Globally, crickets are gaining recognition as a valuable alternative protein source for human consumption due to their lower resource requirement and ecological footprint compared to traditional livestock. In this paper, we examine strategies that may expedite the sustainable domestication of crickets as a food source. Using survey data from 306 households in western Kenya, we find that supplying cricket production starter kits, granting access to credit facilities, encouraging participation in farmer groups, and fostering partnerships can enhance the adoption of cricket farming. Moreover, we provide new evidence that institutional training significantly increases cricket yields while embracing cricket consumption (i.e. entomophagy) increases market supply. These findings underscore the importance of technical training, provision of production starter kits, and raising awareness about entomophagy to achieve sustainable mass production and adoption of cricket farming. The United Nations (UN) projects a global population growth of approximately 9.5 billion by 2050, which will result in an unprecedented increase in the global demand for food UN . Preciseicipe) and other partners have over the past decade invested in research and technologies to increase awareness, acceptance, production, and consumption of insects both for food and livestock feed in sub-Saharan Africa (SSA). This collaboration has led to the discovery of a new, previously scientifically undescribed edible cricket (Scapsipedus icipe) with great promise for mass production for human consumption and inclusion as an alternative protein ingredient in chicken feed  and the probability of adopting cricket farming. Conversely, factors such as group membership, household acceptance of crickets as food, and the provision of cricket production starter kits post-training are positively correlated with the likelihood of household adoption. In the second stage of the model, we find that remoteness negatively impacts yield, while access to credit appears to boost yield.In the second research question, we examine the impact of institutional training sources on cricket production the multinomial endogenous treatment effects (METE) model. We find that access to training from NGOs and a public research university increase cricket yields by 55.4% and 70.8%, respectively. Lastly, we examine the impact of consumption behavior on market supply using an endogenous switching regression (ESR) model. The first stage ESR estimates reveal that geographical remoteness and market inaccessibility negatively correlate with the likelihood of cricket consumption. In contrast, the awareness of crickets as a nutritious food source is positively linked to consumption rates. Furthermore, collective cricket farming activities show a positive correlation with market supply among cricket-consuming households. We also observe a marginal association between higher education levels and increased market supply among non-consuming households. Estimates from the second stage of the ESR model reveal that cricket consumption boosts market supply by 20.5%. Additionally, we find suggestive evidence that if non-consuming households were to adopt cricket consumption, there is potential to augment their market supply by 29.5%.The rest of the paper is organized as follows: We briefly present a literature review in “Livestock value chains are already an ecological stressor and a threat to global ecosystems. For instance, there is a growing consensus that animal-based foods cause more greenhouse gas (GHG) emissions as compared to insect-based foods  in Kenya is not well established, and consumption is likely constrained by various factors. Firstly, most insects are still harvested from their natural environments , implying that their supply is dependent on location and seasonality Nonaka . SecondlWe conducted household surveys across three counties in Western Kenya: Kisumu, Siaya, and Homabay  survey tool. The questionnaire contained different modules that captured detailed information on household socioeconomic characteristics, awareness and perceptions on insects farming and consumption, access to cricket farming training, cricket production, marketing and associated constraints, and institutional support services.The data collection exercise was conducted by well-trained enumerators supervised by Domestic cricket farming being relatively new  cricket farming after receiving training and Yi = 0 if otherwise (non-adopters). β0 is the intercept, βij is a vector of parameters to be estimated; Xij is a vector of explanatory variables which explain cricket farming adoption behavior; Xij and symmetrically distributed about 0.The first (selection) equation in a Heckman model is a probit estimator. We, therefore, specify our selection equation as:λ which is the ratio of the ordinate of a standard normal distribution to the tail area of the distribution  n Greene  as shownAccording to Heckman , if the Xij is a vector of explanatory variables, The second stage (outcome equation) estimated using OLS estimator and is specified as:i will therefore choose a training source or a bundle of training sources j, over any other alternative source k, if Vij > Vik, k ≠ j where Vi is the indirect utility derived from any of the training sources.In this study, potential cricket farmers had two main cricket production training sources: Public university and non-governmental organizations. In addition, due to high possibility of knowledge spillovers, farmers are also likely to receive training from fellow farmers who have receieved institutional training. Nonetheless, our aim is to evaluate the effects of institutional training sources. We theorize that farmers will choose a training source that maximizes expected utility subject to socio-economic and institutional constraints. A farmer However, when the farmers are required to choose from available cricket production training sources (providers), selection bias and endogeneity problems may arise as the choice decision could be influenced by other unobservable characteristics. Failure to account for endogeneity and selection bias can either overestimate or underestimate the actual effects of the treatment variable on the outcome variable of interest. As a result, we follow Deb and Trivedi  and applVij, specified as:zi is a vector of household socioeconomic characteristics; The METE model estimation proceeds in two stages. The first stage applies a multinomial logit which models potential cricket farmers’ adoption decisions for the two main training sources. This first stage assumes that farmers are rational and will therefore choose a training source that maximizes their indirect utility yi is the quantityi; xi represents exogenous covariates with parameter vector β. Parameters γj denote the treatment effects of participating in training relative to the non-participants. If the farmer’s decision to participate in training is endogenous and assuming dij to be exogenous, estimates of γj would be biased and inconsistent thus necessitating exogeneity tests in outcome Eq.  of crickets harvested. We specify the outcome equation as:Estimation of cricket production and supply gains from pre-existing consumption behavior of households based on non-experimental data is not trivial because of the need to find a good counterfactual. What we cannot observe is the production effects of those households that are already cricket consumers should they have chosen not to be cricket consumers. Neither can we observe the effects on non-consumers had they been consumers. Experimental and quasi-experimental studies would effectively address this problem by having two non-consuming insect groups to begin with: treatment and control. With this approach, the treatment group would receive adequate training on how and why they should eat crickets including how to prepare the crickets as food and the benefits of consuming insects in general (entomophagy). We would then presume that the outcomes observed on the control are statistically representative of what would have occurred without adoption.However, routine cricket consumption is not well established in Kenya and therefore not randomly distributed into two well-defined groups of the consuming and non-consuming households, but rather the households themselves decide to consume or not to consume based on their awareness and perceptions towards crickets as an alternative food. As such, there may exist systematic differences between consumers and non-consumers due to observable household characteristics such as education, household size, access to information, income levels, etc. Further, unobservable characteristics such as attitudes, skills, and individual motivations may influence cricket consumption decisions and subsequently affect potential yields and market supply by the households  and E(ε2 |C = 0) are given as shown:1 and λ2 which represent the selectivity terms. If the estimated covariance In light of the above-mentioned issues, we follow Maddala and Nelson  to estimWhile the model parameters can be identified through interpretation of the selectivity terms (inverse mills ratio coefficients), it is recommended to adopt a plausible instrument(s) in the outcome equation for more robust identification  and untreated group on market supply is computed as:In Eq.  the termFinally, we estimate the transitional heterogeneity effects (TH), which is whether the effect is larger or smaller for the households that practice cricket consumption or for the households that did not practice cricket consumption in the counterfactual case that they did practice cricket consumption [i.e. the difference between Eqs.  and 11)11)]. A cGiven the assumption that the error terms have a trivariate normal distribution Eq. , the ESRTable About 26%, 35%, and 39% of the sampled farmers received training on cricket farming from a public university, NGOs, and fellow farmers respectively. This finding underscores the important role of rural networks in generating knowledge spillovers (positive) necessary for the adoption of new farm enterprises and technologies (Pratiwi and Suzuki The average age of the farmers was 48 years with non-adopters older (49 years) than adopters (46 years) with a marginal statistical difference. Years of formal education and household size were averagely 9 and 5 years, respectively, and no significant statistical difference was observed between adopters and non-adopters. The average statistically significant difference in annual income between adopters  and non-adopters  emphasizes the economic importance of cricket farming in transforming rural incomes.With regard to access to institutional support services, 35% of both adopters and non-adopters reported having accessed credit. Interestingly, there was a significant difference (at 5% level) between the two groups with regards to membership in a rural group with about 86 and 77% positive responses among adopters and non-adopters, respectively. This finding is consistent with existing literature on the role of social networks and rural institutions in the adoption of new farming enterprises such as cricket farming  equation reveal that distance to main road, group membership, insect consumption, and provision of rearing equipment affects the probability of adoption of cricket farming after receiving training. Notably, holding all other factors constant, longer distance to the main road undermines adoption of cricket farming as it is associated with a reduction in the probability of adopting the enterprise after exposure to training by 2.4%. Previous studies have shown that poor rural road network could potentially limit timely access to inputs, technical support and financial resources necessary for agricultural production  on other farm or non-farm activities. Finally, the negative association between inaccessibility to roads (remoteness) with cricket yields corroborates evidence from existing studies that poor road network in rural areas limits farmers’ access to both factor and output markets thus undermining farmers’ full production potential  respectively. The differential effects could be attributed to differences in skill-set endowments and specialization among trainers from the two sources of training, although this assessment is beyond the scope of the current study.The estimates of the first stage of the ESR model are presented in Table The statistically significant likelihood ratio test of independence of the selection and outcome equations indicates that there is a positive correlation between cricket consumption and market supply. This implies the presence of endogeneity problem hence justifying the use of ESR model. Additionally, the Wald test is statistically significant indicating the goodness of fit of our ESR model.First stage ESR estimates show that determinants of cricket consumption are distance to the main road, distance to the main market, nutritional perception, and entomophagy awareness. Both distances to main road and the market had negative effects on the decision to consume crickets. This is probably because access to roads and shorter distances to markets may facilitate access to information. As a result, households that are favored by this proximity may be relatively more informed on alternative food sources compared to their remote counterparts. Besides, existing literature points out that good road networks and market accessibility in rural areas improve household decision making and economic outcomes due to increased access to agricultural information services Jacoby .Farmers’ awareness of crickets as source of food and perceing them as nutritional had positive effects on consumption decisions. These findings are consistent with current literature on drivers of insect consumption. For instance, Schouteten et al.  observedWith regard to determinants of market supply, the mode of operating cricket farming affected market supply among cricket consumers. As such, operating cricket farming/enterprise with partners or as a group with other farmers increases the quantities sold among the cricket-consuming households. This could be attributed to efficiencies resulting from labor pooling and diverse skill-sets when operating with partners as cricket farming is labor-intensive  is significantly different from zero. The results suggest that both observed and unobserved factors influence market supply gains (welfare outcomes) given the consumption decision. The significance of the coefficient of correlation between the consumption equation and the outcome equation indicates that self-selection occurred in the decision to accept and consume crickets as an alternative food/protein group.The estimates for the average treatments effects (ATT and ATU) and the heterogeneity effect (HE) are presented in Table The results reveal that cricket consumption (entomophagy) significantly increased market supply and has the potential to increase that of non-consuming producing households. Interestingly, the results in Table In this study, we analyzed socio-economic and institutional factors that would sustain the adoption of edible cricket farming and estimated the effects of institutional training and cricket consumption on production and market supply. Firstly, in assessing what determines how much adopters produce, we hypothesized that this hurdle is dependent on the decision to take up cricket farming after receiving training. We applied the Heckman sample selection model to assess socio-economic and institutional factors that influence adoption and production. Secondly, we hypothesized that the training provider determines the ultimate quantities produced. To that effect, we estimated the effect of the training sources on the production of edible crickets using a multinomial endogenous treatment effects model. Lastly, we deploy an endogenous switching regression to estimate the effects of household consumption of crickets (entomophagy) on market supply.Our findings confirm that after being exposed to training, cricket adoption is enhanced by shorter distance to the markets, membership in rural institutions, provision of rearing equipment (starter kits), and practicing entomophagy. The implication is that uptake of new cricket farming enterprises would require both national and county governments in Kenya in collaboration with development partners to implement policies that address road infrastructural challenges to the market. The policies should further encourage and promote rural institutional capacity building of community members on cricket farming technologies for food and feed. Lastly, sensitization and awareness creation on the nutritional and health benefits of entomophagy to households and economic wellbeing of youth and women is crucial.Cricket yield is significantly higher for adopters that received technical training from various institutions. This implies that future programs should take a collaborative approach in providing specialized training and technical backstopping to assist cricket farmers in overcoming adoption barriers. Research and development partners should endeavor to provide support through the provision of affordable production equipment, entrepreneurial skills, financial support, value addition, professional training and market access.Our results further confirm an effect of cricket consumption on market supply. Thus, commercial cricket mass production and market demand would readily be achieved if frequent sensitization campaigns on cricket consumption are widely carried out to encourage adoption, acceptability, and practices of entomophagy in the various communities and beyond. Nevertheless, despite the aforementioned insights and policy implications, this study has some limitations that can be addressed by future research. Firstly, the current study focuses on the cricket enterprise from a supply-side only. Secondly, this study does not look into the cricket farmers’ disadoption behavior and lastly the study relies on cross-section data which has empirical limitations in estimating causal effects. Future studies should therefore address these limitations by assessing the cricket demand drivers, farmers’ disadoption behavior and endeavor to overcome data and study design limitations to analyze the dynamics of adoption, demand and supply as well as causal effects of institutional and behavioral interventions on consumption and yields.


In addition, the area % of the nanoscale surface irregularity (nSI%) was calculated from the AFM images. The effects of nSI%, Ra and θc on the μSBS were analyzed by multiple linear regression analysis (p < 0.05). Multiple regression analysis revealed that the nSI% was the most predominant factor for the μSBS (p < 0.001). A surface with larger nSI%, higher Ra and relatively lower θc was essential for establishing a reliable resin-zirconia bond.Zirconia ceramics have been widely used as dental restorations due to their esthetic appearance and high flexural strength. The bonding of zirconia with resin cement should rely on both mechanical and chemical bonds. This study was performed to investigate the effect of zirconia surface topography and its wettability after surface pretreatments on the microshear bond strength (μSBS) of a resin cement. Zirconia slabs were prepared and randomly divided into 5 groups based on the surface treatment as follows: no treatment (control), air abrasion (AB), etching with hydrofluoric acid (F), the mixture of hydrofluoric acid and nitric acid (FN), or the mixture of hydrochloric acid and nitric acid (CN) for 10 min. The specimens were subjected to investigation of surface roughness characteristics  using atomic force microscopy (AFM) and measurements of surface contact angle ( However, the bonding of zirconia with resin cement is still considered problematic. Unlike glass ceramics, these ceramics cannot be etched with conventional 5–9% hydrofluoric acid (HF) to create the surface roughness necessary for mechanical interlocking. Thus, the standard protocol is airborne-particle abrasion for mechanically roughening the surface3, accompanied by the application of 10-methacryloyloxydecyl dihydrogen phosphate (10-MDP) monomer to facilitate chemical bonding4. This protocol has been reported to be successful in achieving optimal zirconia-resin bond strength and bond durability7.Yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) ceramics have been widely used in restorative dentistry due to their esthetic appearance and high flexural strength8. Moreover, a too deep and narrow rough surface of adherend materials would reduce the penetration of adhesives or resin cements10, leading to prevention of the optimal formation of mechanical interlocking and/or chemical bonding. Meanwhile, it was speculated that a shallow and wide-open rough surface would be more favorable in allowing complete filling of resin cement11.However, air abrasion was reported to produce sharp-edged irregularities, which might cause initiation of crack propagation in the material12. Although the formation of nano-porosity did not drastically increase the average surface roughness (Ra) value, compared to air abrasion, its presence was found to increase surface wettability and improve the resin-zirconia bond strength11.Concurrently, HF at a high concentration was introduced to etch the zirconia and provide roughness on the zirconia surface, manifested as micro- and nano-porosity13. Therefore, other surface roughness parameters, such as the average distance from the tips of peaks to valleys (Rpv), the height distribution and tip geometry of peaks and valleys evaluated by skewness (Rsk) and kurtosis (Rku), and the presence of nanoscale surface irregularity (nSI), should be investigated because they might be able to affect resin penetration and, consequently, the resin-zirconia bond strength.Regarding roughness measurement, zirconia bonding studies generally report treated zirconia surface topography by the Ra value, which evaluates the surface irregularities in the vertical direction. It should be noted that Ra only measures the height average deviation from the mean line, where the slopes, sizes, geometries of peaks and valleys, or nanoscale irregularity on the microrough surface cannot be exactly detailedThus, the objective of this study was to investigate the effect of surface topography, determined by Ra, Rpv, Rsk, and Rku, the surface wettability, and the nSI of pretreated zirconia ceramics on the microshear bond strengths (μSBS) of a resin cement to zirconia. The null hypothesis tested was that the surface topography and wettability do not affect resin-zirconia bond strengths.