Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies

Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.

Impact of extended nursing model after multi-disciplinary treatment on young patient with post-stroke

BACKGROUND Stroke has become one of the most serious life-threatening diseases due to its high morbidity,disability,recurrence and mortality rates.AIM To explore the intervention effect of multi-disciplinary treatment(MDT)extended nursing model on negative emotions and quality of life of young patients with post-stroke.METHODS A total of 60 young stroke patients who were hospitalized in the neurology department of our hospital from January 2020 to December 2021 were selected and randomly divided into a control group and an experimental group,with 30 patients in each group.The control group used the conventional care model and the experimental group used the MDT extended nursing model.After the inhospital and 3-mo post-discharge interventions,the differences in negative emotions and quality of life scores between the two groups were evaluated and analyzed at the time of admission,at the time of discharge and after discharge,respectively.RESULTS There are no statistically significant differences in the negative emotions scores between the two groups at admission,while there are statistically significant differences in the negative emotions scores within each group at admission and discharge,at discharge and post-discharge,and at discharge and post-discharge.In addition,the negative emotions scores were all statistically significant at discharge and after discharge when compared between the two groups.There was no statistically significant difference in quality of life scores at the time of admission between the two groups,and the difference between quality of life scores at the time of admission and discharge,at the time of discharge and post-discharge,and at the time of admission and post-discharge for each group of patients was statistically significant.CONCLUSION The MDT extended nursing mode can improve the negative emotion of patients and improve their quality of life.Therefore,it can be applied in future clinical practice and is worthy of promotion.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical,genetic,environmental,and lifestyle factors to optimize medication management.This study investigates how artificial intelligence(AI)and machine learning(ML)can address key challenges in integrating pharmacogenomics(PGx)into psychiatric care.In this integration,AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions.AI-driven models integrating genomic,clinical,and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder.This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry,highlighting the importance of ethical considerations and the need for personalized treatment.Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care.Future research should focus on developing enhanced AI-driven predictive models,privacy-preserving data exchange,and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

A Novel Treatment Optimization System and Top Gene Identification via Machine Learning with Application on Breast Cancer

Traditional treatment selection of cancers mainly relies on clinical observations and doctor’s judgment, but most outcomes can hardly be predicted. Through Genomics Topology, we use 272 breast cancer patients’ clinical and gene information as an example to propose a treatment optimization and top gene identification system. This study faces certain challenges such as collinearity and the Curse of Dimensionality within data, so by the idea of Analysis of Variance (ANOVA), Principal Component Analysis (PCA) is implemented to resolve this issue. Several genes, for example, SLC40A1 and ACADSB, are found to be both statistically significant and biological-studies supported;the model developed can precisely predict breast cancer mortality, recurrence time, and survival time, with an average MSE of 3.697, accuracy rate of 88.97%, and F1 score of 0.911. The result and methodology used in this study provide a channel for people to further look into the more precise prediction of other cancer outcomes through machine learning and assist in the discovery of targetable pathways for next-generation cancer treatment methods.

Multi-disciplinary treatment for hepatocellular carcinoma in primary hospitals in China during the COVID-19 epidemic

Hepatocellular carcinoma(HCC)is a common malignant tumor in the Chinese population.Due to its high degree of malignancy,rapid progression,and poor prognosis,it mainly requires multi-disciplinary treatment(MDT)in the clinic.In December 2019,COVID-19,a novel coronavirus pneumonia,broke out in Wuhan,China.It has rapidly spread across the country,with various places launching a level I response to major public health emergencies and traffic being restricted.Most patients with HCC were only able to attend primary hospitals,while the MDT model for HCC in provincial hospitals was restricted.Therefore,it was a huge task for clinicians in primary hospitals to ensure MDT was given to patients with HCC during the level I response to major public health emergencies.How to formulate a reasonable MDT mode for patients with HCC according to local conditions was worthy of consideration by hepatobiliary surgeons in primary hospitals.

Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment

The potential for reducing greenhouse gas(GHG)emissions and energy consumption in wastewater treatment can be realized through intelligent control,with machine learning(ML)and multimodality emerging as a promising solution.Here,we introduce an ML technique based on multimodal strategies,focusing specifically on intelligent aeration control in wastewater treatment plants(WWTPs).The generalization of the multimodal strategy is demonstrated on eight ML models.The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control,exhibiting exceptional performance and interpretability.Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models,with a mean absolute percentage error of 4.4%and a coefficient of determination of 0.948.Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8%compared to traditional fuzzy control methods.The potential application of these strategies in critical water science domains is discussed.To foster accessibility and promote widespread adoption,the multimodal ML models are freely available on GitHub,thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

