BTB与CNC同源蛋白1通过调节MYC相关因子X二聚化蛋白1基因对套细胞淋巴瘤发生发展的影响

目的 探讨BTB与CNC同源蛋白1(BACH1)通过调节MYC相关因子X二聚化蛋白1(MXD1)基因对套细胞淋巴瘤(MCL)发生发展的影响。方法 采用靶向剪切及转座酶技术(CUT&Tag)检测BACH1下游靶基因,并通过序列比对分析寻找BACH1与MXD1的结合位点。通过双荧光素酶报告基因系统验证BACH1对MXD1的调控作用,并采用慢病毒技术构建BACH1过表达及敲低的稳转MCL细胞株,采用蛋白质印迹法(Western blot)检测MXD1蛋白表达水平。应用GSE16411数据集分析MCL细胞和正常B细胞中MXD1 m RNA相对表达量。应用GSE132929及GSE21452数据集筛选MXD1共表达基因,对MXD1共表达基因进行基因本体(GO)和京都基因与基因组百科全书(KEGG)信号通路富集分析。采用噻唑蓝(MTT)法检测MXD1正相关基因应激诱导磷蛋白1同源物含U-框蛋白1(STUB1)的特异性激活剂YL109处理后MCL细胞的活力。结果 BACH1能够靶向结合MXD1并抑制MXD1基因的启动子活性。BACH1敲低后MXD1蛋白表达水平升高,而BACH1过表达后MXD1蛋白表达水平降低。通过GSE16411数据集分析发现,MCL细胞中MXD1 mRNA相对表达量明显低于正常B细胞,差异有统计学意义(P﹤0.01)。在GSE132929数据集中筛选出2222个MXD1共表达基因,在GSE21452数据集中筛选出503个MXD1共表达基因。GO分析显示,MXD1共表达基因主要富集于信号转导、蛋白质磷酸化、细胞葡萄糖醛酸化、类黄酮葡萄糖醛酸化等代谢相关的生物学过程;KEGG信号通路分析显示,MXD1共表达基因主要富集于肝脏发育的代谢途径、蛋白质的消化和吸收、Janus激酶(JAK)/信号转导及转录激活因子(STAT)信号通路等。采用YL109处理MCL细胞后可显著降低细胞存活率。结论 BACH1通过结合MXD1近端启动子负向调控MXD1的表达水平,且BACH1介导的MXD1表达改变很可能通过调控代谢过程参与MCL的发生发展。

基于SKIP宏程序编程CNC无线测头在线智能检测系统设计

针对数控铣削加工精密零件尺寸精度测量时,因离线测量易产生测量误差、效率低、成本高等问题,通过赋值相关检测路径及补偿的参数,编写零件在线检测及误差补偿宏程序,开发了基于三菱M70数控系统跳过指令SKIP在线检测与误差补偿宏程序控制系统。应用结果表明:该系统在线检测重复定位精度能达到0.001~0.003 mm,能适用于精密零件数控铣削加工,具有一定的推广使用价值。

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

In vivo astrocyte reprogramming following spinal cord injury

Harmful and helpful roles of astrocytes in spinal cord injury(SCI):SCI induce gradable sensory,motor and autonomic impairments that correlate with the lesion severity and the rostro-caudal location of the injury site.The absence of spontaneous axonal regeneration after injury results from neuron-intrinsic and neuron-extrinsic parameters.Indeed,not only adult neurons display limited capability to regrow axons but also the injury environment contains inhibitors to axonal regeneration and a lack of growth-promoting factors.Amongst other cell populations that respond to the lesion,reactive astrocytes were first considered as only detrimental to spontaneous axonal regeneration.Indeed,astrocytes.

Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity

Enhanced osteoclastogenesis and osteoclast activity contribute to the development of osteoporosis,which is characterized by increased bone resorption and inadequate bone formation.As novel antiosteoporotic therapeutics are needed,understanding the genetic regulation of human osteoclastogenesis could help identify potential treatment targets.This study aimed to provide an overview of transcriptional reprogramming during human osteoclast differentiation.Osteoclasts were differentiated from CD14+monocytes from eight female donors.RNA sequencing during differentiation revealed 8980 differentially expressed genes grouped into eight temporal patterns conserved across donors.These patterns revealed distinct molecular functions associated with postmenopausal osteoporosis susceptibility genes based on RNA from iliac crest biopsies and bone mineral density SNPs.Network analyses revealed mutual dependencies between temporal expression patterns and provided insight into subtype-specific transcriptional networks.The donor-specific expression patterns revealed genes at the monocyte stage,such as filamin B(FLNB)and oxidized low-density lipoprotein receptor 1(OLR1,encoding LOX-1),that are predictive of the resorptive activity of mature osteoclasts.The expression of differentially expressed G-protein coupled receptors was strong during osteoclast differentiation,and these receptors are associated with bone mineral density SNPs,suggesting that they play a pivotal role in osteoclast differentiation and activity.The regulatory effects of three differentially expressed G-protein coupled receptors were exemplified by in vitro pharmacological modulation of complement 5 A receptor 1(C5AR1),somatostatin receptor 2(SSTR2),and free fatty acid receptor 4(FFAR4/GPR120).Activating C5AR1 enhanced osteoclast formation,while activating SSTR2 decreased the resorptive activity of mature osteoclasts,and activating FFAR4 decreased both the number and resorptive activity of mature osteoclasts.In conclusion,we report the occurrence of transcriptional reprogramming during human osteoclast differentiation and identified SSTR2 and FFAR4 as antiresorptive G-protein coupled receptors and FLNB and LOX-1 as potential molecular markers of osteoclast activity.These data can help future investigations identify molecular regulators of osteoclast differentiation and activity and provide the basis for novel antiosteoporotic targets.

Glia-to-neuron reprogramming to the rescue?

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

Neurocircuit regeneration by extracellular matrix reprogramming

The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.

CNC机床丝杠热误差实时补偿设计及自动补偿试验

为了降低数控机床丝杠传动系统因热误差引起的定位误差,完成机床工作台各运行阶段的实时补偿。用非接触模式为丝杠非电机连接端安装位移检测器,以电涡流传感器时间监测丝杠端面位置数据,获得丝杠产生的总热误差,通过热补偿得到每段丝杠的热误差程度,确定每段坐标系原点发生的偏移,完成自主补偿机床丝杠热误差的效果。研究结果表明:采用分段补偿方法可以获得比其他热误差补偿模型更优的丝杠全段补偿性能。分别检测丝杠各段发生的热误差再对其实施补偿,可以根据丝杠各部位热误差程度实施补偿。

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Key Factors to Consider When Introducing a New Vaccine in Low-Income Settings: Lessons from Malawi Expanded Program on Immunization