® Shaded; Glidewell Laboratories, CA, USA) by using a low-speed diamond saw , and the top and bottom surfaces of slabs were polished under water running with abrasive SiC papers  using a grinding machine . Then, all the specimens were fully sintered into the final dimensions of 10 × 10 × 2 mm following the manufacturer’s instructions and randomly assigned into 5 groups (n = 12) as follows: Control: no surface treatment, Group AB: air abrasion with 50 μm alumina particles under 2 bars at 10 mm from the specimen top surface for 10 s followed by 10 min ultrasonic cleaning in deionized water, Group F: etching with 48% HF, Group FN: etching with 48% HF mixed with 68% nitric acid (HNO3) at a 1:1 ratio by volume, and Group CN: etching with 37% hydrochloric acid (HCl) mixed with 68% HNO3 at a 4:1 ratio by volume. In the acid etching groups, 50 µL of designated acid was dropped and thus wetted the zirconia surface for 10 min, followed by rinsing with deionized water for 1 min. The chemical agents used are listed in Table Sixty zirconia slabs were prepared from partially-sintered zirconia milling blanks (BruxZirp < 0.05).All the specimens were investigated for surface roughness characteristics using AFM  in noncontact mode. The parameters Ra, Rpv, Rsk, and Rku were calculated using analysis software . The Ra, Rpv, Rsk, and Rku data were analyzed statistically with one-way ANOVA and Tukey’s test  by manual counting using a simple hundred-square grid from a 2D image. If a clearly smooth grain surface presented more than 50% of the square grid, it was counted as a smooth grain surface grid . The nSI% was analyzed statistically with one-way ANOVA and Tukey’s test (p < 0.05).Twelve images from each group were measured by 2 observers, 2 times with a 1-week interval. A validation of the method used in this study was performed by both intraobserver and interobserver reliability analysis using the intraclass correlation coefficient (ICC) test, whose correlation ratios were 0.988 and 0.972, respectively .All the treated specimens were used for contact angle measurement by the sessile drop technique. One microliter of deionized water was dropped on the specimen surface and left for 1 min prior to the measurement using a goniometer . The ®, Norton Performance Plastic, OH, USA) with an internal diameter of 0.8 mm and a height of 0.5 mm on the zirconia surface, resin cement  was subsequently injected into the tube and light cured for 20 s from the top surface using a light-curing unit . All specimens were stored in 37 °C distilled water for 24 h. After carefully removing the tubes, the specimens were subjected to μSBS testing using a universal testing machine  using the wire-loop technique at a crosshead speed of 0.5 mm/min. The materials used are listed in Table p < 0.05).After the investigations, all the specimen surfaces were applied with a 10-MDP containing primer . After placing polyethelene tubes (Tygon15: adhesive failure (80–100% of the failure occurred at the interface between resin cement and zirconia), cohesive failure in resin cement or zirconia (80–100% of the failure occurred within resin cement or zirconia), and mixed failure (mixed with adhesive and cohesive failure patterns in the same specimen). The failure mode data were analyzed statistically with the chi-square test (p < 0.05).After the μSBS test, failure mode analysis was carried out using a stereoscopic microscope  at 56 × magnification. Failure modes were categorized into 3 types as followsθc on μSBS, multiple regression analysis was performed by using IBM SPSS Statistics version 27.0  (p < 0.05). Model fit was assessed using the coefficient of determination (R squared).To investigate the effect of nSI%, Ra and p < 0.05). The AB and F groups exhibited the highest Ra and Rpv values among the treatments. The Rsk values were negative in the control and CN groups, whereas they were near zero in the AB group and positive in the F and FN groups. The Rku value of the control group was more than 3. All the surface treatments caused an insignificant change in Rku compared with the control group (p > 0.05). The only significant difference in Rku was found between the CN group  and the F group  (p < 0.05).The means of Ra, Rpv, Rsk, Rku and their standard deviations are presented in Table Representative AFM images exhibiting the surface topography of nontreated and surface-treated zirconia are shown in Fig. p < 0.05), and the F and FN groups showed significantly higher nSI% than the AB and CN groups (p < 0.05).The means and standard deviations of nSI% are shown in Table θc and standard deviations are shown in Table θc compared with the control group (p < 0.05), in which the FN group exhibited significantly lower θc among the treatment groups (p < 0.05).The mean p < 0.05), and the F group exhibited significantly higher μSBS among the treatment groups (p < 0.05).The means and standard deviations of μSBS in each group are shown in Table p < 0.05). The majority of the failure mode of the control was adhesive failure, whereas it was mixed failure in the AB, F, FN, and CN groups.The failure mode distribution is displayed in Fig. θc was significantly fitted to μSBS [μSBS = 9.487 + 0.089(nSI%) + 0.080(Ra) – 0.079(θc)] , in which nSI%, Ra, and θc were statistically significant predictors . The nSI% was the most predominant factor for resin-zirconia bond strength (standardized coefficient beta = 0.499)  of the zirconia surface and affected the 16.AFM was employed in this study since it is an essential tool for characterizing surface topography, particularly on the nanoscale, providing a direct view of surface features at high resolution without surface coating being required3, including a mixture of HCl with HNO3, were used. Air-pressurized abrasion can roughen the surface with large surface defects and mechanically damage the zirconia grains in the top 1- to 1.5-µm zone17. HF etching using a high concentration can corrode both intergranular and intragranular parts of the zirconia surface, being slightly faster at the grain boundaries18, leading to a size reduction and dislodgement of zirconia grains12. These two methods increased Ra and Rpv to the highest values, whereas the mixture of HF with HNO3 (FN group) and the mixture of HCl with HNO3 (CN group) caused the lesser increase in Ra and Rpv. The results of this study were consistent with previous studies, which reported that the mixture of HF with HNO3 was capable of roughening a zirconia surface20 but to a lesser extent than HF etching alone21. It was speculated that adding HNO3 reduced the penetration of the mixture into grain boundaries, no longer dislodging the deeper-part grains. Last, the mixture of HCl with HNO3 barely etched the zirconia substrate, although its strong oxidative effect22 and its ability to readily dissolve common metallic oxides and hydroxides23 have been addressed.With the aim of creating zirconia surfaces with various micro- and nanoscale irregularities, the treatments of airborne particle abrasion or acid etching with HF and its mixture with HNO24. Good surface wetting, determined by a low contact angle, leads to intimate contact between adhesive and adherend and enhances mechanical interlocking26. Thus, surface wettability could be one of the parameters used to predict bonding efficacy. It has been documented that the contact angle is the parameter manifested from the substrate’s surface roughness and surface chemistry27. To eliminate the effect of chemical heterogeneity on the treated surfaces, each specimen was stored in a closed container for 21 days before contact angle measurement. This aging condition possibly exerted airborne hydrocarbon adsorption, which subsequently increased the contact angle28. Although this method overestimated the values of measured θc, it aided the surface chemistry of all treated groups to be homogenized, and hence, the wettability derived from the effect of surface topography could be predominant29.In adhesive dentistry, adequate surface wetting of the adhesive on the substrate is the primary requirement to achieve good adhesion31. However, several studies demonstrated that the contact angle decreases following increasing roughness to a certain extent, where no greater improvement in wetting is expected for highly rough surfaces33. It should be noted that the wettability was determined not only by the average surface roughness but also by the surface geometry in three dimensions, such as the distance between peaks compared to their height, of which the proper distance and height could facilitate the liquid to better wet the surface by forming a capillary action32. Excessively high peaks and symmetrically distributed high peaks and deep valleys (near-zero Rsk) conversely decrease wettability (increase θc) due to trapped air in surface cavities34, whereas flat-shaped peaks and valleys (Rku < 3) are more favourable for enhancing surface wettability35. From the results of this study, the FN group exhibited the lowest θc, which could be attributed to an optimal height and a predominance of peaks (positive Rsk) having a symmetric Gaussian shape (Rku = 3). On the other hand, both the AB and F groups created higher rough surfaces, which might be too high, with a more symmetric topography (near-zero Rsk). Thus, the wettability of the AB and F groups was hampered compared to that of the FN group. When compared to each other, the shape of peaks and valleys was relatively sharp in the AB group (Rku > 3) and flat in the F group (Rku < 3), leading to the better wettability of the F group than that of the AB group. Last, the θc values of the control and CN groups were the highest among the studied groups due to their low surface roughness and predominance of sharp valleys (negative Rsk and Rku > 3). Presumably, the moderately high roughness, with flat peaks , might be preferable to enhance liquid wettability on the zirconia surface.The Wenzel theory states that an increase in surface roughness could decrease the contact angle for a hydrophilic surfaceInterestingly, nSI was obviously observed on the grain surfaces of treated zirconia, especially in the F and FN groups. Since this nSI could not be quantified by the roughness parameters described above, the nSI% was evaluated manually by counting under the hundred-square grid method. It was speculated that a zirconia surface with a higher nSI% could increase the surface area for adhesion; thus, a higher bond strength could be expected. Accordingly, the F and FN groups, including the AB group, which had a high nSI%, demonstrated a higher µSBS than the groups with a lower nSI%.θc, and the nSI%, but also the surface roughness (Ra), which was highest in the F and AB groups, could cooperatively contribute to improving the bond strength of resin cement to zirconia. This speculation was confirmed by the multiple regression analysis, revealing that nSI%, Ra and θc significantly affected the µSBS , in which nSI% was the most predominant factor contributing to the bonding to zirconia (standardized coefficient beta = 0.499), and Ra was a more significant predictor than θc   phase transformation, which is a critical consideration for the surface treatment of Y-TZP ceramics, was not evaluated in this study. Previous studies demonstrated that gentle air abrasion protocols, as used in this study, generally trigger t–m phase transformation44, whereas HF etching with conditions close to the current study was found to induce phase transformation to the lesser degree than air abrasion45. Nevertheless, such a small amount of monoclinic phase presented on the zirconia surfaces prior to the bonding procedure was found to not influence the resin bond strength47.Within the methodology in the current study, there are some clinical limitations to be considered. First, it would be impractical to treat the intaglio surface of a crown and wait 3 weeks before proceeding with cementation. Second, the hazards of HF are well recognized48 and the decrease in bond strengths of resin-zirconia50 were widely reported. However, the study revealing the negative correlation between monoclinic contents and bond strengths after aging is scarce. On the other hand, some previous studies showed increased monoclinic contents without a significant drop in resin bonding52. It was concluded that the effect of resin hydrolysis is responsible for the decreased bond strength more than the phase transformation52. Moreover, it is still controversial whether the development of residual stresses at the interface following phase transformation may negatively affect surface adhesion50. Therefore, further study investigating the effect of t–m phase transformation on both immediate and long-term bond strengths is needed.After aging, the increase in monoclinic contentsθc could concomitantly affect the bond strengths of resin cement to zirconia. The surface roughness properties Rsk and Rku are important parameters for facilitating surface wettability.It could be concluded based on this in vitro study that appropriate surface topography and surface wettability are essential for establishing reliable resin-zirconia bond strength. Of all the factors evaluated, nSI% was the most predominant factor, whereas Ra and


