Single-cell sequencing technology in diabetic wound healing:New insights into the progenitors-based repair strategies

Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.

Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

Terahertz shaping technology based on coherent beam combining

The generation of terahertz(THz) waves by focusing a femtosecond pulsed laser beam at a distance is able to overcome the strong absorption properties of air and has rapidly attracted the attention of industry. However, the poor directionality of the THz wave radiation generated by this method is not conducive to THz wave applications. By controlling the morphology of the ultrafast laser-excited plasma filament and its electron density distribution through coherent beam combining technology, we achieve direct THz beam shaping and are able to obtain THz wave radiation of Gaussian or arbitrary transverse distribution. The novel experimental approach proposed in this paper opens up the research field of direct THz wave shaping using plasma. Moreover, it innovates multi-parameter convergence algorithms and, by doing so, has the potential to find beam patterns with higher energy conversion efficiency and break the energy limit of THz waves emitted by lasers at high power.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast‑Charging Performance of SiO‑Based Anode for Lithium‑Ion Batteries

Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Grindability Evaluation of Ultrasonic Assisted Grinding of Silicon Nitride Ceramic Using Minimum Quantity Lubrication Based SiO_(2)Nanofluid

Minimum quantity Lubrication(MQL)is a sustainable lubrication system that is famous in many machining systems.It involve the spray of an infinitesimal amount of mist-like lubricants during machining processes.The MQL system is affirmed to exhibit an excellent machining performance,and it is highly economical.The nanofluids are understood to exhibit excellent lubricity and heat evacuation capability,compared to pure oil-based MQL system.Studies have shown that the surface quality and amount of energy expended in the grinding operations can be reduced considerably due to the positive effect of these nanofluids.This work presents an experimental study on the tribological performance of SiO_(2)nanofluid during grinding of Si_(3)N_(4)ceramic.The effect different grinding modes and lubrication systems during the grinding operation was also analyzed.Different concentrations of the SiO_(2)nanofluid was manufactured using canola,corn and sunflower oils.The quantitative evaluation of the grinding process was done based on the amount of grinding forces,specific grinding energy,frictional coefficient,and surface integrity.It was found that the canola oil exhibits optimal lubrication performance compared to corn oil,sunflower oil,and traditional lubrication systems.Additionally,the introduction of ultrasonic vibrations with the SiO_(2)nanofluid in MQL system was found to reduce the specific grinding energy,normal grinding forces,tangential grinding forces,and surface roughness by 65%,57%,65%,and 18%respectively.Finally,regression analysis was used to obtain an optimum parameter combinations.The observations from this work will aid the smooth transition towards ecofriendly and sustainable machining of engineering ceramics.

Overexpression of PbrGA2ox1 enhances pear drought tolerance through the regulation of GA_(3)-inhibited reactive oxygen species detoxification and abscisic acid signaling

Drought stress is a devastating natural disaster driven by the continuing intensification of global warming,which seriously threatens the productivity and quality of several horticultural crops,including pear.Gibberellins(GAs)play crucial roles in plant growth,development,and responses to drought stress.Previous studies have shown significant reductions of GA levels in plants under drought stress;however,our understanding of the intrinsic regulation mechanisms of GA-mediated drought stress in pear remains very limited.Here,we show that drought stress can impair the accumulation of bioactive GAs(BGAs),and subsequently identified PbrGA2ox1 as a chloroplast-localized GA deactivation gene.This gene was significantly induced by drought stress and abscisic acid(ABA)treatment,but was suppressed by GA_(3)treatment.PbrGA2ox1-overexpressing transgenic tobacco plants(Nicotiana benthamiana)exhibited enhanced tolerance to dehydration and drought stresses,whereas knock-down of PbrGA2ox1 in pear(Pyrus betulaefolia)by virus-induced gene silencing led to elevated drought sensitivity.Transgenic plants were hypersensitive to ABA,and had a lower BGAs content,enhanced reactive oxygen species(ROS)scavenging ability,and augmented ABA accumulation and signaling under drought stress compared to wild-type plants.However,the opposite effects were observed with PbrGA2ox1 silencing in pear.Moreover,exogenous GA_(3)treatment aggravated the ROS toxic effect and restrained ABA synthesis and signaling,resulting in the compromised drought tolerance of pear.In summary,our results shed light on the mechanism by which BGAs are eliminated in pear leaves under drought stress,providing further insights into the mechanism regulating the effects of GA on the drought tolerance of plants.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