Introduction: As new vaccines become available, countries must assess the relevance to introduce them into their vaccination schedules. Malawi has recently introduced several new vaccines and plans to introduce more. This study was conducted to identify key factors that need to be considered when deciding to introduce a new vaccine and current challenges faced by low and middle income countries using Malawi as an example. Methodology: The study employed a desk review approach, examining published literature from various sources such as PubMed, Medline, and Google Scholar. Policy documents from organizations like the World Health Organization, GAVI the Alliance, and the Ministry of Health for Malawi were also included. A total of 99 articles and documents on new vaccine introduction, challenges of immunization, policy documents in immunization and health systems strengthening were included. The review focused on addressing five key areas critical to new vaccine introduction namely: the need for a vaccine, availability of the vaccine, safety and effectiveness of the vaccine, demand for the vaccine, and the prudent use of public or private funds. Results: Malawi considered the burden of cervical cancer and the significance of malaria in the country when introducing the HPV and malaria vaccines. The country opted for vaccines that can be handled by the cold chain capacity and available human resources. Despite that malaria vaccine and Typhoid Conjugate Vaccine trials were done in country, there are limited vaccine safety and efficacy trials conducted in Malawi, leading to a reliance on WHO-prequalified vaccines. Demand for newly introduced vaccines varied, with high demand for Oral Cholera Vaccine during a cholera outbreak, while demand for COVID-19 vaccines decreased over time. Although cost-effectiveness studies were limited in the country, 2 studies indicated that Typhoid Conjugate Vaccine and malaria vaccine would be cost effective. All these have been implemented despite having challenges like lack of accurate surveillance data, inadequate cold chain capacity, limited safety and efficacy vaccine clinical trials, political influence, and limited funding. Conclusion: Despite several challenges Malawi set a good example of the careful considerations required before introducing a new vaccine. The process involves data review, priority setting, precise planning, and consultation with stakeholders. Low-income countries should invest in vaccine safety, efficacy, and cost-effectiveness trials.

Research and Practice of Cardiopulmonary Rehabilitation Training Programs

In today’s society, the incidence of cardiopulmonary diseases is increasing annually, seriously affecting patients’ quality of life. Therefore, developing a scientific and effective rehabilitation training program is of great significance. This study first analyzes the theoretical basis of cardiopulmonary rehabilitation training, including the effects of aerobic exercise, interval training, and strength training on cardiopulmonary function. Based on this, a comprehensive rehabilitation training program is designed, which includes personalized training plans, comprehensive interventions, multidisciplinary collaboration, patient education, and regular follow-up visits. The cardiopulmonary rehabilitation training plan developed in this study has certain scientific practicability, which provides a theoretical basis for cardiopulmonary rehabilitation training, and also provides a reference for medical institutions, rehabilitation centers and communities, which is helpful for promotion and application to a wider range of patients with cardiopulmonary diseases.

Quercetin Alleviates Lipopolysaccharide-Induced Cardiac Inflammation via Inhibiting Autophagy and Programmed Cell Death

Objective The aim of this study is to explore the potential modulatory role of quercetin against Endotoxin or lipopolysaccharide(LPS)induced septic cardiac dysfunction.Methods Specific pathogen-free chicken embryos(n=120)were allocated untreated control,phosphate buffer solution(PBS)vehicle,PBS with ethanol vehicle,LPS(500 ng/egg),LPS with quercetin treatment(10,20,or 40 nmol/egg,respectively),Quercetin groups(10,20,or 40 nmol/egg).Fifteenday-old embryonated eggs were inoculated with abovementioned solutions via the allantoic cavity.At embryonic day 19,the hearts of the embryos were collected for histopathological examination,RNA extraction,real-time polymerase chain reaction,immunohistochemical investigations,and Western blotting.Results They demonstrated that the heart presented inflammatory responses after LPS induction.The LPS-induced higher mRNA expressions of inflammation-related factors(TLR4,TNFα,MYD88,NF-κB1,IFNγ,IL-1β,IL-8,IL-6,IL-10,p38,MMP3,and MMP9)were blocked by quercetin with three dosages.Quercetin significantly decreased immunopositivity to TLR4 and MMP9 in the treatment group when compared with the LPS group.Quercetin significantly decreased protein expressions of TLR4,IFNγ,MMP3,and MMP9 when compared with the LPS group.Quercetin treatment prevented LPS-induced increase in the mRNA expression of Claudin 1 and ZO-1,and significantly decreased protein expression of claudin 1 when compared with the LPS group.Quercetin significantly downregulated autophagyrelated gene expressions(PPARα,SGLT1,APOA4,AMPKα1,AMPKα2,ATG5,ATG7,Beclin-1,and LC3B)and programmed cell death(Fas,Bcl-2,CASP1,CASP12,CASP3,and RIPK1)after LPS induction.Quercetin significantly decreased immunopositivity to APOA4,AMPKα2,and LC3-II/LC3-I in the treatment group when compared with the LPS group.Quercetin significantly decreased protein expressions of AMPKα1,LC3-I,and LC3-II.Quercetin significantly decreased the protein expression to CASP1 and CASP3 by immunohistochemical investigation or Western blotting in treatment group when compared with LPS group.Conclusion Quercetin alleviates cardiac inflammation induced by LPS through modulating autophagy,programmed cell death,and myocardiocytes permeability.

