Upcycling Lactobacillus casei culture waste into vegan cheese analogue using inulin, locust bean gum, and κ-carrageenan.

The growing popularity of probiotics has led to the generation of substantial by-products. Among these, cell-free supernatant is recognized for containing beneficial postbiotics. Here, we upcycled Lactobacillus casei-free supernatant (LFS) into cheese analogues using inulin (INU), locust bean gum (LBG), and kappa-carrageenen (kCG). In this system, LBG/kCG established the primary structure, while interstitial spaces were progressively filled by INU. Despite the absence of milk proteins and fats, the cheese analogue with 35% w/w INU, 0.2% w/w LBG, and 0.8% kCG exhibited a texture and appearance resembling commercial processed cheese, as determined by texture profile analysis and dynamic small amplitude oscillatory rheometry technique. This can be attributed to the effective fat-replacing activity of INU regarding texture and rheology. Furthermore, the potassium-dominated salt composition of LFS proved advantageous for the LBG/kCG-derived structure-forming. These findings hold significant promise for upcycling probiotics wastewater into low-fat vegan cheese analogues, enriched with both prebiotics and postbiotics.

Optimization of Ultrasound-Assisted Pretreatment for Accelerating Rehydration of Adzuki Bean (Vigna angularis).

Adzuki bean (Vigna angularis), which provides plant-based proteins and functional substances, requires a long soaking time during processing, which limits its usefulness to industries and consumers. To improve this, ultrasonic treatment using high pressure and shear force was judged to be an appropriate pretreatment method. This study aimed to determine the optimal conditions of ultrasound treatment for the improved hydration of adzuki beans using the response surface methodology (RSM). Independent variables chosen to regulate the hydration process of the adzuki beans were the soaking time (2-14 h, X1), treatment intensity (150-750 W, X2), and treatment time (1-10 min, X3). Dependent variables chosen to assess the differences in the beans post-immersion were moisture content, water activity, and hardness. The optimal conditions for treatment deduced through RSM were a soaking time of 12.9 h, treatment intensity of 600 W, and treatment time of 8.65 min. In this optimal condition, the values predicted for the dependent variables were a moisture content of 58.32%, water activity of 0.9979 aw, and hardness of 14.63 N. Upon experimentation, the results obtained were a moisture content of 58.28 ± 0.56%, water activity of 0.9885 ± 0.0040 aw, and hardness of 13.01 ± 2.82 g, confirming results similar to the predicted values. Proper ultrasound treatment caused cracks in the hilum, which greatly affects the water absorption of adzuki beans, accelerating the rate of hydration. These results are expected to help determine economically efficient processing conditions for specific purposes, in addition to solving industrial problems associated with the low hydration rate of adzuki beans.

Characterization of yeast protein isolates extracted via high-pressure homogenization and pH shift: A promising protein source enriched with essential amino acids and branched-chain amino acids.

In the global food industry, plant-based protein isolates are gaining prominence as an alternative to animal-based counterparts. However, their nutritional value often falters due to insufficient essential amino acids. To address this issue, our study introduces a sustainable protein isolate derived from yeast cells, achieved through high-pressure homogenization (HPH) and alkali pH-shifting treatment. Subjected to HPH pressures ranging from 60 to 120 MPa and 1 to 10 cycles, higher pressure and cycle numbers resulted in enhanced disruption of yeast cells. Combining HPH with alkali pH-shifting treatment significantly augmented protein extraction. Four cycles of HPH at 100 MPa yielded the optimized protein content, resulting in a yeast protein isolate (YPI) with 75.3 g protein per 100 g powder, including 30.0 g of essential amino acids and 18.4 g of branched-chain amino acids per 100 g protein. YPI exhibited superior water and oil-holding capacities compared to pea protein isolate, whey protein isolate (WPI), and soy protein isolate. Although YPI exhibited lower emulsifying ability than WPI, it excelled in stabilizing protein-stabilized emulsions. For foaming, YPI outperformed others in both foaming ability and stabilizing protein-based foam. In conclusion, YPI surpasses numerous plant-based protein alternatives in essential amino acids and branched-chain amino acids contents, positioning it as an excellent candidate for widespread utilization as a sustainable protein source in the food industry, owing to its exceptional nutritional advantages, as well as emulsifying and foaming properties. PRACTICAL APPLICATION: This study introduces a sustainable protein isolate derived from yeast cells. YPI exhibited considerable promise as a protein source. Nutritionally, YPI notably surpassed plant-based protein isolates in EAA and BCAA contents. Functionally, YPI demonstrated superior water-holding and oil-holding capacities, as well as an effective emulsion and foam stabilizer.

A label-free aptamer-based colorimetric biosensor for rapid gliadin detection in foods: a focus on pasta, bread and cookies.

