Research on the impacts of dysphagia and its interventions on quality of life, along with research on food-shaping, indicates that 3D food printing may help to improve mealtime experiences and mealtime-related quality of life for adults with dysphagia. To synthesise an integrated set of studies on the impact of dysphagia and its interventions on quality of life and the views of stakeholders on 3D food printing. This synthesis was done to inform the development of an evidence-based framework guiding future clinical practice and research into food design in dysphagia. A qualitative meta-synthesis of six integrated, related studies on dysphagia, quality of life and 3D food printing was conducted to identify content themes. A Framework of Mealtime Quality of Life for Adults with Dysphagia was established, illustrating the connections between the themes. Impacts of dysphagia and its interventions (texture-modified foods in particular) on quality of life for adults with dysphagia include reduced physical health, reduced choice and control, reduced social engagement, and poor mealtime experiences. There are a range of barriers and facilitators to improving quality of life for adults with dysphagia. Stakeholders viewed 3D food printing as a strategy that could, with further development, improve mealtime experiences; however, various feasibility issues and other identified problems would need to be addressed for such potential to be realised. Dysphagia and its interventions impact quality of life in many ways. The influence of food design on quality of life and mealtime experiences should be considered. The evidence-based framework established in this meta-synthesis for dysphagia, quality of life and food design could be used by health professionals to guide their considered assessment and interventions in mealtime management. What is already known on this subject Research shows that dysphagia negatively impacts on the quality of life of adults with dysphagia. Research also shows that 3D food printing may be one food design strategy to help improve the mealtime experience for people with dysphagia. However, findings from recent research needed to be synthesised to create a framework connecting the impacts of dysphagia on quality of life and the importance of food design in the mealtime experience. What this paper adds to existing knowledge This study synthesises six recent studies examining the impacts of dysphagia on quality of life and the feasibility of 3D food printing to improve the mealtime experience. From this a framework (Framework of Mealtime Quality of Life for People with Dysphagia) has been created. What are the potential or actual clinical implications of this study? The Framework of Mealtime Quality of Life for People with Dysphagia will encourage allied health professionals have greater consideration of their client.s quality of life as part of their assessment and interventions. Guidelines strategies to implement the framework have been provided (see Box 1).
Incorporating chickpea into wheat noodles is a promising strategy for reducing starch digestibility, attributed to its low glycemic index. However, the addition of chickpea led to a decline in the texture properties of final products, which poses a significant challenge. Therefore, this study examined the effects of xylanase on both the texture properties and in vitro digestibility of noodles containing 60% chickpea flour-a higher incorporation level than conventionally used. The underlying mechanisms were analyzed from various perspectives, including cooking properties, swelling power, free sulfhydryl groups, and protein secondary structure of noodles. The microstructures of raw and cooked noodles were observed using scanning electron microscopy and confocal laser scanning microscopy, respectively. The results indicated that the addition of an optimal amount of xylanase (0.03%) significantly (p < 0.05) reduced the cooking loss and water absorption ratio of chickpea noodles but increased the swelling power of both raw and cooked noodles. The hardness, chewiness, and cohesiveness of chickpea noodles were enhanced, and the starch digestibility in cooked noodles experienced a decreasing trend. Additionally, the incorporation of xylanase optimized the protein network structures, leading to a denser and more uniform protein network with fewer voids in the noodle structure. Therefore, the appropriate addition of xylanase could improve the compactness of the protein network structure in high-content chickpea noodles, enhancing their texture and anti-digestion properties.
Along with flavor, texture is one of the most important attributes of fresh-cut produce, which plays a critical role in consumer acceptance, influencing perceived freshness, quality, and overall eating experience. Salads, specifically green salads, usually contain leafy greens and other fresh-cut fruit vegetables such as cucumbers and tomatoes. Each of these components has its own unique characteristics, together giving the salad a wide range of textures and consumer experiences. Texture can be measured by both instrumental analysis and sensory evaluation techniques. Instrumental analysis consists of destructive and non-destructive methods, whereas sensory evaluation consists of descriptive and consumer studies. However, a comprehensive understanding and correlation of instrumental analysis and sensory evaluation of texture in salad-related fresh-cut fruits and vegetables is lacking in the literature. This review aims to address this gap by providing an in-depth understanding of the techniques used to quantify texture-related parameters and establishing relationships between the two techniques in fresh-cut salad products. The two different modes of analysis, along with the vastly different texture-related characteristics of fresh-cut salad produce, make it challenging to correlate instrumental analysis with sensory evaluation results. Furthermore, the integration of advanced technologies, including hyperspectral imaging and artificial intelligence-driven texture analysis, is emerging as a promising approach to enhance texture characterization and quality control. In addition to providing a comprehensive approach to enhance quality assessment methods, this review highlights the importance of predictability models powered by artificial intelligence and machine learning, enabling correlating instrumental analysis and sensory evaluation.