裂纹加筋和缺口加筋对钢加筋板极限强度的影响

This paper numerically evaluates the effect of the crack position on the ultimate strength of stiffened panels.Imperfections such as notches and cracks in aged marine stiffened panels can reduce their ultimate strength.To investigate the effect of crack length and position,a series of nonlinear finite element analyses were carried out and two cases were considered,i.e.,case 1 with thin stiffeners and case 2 with thick stiffeners.In both cases,the stiffeners have the same cross-section area.To have a basis for comparison,the intact panels were modeled as well.The cracks and notches were in the longitudinal and transverse direction and were assumed to be in the middle part of the panel.The cracks and notches were assumed to be through the thickness and there is neither crack propagation nor contact between crack faces.Based on the numerical results,longitudinal cracks affect the behavior of the stiffened panels in the postbuckling region.When the stiffeners are thinner,they buckle first and provide no reserved strength after plate buckling.Thus,cracks in the stiffeners do not affect the ultimate strength in the case of the thinner stiffeners.Generally,when stiffeners are thicker,they affect the postbuckling behavior more.In that case,cracks in the stiffeners affect the buckling and failure modes of the stiffened panels.The effect of notch was also studied.In contrast to the longitudinal crack in stiffeners,a notch in the stiffeners reduces the ultimate strength of the stiffened panel for both slender and thick stiffeners.

Phenolic Profile and Volatiles of in vitro Propagated Lavandula angustifolia Mill.Seedlings

An effective in vitro propagation protocol was designed for Lavandula angustifolia Miller,a medicinal aromatic plant that is a prominent source of volatile organic compounds(VOCs).Murashige and Skoog media were supplemented with various concentrations of Plant Growth Regulators(PGRs),and the growth parameters of the nodal segments were examined.Nodal explants formed callus when they were supplemented with 2 mg/L of 6-Benzylaminopurine(BAP).The superior hormonal concentration of Murashige and Skoog(MS)media for the proliferation of shoots from callus cultures(39.33%)was 5 mg/L of 2-Isopentenyl adenine(2iP),and the favorable media for the growth of L.angustifolia callus cultures was 1 and 2 mg/L of BAP,with a 98%for-mation rate in each case.The callus cultures and in vitro propagated L.angustifolia seedlings obtained from var-ious PGR concentrations of MS media were exposed to qualitative and quantitative analysis in terms of phenolic profiles,flavonoids,High-performance liquid chromatography(HPLC)analysis of phenolic acids,and headspace-SPME analysis for volatiles.Such analysis revealed that micropropagated seedlings grown in media containing 1 mg/L of 6-Furfurylaminopurine(KIN)accumulated the highest yield(11.95±0.01 mg GAE/g)of phenolic acids.In contrast,the lowest concentration(2.17±0.04 mg GAE/g)was detected in 0.5 mg/L of BAP+0.5 mg/L of Naphthaleneacetic acid(NAA)media.The plantlets grown in 0.5 mg/L of BAP+0.5 mg/L of NAA media showed the highestflavonoid yield(31.67±0.06μg/g QE/g).In contrast,callus samples exhibited the lowest yield(11.59±0.02μg/g QE/g)offlavonoids in MS media supplemented with a concentration of 0.5 mg/L of BAP.HPLC analysis revealed the variability of phenolic acid contents within the callus cultures as well as plantlets,with gallic acid,4-OH benzoic acid,chlorogenic acid,vanillic acid,caffeic acid,cinnamic acid,and rosmarinic acid being the prominent constituents.The presence of twenty-two chemicals was revealed by headspace-SPME analysis.Eucalyptol,nonanal,borneol,carvone,andβ-caryophyllene were the most abundant.This study demonstrated that micropropagation of L.angustifolia may be an effective method to produce large numbers of genetically identical plantlets for the production of high-value bio compounds.

New possibility for PET plastic recycling by a tailored hydrolytic enzyme

Plastic waste puts a huge burden on the ecosystem due to the current lack of mature recycling technology.Poly(ethylene terephthalate)(PET)is one of the most produced plastics in the world.Enzymatic decomposition holds the promise of recovering monomers from PET plastic,and the monomers can be used to regenerate new PET products.However,there are still limitations in the activity and thermal stability of the existing PET hydrolases.The recent study by Lu et al.introduced a novel PET hydrolase via machine learning-aided engineering.The obtained PET hydrolase showed excellent activity and thermal stability in the hydrolysis of PET and is capable of directly degrading large amounts of postconsumer PET products.This approach provides an effective method for recycling PET waste and is expected to improve the current state of plastic pollution worldwide.