The adjustable loop cortical suspension device (ALD) is a useful femoral fixation device in anterior cruciate ligament (ACL) reconstructions, but the possibility of loosening has been suggested. The purpose of this study was to evaluate the elongation of an adjustable loop and the position of the hamstring graft inside the femoral socket.The subjects were 33 patients who underwent ACL reconstruction with a hamstring tendon. The graft was fixed using ALD and completely filled the femoral socket. Magnetic resonance images were taken one week and one year after the operation. The loop length, femoral socket length, and graft length inside the socket were measured and statistically compared with the clinical outcomes.P < 0.001). The gap between the top of the graft and femoral socket was 0.9 ± 1.8 mm one week after surgery and 1.3 ± 1.7 mm one year after surgery (P = 0.259). At one week post-operation, a gap was found in nine patients (27.3%). The loop length and gap did not strongly correlate with clinical findings.The loop length one week after surgery was 18.9 ± 4.4 mm, and 1 year after surgery was 19.9 ± 4.5 mm (ACL reconstruction using ALD showed a gap between the graft and femoral socket at the one week post-operation mark in 27.3% of participants. One year after the surgery, there were cases where the gap increased and/or decreased, but the elongation of the loop was 1 mm on average. Our findings suggest that ALD is clinically safe to use; however, has the possibility of initial loop elongation and non-uniform changes.IV. The cortical suspensory fixation device is widely used for femoral fixation in anterior cruciate ligament (ACL) reconstructions using the hamstring tendon. Conventionally, fixed loop cortical suspension devices (FLDs) have been used, and beneficial biomechanical and clinical results have been reported , 17, 22.There are many studies of biomechanics about ALD, but most of these are in vitro evaluations of ALDs alone or on the knees of cadaveric and animal studies. To date, the in vivo condition of a tendon graft completely filling the bone tunnel using ALD remains unclear. Therefore, the purpose of this study was to evaluate the loop length changes and the position of the graft which is completely filled in the femoral socket after ACL reconstruction with ALD by magnetic resonance imaging (MRI) and compare them with clinical outcomes. Since previous biomechanical studies have shown that the ALD tends to become loosened with cyclic loading, we hypothesize that the loop would exhibit elongation in vivo, and the tendon graft would shift with it. However, the results were not related to clinical results.This study was a retrospective study. The subjects were 33 patients who underwent ACL reconstruction using an ALD from January 2016 to January 2018 at our hospital and related facilities. Inclusion criteria were primary anatomical single-bundle ACL reconstructions, with hamstring autograft. In all cases, the tendon was fixed to the femur using an ALD, and the button was in contact with the femur on a plain radiograph immediately after the operation. Those cases using an FLD (3 cases), double-bundle reconstruction (5 cases), bone patellar tendon-bone graft (BTB) (1 case) and quadriceps tendon-bone graft (2 cases), and revision ACL reconstructions (2 cases) were excluded. Table All operations were performed by senior coauthors and the procedure was the same in all cases. The semitendinosus tendon (ST) was harvested and quadrupled, for a graft diameter of 7–9 mm and a minimum length of 55 mm. When the thickness was not sufficient, the gracilis tendon was also harvested, combined with the ST to make a six-fold graft, and pretensioned. The femoral bone tunnel was drilled using an outside-in technique with an Antero-Lateral Entry Femoral Aimer  inserted with a guide pin into the anatomical centre of the ACL attachment behind the ‘resident’s ridge’. Using a Flip Cutter  of the same diameter as the graft, 12–20-mm femoral socket was prepared according to the prepared graft length. For the tibial tunnel, a guide pin was inserted into the centre of the ACL attachment of the tibial stump using a Director Tibial Guide , and overdrilled with the same diameter as the tendon graft. The femoral side was fixed with TightRope RT . After graft passage and confirming the button was flipped by intraoperative fluoroscopy, the graft tendon was completely inserted in the socket. After applying preconditioning for 60 s at 100 N and repetitive knee flexion–extension to fully remove the creep, the graft was fixed at 20°of knee flexion with a Double Spike Plate System  at 20 N by ligament tensioners . Meniscal resection, sutures, and cartilage procedures were added according to the arthroscopic findings.All patients were rehabilitated according to the rehabilitation protocol performed at our hospital. The knee joint was placed in a brace for one week after surgery. Subsequently, joint range of motion exercise was started, and the range of motion was gradually increased weekly. After six weeks, there was no restriction. Partial weight bearing was started one week after the operation, and full weight bearing was taken in three weeks. When concomitant meniscus repair was performed, weight bearing and range of motion exercise were delayed for one-two weeks. Approximately three months after the operation, the patients were allowed to start running. Jump exercise was allowed at four months and participants returned to sports eight to 12 months after the operation.We had taken MRI with the following protocol in a past study of bone tunnel enlargement. One week and one year after the operation, the MRI  examination of axial 3D proton-density-weighted imaging sequences was performed before knee range of motion exercise for all cases, and the angle of knee flexion was fixed at 10°. The following parameters were used for imaging acquisition: repetition time = 1500 ms, echo time 39 ms, field of view = 130 mm, matrix = 216 × 216, and slice thickness = 0.6 mm. Multiplanar reconstruction was then performed using the Synapse VINCENT medical imaging system . First, an axis parallel to the femoral tunnel was created and adjusted to maximize the diameter of the femoral tunnel, femoral socket, and graft inside the socket. The distance from the lateral wall of the femur to the graft tendon (loop length) (a), the distance from the femoral tunnel aperture to the femoral socket (socket length) (b), and the distance from the femoral tunnel aperture to the graft tendon in the socket (graft length inside socket) (c) were measured  were calculated. The measurements were performed by a single author, who was an orthopaedic surgeon with 12-years of experience.Clinical results were evaluated before, and minimum two years after surgery. A Lachman test was graded as negative (≤ 2 mm), trace (3–5 mm), or positive (≥ 6 mm). Pivot-shift test was graded as equal, glide, clunk, or gross. Furthermore, Lysholm score, Tegner activity scale, and side-to-side difference measurements of a KT-1000 arthrometer  performed at maximal manual forces were evaluated.P < 0.05.Data were expressed as mean ± standard error. One-week and one-year measurements were compared using a paired t-test. Spearman correlation tests were performed between the Lachman and pivot-shift tests and variations in loop length and gaps. Pearson correlation tests were performed between the Tegner activity scale scores, Lysholm scores, KT-1000 measurements, and variations in loop length and gaps. Intra-observer and interobserver reliabilities of socket length and graft length inside socket were assessed using the intraclass correlation coefficient (ICC). The measurements were performed twice with an interval at least eight weeks between measurements to minimize the memory effect. A co-author also performed measurements to assess ICC. The ICC value for the interobserver reliability was 0.62–0.83. The ICC value for the intra-observer reliability was 0.87–0.93. A post-hoc power analysis was performed using alfa = 0.05 and N (number of cases) = 33; the power was over 0.9. For all statistical evaluations SPSS version 26.0  was used, and significance was assumed at P < 0.001). The gap between the top of the graft and the top of the femoral socket was 0.9 ± 1.8 mm one week after surgery and 1.3 ± 1.7 mm one year after surgery, and the change was 0.4 ± 2.1 mm (P = 0.259) , and a gap of 3 mm or more was found in four cases (12.1%). One year after surgery, the gap increased in 12 cases (36.4%) and decreased in seven cases (21.2%). There were four cases (12.1%) in which the gap increased by 3 mm or more and two cases (6.1%) in which the gap decreased by 3 mm or more , glide in eight cases (24.2%), and clunk and gross were not observed. The KT-1000 side-to-side difference measurement was 6.0 ± 2.1 mm preoperatively and 0.2 ± 1.1 mm postoperatively. The Tegnar activity scale was 7.0 ± 1.1 before surgery and 6.9 ± 1.1 after surgery. The Lysholm score was 82.9 ± 10.5 before surgery and 99.6 ± 1.4 after surgery Table .Table 3Cr = -0.54, P = 0.001), mean KT-1000 side-to-side difference measurement , Lachman test (r = 0), pivot-shift test , or Tegnar activity scale . Change of tunnel-graft gap also did not strongly correlate with the Lysholm score , mean KT-1000 side-to-side difference measurement , Lachman test (r = 0), Pivot-shift test , or Tegnar activity scale .The loop length change was not strongly correlate with the Lysholm score  had a gap between the graft and the socket one week after surgery. In our procedure, after confirming the flip of the Tightrope RT button, the loop was tensioned until it did not shrink further, and the graft was fully filled in the bone socket. The presence of a gap between the graft and the socket at one week after the operation indicates that the adjustable loop had already lengthened even after the early postoperative period.Choi et al. reported the presence of a gap between the graft and the femoral socket on MRI images taken the day after an ACL reconstruction using, as in the present study, an ALD , 7. TheyIn this study, at one year after the operation, the gap increased in 12 cases (36%), while the gap decreased in seven cases (21%). This suggests that the mechanical properties of an ALD may affect the dynamics of the tendon in the bone socket, even long after surgery. There have been few reports on gaps in the femoral socket with cortical suspension devices using MRI. Ahn et al. reported about 10% of cases with a tunnel-graft gap at six months after surgery in both FLD and ALD, but this gap was not related to the clinical results . In our To date, several studies have reported the biomechanical properties of ALDs. Several papers have reported that the maximum displacement of loop length after cyclic loading is larger in ALD compare to FLD , 17, 22.This study has several limitations. The first is the potential for measurement accuracy. Although there was no problem in the measurements one week after the operation, there were some cases in which it was difficult to evaluate the bone socket and the tendon graft one year after the operation, depending on the degree of enlargement of the femoral tunnel and ‘ligamentization’ of the graft. Intra-rater and inter-rater errors were evaluated using reconstructed images from high-resolution MRI scans and were considered acceptable. Second, the initial measurement of loop length was performed one week after the operation.There might have been loosening during this period. However, since the knee has been fixed for one week and the load has been limited, this bias was mitigated as much as possible. Third, there was no control group. Since the in vitro biomechanical superiority of an FLD is clear, it is considered that the elongation of the loop is less than that of ALD. We also did not evaluate the enlargement of the femoral tunnel, which is considered one of the advantages of ALDs. Since it is clinically relevant to evaluate femoral tunnel enlargement by ALDs, it is necessary to evaluate this in future studies.ACL reconstructions using an ALD in which the hamstring tendon fully filled the femoral socket showed a gap between the top of the graft and the top of the femoral socket in 27.3% of cases one week after the operation, and one year after the surgery, there were cases where the gap increased and decreased. In this study, ALD showed non-uniform deformation in vivo, but it is clinically safe to use if it is used in consideration of the possibility of initial loop elongation.


