Convergence effect during spatiotemporal succession of lacustrine plastisphere: loss of priority effects and turnover of microbial species.

Succession is a fundamental aspect of ecological theory, but studies on temporal succession trajectories and ecological driving mechanisms of plastisphere microbial communities across diverse colonization environments remain scarce and poorly understood. To fill this knowledge gap, we assessed the primary colonizers, succession trajectories, assembly, and turnover mechanisms of plastisphere prokaryotes and eukaryotes from four freshwater lakes. Our results show that differences in microbial composition similarity, temporal turnover rate, and assembly processes in the plastisphere do not exclusively occur at the kingdom level (prokaryotes and eukaryotes), but also depend on environmental conditions and colonization time. Thereby, the time of plastisphere colonization has a stronger impact on community composition and assembly of prokaryotes than eukaryotes, whereas for environmental conditions, the opposite pattern holds true. Across all lakes, deterministic processes shaped the assembly of the prokaryotes, but stochastic processes influenced that of the eukaryotes. Yet, they share similar assembly processes throughout the temporal succession: species turnover over time causes the loss of any priority effect, which leads to a convergent succession of plastisphere microbial communities. The increase and loss of microbial diversity in different kingdoms during succession in the plastisphere potentially impact the stability of entire microbial communities and related biogeochemical cycles. Therefore, research needs to integrate temporal dynamics along with spatial turnovers of the plastisphere microbiome. Taking the heterogeneity of global lakes and the diversity of global climate patterns into account, we highlight the urgency to investigate the spatiotemporal succession mechanism of plastisphere prokaryotes and eukaryotes in more lakes around the world.

Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio.

Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels. These images feature 11 types of nuts that are commonly consumed. Each photo includes three nut types; each type consists of 2-4 nuts, so 6-9 nuts are in each image. Rectangular bounding boxes were drawn using a visual geometry group (VGG) image annotator to facilitate the identification of each nut, delineating their locations within the images. This approach renders the dataset an excellent resource for training models capable of multi-label classification and object detection, as it was meticulously divided into training, validation, and test subsets. Utilizing transfer learning in Python with the IceVision framework, deep neural network models were adeptly trained to recognize and pinpoint the nuts depicted in the photographs. The ultimate model exhibited a mean average precision of 0.7596 in identifying various nut types within the validation subset and demonstrated a 97.9% accuracy rate in determining the number and kinds of nuts present in the test subset. By integrating specific nutritional data for each type of nut, the model can precisely (with error margins ranging from 0.8 to 2.6%) calculate the combined nutritional content-encompassing total energy, proteins, carbohydrates, fats (total and saturated), fiber, vitamin E, and essential minerals like magnesium, phosphorus, copper, manganese, and selenium-of the nuts shown in a photograph. Both the dataset and the model have been made publicly available to foster data exchange and the spread of knowledge. Our research underscores the potential of leveraging photographs for automated nut calorie and nutritional content estimation, paving the way for the creation of dietary tracking applications that offer real-time, precise nutritional insights to encourage nut consumption.

A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater.

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution.

Diversity and biogeography of bacterial community in the Ili River network varies locally and regionally.

Microorganisms in rivers indeed play a crucial role in nutrient cycling within aquatic ecosystems. Understanding the assembly mechanisms of bacterial communities in river networks is essential for predicting their special composition and functional characteristics in natural rivers. This study employed 16S rRNA gene amplicon sequence variation (ASVs) to scrutinize the bacterial community within the uniquely topographical Ili River network. The bacterial community composition varied across the three tributaries with distinct sources and the mainstream. The confluence of various sources diminished the diversity of the bacterial community and altered the functionality of within mainstream. We suggest that strong dispersal limitation predominantly shaped the community at the regional scale (46.6 %), underscoring the significant contribution of headwater sites to bacterial community composition. Contrary to expectation, the bacterial resources in the mainstream were not enriched by the higher diversity in three tributaries. Instead, confluence disturbance potentially increased the undominated processes (36.7 %) and alter the bacterial community composition at the local scale of the mainstream. The intricate coalescence at the confluence could potentially be an intriguing causative factor. Our research indicates that the composition of bacterial communities within intricate river networks exhibits biogeographic patterns, simultaneously influenced by river confluence and geographical features, necessitating multi-scale analysis.

