Comparison of Remimazolam and Propofol on Postoperative Delirium in Elderly Patients Undergoing Radical Resection of Colon Cancer: A Single-Center Prospective Randomized Controlled Study.

BACKGROUND We compared the effect of remimazolam and propofol intravenous anesthesia on postoperative delirium in elderly patients undergoing laparoscopic radical resection of colon cancer. MATERIAL AND METHODS One hundred patients undergoing elective radical operation of colon cancer under general anesthesia were divided into a remimazolam group (group R) and propofol group (group P) by a random number table method. During anesthesia induction and maintenance, group R was intravenously injected with remimazolam to exert sedation; however, in group P, propofol was injected instead of remimazolam. The occurrence of postoperative delirium was assessed with the Confusion Assessment Method for the Intensive Care Unit scale and postoperative pain was assessed with the visual analogue score (VAS). The primary outcome measures were the incidence and duration of delirium within 7 days following surgery. Secondary outcome measures included postoperative VAS scores, intraoperative anesthetic drug dosage, and adverse reactions, including nausea and vomiting, hypoxemia, and respiratory depression. RESULTS There was no significant difference in baseline data between the 2 groups (P>0.05). There was no statistically significant difference in the incidence and duration of postoperative delirium between the 2 groups (P>0.05). There were no significant differences in VAS scores, remifentanil consumption, and adverse reactions, including nausea and vomiting, hypoxemia, and respiratory depression between the 2 groups (P>0.05). CONCLUSIONS In elderly patients undergoing radical colon cancer surgery, remimazolam administration did not improve or aggravate the incidence and duration of delirium, compared with propofol.

MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.

AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides, MA-PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA-PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA-PEP are available at https://github.com/liangxiaodata/MA-PEP.

SCN-MLTPP: A Multi-Label Classifier for Predicting Therapeutic Properties of Peptides Using the Stacked Capsule Network.

Identifying the function of therapeutic peptides is an important issue in the development of novel drugs. To reduce the time and labor costs required to identify therapeutic peptides, computational methods are increasingly required. However, most of the existing peptide therapeutic function prediction models are used for predicting a single therapeutic function, ignoring the fact that a bioactive peptide might simultaneously consist of multi-activities. Furthermore, in the few existing multi-label classification models, the feature extraction procedures are still rough. We propose a multi-label framework, called SCN-MLTPP, with a stacked capsule network for predicting the therapeutic properties of peptides. Instead of using peptide sequence vectors alone, SCN-MLTPP extracts different view representation vectors from the therapeutic peptides and learns the contributions of different views to the properties of therapeutic peptides based on the dynamic routing mechanism. Benchmarking results show that as compared with existing multi-label predictors, SCN-MLTPP achieves better and more robust performance for different peptides. In addition, some visual analyses and case studies also demonstrate the model can reliably capture features from multi-view data and predict different peptides.

Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the seriousness of clinical outcomes of adverse reactions to drugs. GCAP has two tasks: one is to predict whether adverse reactions to drugs cause serious clinical outcomes, and the other is to infer the corresponding classes of serious clinical outcomes. Experimental results demonstrate that our method is a powerful and robust framework with high extendibility. GCAP can serve as a useful tool to successfully address the challenge of predicting the seriousness of clinical outcomes stemming from adverse reactions to drugs.

MSDRP: a deep learning model based on multisource data for predicting drug response.

MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. RESULTS: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model. AVAILABILITY AND IMPLEMENTATION: The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.

DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing.

Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines.

Boron contamination and its risk management in terrestrial and aquatic environmental settings.