To determine whether texture analysis based on ultrasound images of placenta can be applied to identify fetal growth restriction (FGR) before clinical diagnosis. A total of 200 ultrasound images (100 FGR and 100 normal controls) of placenta (20-24 weeks) were retrospectively collected and randomly divided into a training set and an independent test set at a ratio of 8:2 using a computer-generated random split. To ensure model stability and optimize hyperparameters rigorously, we used five-fold cross-validation exclusively on the training set. Approximately 300 texture features were extracted from placenta using the methods of the grayscale histogram, grayscale co-occurrence matrix, grayscale run-length matrix, absolute gradient, autoregressive model and wavelet transform. Then, 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients and the mutual information coefficient method. After nonlinear discriminant analysis was performed to reduce feature dimensionality, an artificial neural network classifier was conducted based on the statistically most significant texture features and clinical characteristics. Receiver operating characteristic curves were used to evaluate the performance of our methods in identifying FGR fetuses. Maternal and fetal baseline characteristics were similar for the FGR and normal groups, except for fetal abdominal circumference percentile, gestational age at birth and birth weight percentile (p < 0.05). Among the 30 optimal features, 10 features showed statistically significant differences between FGR and normal fetuses. The classification accuracy based on the statistically most significant texture features (p < 0.01) and abdominal circumference percentile can reach 86.50%, and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.89. The combination of texture analysis of placenta and abdominal circumference measurement is a noninvasive, low-cost and convenient method for predicting FGR fetuses.
A gluten-free diet is the most effective prevention of celiac disease. Understanding how starch affects the texture of gluten-free steamed bread would facilitate rational selection of raw material and improvement of eating quality. This study analyzed the texture of gluten-free steamed bread prepared with different starches and characterized fermentation-induced changes in the structures of starch and protein, as well as in dough rheology. During fermentation, the short-range order and relative crystallinity of starch decreased, while the β-sheet content and hydrophobic interactions increased, leading to increased dough volume and enhanced viscoelasticity. The incorporation of different starches regulated the extent of these changes, causing differences in steamed bread texture. Compared with corn starch (CS), tapioca starch (TS) had higher relative crystallinity and short-range order, with a smaller decrease induced by fermentation, which promoted the formation of a more ordered protein network and strengthened hydrophobic interactions. This enhanced structural order imparted greater viscoelasticity to the dough. Additionally, the peak viscosity of TS was 1.68 times higher than that of CS. The higher gelatinization viscosity enabled TS to form a denser gel during steaming, collectively contributing to a softer and more elastic texture. Specifically, the replacement of CS with TS reduced the hardness of the gluten-free steamed bread from 6.81 to 3.47 N and increased its springiness from 0.76 to 0.92. It is thus proposed that starches with more crystalline regions, stronger short-range order, higher swelling capacity and gelatinization viscosity could be used to improve the texture of gluten-free steamed bread.