Selected legacy and emerging organic contaminants in sediments of China's Yangtze - the world's third longest river: Response to anthropogenic activities.

To explore contaminant concerns as a result of anthropogenic disturbance of the river system, this study provided the first extensive investigation of the contamination profiles, possible driving factors, and ecological risks of 40 target compounds including pharmaceuticals and personal care products (PPCPs), neonicotinoid pesticides (NNIs), polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs) in sediments of the whole Yangtze River (the world's third longest river). Among these target compounds, PPCPs were the dominant contaminants with a total concentration (∑15PPCPs) of 2.13-14.99 ng/g, followed by ∑7PCBs (

Homogenization of bacterial plastisphere community in soil: a continental-scale microcosm study.

Microplastics alter niches of soil microbiota by providing trillions of artificial microhabitats, termed the "plastisphere." Because of the ever-increasing accumulation of microplastics in ecosystems, it is urgent to understand the ecology of microbes associated with the plastisphere. Here, we present a continental-scale study of the bacterial plastisphere on polyethylene microplastics compared with adjacent soil communities across 99 sites collected from across China through microcosm experiments. In comparison with the soil bacterial communities, we found that plastispheres had a greater proportion of Actinomycetota and Bacillota, but lower proportions of Pseudomonadota, Acidobacteriota, Gemmatimonadota, and Bacteroidota. The spatial dispersion and the dissimilarity among plastisphere communities were less variable than those among the soil bacterial communities, suggesting highly homogenized bacterial communities on microplastics. The relative importance of homogeneous selection in plastispheres was greater than that in soil samples, possibly because of the more uniform properties of polyethylene microplastics compared with the surrounding soil. Importantly, we found that the degree to which plastisphere and soil bacterial communities differed was negatively correlated with the soil pH and carbon content and positively related to the mean annual temperature of sampling sites. Our work provides a more comprehensive continental-scale perspective on the microbial communities that form in the plastisphere and highlights the potential impacts of microplastics on the maintenance of microbial biodiversity and ecosystem functioning.

Microplastics in aquatic ecosystems of Africa: A comprehensive review and meta-analysis.

Microplastic pollution is a global issue of great public concern. Africa is flagged to host some of the most polluted water bodies globally, but there is no enough information on the extent of microplastic contamination and the potential risks of microplastic pollution in African aquatic ecosystems. This meta-analysis has integrated data from published articles about microplastic pollution in African aquatic ecosystems. The data on the microplastic distribution and morphological characteristics in water, sediments and biota from African rivers, lakes, oceans and seas were extracted from 75 selected studies. Multivariate statistics were used to critically analyze the effects of sampling and detection methods, ecological risks, spatial distribution and similarity of microplastics in relation to the geographical distance between sampling sites. This study found that sampling methods have significant effect on abundance and morphological characteristics of microplastics and that African aquatic ecosystems are highly contaminated with microplastics compared to global data. The most prevalent colors were white, transparent and black, the most prevalent shapes were fibres and fragments, and the most available polymers were polypropylene (PP), polystyrene (PS) and polyethene terephthalate (PET). Microplastic polymers similarity decreased with an increase in geographical distance between sites. Risk levels of microplastics in African aquatic ecosystems were comparatively high, and more than 40 % of water and sediments showed highest level of ecological risk. This review provides recent information on the prevalence, distribution and risks of microplastics in African aquatic ecosystems.

Occurrence, source apportionment and risk assessment of perfluorinated compounds in sediments from the longest river in Asia.