Despite numerous advancements in gluten detection, a substantial need remains for innovative, cost-effective, in situ methods that can be employed without complex analytical instruments. Addressing this demand, this study introduces a pioneering label-free colorimetric biosensor for the in situ detection of gliadin, a major component of gluten, which is a prevalent trigger of food allergies. Our novel approach employs the strategic coating of gold nanoparticles (AuNP) with gliadin-specific aptamers. In the absence of gliadin, these aptamers stably disperse AuNP, preventing their aggregation. However, upon the introduction of gliadin and in the presence of sodium chloride, AuNP aggregate, yielding a measurable colorimetric signal that facilitates the precise quantification of gliadin. Under rigorously optimized conditions, this AuNP/aptamer-based colorimetric biosensor demonstrated exceptional sensitivity and selectivity, with a detection limit of 32.1 ng mL-1 and a linear response range of 0-300 ng mL-1. Critically, the sensor maintained reliable performance when applied to real-world food samples, including gluten-free bread, cookies, and pasta. Due to its simplicity, selectivity, speed, and cost-effectiveness, this assay represents a significant advancement over current gluten detection methods. Moreover, the developed AuNP/aptamer-based colorimetric biosensor design holds promising potential for adaptation to detect other food allergens or protein toxins through selective aptamer modifications.

Retrograded octenylsuccinylated maize starch-based emulgels for a promising oral delivery system of curcumin.

Emulgels are a type of soft solid delivery system that exploit the merits of both emulsions and gels, namely, bioactive encapsulability and structural stability, respectively. We utilized retrograded/octenylsuccinylated maize starch (ROMS) to fabricate the curcumin-loaded emulgel. Emulgels (oil volume fraction, 0.20) prepared with 1-4 % w/w ROMS exhibited fluid-like behaviors while emulgels with 5-8 % w/w ROMS exhibited a gel-like consistency. Compared to a fluidic emulsion stabilized with 3 % w/w octenylsuccinylated maize starch, the emulgels showed more sustained lipolysis and controlled curcumin release patterns. These results were attributed to rigid ROMS structures at the outer layer of oil droplets, hindering the lipase approach onto the oil/water interface and curcumin diffusion from the interface. Additionally, the bioaccessibility of curcumin in ROMS-stabilized emulgels was enhanced >9.6-fold compared to that of a curcumin solution. Furthermore, emulgels prepared with 8 % w/w ROMS exhibited a high yield stress (376.4 Pa) and maintained appearance and droplet size for 60 days of storage at 4 °C. Consequently, this emulgel has potential as a lipophilic bioactive-containing soft gel with sustained digestion and controlled release properties. Our findings may provide insights into rational delivery system designs.

Comparison of the pretreatment methods for enhancing hydration of water-soaked adzuki beans (Vigna angularis).

Five pretreatments methods, cold plasma, pressure drop, heating, and bath-type and probe-type sonications were compared to shorten the rehydration process of adzuki bean (Vigna angularis) soaked before the cooking in terms of the hydration and softening efficacies. Moisture content and water activity of the probe-type sonicated beans were most dramatically increased as 11-45% and 0.59-0.97 after soaking for only 2 h, respectively (non-treated: 11-12% and 0.59-0.66). Accordingly, the probe-type sonicated beans were most rapidly softened as 27-5 N in the 2 h-soaking and exhibited the lowest hardness after soaking/cooking as ~ 0.97 N (non-treated: 27-21 N and ~ 5.5 N, respectively). According to scanning electron micrographs, these results can be attributed to formation of prominent fissures or scars in the hilum of the probe-type sonicated beans. Consequently, this study will be provide valuable information for developing a rational process in food industry to shorten the rehydration of the adzuki beans.

Applications and perspectives of polyphenol-loaded solid lipid nanoparticles and nanostructured lipid carriers for foods.

Imbalanced nutrition in modern society is one of the reasons for disorders, such as cancer, cardiovascular disease, and diabetes, which have attracted the interest in bioactives (particularly polyphenols) to assist in the balanced diet of modern people. Although stability can be maintained during preparation and storage, the ingested polyphenols undergo harsh gastrointestinal digestion processes, resulting in limited bioaccessibility and low gut-epithelial permeation and bioavailability. Several lipid-based formulations have been proposed to overcome these issues. Solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have also been highlighted as carrier systems for the oral delivery of lipophilic bioactives, including polyphenols. This paper summarizes the research on the ingredients, production methods, post-processing procedures, general characteristics, and advantages and disadvantages of SLNs and NLCs. Overall, this paper reviews the applications and perspectives of polyphenol-loaded SLNs and NLCs in foods, as well as their regulation, production, storage, and economic feasibility.

Delayed Quality Deterioration of Low-Moisture Cereal-Based Snack by Storing in an Active Filler-Embedded LDPE Zipper Bag.

