficacy of laparoscopic cholecystectomy via cystic plate approach for the treatment of gallstones with chronic cholecystitis. Materials and Methods: Totally 184 gallstone patients with chronic cholecystitis who underwent laparoscopic cholecystectomy in The First People's Hospital of Wuhu from January 2021 to October 2022 were randomly divided into a control group (n = 92) and an observation group (n = 92). In the observation group and control group, the gallbladder was removed using the cystic plate approach and traditional approach, respectively. Surgical indicators and complications of patients were compared. Serum levels of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) were measured by enzyme-linked immunosorbent assay. The quality of life of patients was assessed using the SF-36 scale. Results: The recovery time of gastrointestinal function, intraoperative blood loss, and postoperative drainage volume in the observation group were significantly lower than those in the control group (P < .05). At 24 hours after surgery, the serum levels of IL-6, TNF-α, and CRP in the observation group were much lower than those in the control group (P < .05). Three months after surgery, the observation group showed a much higher quality of life score than the control group (P < .05). Conclusion: Laparoscopic cholecystectomy via cystic plate approach can effectively treat chronic gallstones with chronic cholecystitis. It shortened the recovery time of gastrointestinal function, reduced postoperative inflammation, and improved the quality of life.

Error-corrected duplex sequencing enables direct detection and quantification of mutations in human TK6 cells with strong inter-laboratory consistency.

Error-corrected duplex sequencing (DS) enables direct quantification of low-frequency mutations and offers tremendous potential for chemical mutagenicity assessment. We investigated the utility of DS to quantify induced mutation frequency (MF) and spectrum in human lymphoblastoid TK6 cells exposed to a prototypical DNA alkylating agent, N-ethyl-N-nitrosourea (ENU). Furthermore, we explored appropriate experimental parameters for this application, and assessed inter-laboratory reproducibility. In two independent experiments in two laboratories, TK6 cells were exposed to ENU (25-200 µM) and DNA was sequenced 48, 72, and 96 h post-exposure. A DS mutagenicity panel targeting twenty 2.4-kb regions distributed across the genome was used to sample diverse, genome-representative sequence contexts. A significant increase in MF that was unaffected by time was observed in both laboratories. Concentration-response in the MF from the two laboratories was strongly positively correlated (r = 0.97). C:G>T:A, T:A>C:G, T:A>A:T, and T:A>G:C mutations increased in consistent, concentration-dependent manners in both laboratories, with high proportions of C:G>T:A at all time points. The consistent results across the three time points suggest that 48 h may be sufficient for mutation analysis post-exposure. The target sites responded similarly between the two laboratories and revealed a higher average MF in intergenic regions. These results, demonstrating remarkable reproducibility across time and laboratory for both MF and spectrum, support the high value of DS for characterizing chemical mutagenicity in both research and regulatory evaluation.

Duplex sequencing provides detailed characterization of mutation frequencies and spectra in the bone marrow of MutaMouse males exposed to procarbazine hydrochloride.

Mutagenicity testing is an essential component of health safety assessment. Duplex Sequencing (DS), an emerging high-accuracy DNA sequencing technology, may provide substantial advantages over conventional mutagenicity assays. DS could be used to eliminate reliance on standalone reporter assays and provide mechanistic information alongside mutation frequency (MF) data. However, the performance of DS must be thoroughly assessed before it can be routinely implemented for standard testing. We used DS to study spontaneous and procarbazine (PRC)-induced mutations in the bone marrow (BM) of MutaMouse males across a panel of 20 diverse genomic targets. Mice were exposed to 0, 6.25, 12.5, or 25 mg/kg-bw/day for 28 days by oral gavage and BM sampled 42 days post-exposure. Results were compared with those obtained using the conventional lacZ viral plaque assay on the same samples. DS detected significant increases in mutation frequencies and changes to mutation spectra at all PRC doses. Low intra-group variability within DS samples allowed for detection of increases at lower doses than the lacZ assay. While the lacZ assay initially yielded a higher fold-change in mutant frequency than DS, inclusion of clonal mutations in DS mutation frequencies reduced this discrepancy. Power analyses suggested that three animals per dose group and 500 million duplex base pairs per sample is sufficient to detect a 1.5-fold increase in mutations with > 80% power. Overall, we demonstrate several advantages of DS over classical mutagenicity assays and provide data to support efforts to identify optimal study designs for the application of DS as a regulatory test.