This study investigated the pollution of perfluorinated compounds (PFCs) in sediments from the main stream of the Yangtze River, the longest river in Asia. Totally, 13 of 15 PFASs were detected in the sediments and the total concentrations ranged from 0.058 ng/g to 0.89 ng/g dry weight (dw), with dominant contaminants by perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). Concentrations of PFASs in the downstream were higher than those of upstream and midstream. Four main sources were analysed using the Unmix model, textile treatments and food packaging dominantly accounted for approximately half of the total sources, followed by metal electroplating (26.8%), fluoropolymer products (16.3%) and fluororesin coatings (7.4%). Total organic carbon (TOC), total nitrogen (TN) and grain size had significant correlation with the concentration of PFASs in sediments, indicating that the physical and chemical parameters could directly affect the adsorption process of PFASs. In addition, anthropogenic factors such as urbanization rate and per capita GDP also had a direct impact on the distribution of PFASs. Environmental risk assessment showed that PFOS posed low to medium risks to the Yangtze River, indicating that sustained attentions were needed.

Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome.

It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.

The "Microplastome" - A Holistic Perspective to Capture the Real-World Ecology of Microplastics.

Microplastic pollution, an emerging pollution issue, has become a significant environmental concern globally due to its ubiquitous, persistent, complex, toxic, and ever-increasing nature. As a multifaceted and diverse suite of small plastic particles with different physicochemical properties and associated matters such as absorbed chemicals and microbes, future research on microplastics will need to comprehensively consider their multidimensional attributes. Here, we introduce a novel, conceptual framework of the "microplastome", defined as the entirety of various plastic particles (<5 mm), and their associated matters such as chemicals and microbes, found within a sample and its overall environmental and toxicological impacts. As a novel concept, this paper aims to emphasize and call for a collective quantification and characterization of microplastics and for a more holistic understanding regarding the differences, connections, and effects of microplastics in different biotic and abiotic ecosystem compartments. Deriving from this lens, we present our insights and prospective trajectories for characterization, risk assessment, and source apportionment of microplastics. We hope this new paradigm can guide and propel microplastic research toward a more holistic era and contribute to an informed strategy for combating this globally important environmental pollution issue.

Health risk ranking of antibiotic resistance genes in the Yangtze River.

Antibiotic resistance is an escalating global health concern, exacerbated by the pervasive presence of antibiotic resistance genes (ARGs) in natural environments. The Yangtze River, the world's third-longest river, traversing areas with intense human activities, presents a unique ecosystem for studying the impact of these genes on human health. Here, we explored ARGs in the Yangtze River, examining 204 samples from six distinct habitats of approximately 6000 km of the river, including free-living and particle-associated settings, surface and bottom sediments, and surface and bottom bank soils. Employing shotgun sequencing, we generated an average of 13.69 Gb reads per sample. Our findings revealed a significantly higher abundance and diversity of ARGs in water-borne bacteria compared to other habitats. A notable pattern of resistome coalescence was observed within similar habitat types. In addition, we developed a framework for ranking the risk of ARG and a corresponding method for calculating the risk index. Applying them, we identified water-borne bacteria as the highest contributors to health risks, and noted an increase in ARG risks in particle-associated bacteria correlating with heightened anthropogenic activities. Further analysis using a weighted ARG risk index pinpointed the Chengdu-Chongqing and Yangtze River Delta urban agglomerations as regions of elevated health risk. These insights provide a critical new perspective on ARG health risk assessment, highlighting the urgent need for strategies to mitigate the impact of ARGs on human health and to preserve the ecological and economic sustainability of the Yangtze River for future human use.

Can "Risk-Sharing" Mechanisms Help Clonal Aquatic Plants Mitigate the Stress of Nanoplastics?