Boron (B) is released to terrestrial and aquatic environments through both natural and anthropogenic sources. This review describes the current knowledge on B contamination in soil and aquatic environments in relation to its geogenic and anthropogenic sources, biogeochemistry, environmental and human health impacts, remediation approaches, and regulatory practices. The common naturally occurring sources of B include borosilicate minerals, volcanic eruptions, geothermal and groundwater streams, and marine water. Boron is extensively used to manufacture fiberglass, thermal-resistant borosilicate glass and porcelain, cleaning detergents, vitreous enamels, weedicides, fertilizers, and B-based steel for nuclear shields. Anthropogenic sources of B released into the environment include wastewater for irrigation, B fertilizer application, and waste from mining and processing industries. Boron is an essential element for plant nutrition and is taken up mainly as boric acid molecules. Although B deficiency in agricultural soils has been observed, B toxicity can inhibit plant growth in soils under arid and semiarid regions. High B intake by humans can be detrimental to the stomach, liver, kidneys and brain, and eventually results in death. Amelioration of soils and water sources enriched with B can be achieved by immobilization, leaching, adsorption, phytoremediation, reverse osmosis, and nanofiltration. The development of cost-effective technologies for B removal from B-rich irrigation water including electrodialysis and electrocoagulation techniques is likely to help control the predominant anthropogenic input of B to the soil. Future research initiatives for the sustainable remediation of B contamination using advanced technologies in soil and water environments are also recommended.

Scalable fabrication of self-assembled GeSn vertical nanowires for nanophotonic applications.

In this work, scalable fabrication of self-assembled GeSn vertical nanowires (NWs) based on rapid thermal annealing (RTA) and inductively coupled-plasma (ICP) dry etching was proposed. After thermal treatment of molecular-beam-epitaxy-grown GeSn, self-assembled Sn nanodots (NDs) were formed on surface and the spontaneous emission from GeSn direct band was enhanced by ∼5-fold. Employing the self-assembled Sn NDs as template, vertical GeSn NWs with a diameter of 25 ± 6 nm and a density of 2.8 × 109 cm-2 were obtained by Cl-based ICP dry etching technique. A prototype GeSn NW photodetector (PD) with rapid switching ability was demonstrated and the optoelectronic performance of Ge NW PD was systematically studied. The GeSn NW PD exhibited an ultralow dark current density of ∼33 nA/cm2 with a responsivity of 0.245 A/W and a high specific detectivity of 2.40 × 1012 cm Hz1/2 W-1 at 1550 nm under -1 V at 77 K. The results prove that this method is prospective for low-cost and scalable fabrication of GeSn NWs, which are promising for near infrared or short wavelength infrared nanophotonic devices.

GIFDTI: Prediction of Drug-Target Interactions Based on Global Molecular and Intermolecular Interaction Representation Learning.

Drug discovery and drug repurposing often rely on the successful prediction of drug-target interactions (DTIs). Recent advances have shown great promise in applying deep learning to drug-target interaction prediction. One challenge in building deep learning-based models is to adequately represent drugs and proteins that encompass the fundamental local chemical environments and long-distance information among amino acids of proteins (or atoms of drugs). Another challenge is to efficiently model the intermolecular interactions between drugs and proteins, which plays vital roles in the DTIs. To this end, we propose a novel model, GIFDTI, which consists of three key components: the sequence feature extractor (CNNFormer), the global molecular feature extractor (GF), and the intermolecular interaction modeling module (IIF). Specifically, CNNFormer incorporates CNN and Transformer to capture the local patterns and encode the long-distance relationship among tokens (atoms or amino acids) in a sequence. Then, GF and IIF extract the global molecular features and the intermolecular interaction features, respectively. We evaluate GIFDTI on six realistic evaluation strategies and the results show it improves DTI prediction performance compared to state-of-the-art methods. Moreover, case studies confirm that our model can be a useful tool to accurately yield low-cost DTIs. The codes of GIFDTI are available at https://github.com/zhaoqichang/GIFDTI.

RNPredATC: A Deep Residual Learning-Based Model With Applications to the Prediction of Drug-ATC Code Association.