Modern endovascular hybrid operating rooms generate large amounts of medical images during a procedure, which are currently mostly assessed by eye. In this paper, we present fully automatic segmentation of the stent graft on the completion digital subtraction angiography during endovascular aneurysm repair, utilizing a deep learning network.Completion digital subtraction angiographies (cDSAs) of 47 patients treated for an infrarenal aortic aneurysm using EVAR were collected retrospectively. A two-dimensional convolutional neural network (CNN) with a U-Net architecture was trained for segmentation of the stent graft from the completion angiographies. The cross-validation resulted in an average Dice similarity score of 0.957 ± 0.041 and median of 0.968 (IQR: 0.950 – 0.976). The mean and median of the average surface distance are 1.266 ± 1.506 mm and 0.870 mm (IQR: 0.490 – 1.430), respectively.We developed a fully automatic stent graft segmentation method based on the completion digital subtraction angiography during EVAR, utilizing a deep learning network. This can provide the platform for the development of intraoperative analytical applications in the endovascular hybrid operating room such as stent graft deployment accuracy, endoleak visualization, and image fusion correction. Technical innovations in the past decade have led to the replacement of the mobile fluoroscopic C-arm with modern high-tech hybrid operating rooms. With the increased imaging capabilities of the contemporary hybrid operating room, large numbers of fluoroscopy images and digital subtraction angiography (DSA) images are generated during and after the EVAR procedure. Intraoperative clinical decision-making has been predominantly based on visual inspection of images by the operating team, with the exception of computed tomography-fluoroscopy image fusion for navigational purposes. We propose that these techniques can be used to fully exploit all acquired intraoperative images at no additional burden to the operating team. A considerable advantage of using deep learning algorithms is the objective analysis of images versus the subjective visual inspection by the operating team. Moreover, because the number of examples that a deep learning algorithm is exposed to during training is only limited by the availability of data, its exposure could be higher than that of a single observer, potentially outperforming humans. In terms of clinical implementation, this has the potential of improving interobserver agreement during clinical decision-making in the operating room.The interest in artificial intelligence techniques, and particularly deep learning, has exploded in the past years, driven by an increase in computational power, the utilization of large data sets, and image-guided surgery.Completion digital subtraction angiographies (cDSAs) performed at the end of the procedure after stent graft deployment contain information on the stent graft’s position, possible endoleaks, patency of arteries and stent-graft limbs, and blood flow dynamics. Currently, the cDSAs are assessed by the operating team during the procedure by eye. Since the aforementioned information can be subtle, this important information can be missed by the operating team such as a small endoleak. When this information is known during the procedure and the physician is alerted by the algorithm, extra steps can be performed intraoperatively to prevent reintervention. To fully exploit the possible hidden and valuable information in these images, we propose to analyze these images using deep learning. In this article, we present fully automatic segmentation of the stent graft on a cDSA, utilizing a deep learning network. We hypothesize that an automatic stent graft segmentation method can be developed which can be incorporated into intraoperative clinical applications such as stent graft deployment accuracy analysis during the procedure, endoleak visualization, and image fusion overlay correction, all performed by artificial intelligence–based techniques.Completion digital subtraction angiographies (cDSAs) performed at the end of endovascular aneurysm repair (EVAR) contain important information of the stent graft position, patency of arteries and stent graft limbs, and possible endoleaks. All these clinical features are currently assessed by the operating team during the procedure by eye. It is possible that some of these clinical important features can be missed, as some endoleaks are subtle. Also, the position of the stent graft relative to the renal arteries is not assessed in millimeters during the procedure. An artificial intelligence–based algorithm can analyze the cDSA images on a pixel-detailed level, which can be a challenge for humans to see. When this information is analyzed during the procedure, the physician can be alerted by the algorithm and extra steps can be performed intraoperatively to prevent reintervention, such as additional ballooning, placement of a proximal cuff, use of endoanchors, and extension of EVAR limbs. We hypothesize that our fully automated stent graft segmentation method can be incorporated into intraoperative analytical applications such as advanced stent graft deployment assessment during the procedure, endoleak visualization, and image fusion overlay correction, all performed fully automated by artificial intelligence–based techniques.2 to 0.370 × 0.370 mm2. The cDSAs were acquired with a multiphase acquisition protocol; phase 1 for 6 seconds at 3 frames per second (fps), phase 2 for 5 seconds at 1 fps, and phase 3 at 0.5 fps indefinitely. Typically, a cDSA contained between 18 and 43 images per series. A purposely induced apnoea minimalized visceral and stent graft motion during the recording of all series.Completion digital subtraction angiographies of 47 patients treated for an infrarenal aortic aneurysm using EVAR were collected retrospectively. Patients in the study cohort received a bifurcated stent graft, bifurcated stent graft with an iliac branch device, or a single-limb aortoiliac stent graft. The collection of cDSAs from the patients’ electronic medical records was performed with the approval of the local medical ethical review board. All cDSAs were acquired using a C-arm x-ray system in a Philips Azurion FlexMove 7 C20 Hybrid Operating Room  in the Amsterdam University Medical Centers, location VUmc, in the period from May 2017 to April 2020. Images were acquired with isotropic and anisotropic dimensions ranging from 790 × 1024 to 1904 × 1904 pixels with isotropic pixel sizes ranging from 0.154 × 0.154 mm2 isotropic pixel spacing using bilinear interpolation and nearest neighbor interpolation, respectively.We assumed that the stent graft did not move during a cDSA series and therefore posed our segmentation problem as a 2-dimensional (2D) segmentation problem, in which one stent graft segmentation is obtained per cDSA series. To represent a cDSA series as a 2D image, a maximum intensity projection (MaxIP) was obtained along its temporal axis. This resulted in one projection image per cDSA, in which the visibility of the injected contrast agent was minimized, thereby maximizing the visibility of the stent graft. To train and evaluate the deep learning network, pixel-wise manual segmentation masks of the stent graft were created by an expert for all cDSA series to serve as the ground truth. Subsequently, all 2D projection images and corresponding ground truth masks were resampled to 0.4 × 0.4 mm Training of the neural network was performed using randomly-extracted image patches with size 512 × 512 pixels from the projection images. Training patches were augmented to create additional synthetic data to increase the generalizability of the network. Data augmentation consisted of horizontal flipping with a probability of 0.5 and rotation around the image center with a maximum deviation of ±14°. The network was trained by minimizing a Dice loss with an Adam optimizer algorithm using a learning rate of 0.001, dropout of 0.2, and a batch size of 8. The training was performed for 2000 epochs with a reduction of the learning rate by a factor 10 after 1000 and 1500 epochs.A 2D convolutional neural network with a U-Net architecture was trained for segmentation of the stent graft on the 2D projection images, as illustrated in  and run on a workstation with a NVIDIA GeForce RTX 3090 Graphics Card with 24 GB of memory.The network was implemented using the PyTorch-based framework Medical Open Network for AI (MONAI)The performance of the network was evaluated based on a Dice similarity score, which measures the overlap between the predicted segmentation by the model and the ground truth mask. Moreover, we computed the average surface distance between the ground truth and predicted stent graft segmentations. This is the average distance (in millimeters) from all points on the boundary of the predicted segmentation to the closest point on the boundary of the ground truth mask.We performed a 9-fold cross-validation on the full data set. Automatic stent graft segmentations with a high Dice similarity score on unseen test images are shown in Quantitative results are shown in The results of our study demonstrate that a deep learning–based method can fully automatically segment the stent graft on cDSA images in 1.5 seconds. The proposed method can provide the basis for further development of clinical analytic applications in the modern endovascular hybrid operating room. This includes objective assessment of stent graft deployment, presence of endoleaks, patency of arteries and stent graft limbs, and blood flow dynamics.Inspection of the results shows that our proposed method is capable of segmenting different configurations of stent grafts, i.e., bifurcated stent grafts with and without iliac branch devices and aorto-uni-iliac stent grafts. The different strut structures and suprarenal and infrarenal fixation did not seem to influence the performance of the deep learning network.611 Closest to our work is the study of Breininger et al, who also proposed a fully automatic deep learning–based method for stent graft segmentation in single 2D fluoroscopic images. Note that in contrast to their method, we perform automatic segmentation on a 2D projection obtained from the entire 2D + time cDSA series. This leads to results that appear to be slightly more accurate than those reported by Breininger et al, who obtained an average Dice coefficient score of 0.943 ± 0.043, whereas the average of our Dice coefficient was 0.957 ± 0.041.Several previous studies have