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.

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.

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.

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.

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.

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. PURPOSE: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. METHODS: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). RESULTS: The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. CONCLUSION: The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.

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.

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.

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.

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.

Adopting Duplex Sequencing™ Technology for Genetic Toxicity Testing: A Proof-of-Concept Mutagenesis Experiment with N-Ethyl-N-Nitrosourea (ENU)-Exposed Rats.

Duplex sequencing (DuplexSeq) is an error-corrected next-generation sequencing (ecNGS) method in which molecular barcodes informatically link PCR-copies back to their source DNA strands, enabling computational removal of errors by comparing grouped strand sequencing reads. The resulting background of less than one artifactual mutation per 10 7 nucleotides allows for direct detection of somatic mutations. TwinStrand Biosciences, Inc. has developed a DuplexSeq-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. HIGHLIGHTS: DuplexSeq is an ultra-accurate NGS technology that directly quantifies mutationsENU-dependent mutagenesis was detected 24 h post-exposure in proliferative tissuesMultiple tissues exhibited the canonical ENU mutation spectrum 7 d after exposureResults obtained with DuplexSeq were highly concordant between laboratoriesThe Rat-50 Mutagenesis Assay is promising for applications in genetic toxicology.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace.

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Efficacy and outcome of extensive intraoperative peritoneal lavage plus surgery vs surgery alone with advanced gastric cancer patients.

BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors. After resection, one of the major problems is its peritoneal dissemination and recurrence. Some free cancer cells may still exist after resection. In addition, the surgery itself may lead to the dissemination of tumor cells. Therefore, it is necessary to remove residual tumor cells. Recently, some researchers found that extensive intraoperative peritoneal lavage (EIPL) plus intraperitoneal chemotherapy can improve the prognosis of patients and eradicate peritoneal free cancer for GC patients. However, few studies explored the safety and long-term outcome of EIPL after curative gastrectomy. AIM: To evaluate the efficacy and long-term outcome of advanced GC patients treated with EIPL. METHODS: According to the inclusion and exclusion criteria, a total of 150 patients with advanced GC were enrolled in this study. The patients were randomly allocated to two groups. All patients received laparotomy. For the non-EIPL group, peritoneal lavage was washed using no more than 3 L of warm saline. In the EIPL group, patients received 10 L or more of saline (1 L at a time) before the closure of the abdomen. The surviving rate analysis was compared by the Kaplan-Meier method. The prognostic factors were carried out using the Cox appropriate hazard pattern. RESULTS: The basic information in the EIPL group and the non-EIPL group had no significant difference. The median follow-up time was 30 mo (range: 0-45 mo). The 1- and 3-year overall survival (OS) rates were 71.0% and 26.5%, respectively. The symptoms of ileus and abdominal abscess appeared more frequently in the non-EIPL group (P < 0.05). For the OS of patients, the EIPL, Borrmann classification, tumor size, N stage, T stage and vascular invasion were significant indicators. Then multivariate analysis revealed that EIPL, tumor size, vascular invasion, N stage and T stage were independent prognostic factors. The prognosis of the EIPL group was better than the non-EIPL group (P < 0.001). The 3-year survival rate of the EIPL group (38.4%) was higher than the non-EIPL group (21.7%). For the recurrence-free survival (RFS) of patients, the risk factor of RFS included EIPL, N stage, vascular invasion, type of surgery, tumor location, Borrmann classification, and tumor size. EIPL and tumor size were independent risk factors. The RFS curve of the EIPL group was better than the non-EIPL group (P = 0.004), and the recurrence rate of the EIPL group (24.7%) was lower than the non-EIPL group (46.4%). The overall recurrence rate and peritoneum recurrence rate in the EIPL group was lower than the non-EIPL group (P < 0.05). CONCLUSION: EIPL can reduce the possibility of perioperative complications including ileus and abdominal abscess. In addition, the overall survival curve and RFS curve were better in the EIPL group.

Technology Roadmap for Flexible Sensors.

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.