Through the use of prebiotics and probiotics, fermented foods offer significant health benefits by enhancing host nutrition and microbiota composition while providing distinctive flavor profiles. Fermentation substantially alters the bioactive compounds in these foods compared to their natural state. Additionally, fermented foods contain probiotics that can modulate consumers’ gut microbiomes, which in turn regulate host biochemistry to help combat various metabolic diseases. Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a growing global health burden. Gut microbiome dysbiosis, combined with unbalanced nutritional intake, is considered a primary driver of disease pathogenesis. Fermented foods can modify the bioavailability of micronutrients—including carbohydrates, polyphenols, and vitamins—thereby influencing host metabolism. Moreover, the probiotics present in fermented foods, along with their modulatory effects on the gut microbiota, contribute to both the management and prevention of MASLD. Modern fermentation approaches, leveraging synthetic biology, systems biology, and metabolic engineering, can further maximize these health benefits. This review summarizes the components, bioactive compounds, and mechanistic pathways by which fermented foods influence the pathogenesis of MASLD, and highlights the potential applications of modern fermentation technologies to enhance their health-promoting properties.
A Fingerprint plays an important role in identifying an individual in forensic and criminal investigations. Fingerprint ridge density is considered one of the most important features for sex classification. The present study intends to classify sex using fingerprint ridge density through a machine learning model, i.e., Random Forest. A total of 2040 fingerprints of 204 participants (102 males and 102 females) were collected from the north Indian population using a standard methodology. Ridge density in the three topological areas of fingerprints,i.e., radial, ulnar, and proximal areas, was assessed. Taking all the areas into consideration, the data of fingerprint ridge density was used to train the Random forest algorithm. The training and testing of the model data were taken in a ratio of 70:30, respectively (training dataset = 1428; testing dataset = 612). Random forest provided an accuracy of 81.53% in sex classification using fingerprint ridge density. The paper discusses the evaluation report of the accuracy of the parameters of the Random forest in detail. The study concludes that the machine learning models, such as Random forest can be utilized for sex classification from fingerprint ridge density. The study proposes its direct application in forensic examinations, especially when there is no clue about the perpetrator, and the sex of the perpetrator can be predicted from fingerprints recovered from the crime scene using the present customized model.
Self-determination is closely associated with individuals’ autonomy and independence and is crucial for people with intellectual and developmental disabilities. This study investigated the self-determination of adolescents with intellectual and developmental disabilities in China. Using the AIR Self-Determination Scale, data were collected from 116 students and 29 corresponding special education teachers. Findings indicated that the adolescent with intellectual and developmental disabilities had a moderate level of self-determination. However, teachers consistently rated students’ self-determination lower than students’ self-rating. Students’ self-evaluations of their self-determination were significantly influenced by geographic location, age, and disability severity, and teacher evaluations were affected by students’ age and disability severity, as well as teachers’ teaching experience and subject area. The study revealed that teachers face notable challenges in their conceptual understanding and pedagogical implementation of self-determination instruction. Based on these findings, recommendations are proposed across four domains: parents, teachers, schools, and broader society.
In this paper, the effect of filler metal and type of welding on the strength and ductility of dissimilar welding of two different grades of stainless steel was investigated. One of the benefits of stainless steel is its corrosion resistance, which is often necessary for equipment longevity in these facilities. During shipbuilding, as required, stainless steel 316L needs to be welded to the shipbuilding-grade carbon steel A131. In these applications, welding between the two should demonstrate superior strength during vessel construction. To provide a clear illustration, experimental work was needed to allow a careful selection of the joining procedure and filler metal or electrode. The current research work includes a comparative experimental analysis of dissimilar-metal welding (SS-316L & A131 steel). The reasons for choosing these two materials are their greater corrosion resistance and high strength in humid environments. Furthermore, two different welding methods (SMAW & TIG) with varying filler metals were employed in the experiment. The ultimate tensile strength and yield strength of the SMAW welds using E308-16 filler metal were the highest among all, while the TIG welds with ER308L showed superior bending strength. Observations suggest that SMAW with the E308-16 electrode exhibits superior tensile strength, while TIG joints with ER 308L filler provide better bending strength for the welding of SS-316L and shipbuilding (SB) grade A131 steels.
Exposure to air pollution contributes to increased dementia risk, which may be mediated through its impact on psychological and brain structural changes. However, the underlying mechanisms remain poorly understood. We analyzed data from the UK Biobank of 263,095 participants aged 40 years and older. We examined the association between air pollution and incident dementia using Cox proportional hazard regression models. Mediation analysis was performed to explore the mediating roles of psychological and brain structural factors on these associations. Structural equation model (SEM) and bidirectional Mendelian randomization (MR) analyses were further employed to explore the potential pathways involving psychological factors and brain structure in this relationship. During an over 13-year follow-up, a total of 3039 dementia cases were identified. We observed significant associations between air pollution and dementia, with each interquartile range (IQR) increase in air pollution associated with a 5~9% increased risk of dementia. We observed that both psychological and brain structural factors mediated the air pollution-dementia association, particularly loneliness, social isolation, lack of enthusiasm, and reductions in volumes of the amygdala, hippocampus, temporal pole, and frontal pole, with mediation proportions ranging from 4.23% to 11.11%. SEM and MR analyses revealed bidirectional pathways linking air pollution exposure to dementia through psychological factors and brain structural changes: (1) air pollution → psychological disturbances → brain structural damage → dementia; (2) air pollution → brain structural damage → psychiatric disorders → dementia. These findings elucidate the interplay between psychological well-being and neuroanatomical integrity in mediating the neurocognitive impacts of air pollution, offering insights for targeted interventions to mitigate dementia risk associated with environmental exposures.