The Anatomical Therapeutic Chemical (ATC) classification system, designated by the World Health Organization Collaborating Center (WHOCC), has been widely used in drug screening, repositioning, and similarity research. The ATC classification system assigns different codes to drugs according to the organ or system on which they act and/or their therapeutic and chemical characteristics. Correctly identifying the potential ATC codes for drugs can accelerate drug development and reduce the cost of experiments. Several classifiers have been proposed in this regard. However, they lack of ability to learn basic features from sparsely known drug-ATC code associations. Therefore, there is an urgent need for novel computational methods to precisely predict potential drug-ATC code associations in multiple levels of the ATC classification system based on known associations between drugs and ATC codes. In this paper, we provide a novel end-to-end model, so-called RNPredATC, to predict potential drug-ATC code associations in five ATC classification levels. RNPredATC can extract dense feature vectors from sparsely known drug-ATC code associations and reduce the impact from the degradation problem by a novel deep residual learning. We extensively compare our method with some state-of-the-art methods, including NetPredATC, SPACE, and some multi-label-based methods. Our experimental results show that RNPredATC achieves better performances in five-fold and ten-fold cross validations. Furthermore, the visualization analysis of hidden layers and case studies of predicted associations at the fifth ATC classification level confirm that RNPredATC can effectively identify the potential ATC codes of drugs.

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method.

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schrödinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and strong generalization ability with high accuracy, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be generalized accurately during the predicting process. The results represent a remarkable improvement in ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.

Perioperative pectoral nerve block type II and postoperative recurrence in breast cancer: a randomized controlled trial.

BACKGROUND: A new technique for analgesia called pectoral nerve block is widely used in surgeries of breast cancer. Pectoral nerve block type II (Pecs II) block has less influence on immunity when compared with general anesthesia method. The purpose of this research is to demonstrate whether Pecs II block has influence on the recurrence of breast cancer after surgical operation. METHODS: 526 breast cancer patients were recruited in this research and randomized into general anesthesia group and general anesthesia with Pecs II block group. Recurrence-free survival (RFS), distant recurrence-free survival (DRFS), and overall survival (OS) were evaluated for the two groups. RESULTS: Based on the statistical data, only the consumption of remifentanil was dramatically reduced by the performance of Pecs II block when compared with general anesthesia method. The performance of Pecs II block had no significant influence on OS, RFS, and DRFS of breast cancer patients after surgery. ASA physical status III, TNM stage 2 + 3, and mastectomy were proved to have association with lower recurrence-free survival. CONCLUSION: In conclusion, the performance of Pecs II block declined the remifentanil consumption during surgery of breast cancer. Meanwhile, the performance of Pecs II block had no significant influence on the OS, RFS, and DRFS of breast cancer patients after surgical resection.

Focal U-Net: A Focal Self-attention based U-Net for Breast Lesion Segmentation in Ultrasound Images.

Accurate breast lesion segmentation in ultrasound images helps radiologists to make exact diagnoses and treatments, which is important to increase the survival rate of breast cancer patients. Recently, deep learning-based methods have demonstrated remarkable results in breast lesion segmentation. However, the blurry breast lesion boundaries and noise artifacts in ultrasound images still limit the performance of the deep learning-based methods. In this paper, we propose a novel segmentation network equipped with a focal self-attention block for improving the performance of breast lesion segmentation. The focal self-attention block can incorporate fine-grained local and coarse-grained global information. The fine-grained local information is useful to enhance features of breast lesion boundaries, while the coarse-grained global information effectively reduces noise interference. To verify the performance of our network, we implement breast lesion segmentation on our collected dataset of 9836 ultrasound images. The results demonstrate that the focal self-attention block enhances features of breast lesion boundaries and improves the accuracy of breast lesion segmentation.

SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry.

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks.

As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Effect of Perioperative Dexmedetomidine Infusion on Postoperative Delirium in Elderly Patients Undergoing Oral and Maxillofacial Surgery: A Randomized Controlled Clinical Trial.