reported automatic feature extraction of angiographic images during EVAR. These studies mainly focus on characterization and quantification of arterial deformations after insertion of stiff guide wires, intraoperative correction of image fusion, and automatic segmentation of guidewires.The study was limited by the number of available cDSAs. Data augmentation was used to partially compensate for the limited number of available imaging. However, expansion of the data set would likely further improve the performance and robustness of the deep learning network. Despite the high Dice similarity scores of our experiment, one major outlier as a result of under-segmentation can be observed in Our proposed method for fully automatic stent graft segmentation lays the foundation for further development of clinical analytical applications for cDSAs to fully exploit possible valuable information. Future work can focus on the development of deep learning networks for segmentation of anatomical structures and analysis of flow parameters. The merging of separate models can lead to the development of fully automatic analytical applications to assist the surgeon in more objective clinical decision-making in the hybrid operation room.We developed a fully automatic stent graft segmentation method based on the cDSA during EVAR, using a deep learning network. This can provide the platform for the development of intraoperative analytical applications in the endovascular hybrid operating room such as stent graft deployment accuracy assessment, endoleak visualization, and image fusion correction.


Yu-Ping-Feng polysaccharides (YPF-P) is the primary component of Yu-Ping-Feng San (YPF-S) medicine prescription, which consists of three polysaccharides from Astragalus Membranaceus polysaccharide (AM-P), Atractylodes Macrocephala polysaccharide (AM-P), and Saposhnikovia Divaricata polysaccharide (SD-P). The use of Yu-Ping-Feng polysaccharides dates back to the Yuan Dynasty when Yilin Wei first utilized it. The remedy is included in “Effective Formulae Handed Down for Generations” and “The Pharmacopoeia of the People’s Republic of China.” Yu-Ping-Feng polysaccharides is known to promote growth and enhance the body’s immune function in animals. As such, it has promising application prospects in animal husbandry. This review mainly introduces the main components and characteristics of Yu-Ping-Feng polysaccharides, its effects on animal production, and its impact on animal immune function. Additionally, this paper offers a preliminary discussion on the development and utilization of Yu-Ping-Feng polysaccharides, laying the foundation for further research and application. This review may also provide insight and reference for the development of the farming industry, reducing production costs and improving productivity. However, these chemically synthesized formulations could lead to permanent residues within animals, posing potential health threats to both animals and humans.Chinese herbal compound polysaccharides have demonstrated significant effects in immune regulation, antiviral activity, antioxidation, and blood glucose reduction, among other aspects. Moreover, due to the lack of toxic side effects on tissues and cells, these advantages contribute to a broad potential application of polysaccharides in antibiotic-free animal farming.Research has confirmed that the Chinese herbal compound polysaccharide - specifically Yu-Ping-Feng polysaccharide - can enhance animal growth performance and immunity. It holds the potential to replace antibiotics and become a viable non-antibiotic feed additive. This article provides a comprehensive review of the composition, characteristics, and functions of Yu-Ping-Feng polysaccharides in animal production, offering insights for further theoretical research and practical applications in animal farming .2.Astragalus Membranaceus (Huang qi), Atractylodes Macrocephala (Bai zhu), and Saposhnikovia Divaricata (Fang feng) in a ratio of 3:1:1 by weight. Yu-Ping-Feng San is a classical Chinese medicine formula that has a broad range of clinical applications and is effective in treating various diseases, including those related to immunity, inflammation, tumors, viral infections, and lungs  (p < 0.05) (p < 0.05), however, the combination of Yu-Ping-Feng polysaccharides and probiotics was more beneficial for piglets  . Yu-Ping < 0.05) . And rel < 0.05) , 27. In  < 0.05) . In addi < 0.05) . Other s piglets . In conc3.2.SGLT1, GLUT2, GLUT5) in chicken intestinal mucosal epithelial cells, and promoting faster nutrient absorption. As a result, feed utilization is enhanced and the growth characteristics of poultry are altered (p < 0.05) . A related study demonstrated the effects of five Chinese herbals on chick growth and development and found that one of them, the Yu-Ping-Feng polysaccharides, promoted chick growth and development . Related altered , 31. How altered . In addi altered . Interes altered . Subsequ altered . In addi altered . After t < 0.05) . Other e < 0.05) . These r3.3.In earlier times, local veterinarians commonly used Yu-Ping-Feng polysaccharides to treat various conditions in domestic animals, such as excessive sweating, night sweats, nocturnal sweats, and constipation . In modeSome studies have shown that the addition of Yu-Ping-Feng polysaccharides to the diet can significantly improve the slaughter rate of fattening cull cattle and have some fattening effect , 47. It In one experiment, goats in three groups were fed a normal diet, Yu-Ping-Feng medicinal residue + diet, and Yu-Ping-Feng medicinal residue fermented matter + diet; the results showed optimal improvement in meat quality and mutton odor taste in the goats fed the Yu-Ping-Feng ferment compared to the control group . AnotherAs evident from the above, Yu-Ping-Feng polysaccharides can enhance the production performance of various economically important animals . Serving4.The strength of the animal’s immune directly affects the animal’s health. Yu-Ping-Feng polysaccharides, a traditional Chinese medicine compound, can have a protective effect on the immune system of the animal body .4.1.Chicks can gradually develop their immune barrier from the time they hatch out of their shells. In this process, the immune performance of chicks can be enhanced by feeding some antibiotic-free probiotics or natural herbs, thus improving the production efficiency at a later stage. It has been shown that the addition of Yu-Ping-Feng polysaccharides to chick diets could strengthen the antioxidant enzyme activity in chicks, effectively eliminate excess free radicals in the body and improve their immune capacity . Subsequ+ and CD8+ in cells, and enhance the immune function of animals (p < 0.05) and increases total serum protein content (p < 0.05) (Newcastle disease virus (NDV) on chicken embryonic fibroblasts, but also effectively reduced the virulence of NVD  . Anothere of NVD . Yu-Pinge of NVD , 71. Sime of NVD .The polysaccharide components in Yu-Ping-Feng polysaccharides are the material basis for enhancing animal immunity; it has been found during waterfowl research that Yu-Ping-Feng polysaccharides can enhance the immune function of meat ducks by increasing the content of immune factors and improving thymic function . In addi4.2.p < 0.05) , immunoglobulins , interleukins (IL-2) and γ-interferon (IFN-γ), and T-lymphocyte subsets  compared to the blank group ( < 0.05) , 27. Whe < 0.05) . Moreove < 0.05) .According to the relevant literature, the effects of the addition of the traditional Chinese medicine compound Yu-Ping-Feng polysaccharides on the immune performance of different species of economic animals are summarized as follows. As shown in , CompounBased on the available evidence, it appears that Yu-Ping-Feng polysaccharides can be utilized to improve the immune function in poultry and pigs. Various studies have demonstrated that Yu-Ping-Feng polysaccharides has multiple immune-enhancing effects, including the stimulation of immune cell proliferation and cell cycle distribution, increasing protein content in serum, and maintaining the potency of immune vaccines.5.Yu-Ping-Feng polysaccharides has promising market potential in animal production. Extensive experimentation has demonstrated the value of Yu-Ping-Feng polysaccharides, whether used simply as a feed additive or in combination with other substances, in promoting animal growth and development, as well as enhancing disease immunity. In the context of modern animal production processes, the use of Yu-Ping-Feng polysaccharides or its derivatives can yield significant productivity gains, thereby ensuring improved profitability for farms.The polysaccharides from Yu-Ping-Feng have positive effects in the field of economic animal breeding, but there are still some issues: (1) In poultry and livestock farming, Yu-Ping-Feng polysaccharides have great potential as a natural feed additive for antibiotic substitution. However, there are limited comparative experiments between antibiotics and Yu-Ping-Feng polysaccharides on animals, and there is a lack of scientific data to support this. More research is needed to confirm the substitutive ability of Yu-Ping-Feng polysaccharides for antibiotics. (2) Yu-Ping-Feng polysaccharides can be extracted from the medicinal residue byproducts of Yu-Ping-Feng prescriptions produced by traditional Chinese medicine companies. However, the utilization of these medicinal residues by traditional Chinese medicine companies is not sufficient. It is hoped that secondary development and utilization of these residues can turn waste into valuable resources. Resolving the above-mentioned issues can further enhance the understanding and utilization of Yu-Ping-Feng polysaccharides, which is of significant importance for non-antibiotic animal breeding.HC: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing. YZ: Formal analysis, Supervision, Writing – review & editing. HY: Conceptualization, Supervision, Writing – review & editing. SC: Supervision, Writing – review & editing. ZM: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing. HL: Conceptualization, Supervision, Writing – review & editing.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is supported by the National Natural Science Foundation of China (No.32002156), the Natural Science Foundation of Guangdong (No.2019A1515110454), and the Key Area Research and Development Program of Guangdong Province (2019B110209005).