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Cognitive impairment (CI) is very common in patients with Parkinson’s Disease (PD) and progressively develops on a spectrum from mild cognitive impairment (PD-MCI) to full dementia (PDD). Identification of PD patients at risk of developing cognitive decline, therefore, is unmet need in the clinic to manage the disease. Previous studies reported that oral microbiota of PD patients was altered even at early stages and poor oral hygiene is associated with dementia. However, data from single modalities are often unable to explain complex chronic diseases in the brain and cannot reliably predict the risk of disease progression. Here, we performed integrative metaproteogenomic characterization of salivary microbiota and tested the hypothesis that biological molecules of saliva and saliva microbiota dynamically shift in association with the progression of cognitive decline and harbor discriminatory key signatures across the spectrum of CI in PD. We recruited a cohort of 115 participants in a multi-center study and employed multi-omics factor analysis (MOFA) to integrate amplicon sequencing and metaproteomic analysis to identify signature taxa and proteins in saliva. Our baseline analyses revealed contrasting interplay between the genus Neisseria and Lactobacillus and Ligilactobacillus genera across the spectrum of CI. The group specific signature profiles enabled us to identify bacterial genera and protein groups associated with CI stages in PD. Our study describes compositional dynamics of saliva across the spectrum of CI in PD and paves the way for developing non-invasive biomarker strategies to predict the risk of CI progression in PD.

Background: Currently, medications and behavioral modifications have limited success in the treatment of functional constipation (FC). An individualized diet based on microbiome analysis may improve symptoms in FC. In the present study, we aimed to investigate the impacts of microbiome modulation on chronic constipation. Methods: Between December 2020–December 2021, 50 patients fulfilling the Rome IV criteria for functional constipation were randomized into two groups. The control group received sodium picosulfate plus conventional treatments (i.e., laxatives, enemas, increased fiber, and fluid intake). The study group underwent microbiome analysis and received an individualized diet with the assistance of a soft computing system (Enbiosis Biotechnology®, Sariyer, Istanbul). Differences in patient assessment constipation–quality of life (PAC-QoL) scores and complete bowel movements per week (CBMpW) were compared between groups after 6-weeks of intervention. Results: The mean age of the overall cohort (n = 45) was 31.5 ± 10.2 years, with 88.9% female predominance. The customized diet developed for subjects in the study arm resulted in a 2.5-fold increase in CBMpW after 6-weeks (1.7 vs. 4.3). The proportion of the study group patients with CBMpW > 3 was 83% at the end of the study, and the satisfaction score was increased 4-fold from the baseline (3.1 to 10.7 points). More than 50% improvement in PAC-QoL scores was observed in 88% of the study cohort compared to 40% in the control group (p = 0.001). Conclusion: The AI-assisted customized diet based on individual microbiome analysis performed significantly better compared to conventional therapy based on patient-reported outcomes in the treatment of functional constipation.

The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called “gut-brain axis.” Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

•A significant part of the health benefits provided by the Mediterranean diet (MedDiet) can be attributed to its microbiome mediation.•Gut microbiota assembly and biosynthetic capacity are responsive to the MedDiet, where the microbiome-reachable nutrients shape and modulate the microbiome.•The MedDiet is a main substrate supplier for several bioactive compound transformations mediated by the gut microbiome.•Based on individual microbiome profiles, the MedDiet can be fine-tuned and personalized for optimal outcomes. The Mediterranean diet (MedDiet) is recognized as one of the United Nations Educational, Scientific and Cultural Organization Intangible Cultural Heritage assets associated with lower rates of cardiometabolic diseases; lower prevalence of cancer, Alzheimer's disease, depression, and onset of inflammatory bowel disease; and more generally low-grade inflammation and mortality risks. Beyond being an input source of beneficial micronutrients, it recently has been discovered that the MedDiet plays a role in a more complex human microbiome–mediated mechanism. An interesting hypothesis suggests a bidirectional relationship between the MedDiet and the gut microbiome, where gut microbiota assembly and biosynthetic capacity are responsive to the diet; in return, the microbiome-reachable nutrients shape and modulate the microbiome toward a characteristic probiotic state. It can be speculated that that primary health benefits of the MedDiet exerted via the gut microbiome are mediated by the bioactive compounds transformed by the microbiome. Furthermore, it is possible that additional probiotic properties of the organisms promoted by diet adherence have secondary benefits. As more detailed omic-based studies take place, more evidence on the MedDiet as a core generic probiotic microbiome modulation strategy surface. However, individual–specific microbiome compositions might impose personal variations on the diet outcome. Therefore, a prospective strategy of a fine-tuned precision nutrition approach might deliver optimized benefits of the MedDiet.