CBL教学法联合MDT翻转课堂在乳腺癌超声教学中的应用

基于案例教学法(case-based learning,CBL)联合多学科联合会诊(multi-disciplinary treatment,MDT)翻转课堂是“以学生为中心”的全新教学模式,改变了过去“以教师为中心”的传统教学模式。乳腺超声是一门实践性强的学科,这种全新的教学理念和教学模式,让学生进一步了解乳腺癌的诊疗模式,有助于拓展学生思维,提高自主学习能力;加强学生之间的交流合作,培养团队协作能力;培养学生学习兴趣,提高综合分析病例的能力。在今后的教学中,基于CBL教学法联合MDT翻转课堂在乳腺癌的超声教学中将被不断推广应用,进一步推动不同学科之间的融合式教学,为传统课堂教学变革带来新的曙光。

基于深度学习神经网络技术的脊柱椎弓根螺钉自动规划研究

目的:针对骨科手术机器人螺钉手工规划效率低下的问题,实现基于CT的脊柱椎弓根螺钉自动、高效、高质量规划。方法:采用深度学习神经网络对标注分割和螺钉的CT图像进行有监督的机器学习,实现脊柱椎弓根螺钉的自动规划;本实验使用44例腰椎CT共440枚螺钉作为训练集,使用11例CT生成110枚螺钉作为测试集,以手工规划作为对照组,通过盲法专家评价评估螺钉规划效果,并通过记录规划时间评估规划效率。结果:该自动规划方法生成的螺钉规划临床可用率为95.4%,自动规划时间与平均手工规划时间分别为68.8 s和177.6 s。结论:该自动规划方法可初步实现高效、高质量的脊柱椎弓根螺钉自动规划,但仍需临床医生监督复核。

Mini-CEX联合MDT在急诊科住院医师规范化培训中的应用效果

目的探讨迷你临床演练评估(mini-clinical evaluation exercise,Mini-CEX)联合多学科综合治疗协作组(multidisciplinary team,MDT)在急诊科住院医师规范化培训中的运用效果。方法选择于医院急诊科进行规范化培训的住院医师,2022年6—8月进行规范化培训者为传统培训组,2022年9—11月进行规范化培训者为Mini-CEX联合MDT组。传统培训组35名,给予常规教学方式教学,Mini-CEX联合MDT组35名,给予Mini-CEX联合MDT教学,教学完成后评估规范化培训住院医师的理论成绩及实际操作技能,教学前、后通过Mini-CEX考核规范化培训住院医师的问诊能力、临床诊断、操作能力、查体能力、治疗方案、沟通能力,给予评判性思维能力量表-中文版(critical thinking disposition inventory—Chinese version,CTDI-CV)、学生自主学习能力评价量表评价,比较2组教学满意度。结果Mini-CEX联合MDT组理论知识、实际操作技能评分较传统培训组高,差异有统计学意义(P<0.001);Mini-CEX联合MDT组问诊能力、临床诊断、操作能力、查体能力、治疗方案、沟通能力评分较传统培训组高,差异有统计学意义(P<0.001);Mini-CEX联合MDT组CTDI-CV评分较传统培训组低,自主学习能力评分、教学满意度较传统培训组高,差异有统计学意义(P<0.001)。Mini-CEX联合MDT组教学满意度(97.14%)较传统培训组高(85.71%),差异有统计学意义(P<0.05)。结论在急诊科住院医师规范化培训中运用Mini-CEX联合MDT教学,可提升规范化培训住院医师理论及实践操作成绩,提升其综合临床能力、评判性思维能力、自学能力及教学满意度。

智慧水厂能耗监测评价与异常诊断管理平台研究

为提升污水处理厂水处理过程的运行能效,加速推进智慧水厂向节能化、数字化方向转型,设计了一种面向污水处理厂的能耗监测评价与异常诊断管理平台。平台基于建筑信息模型和能耗管理系统,通过采集污水处理厂运行数据与气象数据等信息,建立基于机器学习算法的动态监督能耗预测模型,实现厂区运行状况实时监测与评价。运维管理人员可依据平台生成的状态评价指数,进行厂区能源结构调整与异常用能识别与诊断。最后,以实际案例数据验证了平台各功能模块的可行性与有效性。分析结果表明,平台预警模块可以监测反馈96%的异常用能情况。