Most aquatic plants applied to ecological restoration have demonstrated a clonal growth pattern. The risk-spreading strategy plays a crucial role in facilitating clonal plant growth under external environmental stresses via clonal integration. However, the effects of different concentrations of nanoplastics (NPs) on the growth traits of clonal aquatic plants are not well understood. Therefore, this study aimed to investigate the impact of NPs exposure on seedlings of parent plants and connected offspring ramets. A dose response experiment (0.1, 1, and 10 mg L-1) showed that the growth of Eichhornia crassipes (water hyacinth) was affected by 100 nm polystyrene nanoplastics after 28 days of exposure. Tracer analysis revealed that NPs are accumulated by parent plants and transferred to offspring ramets through stolon. Quantification analysis showed that when the parent plant was exposed to 10 mg L-1 NPs alone for 28 days, the offspring ramets contained approximately 13 ± 2 µg/g NPs. In the case of connected offspring ramets, leaf and root biomass decreased by 24%-51% and 32%-51%, respectively, when exposed to NP concentrations ranging from 0.1 to 10 mg L-1. Excessive enrichment of NPs had a detrimental effect on the photosynthetic system, decreasing the chlorophyll content and nonphotochemical quenching. An imbalance in the antioxidant defense systems, which were unable to cope with the oxidative stress caused by NP concentrations, further damaged various organs. The root system can take up NPs and then transfer them to the offspring through the stolon. Interference effects of NPs were observed in terms of root activity, metabolism, biofilm composition, and the plant's ability to purify water. However, the risk-spreading strategy employed by parent plants (interconnected offspring ramets) offered some relief from NP-induced stress, as it increased their relative growth rate by 1 to 1.38 times compared to individual plants. These findings provide substantial evidence of the high NP enrichment capacity of E. crassipes for ecological remediation. Nevertheless, we must also remain aware of the environmental risk associated with the spread of NPs within the clonal system of E. crassipes, and contaminated cloned individuals need to be precisely removed in a timely manner to maintain normal functions.

Interaction of microplastics with heavy metals in soil: Mechanisms, influencing factors and biological effects.

Microplastics (MPs) and heavy metals (HMs) in soil contamination are considered an emerging global problem that poses environmental and health risks. However, their interaction and potential biological effects remain unclear. Here, we reviewed the interaction of MPs with HMs in soil, including its mechanisms, influencing factors and biological effects. Specifically, the interactions between HMs and MPs mainly involve sorption and desorption. The type, aging, concentration, size of MPs, and the physicochemical properties of HMs and soil have significant impacts on the interaction. In particular, MP aging affects specific surface areas and functional groups. Due to the small size and resistance to decomposition characteristics of MPs, they are easily transported through the food chain and exhibit combined biological effects with HMs on soil organisms, thus accumulating in the human body. To comprehensively understand the effect of MPs and HMs in soil, we propose combining traditional experiments with emerging technologies and encouraging more coordinated efforts.

Rare resistome rather than core resistome exhibited higher diversity and risk along the Yangtze River.

As important freshwater ecosystems, the occurrence and distribution of antibiotic resistance genes (ARGs) in rivers are relevant to public health. However, studies investigating ARGs of different environmental media in river ecosystems are limited. In this study, we analyzed the ARGs of microbes in free-living setting, particle-associated setting, sediment and bank soil of the Yangtze River using metagenomics. Twenty-six ARGs were found in all samples regardless of media (core resistome) with a diversity of 8.6 %-34.7 %, accounting for 22.7 %-89.2 % of the relative abundance of the overall ARGs. The core resistome of the Yangtze River was dominated by multidrug resistance genes consisting mainly of efflux pumps and bacitracin resistance genes. The rare resistome was dominated by multidrug, sulfonamide, and aminoglycoside resistance genes. The core resistome was more prevalent in chromosomes, implying that these ARGs with low diversity and high relative abundance may be intrinsic to microbes in the Yangtze River. The rare resistome was more prevalent in plasmids, suggesting these ARGs with high diversity and low relative abundance were acquired under environmental stresses and had transfer potential. Additionally, we found that core and rare resistome were mainly carried by specific bacteria. Noteworthily, twenty-two ARGs of high clinical concern were identified in rare resistome, especially aac(6')-I, sul1, and tetM, which were plasmid-borne and hosted by clinically relevant pathogens. Both core and rare resistome hosts showed the highest niche breadths in particle-associated setting compared to other media, and particle-associated setting could provide more stable and ideal conditions for resistome hosts to survive. This study elucidated the genetic locations of ARGs and the community assembly mechanisms of ARG hosts in freshwater environments.

