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BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.FindingsThe application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).InterpretationOur results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.FundingThe specific funding of this article is provided in the acknowledgements section.

ABSTRACT Introduction In Iran, the assessment of osteoporosis through tools like dual‐energy X‐ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML) techniques to evaluate the accuracy and precision of each method, with the aim of identifying the most accurate pattern for diagnosing the osteoporosis risks. Methods We analysed the data related to osteoporosis risk factors obtained from the Fasa Adults Cohort Study in eight ML methods, including logistic regression (LR), baseline LR, decision tree classifiers (DT), support vector classifiers (SVC), random forest classifiers (RF), linear discriminant analysis (LDA), K nearest neighbour classifiers (KNN) and extreme gradient boosting (XGB). For each algorithm, we calculated accuracy, precision, sensitivity, specificity, F1 score and area under the curve (AUC) and compared them. Results The XGB model with an AUC of 0.78 (95% confidence interval [CI]: 0.74–0.82) and an accuracy of 0.79 (0.75–0.83) demonstrated the best performance, while AUC and accuracy values of RF were achieved (0.78 and 0.77), LR (0.78 and 0.77), LDA (0.78 and 0.76), DT (0.76 and 0.79), SVC (0.71 and 0.64), KNN (0.63 and 0.59) and baseline LR (0.72 and 0.82), respectively. Conclusion The XGB model had the best performance in assessing the risk of osteoporosis in the Iranian population. Given the disadvantages and challenges associated with traditional osteoporosis diagnostic tools, the implementation of ML algorithms for the early identification of individuals with osteoporosis can lead to a significant reduction in morbidity and mortality related to this condition. This advancement not only alleviates the substantial financial burden placed on the healthcare systems of various countries due to the treatment of complications arising from osteoporosis but also encourages health policies to shift toward more preventive approaches for managing this disease.

IntroductionPosttraumatic stress disorder (PTSD) is a debilitating disorder characterized by intrusive memories, avoidance, negative thoughts and moods, and heightened arousal. Many patients also report gastrointestinal symptoms. Cognitive behavioral therapy (CBT) is an evidence-based treatment approach for PTSD that successfully reduces symptoms. However, many patients still meet criteria for PTSD after treatment or continue to have symptoms indicating the need for new treatment strategies for PTSD. Patients with PTSD have a disrupted intestinal microbiome (i.e., dysbiosis) which can promote neuroinflammation; thus, modulation of the microbiome could be an alternative or adjunct treatment approach for PTSD.MethodsThe current study was a 12-week, double-blind, placebo-controlled trial seeking to understand if CBT combined with a microbiota-modifying, prebiotic fiber intervention would beneficially impact clinical outcomes in veterans with PTSD (n = 70). This proof-of-concept, pilot trial was designed to assess: (1) the relationship between severity of PTSD symptoms and microbiota composition and SCFA levels (i.e., acetate, propionate, butyrate), (2) if CBT treatment with a concomitant prebiotic fiber intervention would beneficially impact clinical outcomes in veterans with PTSD, (3) evaluate the feasibility and acceptability of a prebiotic intervention as an adjunct treatment to CBT, and (4) assess the impact of treatment on the intestinal microbiota and stool SCFA (i.e., mechanism).ResultsThis study found that PTSD severity may be associated with reduced abundance of taxa capable of producing the SCFA propionate, and that a subset of individuals with PTSD may benefit from a microbiota-modifying prebiotic intervention.ConclusionThis study suggests that targeting the intestinal microbiome through prebiotic supplementation could represent a promising avenue for enhancing treatment outcomes in some individuals with PTSD.Clinical trial registrationhttps://clinicaltrials.gov/, identifier NCT05424146.