Burden of routine orthopedic implant removal a single center retrospective study

BACKGROUND Open reduction and internal fixation represent prevalent orthopedic procedures,sparking ongoing discourse over whether to retain or remove asymptomatic implants.Achieving consensus on this matter is paramount for orthopedic surgeons.This study aims to quantify the impact of routine implant removal on patients and healthcare facilities.A retrospective analysis of implant removal cases from 2016 to 2022 at King Fahad Hospital of the University(KFHU)was conducted and subjected to statistical scrutiny.Among these cases,44%necessitated hospitalization exceeding one day,while 56%required only a single day.Adults exhibited a 55%need for extended hospital stays,contrasting with 22.8%among the pediatric cohort.The complication rate was 6%,with all patients experiencing at least one complication.Notably,34.1%required sick leave and 4.8%exceeded 14 d.General anesthesia was predominant(88%).Routine implant removal introduces unwarranted complications,particularly in adults,potentially prolonging hospitalization.This procedure strains hospital resources,tying up the operating room that could otherwise accommodate critical surgeries.Clearly defined institutional guidelines are imperative to regulate this practice.AIM To measure the burden of routine implant removal on the patients and hospital.METHODS This is a retrospective analysis study of 167 routine implant removal cases treated at KFHU,a tertiary hospital in Saudi Arabia.Data were collected in the orthopedic department at KFHU from February 2016 to August 2022,which includes routine asymptomatic implant removal cases across all age categories.Nonroutine indications such as infection,pain,implant failure,malunion,nonunion,restricted range of motion,and prominent hardware were excluded.Patients who had external fixators removed or joints replaced were also excluded.RESULTS Between February 2016 and August 2022,360 implants were retrieved;however,only 167 of those who met the inclusion criteria were included in this study.The remaining implants were rejected due to exclusion criteria.Among the cases,44%required more than one day in the hospital,whereas 56%required only one day.55%of adults required more than one day of hospitalization,while 22.8%of pediatric patients required more than one day of inpatient care.The complication rate was 6%,with each patient experiencing at least one complication.Sick leave was required in 34.1%of cases,with 4.8%requiring more than 14 d.The most common type of anesthesia used in the surgeries was general anesthesia(88%),and the mean(SD)surgery duration was 77.1(54.7)min.CONCLUSION Routine implant removal causes unnecessary complications,prolongs hospital stays,depletes resources and monopolizing operating rooms that could serve more critical procedures.

Control of GaN inverted pyramids growth on c-plane patterned sapphire substrates

Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985 ℃ and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.

未热解厚热障涂层的隔热能力和黏接强度

对比研究使用未热解粉末制备的厚热障涂层(UPTTBC)和传统TBC、厚TBC(TTBC)及密集垂直裂纹(DVC)TTBC 4种涂层的隔热能力和黏接强度。采用降温法测试涂层的隔热能力;根据ASTM C633标准,采用拉拔试验检测涂层的黏接强度。结果显示,UPTTBC和DVC TTBC的黏接强度分别比TTBC高35%和25%。这可能是由于UPTTBC结构中存在亚微米尺度的未熔化区域,起到了阻止裂纹扩展的作用。此外,隔热测试的结果表明,DVCTTBC涂层的隔热能力最差,为0.28℃/μm,而UPTTBC涂层的隔热能力最强,为0.40℃/μm。由于UPTTBCs显微组织中存在亚微米区域,使涂层展现出优越的隔热能力。相反,DVCTTBC因其致密的结构,具有最低的隔热能力。

Novel Batch Polishing Method of Ceramic Cutting Inserts for Reducing Tool Wear

Ceramic cutting inserts are a type of cutting tool commonly used in high-speed metal cutting applications.However,the wear of these inserts caused by friction between the workpiece and cutting inserts limits their overall effectiveness.In order to improve the tool life and reduce wear,this study introduces an emerging method called magnetic field-assisted batch polishing(MABP)for simultaneously polishing multiple ceramic cutting inserts.Several polishing experiments were conducted under different conditions,and the wear characteristics were clarified by cutting S136H steel.The results showed that after 15 min of polishing,the surface roughness at the flank face,edge,and nose of the inserts was reduced to below 2.5 nm,6.25 nm,and 45.8 nm,respectively.Furthermore,the nose radii of the inserts did not change significantly,and there were no significant changes in the weight percentage of elements before and after polishing.Additionally,the tool life of the batch polished inserts was found to be up to 1.75 times longer than that of unpolished inserts.These findings suggest that the MABP method is an effective way to mass polish ceramic cutting inserts,resulting in significantly reduced tool wear.Furthermore,this novel method offers new possibilities for polishing other tools.