Background The capacity of the current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in the current paradigm of shallow tracing is unlikely to reach the silent clusters that might be as important as the symptomatic cases in the spread of the disease. Group testing is a feasible and promising approach when the resources are scarce and when a relatively low prevalence regime is observed on the population. Methods We employed group testing with a sparse random pooling scheme and conventional group test decoding algorithms both for exact and inexact recovery. Results Our simulations showed that significant reduction in per case test numbers (or expansion in total test numbers preserving the number of actual tests conducted) for very sparse prevalence regimes is available. Currently proposed COVID-19 group testing schemes offer a gain up to 15X-20X scale-up. There is a good probability that the required scale up to achieve massive scale testing might be greater in certain scenarios. We investigated if further improvement is available, especially in sparse prevalence occurrence where outbreaks are needed to be avoided by population scans. Conclusion Our simulations show that sparse random pooling can provide improved efficiency gains compared to conventional group testing or Reed-Solomon error correcting codes. Therefore, we propose that special designs for different scenarios could be available and it is possible to scale up testing capabilities significantly.

INTRODUCTION:Personalized management strategies are pivotal in addressing irritable bowel syndrome (IBS). This multicenter randomized controlled trial focuses on comparing the efficacy of a microbiome-based artificial intelligence-assisted personalized diet (PD) with a low-fermentable oligosaccharides, disaccharides, monosaccharides, and polyols diet (FODMAP) for IBS management.METHODS:One hundred twenty-one patients participated, with 70 assigned to the PD group and 51 to the FODMAP diet group. IBS subtypes, demographics, symptom severity (IBS-SSS), anxiety, depression, and quality of life (IBS-QOL) were evaluated. Both interventions spanned 6 weeks. The trial's primary outcome was the within-individual difference in IBS-SSS compared between intervention groups.RESULTS:For the primary outcome, there was a change in IBS-SSS of −112.7 for those in the PD group vs −99.9 for those in the FODMAP diet group (P = 0.29). Significant improvement occurred in IBS-SSS scores (P < 0.001), frequency (P < 0.001), abdominal distension (P < 0.001), and life interference (P < 0.001) in both groups. In addition, there were significant improvements in anxiety levels and IBS-QOL scores for both groups (P < 0.001). Importantly, PD was effective in reducing IBS SSS scores across all IBS subtypes IBS-Constipation (IBS-C; P < 0.001), IBS-Diarrhea (IBS-D; P = 0.01), and IBS-Mixed (IBS-M; P < 0.001) while FODMAP diet exhibited comparable improvements in IBS-C (P = 0.004) and IBS-M (P < 0.001). PD intervention significantly improved IBS-QOL scores for all subtypes (IBS-C [P < 0.001], IBS-D [P < 0.001], and IBS-M [P = 0.008]) while the FODMAP diet did so for the IBS-C (P = 0.004) and IBS-D (P = 0.022). Notably, PD intervention led to significant microbiome diversity shifts (P < 0.05) and taxa alterations compared with FODMAP diet.DISCUSSION:The artificial intelligence-assisted PD emerges as a promising approach for comprehensive IBS management. With its ability to address individual variation, the PD approach demonstrates significant symptom relief, enhanced QOL, and notable diversity shifts in the gut microbiome, making it a valuable strategy in the evolving landscape of IBS care.