Acne is the most prevalent skin disorder in the United States, affecting up to 50 million people from all age groups. Treatment options include topical and systemic therapies. Limitation in many treatment options opens avenues for alternative therapies, such as chemical peeling. This exploratory study, funded by Colgate Palmolive company, aimed to evaluate the effectiveness of a new chemical peel (PCAskin Acne Peel Plus) in treating adult acne. The novel peel features a blend of acids and biofunctional ingredients designed to aid in acne management. The study's primary objective was to assess the novel peel's effectiveness in positively influencing acne severity, specifically by reducing acne lesions, papules, and pustules. Sixteen participants aged 25-40 years old, with Fitzpatrick skin types I-VI, presenting evidence of mild-to-moderate acne, were assessed over a 12-week period following treatment initiation. The effects of the test peel on acne were evaluated using a combination of methods. Skin sebum was measured using a moisture meter (BGJOY, SK-IV digital moisture monitor for skin). Photographic data was obtained using the Canfield Visia CR System (Canfield, Fairfield, NJ; model Generation 7, software version 8) for determining acne severity, appearance of skin pores, texture and redness. Acne severity was assessed by the study investigator using the Investigator Global Assessment (IGA) acne severity scale from the Visia images. Subjective assessment of skin parameters (acne severity, oiliness of the skin, pore size, skin discoloration, skin texture/smoothness, overall clarity of skin tone, and changes in scarring appearance) was also obtained at the start (Day 0) and end (Week 12) of the study using self-assessment questionnaires filled out by the study subjects. Significant decreases in total acne lesions (papules + pustules; p-value = 0.012) and papules (p-value = 0.023) were observed at the outset of the study (Week 12) compared to baseline (Day 0), with an average change (standard deviation) of -2.0 (2.6) and -1.8 (2.5) lesions, respectively. In addition, significant improvements in sebum content (Week 12, p-value = 0.042), erythema (Week 12, p-value = 0.030), and pore appearance (Day 1; p-value = 0.005; Week 12, p-value = 0.003) were observed compared to baseline. Positive perceptions of the treatment among participants and perceived improvements in acne severity (p-value = 0.004) and skin clarity (p-value = 0.036) were also highlighted. No adverse effects were observed during the study. This preliminary exploratory study indicates that treatment with a novel chemical peel appeared to yield a range of benefits for adults with acne-prone skin, supporting its potential as a safe, inexpensive, and minimally invasive treatment for the management of mild-to-moderate forms of adult acne. Further large scale, controlled studies are necessary to confirm these initial findings.
Beyond oral function, the physicochemical properties of food, such as acidity and hardness, significantly influence nutritional intake. Clarifying the relationship between subjective and objective metrics for assessing oral function can help establish simple methods for selecting food with safe and suitable properties tailored to individual oral functions. To objectively and subjectively evaluate the chewing and swallowing processes of gummies with varying hardness and acidity in healthy young adults and to examine relationships between subjective and objective evaluation metrics. Ten healthy young adult men (mean age: 29.8 ± 3.6 years) with no history of oral hypofunction or dysphagia were included. Participants chewed and swallowed four types of gummies (hard with acid, hard without acid, soft with acid, and soft without acid) at their own pace. The subjective evaluation included sensory assessment of chewing and swallowing. Objective evaluations included electromyographic analysis of masticatory and swallowing-related muscles and analysis of the food properties of gummies that were spat out just before swallowing. Correlations between subjective and objective evaluation metrics were assessed using Peason's correlation analysis. The physical properties and acidity of food significantly affected muscle activity and sensory evaluations of masticatory and swallowing functions. Hard gummies, particularly those without acidity, showed significantly greater masseter and temporalis muscle activity, a higher number of chewing cycles, and longer chewing time than soft gummies. In contrast, no significant differences were observed in swallowing-related muscle activity or swallowing duration among the gummy types. Furthermore, strong correlations were observed between subjective evaluations and mastication-related objective parameters, whereas correlations with swallowing-related metrics were limited. Differences in food hardness caused by the presence or absence of acidity affected the masticatory and swallowing functions of healthy young adults. Furthermore, we identified a relationship between subjective and objective evaluations. Our findings provide fundamental data for investigating the effects of food physical properties on sensory evaluation, mastication, and swallowing functions.
Understanding the interactions between polysaccharides and plant proteins is essential for controlling texture in the formulation of alternative food products. This study investigates the influence of pH on rheology and microstructure of composite gels formed from curdlan and soy protein isolate (SPI). Composite gels, prepared at a 1:1 weight ratio of curdlan to SPI to eliminate the influence of mixing ratio, were compared with their respective pure components across pH 5, 7, and 9. In pure curdlan gel, a higher pH (pH 9) resulted in increased linear viscoelasticity, which can be linked to enhanced curdlan solubility, as revealed by scanning electron microscopy. In contrast, the curdlan-SPI composite gels exhibited the strongest gelation at pH 5 (G'~7535 Pa). Microstructural analysis showed that aggregated SPI particles were embedded within the curdlan network at this pH, thus reinforcing its gel strength. At pH 9, the increased solubility of SPI hindered associations of curdlan molecules, leading to weakened gel structure. Under large amplitude oscillatory shear, the composite gel at pH 9 exhibited an earlier onset of nonlinear response compared to the other pH conditions, consistent with diminished structural integrity. X-ray diffraction analysis confirmed the presence of both single- and triple-helix conformations of curdlan, which contribute to gel network formation. However, incorporation of SPI reduced the degree of triple-helix ordering, indicating disruption of curdlan's structural organization. These findings underscore the critical role of pH in modulating the phase behavior and rheological properties of nonionic polysaccharide-protein gels. Such insights are valuable for optimizing the formulation of plant-based gel systems with tailored textural properties for the development of alternative food products.