Purpose: The aim of the study was to determine whether perioperative dexmedetomidine administration can improve postoperative delirium in elderly patients undergoing oral and maxillofacial surgery. Patients and Methods: This was a prospective double-blind randomized controlled clinical trial conducted in Cangzhou Central Hospital from December 2021 to March 2022. Patients aged 65 and older underwent oral and maxillofacial surgery under general anesthesia. Eligible patients were randomly assigned to dexmedetomidine or control group. Dexmedetomidine was injected intravenously from 10 min before induction of anesthesia to 30 min before the end of surgery in dexmedetomidine group, while patients in the control group were given normal saline at the same rate during the same time period. The primary measurement indicators were the incidence and duration of delirium in the first five days after surgery. The secondary measurement indicators were Visual Analogue Score (VAS) for the first 24 hours following surgery, subjective sleep quality score within 24 hours postoperatively and intraoperative adverse reactions. Results: One hundred and twenty patients were randomly assigned. Baseline characteristics were similar between two groups. The incidence and duration of postoperative delirium did not differ statistically between two groups (all P > 0.05). Compared with control group, VAS scores in dexmedetomidine group were significantly lower at 6, 12, and 24 hours after surgery (all P < 0.05); moreover, Richards-Campbell Sleep Questionnaire (RCSQ) results were significantly improved 1 day after surgery in dexmedetomidine group (P < 0.05). Dexmedetomidine-related adverse reactions were similar in both groups (P > 0.05). Conclusion: Intravenous infusion of dexmedetomidine 10 min before induction of anesthesia to half an hour before the end of surgery did not improve postoperative delirium in elderly patients undergoing oral and maxillofacial surgery; however, dexmedetomidine may be associated with decreased postoperative pain and improved postoperative sleep quality.

Blockade of integrin signaling reduces chemotherapy-induced premature senescence in collagen cultured bladder cancer cells.

Background: Diminished sensitivity towards chemotherapy remains the major impediment to the clinical treatment of bladder cancer. However, the critical elements in control of chemotherapy resistance remain obscure. Methods: We adopted improved collagen gels and performed cytotoxicity analysis of doxorubicin (DOX) and mitomycin C (MMC) of bladder cancer cells in a 3D culture system. We then detected the expression of multidrug resistant gene ABCB1, dormancy-associated functional protein chicken ovalbumin upstream-transcription factor 1 (COUPTF1), cell proliferation marker Ki-67, and cellular senescence marker senescence-associated ß-galactosidase (SA-ß-Gal) in these cells. We further tested the effects of integrin blockade or protein kinase B (AKT) inhibitor on the senescent state of bladder cancer. Also, we examined the tumor growth and survival time of bladder cancer mouse models given the combination treatment of chemotherapeutic agents and integrin α2ß1 ligand peptide TFA (TFA). Results: Collagen gels played a repressive role in bladder cancer cell apoptosis induced by DOX and MMC. In mechanism, collagen activated the integrin ß1/AKT cascade to drive bladder cancer cells into a premature senescence state via the p21/p53 pathway, thus attenuating chemotherapy-induced apoptosis. In addition, TFA had the ability to mediate the switch from senescence to apoptosis of bladder cancer cells in xenograft mice. Meanwhile, TFA combined with chemotherapeutic drugs produced a substantial suppression of tumor growth as well as an extension of survival time in vivo. Conclusions: Based on our finding that integrin ß1/AKT acted primarily to impart premature senescence to bladder cancer cells cultured in collagen gel, we suggest that integrin ß1 might be a feasible target for bladder cancer eradication.

Mobilization of contaminants: Potential for soil remediation and unintended consequences.