Abstract Background Lumbar degenerative spondylolisthesis (LDS), characterized as degeneration of the intervertebral disc and structural changes of the facet joints, is a condition with varying degrees of instability that may lead to pain, canal stenosis, and subsequent surgical intervention. However, the etiology of LDS remains inconclusive. Gut microbiome dysbiosis may stimulate systemic inflammation in various disorders. However, the role of such dysbiosis upon spine health remains under‐studied. The current study assessed the association of gut microbiome dysbiosis in symptomatic patients with or without LDS. Methods A cross‐sectional analysis within the framework of a prospective study was performed. DNA was extracted from fecal samples collected from adult symptomatic patients with (n = 21) and without LDS (n = 12). Alpha and beta diversity assessed differences in fecal microbial community between groups. Taxon‐by‐taxon analysis identified microbial features with differential relative abundance between groups. Subject demographics and imaging parameters were also assessed. Results There was no significant group differences in age, sex, race, body mass index, smoking/alcohol history, pain profiles, spinopelvic alignment, and Modic changes (p >0.05). LDS subjects had significantly higher disc degeneration severity (p = 0.018) and alpha diversity levels compared to non‐LDS subjects (p = 0.002–0.003). Significant differences in gut microbial community structure were observed between groups (p = 0.046). Subjects with LDS exhibited distinct differences at the phylum level, with a significantly higher Firmicutes to Bacteroidota ratio compared to non‐LDS (p = 0.003). Differential relative abundance analysis identified six taxa with significant differences between the two groups, with LDS demonstrating an increase in putative pro‐inflammatory bacteria (Dialister, CAG‐352) and a decrease in anti‐inflammatory bacteria (Slackia, Escherichia‐Shigella). Conclusion This study is the first to report a significant association of gut microbiome dysbiosis and LDS in symptomatic patients, noting pro‐inflammatory bacterial taxa. This work provides a foundation for future studies addressing the role of the gut microbiome in association with spine health and disease.

IntroductionAlzheimer’s disease (AD) prevalence has increased in the last century which can be attributed to increased lifespan, but environment is also important. Exposure to artificial light at night is one environmental factor that may influence AD.MethodsThis study evaluated the relationship between outdoor nighttime light exposure and AD prevalence in the United States using satellite acquired outdoor nighttime light intensity and Medicare data.ResultsHigher outdoor nighttime light was associated with higher prevalence of AD. While atrial fibrillation, diabetes, hyperlipidemia, hypertension, and stroke were associated more strongly with AD prevalence than nighttime light intensity, nighttime light was more strongly associated with AD prevalence than alcohol abuse, chronic kidney disease, depression, heart failure, and obesity. Startlingly, nighttime light exposure more strongly associated with AD prevalence in those under the age of 65 than any other disease factor examined.DiscussionThese data suggest light exposure at night may influence AD, but additional studies are needed.

We developed a home-based electronic nose (E-Nose) to passively monitor volatile organic compounds (VOCs) emitted following bowel movements and assessed its validity by correlating the output with prebiotic fiber intake. Healthy, non-overweight participants followed a three-week protocol which included the following: (1) installing the E-Nose in their bathroom; (2) activating the device following each bowel movement; (3) recording their dietary intake; (4) consuming a fiber bar (RiteCarbs) containing a blend of 10 g of prebiotic fiber daily during weeks two and three; and (5) submit stool specimens at the beginning and end of the study for 16S rRNA gene sequencing and analysis. Participants’ fecal microbiome displayed significantly increased relative abundance of putative total SCFA-producing genera (p = 0.0323) [total acetate-producing genera (p = 0.0214), total butyrate-producing genera (p = 0.0131)] and decreased Gram-negative proinflammatory genera (p = 0.0468). Prebiotic intervention significantly increased the participants’ fiber intake (p = 0.0152), E-Nose Min/Max (p = 0.0339), and area over the curve in VOC–to–fiber output (p = 0.0044). Increased fiber intake was negatively associated (R2 = 0.53, p = 0.026) with decreased relative abundance of putative Gram-negative proinflammatory genera. This proof-of-concept study demonstrates that a prototype E-Nose can noninvasively detect a direct connection between fiber intake and VOC outputs in a home-based environment.