This study focused on controlling the vapor permeability of an active zipper bag and preserving the quality of cereal-based snacks during the storage period at home. The active zipper bag was prepared by extruding low-density polyethylene with active fillers obtained from natural mineral materials. The active zipper bag showed the same transparent appearance as the existing one but showed 21% lower water vapor capability. As a result, during a 20-day storage period, three types of grain-based snacks (biscuits, shortbread cookies, and puffed snacks) showed delayed increases in weight, moisture content, and moisture activity when stored in an active zipper bag. In addition, this also affected the texture of the biscuits and shortbread cookies, in which the area under the curve was reduced significantly after appearing at a peak during the hardness measurement. On the other hand, the decrease in the number of air cell fracture events in puffed snacks was remarkable. This result suggests that the inner microstructure is preserved better when stored in an active zipper bag. In conclusion, the active zipper bag showed poor water vapor permeability, suggesting that the prepared zipper bag can be developed as snack packaging.

The effect of curdlan and the resting process on the quality of the dried whole tofu noodles.

The aim of this study is to make dried noodles having high contents of whole tofu (60% (w/w)). To control the high moisture of the whole tofu, curdlan was added and a high-temperature resting process was applied. The elasticity of the dough sample rested at 45°C for 45 min increased over 50% more than the non-rested one. The addition of curdlan and the high-temperature resting process helped to form a compact internal structure in the dough, which might have been induced by the gelation of curdlan and the swelling of starch. In addition, these treatments resulted in about 20% and 15% reduction in cooking time and cooking loss, respectively. Whole tofu noodles having high protein content with improved texture and cookability was developed. These results could be helpful to the development of the bread based on a high hydration dough. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10068-021-01020-9.

GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-related data to achieve successful solutions. Modeling the properties and functions of proteins is important but challenging, especially when dealing with predictions of the sequence type. RESULT: We propose a method to model compounds and proteins for compound-protein interaction prediction. A graph neural network is used to represent the compounds, and a convolutional layer extended with a bidirectional recurrent neural network framework, Long Short-Term Memory, and Gate Recurrent unit is used for protein sequence vectorization. The convolutional layer captures regulatory protein functions, while the recurrent layer captures long-term dependencies between protein functions, thus improving the accuracy of interaction prediction with compounds. A database of 7000 sets of annotated compound protein interaction, containing 1000 base length proteins is taken into consideration for the implementation. The results indicate that the proposed model performs effectively and can yield satisfactory accuracy regarding compound protein interaction prediction. CONCLUSION: The performance of GCRNN is based on the classification accordiong to a binary class of interactions between proteins and compounds The architectural design of GCRNN model comes with the integration of the Bi-Recurrent layer on top of CNN to learn dependencies of motifs on protein sequences and improve the accuracy of the predictions.

Physician Knowledge Base: Clinical Decision Support Systems.

With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such systems.

Visible on-site detection of Ara h 1 by the switchable-linker-mediated precipitation of gold nanoparticles.

Biosensors have been widely applied in tests for allergens, but on-site detection remains a challenge. Herein, we proposed a detection procedure for peanut Ara h 1 as a representative allergen, which was extracted from a cookie, thereby minimising the need for any complex pretreatment that was difficult to perform, and enabling the visual detection of the target without the use of analytical equipment. The extraction procedure was performed in less than 30 min using a syringe and filter (0.45 µm). The detection method for Ara h 1 was based on the aggregation of switchable linkers (SL) and gold nanoparticles (AuNP), and the presence of 0.19 mg peanut protein per 30 g of cookie could be confirmed within 30 min based on the AuNP/SL concentration ratio by the precipitation. This proposed procedure could be successfully applied to the detection of a wide range of food allergens.

Development of a portable lab-on-a-valve device for making primary diagnoses based on gold-nanoparticle aggregation induced by a switchable linker.

We have developed a low-cost, portable lab-on-a-valve (LOV) integrated microdevice for the detection of pathogens in primary-diagnosis settings. This system was designed for field-based pathogen detection based on the aggregation of gold nanoparticles induced by a switchable linker. A three-way valve, which has attracted much attention as a functional mesofluidic platform for pressure-driven flow, has been designed as a universal reaction platform that combines the functions of fluid flow and a reaction chamber. In addition, we obtain rapid and enhanced visual signals by the use of a syringe filter to remove gold nano-aggregates (Au NAs). Using this device, Salmonella Typhimurium down to 101 CFU mL-1 can be visually detected within 30 min by performing a simple operation that requires no complex equipment. This prototype device has great potential for use in the semi-quantitative and qualitative identification of pathogens in on-site primary diagnoses.

Colorimetric switchable linker-based bioassay for ultrasensitive detection of prostate-specific antigen as a cancer biomarker.