Land treatment has become an essential waste management practice. Therefore, soil becomes a major source of contaminants including organic chemicals and potentially toxic elements (PTEs) which enter the food chain, primarily through leaching to potable water sources, plant uptake, and animal transfer. A range of soil amendments are used to manage the mobility of contaminants and subsequently their bioavailability. Various soil amendments, like desorbing agents, surfactants, and chelating agents, have been applied to increase contaminant mobility and bioavailability. These mobilizing agents are applied to increase the contaminant removal though phytoremediation, bioremediation, and soil washing. However, possible leaching of the mobilized pollutants during soil washing is a major limitation, particularly when there is no active plant uptake. This leads to groundwater contamination and toxicity to plants and soil biota. In this context, the present review provides an overview on various soil amendments used to enhance the bioavailability and mobility of organic and inorganic contaminants, thereby facilitating increased risk when soil is remediated in polluted areas. The unintended consequences of the mobilization methods, when used to remediate polluted sites, are discussed in relation to the leaching of mobilized contaminants when active plant growth is absent. The toxicity of targeted and non-targeted contaminants to microbial communities and higher plants is also discussed. Finally, this review work summarizes the existing research gaps in various contaminant mobilization approaches, and prospects for future research.

Drug repositioning based on multi-view learning with matrix completion.

Determining drug indications is a critical part of the drug development process. However, traditional drug discovery is expensive and time-consuming. Drug repositioning aims to find potential indications for existing drugs, which is considered as an important alternative to the traditional drug discovery. In this article, we propose a multi-view learning with matrix completion (MLMC) method to predict the potential associations between drugs and diseases. Specifically, MLMC first learns the comprehensive similarity matrices from five drug similarity matrices and two disease similarity matrices based on the multi-view learning (ML) with Laplacian graph regularization, and updates the drug-disease association matrix simultaneously. Then, we introduce matrix completion (MC) to add some positive entries in original association matrix based on low-rank structure, and re-execute the multi-view learning algorithm for association prediction. At last, the prediction results of the above two operations are integrated as the final output. Evaluated by 10-fold cross-validation and de novo tests, MLMC achieves higher prediction accuracy than the current state-of-the-art methods. Moreover, case studies confirm the ability of our method in novel drug-disease association discovery. The codes of MLMC are available at https://github.com/BioinformaticsCSU/MLMC. Contact: jxwang@mail.csu.edu.cn.

Effects of Abdominal Wall Blocks on Postoperative Delirium in Elderly Patients Undergoing Laparoscopic Surgery: A Randomized Controlled Study.

BACKGROUND Postoperative delirium (POD) seriously affects the rapid postoperative recovery of elderly patients. We investigated the effect of abdominal wall blocks on POD in elderly patients undergoing laparoscopic radical resection of colon cancer and underlying mechanisms. MATERIAL AND METHODS A total of 100 patients undergoing laparoscopic radical resection of colon cancer were randomly assigned to group C (control) and group R (regional nerve blocks). In group R, 20 mL of local anesthesia-mixed solution was injected into the bilateral transverse abdominis muscle plane and 10 mL was injected into the bilateral posterior sheath of the rectus abdominis muscle. In group C, the same amount of saline was used for nerve block. The consumption of propofol and remifentanil during surgery was recorded. Levels of serum interleukin (IL)-6 and highly sensitive C-reactive protein (hs-CRP) during surgery were evaluated. The Confusion Assessment Method for the Intensive Care Unit Scale and the Richmond Agitation-Sedation Scale were adopted to evaluate POD. RESULTS The incidence of POD was lower in group R than in group C (P=0.048). The consumption of propofol and remifentanil was significantly reduced in group R, compared with group C (P<0.05). Compared with T0, serum IL-6 and hs-CRP levels in both groups were significantly increased at T1 and T2 (P<0.05). Moreover, serum IL-6 and hs-CRP were lower at T1 and T2 in group R compared with group C (P<0.05). CONCLUSIONS Abdominal wall blocks may alleviate POD in elderly patients undergoing laparoscopic surgery, which may be related to the reduction of anesthetic consumption and inflammatory response.