Parkinson's disease (PD) is a movement disorder characterized by neuroinflammation, α-synuclein pathology, and neurodegeneration. Most cases of PD are non-hereditary, suggesting a strong role for environmental factors, and it has been speculated that disease may originate in peripheral tissues such as the gastrointestinal (GI) tract before affecting the brain. The gut microbiome is altered in PD and may impact motor and GI symptoms as indicated by animal studies, although mechanisms of gut-brain interactions remain incompletely defined. Intestinal bacteria ferment dietary fibers into short-chain fatty acids, with fecal levels of these molecules differing between PD and healthy controls and in mouse models. Among other effects, dietary microbial metabolites can modulate activation of microglia, brain-resident immune cells implicated in PD. We therefore investigated whether a fiber-rich diet influences microglial function in α-synuclein overexpressing (ASO) mice, a preclinical model with PD-like symptoms and pathology. Feeding a prebiotic high-fiber diet attenuates motor deficits and reduces α-synuclein aggregation in the substantia nigra of mice. Concomitantly, the gut microbiome of ASO mice adopts a profile correlated with health upon prebiotic treatment, which also reduces microglial activation. Single-cell RNA-seq analysis of microglia from the substantia nigra and striatum uncovers increased pro-inflammatory signaling and reduced homeostatic responses in ASO mice compared to wild-type counterparts on standard diets. However, prebiotic feeding reverses pathogenic microglial states in ASO mice and promotes expansion of protective disease-associated macrophage (DAM) subsets of microglia. Notably, depletion of microglia using a CSF1R inhibitor eliminates the beneficial effects of prebiotics by restoring motor deficits to ASO mice despite feeding a prebiotic diet. These studies uncover a novel microglia-dependent interaction between diet and motor symptoms in mice, findings that may have implications for neuroinflammation and PD.

Long COVID, a type of post-acute sequelae of SARS-CoV-2 (PASC), has been associated with sustained elevated levels of immune activation and inflammation. However, the mechanisms that drive this inflammation remain unknown. Inflammation during acute coronavirus disease 2019 could be exacerbated by microbial translocation (from the gut and/or lung) to blood. Whether microbial translocation contributes to inflammation during PASC is unknown. We did not observe a significant elevation in plasma markers of bacterial translocation during PASC. However, we observed higher levels of fungal translocation - measured as β-glucan, a fungal cell wall polysaccharide - in the plasma of individuals experiencing PASC compared with those without PASC or SARS-CoV-2-negative controls. The higher β-glucan correlated with higher inflammation and elevated levels of host metabolites involved in activating N-methyl-d-aspartate receptors (such as metabolites within the tryptophan catabolism pathway) with established neurotoxic properties. Mechanistically, β-glucan can directly induce inflammation by binding to myeloid cells (via Dectin-1) and activating Syk/NF-κB signaling. Using a Dectin-1/NF-κB reporter model, we found that plasma from individuals experiencing PASC induced higher NF-κB signaling compared with plasma from negative controls. This higher NF-κB signaling was abrogated by piceatannol (Syk inhibitor). These data suggest a potential targetable mechanism linking fungal translocation and inflammation during PASC.

Obesity has repeatedly been linked to reorganization of the gut microbiome, yet to this point obesity therapeutics have been targeted exclusively toward the human host. Here, we show that gut microbe-targeted inhibition of the trimethylamine N-oxide (TMAO) pathway protects mice against the metabolic disturbances associated with diet-induced obesity (DIO) or leptin deficiency (Lepob/ob). Small molecule inhibition of the gut microbial enzyme choline TMA-lyase (CutC) does not reduce food intake but is instead associated with alterations in the gut microbiome, improvement in glucose tolerance, and enhanced energy expenditure. We also show that gut microbial CutC inhibition is associated with reorganization of host circadian control of both phosphatidylcholine and energy metabolism. This study underscores the relationship between microbe and host metabolism and provides evidence that gut microbe-derived trimethylamine (TMA) is a key regulator of the host circadian clock. This work also demonstrates that gut microbe-targeted enzyme inhibitors have potential as anti-obesity therapeutics.