Assembly mechanisms of microbial communities in plastisphere related to species taxonomic types and habitat niches.

A lot of plastic floats are presented in the kelp cultivation zone, enabling us to effectively evaluate the differences between surface water (SW) and plastic-attached (PA) microbial communities. In this study, we explored the microbial communities (both bacteria and protists) in SW and PA niches during the kelp cultivation activities. Effects of habitat niches on the diversity and composition of microbial communities were found. Beta partitioning and core taxa analyses showed species turnover and local species pool governed the microbial community assembly, and they contributed more to bacteria and protists, respectively. Based on the results of null model, bacterial communities presented a more deterministic and homogeneous assembly compared to protistan communities. Moreover, microbial communities in PA niche had higher species turnover and homogenizing assembly compared to the SW niche. The results of this study supplemented the theory of microbial community assembly and expanded our understanding of protists in plastisphere.

The threat of micro/nanoplastic to aquatic plants: current knowledge, gaps, and future perspectives.

Plastics have been recognized as an emerging pollutant and have raised global concerns due to their widespread distribution in the environment and potential harm to living systems. However, research on the threat of micro/nanoplastics (MPs/NPs) to the unique group of aquatic plants is far behind, necessitating a comprehensive review to summarize current research progress and identify future research needs. This review explores the sources and distribution patterns of MPs/NPs in aquatic environments, highlighting their uptake by aquatic plants through roots and leaves, and subsequent translocation via the vascular system facilitated by the transpiration stream. Exposure to MPs/NPs elicits diverse effects on the growth, physiology, and ecological interactions of aquatic plants, with variations influenced by plastic properties, plant species, and experimental conditions. Furthermore, the presence of MPs/NPs can impact the toxicity and bioavailability of other associated toxicants to aquatic plants. This review shows critical knowledge gaps and emphasizes the need for future research to bridge the current understanding of the limitations and challenges posed by MPs/NPs in aquatic ecosystems.

Sentiment Analysis of Tweets on Menu Labeling Regulations in the US.

Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis of public opinions regarding menu labeling regulations, drawing on Twitter data from 2008 to 2022. Tweets were collected through a systematic search strategy and annotated as positive, negative, neutral, or news. Our temporal analysis revealed that tweeting peaked around major policy announcements, with a majority categorized as neutral or news-related. The prevalence of news tweets declined after 2017, as neutral views became more common over time. Deep neural network models like RoBERTa achieved strong performance (92% accuracy) in classifying sentiments. Key predictors of tweet sentiments identified by the random forest model included the author's followers and tweeting activity. Despite limitations such as Twitter's demographic biases, our analysis provides unique insights into the evolution of perceptions on the regulations since their inception, including the recent rise in negative sentiment. It underscores social media's utility for continuously monitoring public attitudes to inform health policy development, execution, and refinement.

[Research Progress on Removing Antibiotic Resistance Genes in Constructed Wetlands].