Geometric Error Identification of Gantry-Type CNC Machine Tool Based on Multi-Station Synchronization Laser Tracers

Laser tracers are a three-dimensional coordinate measurement system that are widely used in industrial measurement.We propose a geometric error identification method based on multi-station synchronization laser tracers to enable the rapid and high-precision measurement of geometric errors for gantry-type computer numerical control(CNC)machine tools.This method also improves on the existing measurement efficiency issues in the single-base station measurement method and multi-base station time-sharing measurement method.We consider a three-axis gantry-type CNC machine tool,and the geometric error mathematical model is derived and established based on the combination of screw theory and a topological analysis of the machine kinematic chain.The four-station laser tracers position and measurement points are realized based on the multi-point positioning principle.A self-calibration algorithm is proposed for the coordinate calibration process of a laser tracer using the Levenberg-Marquardt nonlinear least squares method,and the geometric error is solved using Taylor’s first-order linearization iteration.The experimental results show that the geometric error calculated based on this modeling method is comparable to the results from the Etalon laser tracer.For a volume of 800 mm×1000 mm×350 mm,the maximum differences of the linear,angular,and spatial position errors were 2.0μm,2.7μrad,and 12.0μm,respectively,which verifies the accuracy of the proposed algorithm.This research proposes a modeling method for the precise measurement of errors in machine tools,and the applied nature of this study also makes it relevant both to researchers and those in the industrial sector.

An Empirical Application of User-Guided Program Analysis

Although static program analysis methods are frequently employed to enhance software quality,their efficiency in commercial settings is limited by their high false positive rate.The EUGENE tool can effectively lower the false positive rate.However,in continuous integration(CI)environments,the code is always changing,and user feedback from one version of the software cannot be applied to a subsequent version.Additionally,people find it difficult to distinguish between true positives and false positives in the analytical output.In this study,we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true positives.On the three projects ethereum,go-cloud,and kubernetes,we assessed our methodologies.According to the trial findings,EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of reports.We paired our techniques with GoInsight~1 and discovered a vulnerability.We also offered a patch to the community.

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

Effectiveness of Co-Testing in Cervical Cancer Screening Program in Macao SAR

Background: Cervical cancer remains a significant public health concern in Macao SAR despite the implementation of a cervical cancer screening program and HPV vaccination. To improve early detection, Macao SAR introduced HPV DNA testing alongside cytology (co-testing) as the primary screening method in 2019. This study evaluates the effectiveness of co-testing in identifying cervical precancerous lesions (CIN2+) compared to cytology alone. Methods: We conducted a retrospective analysis of women aged 30 - 65 years who participated in the routine cervical cancer screening program in Macao SAR Primary Healthcare Centers from 2019 to 2022. Data from over 70,000 women were analyzed, comparing the detection rates of CIN2+ through co-testing and cytology alone. Women with abnormal cytology or positive HPV results were referred for colposcopy. Results: The introduction of co-testing led to a significant increase in the detection of CIN2+, particularly in women with atypical squamous cells of undetermined significance (ASCUS) or negative for intraepithelial lesion or malignancy (NILM) cytology results. Between 2019 and 2022, the percentage of women with ASCUS/NILM and any high-risk HPV (hrHPV) positive who were diagnosed with CIN2+ after colposcopy were 24%, 13%, 10% and 7.5% respectively. This highlights the ability of co-testing to identify high-risk individuals who would have been missed by cytology alone. Discussion: Our findings demonstrate the effectiveness of co-testing in improving the sensitivity of cervical cancer screening in Macao SAR. The inclusion of HPV DNA testing allows for better risk stratification of women with ASCUS/NILM cytology, leading to more targeted referrals for colposcopy and timely detection of precancerous lesions. The initial high positive rate in 2019 (24%) might be attributed to the small sample size and potentially reflects a backlog of undiagnosed cases prior to co-testing implementation. Conclusion: The implementation of co-testing in Macao SAR’s cervical cancer screening program significantly improves the early detection of precancerous lesions, particularly in women with ambiguous cytology results. This proactive approach contributes to reducing cervical cancer morbidity and mortality and improving women’s health outcomes in Macao SAR.