Energy demand prediction can benefit electricity consumers, distribution network service providers, and system operators. It heavily depends on auxiliary factors, such as weather parameters (e.g., ambient temperature), which makes this problem more complex. Moreover, for many industrial applications, instead of aiming for the highest prediction accuracy, a more easily understandable and interpretable model that can lead to higher accuracy against the baseline model is the priority. Therefore, this problem still requires more investigation, especially when there is a specified prediction baseline to be compared with. This paper proposes a machine learning (ML) based prediction framework that investigates how temperature combined with energy consumption and simple and interpretable ML methods can be used to provide more precise demand forecasts and thus baselines closer to actual load profiles. The proposed framework is tested on two different real-world energy demand datasets. The analysis shows that using a simple ML model, such as a polynomial regression model, results in a more accurate prediction than the current baselines used in the energy market. The proposed ML models are not black-box type models, and thus are easier to explain and interpret. The ML-based forecasted demand is used as a baseline for demand response (DR) and is compared with the existing baselines used in the demand response market of Australia’s national grid.

Background & Aim: Knee osteoarthritis as a common chronic illness leading to disability, imposes numerous challenges on individuals in terms of daily living and self-care. Therefore, the implementation of tele-rehabilitation nursing interventions that foster patients' internal health locus of control and enhance the development of self-care behaviors may be useful. This study aimed to determine the effect of tele-nursing rehabilitation on the health locus of control in patients with knee osteoarthritis. Methods & Materials: In this randomized clinical trial study, a total of 34 patients with knee osteoarthritis referred to the rehabilitation clinics affiliated with Shiraz University of Medical Sciences in 2022 were enrolled. The intervention group participated in an 8-session tele-rehabilitation nursing program, while the control group received standard routine care. The Multidimensional Health Locus of Control scale was administered to both groups on three separate occasions. The data were subjected to descriptive and inferential statistical analyses using SPSS software version 26. Results: According to the results from a two-way repeated measures analysis of variance (ANOVA), a statistically significant difference was observed solely in the trend of changes in the mean score of internal belief between the two groups and over the study time period (P

Cancer is a disease characterized by abnormal and uncontrolled growth of cells, leading to invasion and metastasis to other tissues. Chemotherapy drugs are some of the primary treatments for cancer, which could detrimentally affect the cancer cells by various molecular mechanisms like apoptosis and cell cycle arrest. These treatment lines have always aligned with side effects and drug resistance. Due to their anticancer effects, medicinal herbs and their active derivative compounds are being profoundly used as complementary treatments for cancer. Many studies have shown that herbal ingredients exert antitumor activities and immune-modulation effects and have fewer side effects. On the other hand, combining phytotherapy and chemotherapy, with their synergistic effects, has gained much attention across the medical community. This review article discussed the therapeutic effects of essential herbal active ingredients combined with chemotherapeutic drugs in cancer therapy. To write this article, PubMed and Scopus database were searched with the keywords “Cancer,” “Combination,” “Herbal,” “Traditional,” and “Natural.” After applying inclusion/exclusion criteria, 110 articles were considered. The study shows the anticancer effects of the active herbal ingredients by inducing apoptosis and cell cycle arrest in cancer cells, especially with a chemotherapeutic agent. This study also indicates that herbal compounds can reduce side effects and dosage, potentiate anticancer responses, and sensitize cancer cells to chemotherapy drugs.