Advancements in metagenomic techniques have provided new tools for profiling human parasites in environmental matrices such as wastewater. This study aimed to profile protozoan parasites in wastewater from a major city, rural area, and hospital in Kayseri, Türkiye, using metagenomic techniques. Shotgun metagenome sequencing was conducted on ten water samples collected from five sampling sites over a two-week period. The sequences were aligned to 80 human parasite genomes to evaluate the presence and relative abundance of each parasite species. A comparative bioinformatic analysis was performed on the metagenomes from each sampling point. The diversity of parasites in the city wastewater exceeded that of the rural and hospital sampling points. spp. subtypes and were dominant in rural wastewater, while , , , and species showed significant abundance in hospital effluent (p<0.01). Moreover, protozoan parasites not previously reported in a clinical setting were identified in the water samples. This is the first study in Türkiye investigating the presence of human parasites in wastewater using metagenomics. The study highlights the risk posed by human parasites in treated wastewater to population using natural resources. Implementing a specialized wastewater treatment targeting parasites could mitigate the potential spread of these pathogens in the environment. The study revealed certain sequences associated with species not previously identified in clinical instances. This finding may result from genomic resemblances with other eukaryotic organisms that were not systematically excluded, or alternatively, the displacement of protozoa linked to the increasing influx of refugees.