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.in situ” for transplantation, and those discarded after the “in situ” evaluation were considered as no transplantable liver grafts, while those grafts transplanted after “in situ” evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.Liver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated “p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85.A total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (The tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation. Throin situ” assessment. This liver graft assessment is a complex process, and the most important tool that liver transplant surgeons (LTS) have, to carry it out, is their own experience in evaluating liver grafts for transplantation  techniques have been developed in different medical fields, LT included –16. TheyMachine learning (ML) is the area of AI that enables the creation of computer models with the ability to learn from real data. In this work, we use prediction systems that are trained from a set of labeled records and generate a model able to classify new data . There aThe main objective of the study is to develop a tool that supports the surgeon responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.The liver graft samples evaluated for LT between the period 2016 and 2018 were studied. They are composed of TLG and NTLG. All of them were evaluated by the same three LTS, which share the same validation criteria for liver graft assessment. The liver donor variables were extracted from LDP and collected prospectively in a database . LDP is The reasons for rejections argued by the LTS were collected . Just asA liver biopsy was made for all grafts belonging to the NTLG group once the LTS classified the liver graft as NTLG, and all histological diagnoses were collected .in situ” by an expert surgeon in liver donation. We used the “Guide to Quality and Safety of Organs Transplantation” (Only the liver after donor brain death (DBD) was included in this study. All liver grafts were initially accepted by telephone based on the characteristics of the LDP and evaluated “ntation”  to assesAll NTLGs were discharged without histological confirmation. The biopsies were made after the LTS classified the liver graft as NTLG, all of them being carried out by liver expert pathologists belonging to the same pathological anatomy unit that shares the same liver graft study protocol. Those that were considered NTLG after an intraoperative histological study were excluded. The TLG group is formed by liver grafts that were used for LT and did not develop primary non-function or early allograft dysfunction. All of them have post-reperfusion biopsy findings that were not pathological. Those TLG that were considered TLG after the intraoperative histological study were excluded.in situ” LTS evaluation and were not used for liver transplantation.NTLG: liver grafts that were accepted by telephone but failed the “in situ” evaluation. All of them were used for liver transplantation. No liver in this group developed primary non-function or early allograft dysfunction, and all of them have a post-reperfusion biopsy without pathological findings.TLG: liver grafts that were accepted by telephone and passed the “Personal medical history (PMH): refers to any systemic pathology other than the cardiovascular risk factors.Personal surgical history (PSH): refers to any previous abdominal surgery.In the ultrasound item , we consThe item “macroscopic appearance”  is indetIn our center, during the study period, we considered pathological steatosis if it was >30% and macrovacuolar. Currently, the pairing policy has changed in our region, and the donor–recipient matching is allowed, so grafts with steatosis of up to 60% are accepted.The sample was divided into two groups: TLG and NTLG.This study was approved by the hospital's ethics committee. All data were fully anonymized before we accessed them.(A)NTLG risk factors studyIn Phase A, the NTLG risk factors were identified through a univariant study. For it, a single center, retrospective and cohort study based on a prospective database that analyzes a liver graft sample was performed  were included in the study. Based on the obtained results in the univariant study, we have designed a computer system that can help in the decision-making process to determine the suitability or not of a graft.The normal distribution of the sample was determined using the Kolmogorov–Smirnov test. We compared them and studied whether there are statistically significant differences between both groups, establishing the odds ratio (OR) that each variable presented. For this univariant study, the Chi-square test has been used for qualitative variables and the Student's (B)Expert machine-learning system for liver graft assessment developedThe objective of the whole process is to obtain a mathematical model capable of predicting the suitability of a liver graft for LT from the diverse factors under study. To do this, a classic supervised learning workflow is applied, consisting of three different phases: (1) pre-processing and selection of the most relevant attributes, (2) conducting tests with different mathematical models that optimize a certain fitness, and (3) validation of results to choose the best model.F-test was applied, then selecting those attributes with the highest statistical value, a sign of a greater difference between the values of these attributes for transplantable and no transplantable grafts.The first step consists of a preliminary pre-processing phase that includes feature selection (FS). This procedure often improves the prediction rate of the classifiers , by chooTo conduct the second step, we have opted for classifiers based on decision tree ensembles. An ensemble is a model resulting from the collaboration between several individual classifiers, such that the final decision is the result of a consensus or vote. One of the most widely used prediction models in this category is the XGBoost  classifihttps://epistasislab.github.io/tpot/). TPOT is a Python-automated machine learning (AutoML) ((1)Add several synthetic features, using Bernoulli, Naïve Bayes, and XGBoost classifiers as estimators.(2)Add two new features, corresponding to the number of zero and non-zero features for each sample.(3)F-value between label/feature.Perform Feature Selection, using ANOVA (4)Perform classification with XGBoost classifier.The final step is validating the model. To avoid overfitting , different validation techniques may be used. In our case, both hold-out and cross-validation have been used. Cross-validation (CV) (Both Phases 1 and 2 have been performed using TPOT ((AutoML) . This toion (CV)  is an ition (CV) .External validation has been excluded in this work mainly due to the sample size. Instead, we have used five different types of validations see , three o•Accuracy: a fraction of all correctly classified instances.•Precision: the proportion of true positives out of all positive predictions.•Recall: the proportion of true positives out of all actual positive instances.•F1-score: the harmonic mean of precision and recall.•AUC: the measure of separability. It indicates the extent to which a model is capable of distinguishing between classes.In the following, a brief explanation of the scores in c) or antibodies against hepatitis C virus (AcHCV) were scarce.A total of 350 liver grafts that were evaluated for LT were studied: 123 NTLG vs. 227 TLG. The mean age was 60 years old, and most donors were males. The most frequent cardiovascular risk factor in the sample was arterial hypertension (AH), and more than 50% of the sample had associated medical antecedents other than the cardiovascular risk factors. The sample presented obesity Grade 1, and the analytical values were in the physiological range. The positive donors for antibodies against hepatitis B capsid antigen .The NTLG risk factors identified in the univariant study were age, dyslipidemia (DLP), PMH, PSH, bilirubinemia (Bb T), and the result of previous liver ultrasound (p > 0.05)  .The best performance reordering in terms of accuracy and AUC corresponds to 76.29% of correct classifications, with an AUC value of 0.79 . In the second column, 70 out of 123 liver grafts classified as NTLG for LTS were classified as NTLG for the mathematical model. In addition, 30 liver grafts classified as TLG for LTS were classified as NTLG for the model. As it can be derived from this matrix, the rate of success is considerably higher for TLG, which is 87% compared with 57% for NTLG. In addition, a study of prediction reliability has been carried out. This is possible because the model provides the probability of confidence for each predicted label. From The same distribution of hits and errors is depicted in Furthermore, the XGBoost classifier provides us with a ranking specifying the relevance of variables. Specifically, the sorted list of variables in this study is the following: PMH, normal ultrasound, pathological ultrasound, PSH, Bb T, gamma-glutamil transferase (GGT), age, body mass index (BMI), DLP, amine doses, glutamate pyruvate transaminase (GPT), amines, AH, sodium (Na), and glutamic oxaloacetic transaminase (GOT). On the other hand, the remaining variables: sex, diabetes mellitus (DM), AcHCV, AcHBV, not performed ultrasound, have no contribution to determining graft validity, according to XGBoost.In addition, we have conducted an overfitting analysis of our tool using learning curves. Learning curves provide insights into how the performance of the classifier evolves as the size of the training set increases. By plotting the training and validation accuracies of the classifier against the number of training instances, it is possible to identify if overfitting or underfitting is occurring.In particular, the classifier is overfitting the training data when the training accuracy is high, and the validation accuracy remains significantly lower along the graph. This means that the classifier is memorizing the training examples and performing poorly on unseen data. On the other hand, when the training and validation accuracy curves converge at a certain accuracy level, it suggests that the performance of the classifier stabilizes, and it is not significantly affected by adding more training examples. The accuracy level of the convergence indicates a situation of underfitting (low level) or a well-generalized classifier (high level). As it can be seen in In this study, we present a mathematical model that initially classifies the liver graft as transplantable or not based only on LDP information. LDP is an official ONT document that is sent to