A real-time performance improvement method for composite time scale

The composite time scale(CTS)provides a stable,accurate,and reliable time scale for modern society.The improvement of CTS’s real-time performance will improve its stability,which strengths related applications’performance.Aiming at this goal,a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper.The determinants of the CTS’s calculation interval(characteristics of the clock ensemble,the measurement noise,the time and frequency synchronization system’s noise and the auxiliary output generator noise floor)are studied and the optimal calculation interval is obtained.We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage.A strategy to get the reasonable initial parameters of ensemble algorithm is designed.The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters.On this basis,we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than1700 s.The experimental result proves that the CTS’s real-time performance is significantly improved.

A comparative in vitro study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells

Cancer frequently develops resistance to the majority of chemotherapy treatments.This study aimed to examine the synergistic cytotoxic and antitumor effects of SGLT2 inhibitors,specifically Canagliflozin(CAN),Dapagliflozin(DAP),Empagliflozin(EMP),and Doxorubicin(DOX),using in vitro experimentation.The precise combination of CAN+DOX has been found to greatly enhance the cytotoxic effects of doxorubicin(DOX)in MCF-7 cells.Interestingly,it was shown that cancer cells exhibit an increased demand for glucose and ATP in order to support their growth.Notably,when these medications were combined with DOX,there was a considerable inhibition of glucose consumption,as well as reductions in intracellular ATP and lactate levels.Moreover,this effect was found to be dependent on the dosages of the drugs.In addition to effectively inhibiting the cell cycle,the combination of CAN+DOX induces substantial modifications in both cell cycle and apoptotic gene expression.This work represents the initial report on the beneficial impact of SGLT2 inhibitor medications,namely CAN,DAP,and EMP,on the responsiveness to the anticancer properties of DOX.The underlying molecular mechanisms potentially involve the suppression of the function of SGLT2.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Nutritional Characterization of Traditional Foods Based on Millet, Sorghum and Cowpea from the North-Central Region of Burkina Faso

The food and nutrition situation in Burkina Faso, like most developing countries in sub-Saharan Africa, is marked by growing food vulnerability. The majority of local dishes are being abandoned in favor of a minority of imported cereal dishes and other ultra-processed foods. This minority of cereal foods is blamed for stunted growth in children, while ultra-processed foods are linked to chronic diseases such as hypertension, certain types of diabetes and cancer. Knowledge of the nutritional value of local foods is needed to determine their nutritional quality. The aim of this study was to determine the nutritional values of local dishes based on millet, sorghum and cowpea in the Centre-North region. The methodology consisted of making an inventory of millet-, sorghum- and cowpea-based dishes using focus groups made up of women and men from three age groups comprising young people, adults and the elderly in the communes of Lebda and Boussouma. The dishes were reproduced, and standard biochemical methods were used for nutritional characterization. A total of 34 dishes were inventoried, including 16 millet/sorghum-based dishes, 8 cowpea-based dishes and 10 dishes composed of millet/sorghum and cowpea or leaves. The mean protein, carbohydrate, ash and iron contents per 100 g DM of the three types of dishes were significantly different (p ≤ 0.05), ranging respectively from 13.61 to 22.63 g, 70.76 to 80.88 g, 1.87 to 5.96 g and 7.67 to 12.06 mg. Those for lipid, energy and zinc were not significantly different, ranging from 5.51 to 6.56 g, from 427 to 433 Kcal and from 2.98 to 3.32 mg respectively. Cowpea-based and mixed dishes cover the nutritional requirements for protein, carbohydrates, iron, zinc and energy recommended for children and adults. The consumption of mixed dishes and cowpea-based dishes could be promoted by nutritional policy to reduce stunting and recommended to obese, hypertensive and diabetic people as part of their diet.