This study investigated whether distraction during chewing affects oral processing and texture perception of mashed potato samples that differ in textural properties due to the addition of dietary hydrocolloids: pectin, which produces a softer product, and psyllium, which produces a harder one. Twenty-six participants chewed the samples both in a habitual manner (control condition) and while watching a video clip (distracted condition). Electromyography was employed to characterize chewing behavior, including chewing duration, rate, and muscle activity. Salivary flow and bolus characteristics were also assessed. Sensory ratings for hardness, homogeneity, moisture, cohesiveness, adhesiveness, and ease of swallowing were compared between the soft (pectin-containing) and hard (psyllium-containing) samples, as well as between oral processing conditions. During video observation, chewing duration was prolonged. The softer sample was chewed at a slower rate, primarily due to longer intervals between chewing strokes, whereas the chewing rate for the harder sample remained unchanged. Increased suprahyoid muscle activity suggested enhanced intraoral tongue manipulation. These changes in oral processing resulted in a bolus that was softer, less adhesive, and less viscous. Additionally, both salivary flow rate and mucin concentration were reduced during video observation. Changes in sensory perception were most pronounced for the softer sample, which was perceived as softer, more adhesive, and more cohesive under distraction. Overall, distraction during chewing alters intraoral manipulation, leading to boluses with modified properties and affecting texture perception. Further research is warranted to determine whether these findings may pose a risk for overeating starchy foods with a high glycemic load.
Background/Objectives: Providing an appropriate diet to older adults with dysphagia can prevent aspiration, choking, and nutritional deficiencies and help preserve their quality of life. Therefore, assessments for determining the appropriateness of food types are required. This multicenter study aimed to determine the reliability and validity of the Meal Rounds Observation Form (MROF), which was developed to identify food forms that can be safely consumed by older adults with dysphagia. Methods: We analyzed 532 food-texture observations obtained from 155 participants (114 men and 41 women). The reliability and validity of the MROF were compared with those of videofluoroscopic (VF) or videoendoscopic (VE) examinations of swallowing. Results: The food-form categories were water (108 pairs), 0j (54 pairs), 0t (118 pairs), 1j (20 pairs), 2-1 (28 pairs), 2-2 (37 pairs), 3 (68 pairs), 4 (67 pairs), and normal food (32 pairs) based on JDD 2021 codes. The AUC was lowest for the water (0.568) category and highest for food forms requiring chewing, such as those of the 4 and normal food (0.678) categories. The sensitivity and specificity of the Gugging Swallowing Screen were 60.1% and 69.1%, respectively (p < 0.001). The agreement between the Gugging Swallowing Screen and the MROF evaluation for food types requiring mastication was 73.2%. Logistic regression analysis revealed asymmetric movement of the corners of the mouth and coughing as important indicators when evaluating food types requiring mastication. Conclusions: The MROF is useful for determining food intake safety when VF or VE tests cannot be performed in medical and nursing care settings and can guide clinical decision-making. However, caution is required in applying it clinically because of its relatively low specificity.
Emerging evidence suggests that platelet activation and aggregation are common factors in both metabolic syndrome (MetS) and severe COVID-19, emphasizing the need to investigate their biological connection. We hypothesized that enhanced platelet aggregation mediates the association between MetS and severe COVID-19. This study aimed to determine whether platelet aggregation serves as a mechanistic link between MetS and COVID-19 severity and to develop an image-based biomarker capable of predicting severe disease. We conducted massive image-based profiling of circulating platelets in a retrospective cohort of 327 COVID-19 patients (63.9% male; median age, 59.0 years) with metabolic records. Morphologic features of platelets, including shape, density and radial distribution, and texture, were extracted. A machine learning model was developed to construct the COVID-19 Platelet Aggregate Formation Index (CoPAFI), which could quantitatively reflect platelet aggregation and predict severe COVID-19. Mediation analysis was then performed to quantify the extent to which platelet aggregation mediated the association between components of MetS and severe COVID-19. The CoPAFI predicted severe COVID-19 with an area under the curve of 0.82 and an odds ratio of 3.76 (95% CI, 2.63-5.38). The CoPAFI was strongly associated with a hypercoagulable state and served as a reliable indicator for assessing the risk of severe COVID-19. In patients with components of MetS, platelet aggregation, as measured by the CoPAFI, accounted for approximately 25% of the increased severity of COVID-19. Enhanced platelet aggregation partially mediates the impact of components of MetS on COVID-19 severity, accounting for approximately 25% of the association, emphasizing the need for integrated metabolic and coagulation management in COVID-19 treatment.