the liver transplant team that is valuing the liver graft, containing the most important features of the donor. It is the first information known and constitutes a very important element in the decision-making process. The information that the mathematical model gives us relates to the possibility of using the liver graft for LT. It does not match the donor–recipient, does not calculate the index of risk of primary graft failure (PGF), and does not make predictions about patient post-transplant mortality, survival, or complications.The liver graft assessment for transplantation is a complex process. The two main tools that the LTS can use to carry out it are the LDP and the experience of the surgeon . This exper se,” but on the percentage and type of steatosis. On the other hand, the macroscopic features of the graft constitute an indeterminate item defined by the absence of the specific macroscopic criteria for rejection. In our study, up to 66.6% of the organs discarded for this reason presented a biopsy without pathological findings. In these cases, an intraoperative biopsy could help the surgeon make decisions, but it is not always possible to perform it.Currently, the process for liver graft assessment can be improved; the rate of NTLG without pathological findings that confirm it is between 20.4%–35.5% , 32. It •The argument of the surgeon: up to 36.5% of the grafts in our sample represent the total percentage of grafts discarded because of steatosis and macroscopic features. In the case of the national series in recent years, this percentage has ranged between 50.6% and 60.9% .•The biopsy reports: up to 60.2% in our series, the summation of NTLG with biopsy, without pathological findings and with steatosis findings.The model proposed in this study can be a useful support tool in the process of liver graft assessment. It not only produces a prediction but also informs of the probability with which each prediction has been selected. The best results obtained by the system in terms of precision corresponded to 76.29% with an AUC value of 0.79. This success rate is established for a confidence interval of >0.8, with an error probability of 0.15. To design the model under the most realistic conditions possible, the inclusion criteria used were the LTS assessments and not the biopsy findings, since it is known later. Therefore, the system has been tested with data in which the group of grafts considered as NTLG contains grafts that could have been used (20.4%). Of these 25 NTLG that presented a biopsy without pathological findings, 17 belong to the 53 NTLG predicted by the mathematical model as a TLG. It is an interesting finding because it means that the model predicts TLG more accurately than NTLG, identifying TLG classified as NTLG by LTS, which is in this case, 17 grafts of 25 (68%). However, it is important to note that classifiers should not replace clinical expertise and domain knowledge. Instead, they should complement the decision-making process by providing additional insights and supporting doctors in their clinical judgment. In this context, using classifiers for making predictions in health applications offers several advantages over the traditional statistical techniques. On the one hand, it allows handling more complex relationships and interactions between multiple variables in the data, since they are well-suited to capture non-linear relationships. Also, classifiers can adapt and update their predictions as new data become available. This adaptability to changing data allows incorporating new evidence to the model, making them very suitable to real-time decision support.For all that, there exists a percentage of liver grafts rejected for LT that could have been used for this purpose or that could have benefited from a more comprehensive assessment process, at least, based on the following:•The assessment we perform with our model is global, based on donor characteristics. We do not focus on just one of the reasons of graft rejection.•We do not need to travel to obtain the images to carry out the assessment. The ONT sends the LPD before moving the surgical team, so the assessment is performed very early. This avoids unnecessary travel, with the consequent economic savings for the health system.As we previously described, we aim to develop a tool that supports the LTS in the decision-making process whether to accept a graft or not using the initial variables available to it. To make the most of the previous information on liver grafts provided by LDP, we propose to study and learn from the previous and accumulated experience of the LTS, using AI techniques. The LTS experience is based on a non-analytical model of clinical reasoning. The outcome of the decisions we make can be optimized by the analytical processes of our AI-based tool (The scientific community has already used math for aiding this complex process and make the liver graft pool available for LT profitable. Based on classical statistics, there are tools that calculate the index of risk of FPI, or even, the survival of the patient with crossed data of donors and recipients , 35. In sed tool , 37. It AI can be understood as the part of science in charge of developing a set of mathematical algorithms that allow computers to perform processes such that they can pose solutions to determinate problems after reaching their conclusions, which are the result of analyzing a large amount of data . For a lThe main limitation of the development of this technology is the lack of data. AI needs large databases to serve as a source of information to develop new models of data analysis and interpretation and to improve the existing ones . For thiCurrently, we are in the initial development phase of this system.The main limitations associated with our study are that it is a retrospective study; the mathematical model has been trained with the results of only one liver transplant team and must be externally validated. In addition, the tool has been trained with a pool of grafts in which steatosis of >30% was considered a criterion for graft rejection. The pairing policy has changed in our region, and donor–recipient matching is allowed, so grafts with steatosis of up to 60% are accepted. Therefore, the tool may classify grafts as NTLG that we can currently use. We hope that all these limitations will be addressed in the future as we train the tool, as machine learning can learn as it is trained with new data. Thanks to this capability, it can adapt to such modifications.For all these reasons, we currently continue to introduce new data in the model, so we hope that the system improves its predictive capacity.The tool presented in this paper obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it; thus, we think that it can become an important tool to help in the decision-making process supporting the valuation of the LTS. The fact that the model presents better results identifying TLG is very interesting because the final objective is to decrease the number of TLG discharged to liver transplantation that could have been used for LT.AI and big data technology need the collaboration of the surgical community to advance. So, these systems learn and improve as they analyze more data. In this way, the creation of large databases that include as many patients as possible is necessary if we want to implement these novel mathematical technologies.


Trigeminal neuralgia (TN) is the most frequent facial pain. It is difficult to treat pharmacologically and a significant amount of patients can become drug-resistant requiring surgical intervention. From an etiologically point of view TN can be distinguished in a classic form, usually due to a neurovascular conflict, a secondary form (for example related to multiple sclerosis or a cerebello-pontine angle tumor) and an idiopathic form in which no anatomical cause is identifiable. Despite numerous efforts to treat TN, many patients experience recurrence after multiple operations. This fact reflects our incomplete understanding of TN pathogenesis. Artificial intelligence (AI) uses computer technology to develop systems for extension of human intelligence. In the last few years, it has been a widespread of AI in different areas of medicine to implement diagnostic accuracy, treatment selection and even drug production. The aim of this mini-review is to provide an up to date of the state-of-art of AI applications in TN diagnosis and management. Trigeminal neuralgia (TN) is a form of neuropathic facial pain which significantly impacts quality-of-life of affected patients . TypicalTwo medical databases (PubMed and Scopus) were screened for eligible scientific reports. The key words “deep learning”, “machine learning”, “artificial intelligence”, “trigeminal neuralgia”, “tic douloureux” (MeSH) have been used in any possible combination. The last search was launched in August 2023. Two reviewers  independently screened the abstracts and the references list. Any difference was solved by consensus with a third senior author (N.M.) A total of 26 articles were identified and reviewed and finally, 17 studies were included in the present mini-review .AI has become increasingly involved in various aspects of TN diagnosis. It plays a pivotal role in identifying signature patterns within the cortical and subcortical regions, delineating the surgical anatomy of the cerebello-pontine angle, and distinguishing different types of facial pain. Liang et al. explored the capabilities of correlation and machine learning (ML) analysis methods in detecting activated cortical centers in TN patients. Interestingly, they found that certain cortical areas were only activated by conventional correlation methods, while others were detected by convolutional neural networks (CNN) and graph convolutional neural networks (GCNN). Their suggestion to combine these methods aims to achieve a more comprehensive understanding of the neural structures underlying TN pain perception . Zhong eIn contrast to Lin et al., Bai et al. introduced MVDNet, a deep learning network focused on real-time blood vessel and cranial nerve segmentation during MVD procedures for facial and trigeminal nerve disorders. MVDNet achieved impressive precision, with a 76.59% Intersection-over-Union (mIoU) accuracy and a rapid 137.6 fps speed, surpassing other real-time models . AI has p < 0.01), absence of immediate postoperative pain remission (p < 0.01) (p = 0.003)  , symptom < 0.01) . A prosp= 0.003)  and a re= 0.003) . Regardi= 0.003) , 34, 35.= 0.003) . Chen et= 0.003) . Microsc