基于深度学习的蔬菜田精准除草作业区域检测方法

【目的】蔬菜生长随机,杂草种类众多。传统杂草识别算法复杂,且仅识别出杂草,未能精准确定除草作业区域。本研究以蔬菜及其伴生杂草为研究对象,拟探索一种基于深度学习的杂草识别与精准除草作业区域检测方法。【方法】通过将原图切分网格图像,利用深度学习模型识别蔬菜、杂草及土壤,将包含杂草的网格图像标记为除草作业区域。选取ShuffleNet、DenseNet和ResNet模型开展识别试验,并采用精度、召回率、F_(1)值和总体准确率、平均准确率分别对验证集和测试集进行评价分析。【结果】所选的3种网络模型均能较好地识别杂草和蔬菜,其中ShuffleNet为杂草识别最优模型,其对杂草的识别具有较为均衡的精度和召回率,分别为95.5%、97%,且其识别速度也达最优,为68.37 fps,能够应用于实时杂草识别。【结论】本研究提出的除草作业区域检测方法具有高度的可行性和极佳的识别效果,可用于蔬菜田间杂草的精准防除。

基于多学科协作的CBL教学法对提高全科医师临床应诊能力的影响研究

背景培养高质量的全科医学人才是维护人民群众身体健康的核心,但我国现有的全科医生培养方式仍存在不足,致使全科医生在基层医疗服务中岗位胜任力不足,故如何提高全科医生的岗位胜任力是目前全科医学教学的重中之重。目的探索基于多学科协作(MDT)的CBL教学法对提高全科医师临床应诊能力的影响。方法选取2020年7月—2023年7月在呼吸与危重症医学科培训的26名全科医生为研究对象,按批次分为三组:传统教学组、CBL教学组和MDT+CBL教学组,同一批次教学方法相同。采用莱斯特评估套件(LAP)对学员临床应诊能力进行评价,并采用自主设计的匿名调查问卷进行教学满意度调查。结果理论考核成绩:MDT+CBL教学组考试成绩优于传统教学组和CBL教学组(P<0.05)。临床应诊能力考核:MDT+CBL教学组LAP总分高于传统教学组和CBL教学组(P<0.05),MDT+CBL教学组在接诊和病史采集、患者管理、解决问题、医生行为和与患者的关系及预防性治疗方面的得分均高于传统教学组和CBL教学组(P<0.05)。教学效果的满意度:三种教学模式中MDT+CBL教学模式总体满意度得分高于CBL教学组和传统教学组(P<0.05),且MDT+CBL教学组学员的知识外延能力、指导健康生活方式能力、处理合并症能力、指导患者康复能力和社区合理用药能力得分更高(P<0.05)。结论基于多学科协作的CBL教学法有助于提高全科医师的临床应诊能力和岗位胜任力,是全科医学教学新模式。

人工智能在眩晕相关疾病诊疗中的应用

眩晕相关疾病发病率高,涉及全身多系统,致病机制复杂,诊断困难,是临床工作的一大难点。近年来人工智能技术发展迅猛,逐渐成为现代眩晕相关疾病临床诊疗的重要助力之一。该文分别着眼于人工智能在眩晕相关疾病诊断、评估和治疗中的应用,综述近年来人工智能在眩晕相关疾病中的进展和前沿,剖析人工智能在眩晕相关疾病诊疗中的优缺点,并展望人工智能在眩晕相关疾病诊疗中的发展前景和方向。

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction

Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease caused by pathogens that may be present in source water. An important index of disinfection efficiency is the free chlorine residual (FCR), a regulated disinfection parameter in the US that indirectly measures disinfectant power for prevention of microbial recontamination during DWT and distribution. This work demonstrates how machine learning (ML) can be implemented to improve FCR forecasting when supplied with water quality data from a real, full-scale chlorine disinfection system in Georgia, USA. More precisely, a gradient-boosting ML method (CatBoost) was developed from a full year of DWT plant-generated chlorine disinfection data, including water quality parameters (e.g., temperature, turbidity, pH) and operational process data (e.g., flowrates), to predict FCR. Four gradient-boosting models were implemented, with the highest performance achieving a coefficient of determination, R2, of 0.937. Values that provide explanations using Shapley’s additive method were used to interpret the model’s results, uncovering that standard DWT operating parameters, although non-intuitive and theoretically non-causal, vastly improved prediction performance. These results provide a base case for data-driven DWT disinfection supervision and suggest process monitoring methods to provide better information to plant operators for implementation of safe chlorine dosing to maintain optimum FCR.