The ultrasonication-assisted infrared cooking (USIC) on the improvement of texture and flavor of aged Indica rice was evaluated, and the role of ultrasonication was discussed considering starch structures and water status modifications, with varying ultrasonic powers (400-1000 W). Results showed that the moisture content in cooked and stir-fried aged Indica rice was increased, the hydrogen bond freedom and the proportion of immobile water were enhanced by USIC. The amylose content was reduced from 23.17 ± 0.06 g/100 g to 18.04 ± 0.17-19.91 ± 0.13 g/100 g, the relative crystallinity of rice starch declined from 18.32% to 3.32%-4.82%. FTIR spectrum analysis indicated the R1047/1022 cm -1 achieved a lower value (1.08-1.09), starch-protein interactions were weakened, starch-lipid complex formation was aggravated. USIC increased rapidly digested starch (RDS) content but decreased slowly digested starch (SDS) and resistant starch (RS) content. At ultrasonic powers ≥ 800 W, RS content was elevated. The hardness, chewiness, and stickiness of both USIC cooked and stir-fried rice were reduced; however, it presented a firstly decreasing and then increasing trend with the ultrasound power, with the lowest value achieved with the ultrasonic power 600 W. Microstructurally, USIC-treated rice exhibited a sponge-like porous structure with a rougher surface and fissured cross-section. USIC had no negative effect on the oil oxidation of stir-fried rice; the prominent flavor of aged Indica rice from sulfides and nitrogen oxides after cooking was attenuated and balanced by electronic nose analysis. It indicated USIC can serve as a novel approach to enhance the sensory quality of aged rice products.
A move to offer alternatives to traditional smokeless tobacco and/or cigarettes has led to the development of oral tobacco-derived nicotine (OTDN) products. One such product that has garnered popularity with adult nicotine users is nicotine gum. Nicotine gums have been identified by many as a potential reduced risk option. Understanding the texture and thermomechanical properties of these products is essential for ensuring product quality, manufacturing efficiency, product development, and compliance with standards. In this study, we present a comprehensive texture hardness and thermomechanical characterization of three commercially available 4 mg nicotine gum brands, each made in two flavors. The products are referred to throughout this article as Brand A-Mint, Brand A-Cinnamon, Brand B-Wintergreen, Brand B-Peppermint, Brand C-Cinnamon, and Brand C-Original. Thermal properties were assessed using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA), while mechanical properties were evaluated via Dynamic Mechanical Analysis (DMA) and Texture Analysis. DSC analysis revealed distinct melting behavior across all nicotine gums. Brand C-Cinnamon showed a single, intense melting peak, while Brand A and Brand B exhibited two to three lower-intensity peaks between 30°C and 100°C. These differences suggest variations in gum matrix crystallinity, likely influenced by composition, sweeteners, and flavoring agents. Texture hardness and viscoelastic analyses showed that Brand A-Cinnamon, Brand B-Wintergreen, and Brand B-Peppermint required more work to chew and break down than Brand A-Mint, Brand C-Cinnamon, and Brand C-Original. Using first-order polynomial extrapolation, we established a robust correlation between texture hardness and storage modulus (R2 = 0.90), enabling predictive modeling across temperature ranges. The measured texture hardness and viscoelastic properties closely mirrored consumer perceptions, demonstrating a clear directional alignment with aggregated feedback from a sensory trial and multiple online sources.
The development of gluten-free pasta with acceptable cooking and textural quality remains challenging due to the absence of a gluten network. In this study, gluten-free pasta was formulated using germinated proso millet flour (GPMF) with the incorporation of different hydrocolloids (guar gum (GG), gum acacia (GA), and xanthan gum (XG) at 1 and 2 g/100 g levels) to improve functional properties. Durum wheat semolina (DWS) pasta was used as the reference sample. The effects of hydrocolloid addition on pasting behavior, cooking quality, texture, color, and microstructure were evaluated. Pasta prepared from GPMF exhibited a shorter optimum cooking time (4.36-5.91 min) compared to the semolina sample (8.76 min). Hydrocolloid addition reduces the gruel loss from 3.46% (GMPF) to 2.08% in the XG2 sample, while increasing water absorption (up to 149.86%) and swelling index (up to 2.11). Texture analysis showed an improvement in uncooked pasta hardness from 1.50 N (GPMF) to 16.73 N in XG2, and an increase in cooked pasta firmness from 0.16 to 0.53 N. Color analysis revealed lower lightness and yellowness in GPMF pasta compared to semolina, while hydrocolloid incorporation improved visual appearance. FTIR analysis confirmed the presence of characteristic starch and protein functional groups, indicating changes in intermolecular interactions without the formation of new chemical bonds. SEM micrographs show hydrocolloid addition promoted the formation of a more compact and continuous starch-protein-hydrocolloid matrix after cooking. This study highlights the potential of germinated proso millet as a value-added ingredient for nutritionally balanced gluten-free pasta products.