Pretreatment can alleviate programmed cell death in mesenchymal stem cells

In this editorial,we delved into the article titled“Cellular preconditioning and mesenchymal stem cell ferroptosis.”This groundbreaking study underscores a pivotal discovery:Ferroptosis,a type of programmed cell death,drastically reduces the viability of donor mesenchymal stem cells(MSCs)after engraftment,thereby undermining the therapeutic value of cell-based therapies.Furthermore,the article proposes that by manipulating ferroptosis mechanisms through preconditioning,we can potentially enhance the survival rate and functionality of MSCs,ultimately amplifying their therapeutic potential.Given the crucial role ferroptosis plays in shaping the therapeutic outcomes of MSCs,we deem it im-perative to further investigate the intricate interplay between programmed cell death and the therapeutic effectiveness of MSCs.

Efficacy comparison of fruquintinib,regorafenib monotherapy or plus programmed death-1 inhibitors for microsatellite stable metastatic colorectal cancer

BACKGROUND Regorafenib(R)and fruquintinib(F)are the standard third-line regimens for colorectal cancer(CRC)according to the National Comprehensive Cancer Network guidelines,but both have limited efficacy.Several phase 2 trials have indicated that R or F combined with immune checkpoint inhibitors can reverse immunosuppression and achieve promising efficacy for microsatellite stable or proficient mismatch repair(MSS/pMMR)CRC.Due to the lack of studies comparing the efficacy between F,R,F plus programmed death-1(PD-1)inhibitor,and R plus PD-1 inhibitors(RP),it is still unclear whether the combination therapy is more effective than monotherapy.AIM To provide critical evidence for selecting the appropriate drugs for MSS/pMMR metastatic CRC(mCRC)patients in clinical practice.METHODS A total of 2639 CRC patients were enrolled from January 2018 to September 2022 in our hospital,and 313 MSS/pMMR mCRC patients were finally included.RESULTS A total of 313 eligible patients were divided into F(n=70),R(n=67),F plus PD-1 inhibitor(FP)(n=95)and RP(n=81)groups.The key clinical characteristics were well balanced among the groups.The median progression-free survival(PFS)of the F,R,FP,and RP groups was 3.5 months,3.6 months,4.9 months,and 3.0 months,respectively.The median overall survival(OS)was 14.6 months,15.7 months,16.7 months,and 14.1 months.The FP regimen had an improved disease control rate(DCR)(P=0.044)and 6-month PFS(P=0.014)and exhibited a better trend in PFS(P=0.057)compared with F,and it was also significantly better in PFS than RP(P=0.030).RP did not confer a significant survival benefit;instead,the R group had a trend toward greater benefit with OS(P=0.080)compared with RP.No significant differences were observed between the R and F groups in PFS or OS(P>0.05).CONCLUSION FP is superior to F in achieving 6-month PFS and DCR,while RP is not better than R.FP has an improved PFS and 6-month PFS compared with RP,but F and R had similar clinical efficacy.Therefore,FP may be a highly promising strategy in the treatment of MSS/pMMR mCRC.

Enhancing Data Analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.