Recently, the issue of environmental pollution and emerging antibiotic resistance genes (ARGs) has gradually gained attention. Antibiotic resistant bacteria (ARB) can be effectively reduced via the conventional water treatment processes. Although the ARB are completely inactivated during the disinfection process, the free ARGs can be incorporated into other microorganisms through transformation or transduction, allowing the ARGs to spread and propagate. Therefore, ARGs in wastewater must be handled by a specific process. It has been demonstrated in several studies that treatment using constructed wetland is an effective, economical, and environmentally friendly method of removing antibiotics and resistance genes from wastewater. Here, the research progress on the removal effect of constructed wetland water treatment systems on ARGs at home and abroad was reviewed. The results revealed that the removal efficiency of ARGs in subsurface flow constructed wetland was higher than that in surface flow-constructed wetlands. The composite-constructed wetland had significantly improved removal efficiency of ARGs compared to that in the ordinarily constructed wetland; however, the parameter setting of the composite process still requires further research. Several studies have reported that the removal efficiency of ARGs using constructed wetlands varies depending on the type of constructed wetland enhancement, plant, temperature, pH, and other factors. The results of the current study revealed that cross-mixing was the best way to combine plants, whereas the selection of plant species has not yet shown a clear dominant species. Temperature and pH affected the removal of ARGs by altering the microbial community in constructed wetlands. Although longer hydraulic residence time could increase the removal efficiency of ARGs, it also increased the enrichment risk of ARGs. The selection of constructed wetland substrate type should focus on fillers with a high specific surface area; the flow direction of the up-flow type was generally more efficient than the down-flow type in removing ARGs. In conclusion, the various factors (such as, the constructed wetland type, substrate type, hydraulic retention time, ambient temperature, and plant species) need to be integrated into the design of the constructed wetland system parameters to achieve the most effective treatment effect. The application of constructed wetlands in removing ARGs from the environment has broad prospects but also faces challenges.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions.

BACKGROUND: This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms; and encourage the adoption of AI methodologies. METHODS: A scoping review was performed in PubMed, Web of Science, Cochrane Library, and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes. AI methodologies were summarized and categorized to identify synergies, patterns, and trends informing future research. Additionally, a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application. RESULTS: The review included 24 studies that met the predetermined eligibility criteria. AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes. Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data. Comparisons of different AI models yielded mixed results, likely due to model performance being highly dependent on the dataset and task. An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed, addressing complex human-machine communication, behavior modification, and decision-making tasks. Six key areas for future AI adoption in PA interventions emerged: personalized PA interventions, real-time monitoring and adaptation, integration of multimodal data sources, evaluation of intervention effectiveness, expanding access to PA interventions, and predicting and preventing injuries. CONCLUSION: The scoping review highlights the potential of AI methodologies for advancing PA interventions. As the field progresses, staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Effects of probiotics in patients with morbid obesity undergoing bariatric surgery: a systematic review and meta-analysis.

BACKGROUND: Probiotics are commonly used after bariatric surgery. However, uncertainty remains regarding their effects. The purpose of this systematic review was to assess the effect of probiotics in patients with morbid obesity undergoing bariatric surgery. METHODS: PubMed, Cochrane Library, Embase, Science Direct, and Web of Science were searched from inception to April 4, 2023. No language restrictions were applied. Relevant randomized controlled trials and controlled clinical trials were included. We used the aggregated data extracted from the trials and assessed the heterogeneity. When severe heterogeneity was detected, a random effect model was used. All stages of the review were done by independent authors. RESULTS: We screened 2024 references and included 11 randomized controlled trials and controlled clinical trials. Compared with the protocol groups, probiotics showed significant effects on regulating aspartate amino transferase level (MD = -4.32 U/L; 95% CI [-7.10, -1.53], p = 0.002), triglycerides (MD = -20.16 mg/dL; 95% CI [-34.51, -5.82], p = 0.006), weight (MD = -1.99 kg; 95% CI [-3.97, -0.01], p = 0.05), vitamin B12 (MD = 2.24 pg/dL; 95% CI [-0.02, 4.51], p = 0.05), dietary energy (MD = -151.03 kcal; 95% CI [-215.68, -86.37], p < 0.00001), dietary protein (MD = -4.48 g/day, 95% CI [-8.76, -0.20], p = 0.04), dietary carbohydrate (MD = -34.25 g/day, 95% CI [-44.87, -23.62], p < 0.00001), and dietary fiber (MD = -2.17 g/day, 95% CI [-3.21, -1.14], p < 0.0001). There were no severe side effects related to probiotics. CONCLUSIONS: Our meta-analysis suggested that probiotics may delay the progression of liver function injury, improve lipid metabolism, reduce weight, and reduce food intake, although the effects on other indicators were insignificant. Probiotics may be helpful for patients undergoing bariatric surgery. The review was registered on PROSPERO (International prospective register of systematic reviews): CRD42023407970. No primary source of funding.