This study aimed to evaluate the role of radiomic analysis applied to ultrasound images in predicting postpartum blood loss at birth in women affected by low-lying placenta or placenta previa. In this retrospective, single-center study, we analyzed singleton pregnancies with placenta previa or a low-lying placenta, initially diagnosed at the second-trimester ultrasound examination. Data were collected from ultrasound examinations conducted in the second and third trimesters, along with birth outcomes. Radiomic analysis was conducted on archival ultrasound images to extract quantitative features. Predictive models were constructed utilizing multivariable generalized linear modeling (Gamma regression with a log link), encompassing radiomics-only, clinical/sonographic-only, and an integrated model. In the final analysis of 107 women, 51 exhibited postpartum blood loss exceeding 500 mL. A prior cesarean delivery was recognized as a notable clinical risk factor. Multiple radiomic features identified in second- and third-trimester ultrasound scans correlated with a heightened risk of significant blood loss during birth. The integrated predictive model exhibited superior accuracy for blood loss exceeding 500 mL, achieving an AUC of 82.32% (95% CI: 74.18%-90.45%). This performance surpassed that of the clinical ultrasound model, which had an AUC of 71.27% (95% CI: 62.27%-80.27%), with a statistically significant difference (p = 0.001). Additionally, it demonstrated a nonsignificant improvement over the radiomics-only model, which recorded an AUC of 77.17% (95% CI: 68.25%-86.09%). Radiomic analysis of ultrasound images enhances risk prediction for postpartum major blood loss in pregnancies affected by placenta previa and low-lying placenta. Integrating radiomics with clinical and sonographic data improves predictive accuracy, offering a promising tool for personalized obstetric risk assessment and management.
Transcatheter aortic valve replacement (TAVR) is a minimally invasive procedure for treating severe aortic valve stenosis. This study aimed to develop an interpretable machine learning (ML) model based on echocardiographic myocardial texture for predicting 1-year clinical endpoints following TAVR via the femoral artery. We retrospectively studied 121 patients who underwent transfemoral TAVR between January 2019 and October 2023, and data from external validation centers were collected between January 2019 and October 2023 to validate and evaluate the model externally. The composite outcome of all-cause mortality and heart failure events was used as the clinical endpoint. Based on echocardiographic imaging of myocardial texture analysis, nine widely used ML algorithms were used to construct models. Three predictive models were then constructed based on the best machine mode, with Shapley additive explanations for the interpretation of the model and the assessment of the contributions of the different characteristics. Among the nine ML models, the Extra Tree (ET) model had the best discriminative ability. An explainable final ET model was established with two features. The final model accurately predicted the clinical endpoint 1 year after transfemoral TAVR in both internal and external validations, and the results in the COX multifactorial regression analysis showed that LVEF <50% was an independent predictor of the clinical endpoint 1 year after transfemoral TAVR. In addition, Kaplan-Meier survival curves showed that patients with high Rad-Score values had lower survival rates than those with low Rad-Score values. This novel method based on echocardiographic myocardial texture with ML may have the potential to be used for predicting clinical endpoints 1 year after transfemoral TAVR and may be used as a convenient tool to facilitate its utility in clinical settings.
This study examines the impact of ozonation on the baking and textural properties of bread made from ozonated Whole wheat flour (WWF). Flour was treated with ozone at 3 L/min for 5, 15, and 25 min to evaluate its effect on bread quality. Dough rheology indicated non-Newtonian behavior, with increasing ozonation time reducing viscosity. Farinographic analysis showed improved dough characteristics at 5-15 min of ozonation, with increased dough stability, increased dough development time, reduced softening, and decreased mixing tolerance index (MTI). However, at 25 min, dough stability and development time declined, MTI and degree of softening increased, indicating quality deterioration. Ozonation enhanced bread characteristics, increasing specific volume and oven spring at 5-15 min. Moreover, bake absorption increased, while bake loss decreased with increase in ozone exposure. The brightness (L*) value increased, while a* and b* values of crumb and crust decreased indicating degradation of pigments by ozonation. The textural properties show that the bread made from 15 min ozone exposure flour has softer texture having lowest hardness value as compared to other breads. FTIR spectra confirmed presence of molecular interactions and functional groups in both crumb and crust of breads. Overall, up to 15 min of ozonation enhanced bread-making properties, yielding bread with superior volume and texture, whereas longer exposure adversely affected bread quality.