BackgroundIrritable bowel syndrome (IBS) is a common disorder that significantly affects the quality of life and is often linked to dysbiosis in the gut microbiome. This study evaluates the long-term effects of a 6-week dietary intervention, comparing a microbiome-based artificial intelligence-assisted Personalized Diet (PD) with a low-FODMAP diet (LFD) by assessing symptom improvement and microbiome changes at 6 and 12 months.MethodsClinical and microbiome data were collected at baseline, 6 weeks, 6 months, and 12 months after a 6-week dietary intervention in IBS patients. Symptom severity, quality of life, and psychological well-being were assessed using validated questionnaires. Fecal samples were analyzed using 16S rRNA sequencing to assess microbiome diversity, with statistical analyses performed using mixed-effects models, ANOVA, and PERMANOVA.ResultsTable 1 presents IBS-SSS, IBS-QOL, HADS-Anxiety, and HADS-Depression scores across time points for the PD and LFD groups (IDDF2025-ABS-0325 table 1). Table 2 provides IBS-SSS scores for each IBS subtype at each time point (IDDF2025-ABS-0325 table 2). There were no significant differences in baseline alpha diversity between the PD and LFD groups. However, from 6 weeks onward, the PD group demonstrated significantly higher alpha diversity compared to the LFD group (6 weeks: t=2.04, p=0.046; 6 months: t=3.05, p=0.0034; 12 months: t=2.12, p=0.039) (IDDF2025-ABS-0325 figure 1). Beta diversity analysis revealed that microbiota composition was comparable at baseline but diverged significantly by 6 weeks (p=0.042), with further divergence observed at 6 months (p=0.001) and sustained at 12 months (p=0.025) (IDDF2025-ABS-0325 figure 2).Abstract IDDF2025-ABS-0325 Table 1IBS-SSS, IBS-QOL, HADS-anxiety, and HADS-depression scores across timelines for PD and LFDOutcomeBaseline (Mean ± SD, p-value)6-Weeks (Mean ± SD, p-value)6-Month (Mean ± SD, p-value)12-Months (Mean ± SD, p-value)IBS-SSS PD314.42 ± 92.79210.64 ± 130.63, p<0.001232.45 ± 132.13, p<0.001236.15 ± 126.51, p<0.001IBS-SSS LFD276.76 ± 90.15176.86 ± 111.09, p<0.001233.41 ± 130.04, p<0.001306.10 ± 111.10, p>0.05 (ns)IBS-QOL PD45.55 ± 22.0655.79 ± 21.85, p<0.00150.82 ± 24.88, p<0.00150.90 ± 26.65, p<0.001IBS-QOL LFD42.65 ± 19.8255.08 ± 23.62, p<0.00152.76 ± 22.52, p<0.00146.37 ± 19.98, p<0.001HADS-Anxiety PD10.27 ± 4.228.15 ± 3.37, p<0.0018.45 ± 4.48, p<0.0016.21 ± 5.97, p<0.001HADS-Anxiety LFD10.74 ± 3.957.86 ± 4.07, p<0.0018.44 ± 4.83, p<0.0018.52 ± 3.93, p<0.001HADS-Depression PD7.57 ± 4.356.22 ± 4.10, p<0.0016.67 ± 4.08, p<0.0014.8 ± 4.96, p<0.001HADS-Depression LFD8.33 ± 4.365.72 ± 4.35, p<0.056.61 ± 4.27, p<0.056.28 ± 3.62, p<0.05IBS-SSS: Irritable Bowel Syndrome Symptom Severity Score, IBS-QOL: Irritable Bowel Syndrome Quality of Life, HADS-Anxiety: Hospital Anxiety and Depression Scale – Anxiety subscale, HADS-Depression: Hospital Anxiety and Depression Scale, PD: microbiome-based artificial intelligence-assisted Personalized Diet, LFD: low-FODMAP dietAbstract IDDF2025-ABS-0325 Table 2IBS symptom severity scores (IBS-SSS) across time points for PD and LFDIBS Subtype & DietBaseline Mean ± SD6 Weeks Mean ± SD (p-value)6 Months Mean ± SD (p-value)12 Months Mean ± SD (p-value)IBS-C PD327.91 ± 97.74201.68 ± 122.67 (p = 0.003)252.32 ± 116.68 (p = 0.019)240.30 ± 122.05 (p = 0.024)IBS-C LFD295.46 ± 85.94192.75 ± 119.45 (p = 0.021)255.00 ± 103.22 (p = 0.166)311.86 ± 116.13 (p = 0.544)IBS-D PD306.06 ± 100.88221.13 ± 126.24 (p = 0.041)227.31 ± 121.78 (p = 0.035)290.86 ± 134.32 (p = 0.562)IBS-D LFD223.08 ± 97.44170.75 ± 109.81 (p = 0.205)258.70 ± 167.36 (p = 0.297)260.20 ± 154.18 (p = 0.175)IBS-M PD300.86 ± 80.01187.41 ± 148.24 (p = 0.004)201.59 ± 155.79 (p = 0.011)200.31 ± 127.46 (p = 0.003)IBS-M LFD290.71 ± 74.47157.82 ± 101.91 (p = 0.001)184.08 ± 128.05 (p = 0.005)321.00 ± 82.99 (p = 0.348)IBS-C: Irritable Bowel Syndrome with constipation, IBS-D: Irritable Bowel Syndrome with Diarrhea, IBS-M: Irritable Bowel Syndrome with Mixed Bowel HabitsAbstract IDDF2025-ABS-0325 Figure 1(a) Mean alpha diversity (Shannon index) over time for both the Low-FODMAP Diet (LFD; orange) and Personalized Diet (PD; blue) groups, plotted with 95% confidence intervals at baseline, 6 weeks, 6 months, and 12 months; (b) Violin plots illustrating the full distribution of alpha diversity values at each time point, superimposed with individual trajectories (thin lines) demonstrating within-subject changes over the study period[Figure omitted. See PDF]Abstract IDDF2025-ABS-0325 Figure 2(a) Boxplots of between-group Bray-Curtis distances at each time point (TO, T1, T2, T3), showing how the LFD and PD groups progressively diverged in overall microbiome composition over the 12-month period.(b) A principal coordinates analysis (PCoA) of Bray-Curtis dissimilarities, with each point representing one sample, colored by diet group (LFD or PD) and connected by a thin line to illustrate each individual’s longitudinal path through baseline, 6 weeks, 6 months, and 12 months[Figure omitted. See PDF]ConclusionsThe PD group exhibited sustained improvements in all outcome measures over 12 months, while the LFD group’s IBS-SSS scores reverted to baseline. PD induced a significant and persistent increase in alpha diversity, accompanied by progressive changes in beta diversity, reflecting a lasting modulation of the microbiome. These results suggest that PD offers not only long-term symptom relief but also sustained restructuring of the gut microbiota. In conclusion, PD represents a promising long-term therapeutic strategy for managing IBS, driven by personalized and microbiome-targeted dietary interventions.