Introduction: Aging studies in humans and mice have played a key role in understanding the intestinal microbiome and an increased abundance of “inflammaging” Gram-negative (Gn) bacteria. The mechanisms underlying this inflammatory profile in the aging microbiome are unknown. We tested the hypothesis that an aging-related decrease in colonic crypt epithelial cell anti-microbial peptide (AMP) gene expression could promote colonic microbiome inflammatory Gn dysbiosis and inflammaging.Methods: As a model of aging, C57BL/6J mice fecal (colonic) microbiota (16S) and isolated colonic crypt epithelial cell gene expression (RNA-seq) were assessed at 2 months (mth) (human: 18 years old; yo), 15 mth (human: 50 yo), and 25 mth (human: 84 yo). Informatics examined aging-related microbial compositions, differential colonic crypt epithelial cell gene expressions, and correlations between colonic bacteria and colonic crypt epithelial cell gene expressions.Results: Fecal microbiota exhibited significantly increased relative abundances of pro-inflammatory Gn bacteria with aging. Colonic crypt epithelial cell gene expression analysis showed significant age-related downregulation of key AMP genes that repress the growth of Gn bacteria. The aging-related decrease in AMP gene expressions is significantly correlated with an increased abundance in Gn bacteria (dysbiosis), loss of colonic barrier gene expression, and senescence- and inflammation-related gene expression.Conclusion: This study supports the proposed model that aging-related loss of colonic crypt epithelial cell AMP gene expression promotes increased relative abundances of Gn inflammaging-associated bacteria and gene expression markers of colonic inflammaging. These data may support new targets for aging-related therapies based on intestinal genes and microbiomes.

ABSTRACT: Objective: The aim of this research was to investigate attitudes towards principles of professional ethics (PPE) amongst Iranian dentists working in Isfahan, Iran. Methods: This pilot cross-sectional study was conducted amongst 273 dentists in Isfahan, Iran. A validated, comprehensive questionnaire including principles of respect for patient autonomy, nonmaleficence, beneficence, and justice was used to evaluate dentistry professional ethics aspects. Age, gender, marital status, type of graduate university, level of education, specialised field of study, work experience, workplace, and participation in ethics workshops and courses were registered from participants. Total and domains scores of used questionnaires were calculated and compared across categories of study participants’ characteristics. Results: Mean ± SD age of participants was 35.4 ± 10.7 years, and 57% were female; about 73% graduated from Isfahan University of Medical Sciences, and 65% were general dentists. The attitude total score of dentists towards PPE was 133.02 ± 13.16. Mean total score of the questionnaire and its domains was different significantly (P < .05) across categories of level of education, marital status, and passing the ethical courses. Conclusions: The attitude of dentists towards PPE was rated at a good level. However, improvement in attitudes of some specific subgroups such as general dentists and newly graduated ones is needed. Conducting specific workshops about professional ethics and incorporating these principles into university curricula can be beneficial.

The rise in patients seeking corneal refractive surgery, despite having systemic autoimmune diseases, underscores the need for special considerations when treating these individuals. Recent studies have expanded our knowledge in identifying and evaluating autoimmune disorders and their potential side effects in the results of corneal refractive surgery with laser. This study briefly examines the pathogenic factors, clinical aspects, and possible complications in patients with systemic autoimmune disorders subjected to these surgeries. In total, 132 articles were selected for this research among the reviewed studies. Considering that the release of various cytokines caused by systemic autoimmune disorders can lead to destructive corneal consequences, the need for early diagnosis before any laser surgery for refractive errors seems essential. Although procedures such as LASIK and PRK are commonly performed on patients with autoimmune disorders, important considerations must be made. Studies have not yet confirmed definitive contraindications to laser refractive surgery for autoimmune disorders.

Abstract Next generation amplicon sequencing has created a plethora of data from human microbiomes. The accessibility to this scientific data and its corresponding metadata is important for its reuse, to allow for new discoveries, verification of published results, and serving as path for reproducibility. Dietary fiber consumption has been associated with a variety of health benefits that are thought to be mediated by gut microbiota. To enable direct comparisons of the response of the gut microbiome to fiber, we obtained 16S rRNA sequencing data and its corresponding metadata from 11 fiber intervention studies for a total of 2,368 samples. We provide curated and pre-processed genetic data and common metadata for comparison across the different studies.