To develop and validate a machine learning-based multimodal radiomics model for predicting central lymph node metastasis (CLNM) in patients with clinically node-negative (cN0) papillary thyroid microcarcinoma (PTMC). A retrospective study was conducted on the clinical data of 532 consecutive cN0 PTMC patients who underwent surgery at the Department of Thyroid Surgery of the First People's Hospital of Changzhou and the Department of Thyroid and Breast Surgery of Suzhou Municipal Hospital between January 2022 and June 2024. Among them, 487 patients from the First People's Hospital of Changzhou were randomly assigned to a training set (n=352) or an internal validation set (n=135), while 45 patients from Suzhou Municipal Hospital served as an external validation set. Clinical feature screening involved collinearity analysis using variance inflation factors, followed by logistic regression to identify independent risk factors for CLNM. Radiomics features were extracted from ultrasound and CT images. An initial feature screening was performed using statistical tests (t-test or Mann-Whitney U test, P<0.05) along with mutual information analysis (score >0.015), followed by least absolute shrinkage and selection operator (LASSO) regression for key feature selection. Using the optimized feature set, four machine learning models were constructed: random forest, gradient boosting machine (GBM), support vector machine, and K-nearest neighbors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), decision curve analysis, and SHapley Additive exPlanations (SHAP) method. Logistic regression identified five clinical features independently associated with CLNM: age <55 years (OR=2.391, 95%CI: 1.072-5.334, P<0.05), coexisting Hashimoto's thyroiditis (OR=3.084, 95%CI: 1.474-6.453, P<0.01), maximum tumor diameter (OR=11.086, 95%CI: 2.881-48.378, P<0.01), monocyte count (OR=0.005, 95%CI: 0.001-0.044, P<0.01), and the lymphocyte-to-monocyte ratio (OR=0.564, 95%CI: 0.486-0.654, P<0.01). LASSO regression selected two key ultrasound and six key CT radiomics features. Among the four models, the GBM model based on multimodal feature fusion performed best, with AUC values of 0.975, 0.833, and 0.916, accuracies of 0.925, 0.748, and 0.863, specificities of 0.950, 0.800, and 0.881, and sensitivities of 0.900, 0.720, and 0.804 in the training, internal validation, and external validation sets, respectively. Decision curve analysis showed that the GBM model provided the highest net clinical benefit within the threshold probability range of 0.1-0.8. SHAP feature importance analysis revealed that the lymphocyte-to-monocyte ratio and monocyte count contributed most to CLNM prediction, followed by maximum tumor diameter and radiomics texture features. The GBM-based multimodal radiomics model can accurately predict the risk of CLNM in patients with cN0 PTMC, which may facilitate individualized preoperative risk stratification and clinical descision-making. 目的: 探索多模态影像组学结合机器学习对临床淋巴结阴性（cN0）甲状腺乳头状微小癌（PTMC）患者颈部中央区淋巴结转移（CLNM）的预测价值。方法: 回顾性研究2022年1月至2024年6月常州市第一人民医院甲状腺外科和苏州市立医院甲乳外科收治的532例cN0 PTMC患者的临床资料，其中常州市第一人民医院487例（随机分为训练集352例和内部验证集135例），苏州市立医院45例作为外部验证集。临床特征筛选采用方差膨胀因子进行共线性分析，排除多重共线性的变量后，再通过logistic回归确定与CLNM相关的独立危险因素。采用三维Slicer软件提取超声影像组学特征874个，采用LIFEx软件提取CT影像组学特征1433个，通过统计学检验和互信息分析进行初筛，再经LASSO回归选择关键特征。基于优化后的关键特征构建随机森林、梯度提升机、支持向量机和K最近邻四种机器学习模型，采用十折交叉验证和网格搜索优化参数，通过受试者操作特征曲线下面积（AUC）、决策曲线分析及沙普利可加性特征解释（SHAP）评估模型性能。结果: logistic回归确定5个与CLNM显著相关的临床特征:年龄<55岁（OR=2.391，95%CI:1.072~5.334，P<0.05）、合并桥本甲状腺炎（OR=3.084，95%CI:1.474~6.453，P<0.01）、肿瘤最大直径（OR=11.086，95%CI:2.881~48.378，P<0.01）、单核细胞计数（OR=0.005，95%CI:0.001~0.044，P<0.01）、淋巴细胞与单核细胞比值（OR=0.564，95%CI:0.486~0.654，P<0.01）。LASSO回归筛选出2个关键超声影像组学特征和6个关键CT影像组学特征。在四种模型中，基于多模态特征融合的梯度提升机模型性能最佳，其训练集、内部验证集和外部验证集的AUC分别为0.975、0.833和0.916，准确率分别为0.925、0.748和0.863，特异度分别为0.950、0.800和0.881，灵敏度分别为0.900、0.720和0.804。决策曲线分析显示梯度提升机模型在0.1~0.8的阈值概率范围内净临床获益最高。SHAP特征重要性分析显示，淋巴细胞与单核细胞比值及单核细胞计数对预测CLNM的贡献最大，肿瘤最大直径和影像组学纹理特征次之。结论: 基于多模态特征融合的梯度提升机模型能准确预测cN0 PTMC患者CLNM风险，有助于制订个体化的术前风险评估和临床决策。.