PBL联合MDT教学在肝胆胰外科住院医师规范化培训中的应用

肝胆胰外科医师是专业性较强,并对综合素质要求较高的群体在规范化培训阶段采用系统的理论教学和规范的实践操作才能保证教学的质量。以问题为基础的教学法(problem based learning,PBL)是基于学生为主体,以问题为导向,让学生以合作的形式共同解决学习过程中发现的问题,实现将抽象的理论知识贯穿于临床实践中的一种教学方式,已在多个学科领域取得了显著的应用效果。与传统教学方法相比,PBL更适合复杂学科疾病的教学,同时鼓励学生制作PPT参加多学科讨论(multidisciplinary treatment,MDT),有助于调动学生的积极性,锻炼缜密的临床诊疗思维,并提高临床操作实践技能。文章探讨了针对肝胆胰外科医师规范化培训过程中将PBL与MDT相结合的应用效果,以期为后续临床教学提供经验指导。

中医智能辨证多决策模型构建思路与方法

辨证受医师诊疗经验影响存在复杂性、模糊性、不确定性等弊端,融入人工智能技术进行智能化辨证是解决这些弊端的重要方法。但当前智能辨证面临使用模型单一、辨证模式难以适用于多病种等问题,使得辨证的准确度与适用度均有待提高。为解决智能化辨证当前存在的复杂问题,将提取文本信息、优化医案数据结构及模型选择与联合构建等作为多决策模型思路构建的重要组成部分,并根据数据和模型特点,采用自然语言处理技术进行医案症状及证型内容信息提取,基于粗糙集的属性约简算法进行数据降维处理,最后采取加权投票融合支持向量机、多标记K-近邻、反向传播(Back Propagation,BP)神经网络算法构建多决策辨证模型,旨在为提升人工智能辨证模型的准确率提供参考,更好地指导临床辨证。

MDT结合PBL教学法培养胃肠外科住培研究生综合临床能力的探讨

目的 探讨多学科诊疗(multidisciplinary diagnosis and treatment,MDT)结合以问题为基础的教学法(problem-based learning,PBL)在胃肠外科住院医师规范化培训(简称住培)研究生综合临床能力培养中的应用效果。方法 选取2019年1月—2021年8月于广州医科大学附属第二医院轮科3个月以上的60名第2年及以上外科临床专业住院住培硕士研究生作为研究对象,轮转入胃肠外科后采用随机数字表法分为A组、B组、C组,每组20名。A组采用PBL法,B组采用MDT模式教学,C组采用MDT结合PBL模式法。比较3组的对MDT了解得分、多学科临床思维得分、主动学习能力得分、出入科成绩得分。结果 3组的对MDT了解得分、多学科临床思维得分、出科测试得分比较,差异有统计学意义(P <0.05);3组的主动学习能力得分、入科测试得分比较,差异无统计学意义(P> 0.05);B组、C组的对MDT了解得分[(3.85±0.67)分、(4.30±0.57)分]、多学科临床思维得分[(82.30±3.85)分、(85.55±3.38)分]均高于A组[(3.05±0.83)分、(77.90±5.86)分],C组的出科测试得分[(86.10±3.28)分]高于A组[(81.90±4.42)分],差异均有统计学意义(P <0.05);C组对MDT了解得分[(4.30±0.57)分]、主动学习能力得分[(4.00±0.73)分]、多学科临床思维得分[(85.55±3.38)分]高于B组[(3.85±0.67)分、(3.45±0.51)分、(82.30±3.85)分],差异均有统计学意义(P <0.05);A组与B组的主动学习能力得分、入科测试得分、出科测试得分比较,差异无统计学意义(P> 0.05);A组与C组的主动学习能力得分、入科测试得分比较,差异无统计学意义(P> 0.05);B组与C组的入科测试得分、出科测试得分比较,差异无统计学意义(P> 0.05)。结论 MDT结合PBL教学模式具有良好的互补作用,将两者结合起来形成一种新的教学模式,能够为培养胃肠外科住培研究生提供一种更好的教学模式。

数字化辅助的CBL在研究生正畸-正颌外科联合治疗教学中的应用

目的:探讨数字化辅助的案例教学法(Case-based learning,CBL)在研究生正畸-正颌外科联合治疗教学中的应用,以提供教学模式新思路。方法:将30名口腔正畸专业一年级研究生随机分为两组,对照组采用PPT讲解知识点配合病例图片展示的传统教学模式,实验组在传统教学模式基础上增加三维数字化辅助的CBL教学,即在常规讲授教学前组织研究生学习三维数字化软件在正畸-正颌外科联合治疗典型病例中的应用。采用随堂测验和问卷调查法对学生知识点掌握情况和教学满意度进行综合评价。结果:随堂测验中,对照组得分为(9.87±1.71)分,实验组得分为(11.40±1.99)分,实验组对知识点的掌握情况显著优于对照组(P<0.05)。两组学生在课前对此次课程的期待程度比较,差异无统计学意义(P>0.05),课程满意度综合评价中,实验组得分均显著高于对照组(P<0.05)。结论:将三维数字化辅助的CBL教学模式应用于研究生正畸-正颌外科联合治疗教学,更能激发学生的学习兴趣,有助于学生在课堂上注意力的集中,使得教学内容更加容易理解,值得在正畸研究生教学中推广。