The use of colorimetric bioassays for protein detection is one of the most interesting diagnostic approaches, but their relatively poor detection limits have been a critical issue. In this study, we developed an efficient colorimetric bioassay based on switchable linkers (SLs) for the detection of prostate-specific antigen (PSA), which is one of the most widely used protein biomarkers for the diagnosis of prostate and breast cancers. SLs can cross-link gold nanoparticles (AuNPs) to generate large-scale aggregates and thereby induce precipitation to achieve visual signal amplification. In addition, when SLs are occupied by target proteins (referred to as 'switch-off'), highly sensitive detection is enabled. To maximize sensitivity, we adjusted the total surface area of AuNPs by controlling their concentration. As a result, PSA was detected at an ultralow concentration of 100 fg mL-1. This SL-based assay is shown to be simple, easy to handle and visualize, and highly sensitive. Therefore, in addition to PSA, the proposed SL-based assay could be used to detect other protein biomarkers.

A pilot study using machine learning methods about factors influencing prognosis of dental implants.

PURPOSE: This study tried to find the most significant factors predicting implant prognosis using machine learning methods. MATERIALS AND METHODS: The data used in this study was based on a systematic search of chart files at Seoul National University Bundang Hospital for one year. In this period, oral and maxillofacial surgeons inserted 667 implants in 198 patients after consultation with a prosthodontist. The traditional statistical methods were inappropriate in this study, which analyzed the data of a small sample size to find a factor affecting the prognosis. The machine learning methods were used in this study, since these methods have analyzing power for a small sample size and are able to find a new factor that has been unknown to have an effect on the result. A decision tree model and a support vector machine were used for the analysis. RESULTS: The results identified mesio-distal position of the inserted implant as the most significant factor determining its prognosis. Both of the machine learning methods, the decision tree model and support vector machine, yielded the similar results. CONCLUSION: Dental clinicians should be careful in locating implants in the patient's mouths, especially mesio-distally, to minimize the negative complications against implant survival.

A Switchable Linker-Based Immunoassay for Ultrasensitive Visible Detection of Salmonella in Tomatoes.

On-site detection for sensitive identification of foodborne pathogens on fresh produce with minimal use of specialized instrumentation is crucial to the food industry. A switchable linker (SL)-based immunoassay was designed for ultrasensitive on-site detection of Salmonella in tomato samples. The assay is based on large-scale aggregation of gold nanoparticles (GNPs), induced by a quantitative relationship among the biotinylated Salmonella polyclonal antibody (b-Ab) used as the SL, the functionalized GNPs, and Salmonella. Important factors such as the concentration of SLs, time required for large-scale aggregation, and selectivity of b-Ab were optimized to minimize the detection time (within 45 min with gentle agitation) and achieve the lowest limit of detection (LOD; 10 CFU/g in tomato samples) possible. This SL-based immunoassay with its relatively low LOD and short detection time may meet the need for rapid, simple, on-site analysis of pathogens in fresh produce. PRACTICAL APPLICATION: The novel switchable linker-based immunoassay is a rapid, specific, and sensitive method that has potential applications for routine diagnostics of Salmonella in tomato products. These advantages make it a practical approach for general use in the processing industry to detect Salmonella rapidly and to implement appropriate regulatory procedures. Furthermore, it could be applied to other fresh products including cantaloupe, strawberry, and cucumbers.

Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach.

OBJECTIVES: To develop a Web-based tool to draw attention to patients positive for human papillomavirus (HPV) who have a high risk of progression to cervical cancer, in order to increase compliance with follow-up examinations and facilitate good doctor-patient communication. METHODS: Records were retrospectively analysed from women who were positive for HPV on initial testing (before any treatment). Information concerning age, Papanicolaou (PAP) smear result and presence of 15 high-risk HPV genotypes was used in a support vector machine (SVM) model, to identify the patient features that maximally contributed to progression to high-risk cervical lesions. RESULTS: Data from 731 subjects were analysed. The maximum number of correct cancer predictions was seen when four features (PAP, HPV16, HPV52 and HPV35) were used, giving an accuracy of 74.41%. A web-based high-risk cervical lesion prediction application tool was developed using the SVM model results. CONCLUSIONS: Use of the web-based prediction tool may help to increase patient compliance with physician advice, and may heighten awareness of the significance of regular follow-up HPV examinations for the prevention of cervical cancer, in Korean women predicted to have heightened risk of the disease.

Application of support vector machine for prediction of medication adherence in heart failure patients.

OBJECTIVES: Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification. METHODS: Data about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set. RESULTS: THE TWO MODELS THAT BEST CLASSIFIED MEDICATION ADHERENCE IN THE HF PATIENTS WERE: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%. CONCLUSIONS: SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.