This study investigated the effect of pre-gelatinized pearl millet flour (PGPMF) supplementation on the quality characteristics of native pearl millet flour and bread prepared from the blends. The PGPMF was prepared by steaming the pearl millet grains, followed by drying and milling. The PGPMF was substituted in the native pearl millet flour in varying proportions and blends were characterized for quality parameters. The bread was prepared from the blends and assessed for quality characteristics. The PGPMF substitution improved the hydration properties of blends and reduced the water solubility index by 28.9%. The PGPMF reduced the emulsion capacity and foaming ability of the blends, while it improved the freeze-thaw stability by 30.6%. The rheological analysis of blends confirmed their shear-thinning behavior, and FTIR spectra indicated broadening of OH stretching bands and improvements in the amorphousness. The PGPMF substitution amended the physical attributes of prepared bread with a significant enhancement in crumb structure. The in-vitro starch digestibility and protein digestibility increased considerably with the substitution of PGPMF. The firmness of bread decreased by 36% and its springiness increased from 0.26 to 0.35 with the substitution of PGPMF. The scanning electron micrography revealed a porous and uniform matrix in PGPMF substituted breads. The substitution level of 40% PGPMF was identified as optimal, resulting in significant improvements. These findings indicate that PGPMF effectively addresses the rheological limitations of pearl millet, improving bread quality, thereby supporting the development of millet-based, gluten-free, and shelf-stable bakery products.
Early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer remains challenging. This study aimed to explore the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired after the second cycle of NAC for early prediction of pCR. A retrospective analysis was conducted on 119 breast cancer patients who underwent NAC at our hospital between March 2020 and August 2023. Patients were categorized into pCR and non-pCR groups based on postoperative Miller-Payne pathological grading as the gold standard. Tumor regions of interest (ROIs) were manually delineated on phase-three DCE-MRI sequences. PyRadiomics extracted 851 features. A rigorous dimensionality reduction process-including stability screening, intergroup differential analysis, and decorrelation analysis-yielded 88 key features. LASSO regression (10-fold cross-validation) ultimately selected three optimal wavelet-based texture features that formed the core components of our radiomics signature: wavelet. LLH_glcm_Idn (inverse difference normalized), wavelet. LLH_glcm_MCC (maximum correlation coefficient), and wavelet. LHL_firstorder_Skewness. The dataset was randomly split into a training set (83 cases) and a validation set (36 cases) at a 7:3 ratio. A support vector machine (SVM) classifier was constructed, and model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis. Among 119 patients, 43(36.13%) achieved pCR. The constructed radiomics model demonstrated an area under the curve (AUC) of 0.667 and 0.647 in the training and validation sets, respectively, with accuracy rates of 66.27% and 73.49%. Decision curve analysis suggested potential clinial utility under hypothetical scenarios when the probability threshold exceeded 0.3, although this finding is exploratory and requires prospective validation. This study developed and internally validated a minimalist radiomics model based on mid-treatment MRI, demonstrating moderate and stable predictive capability for pCR after NAC in breast cancer and showing potential for aiding clinical decision-making. As an exploratory proof-of-concept study, the findings underscore the necessity for future multicenter external validation and integration of multimodal features.