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In microbial community sequencing, involving bacterial ribosomal 16S rDNA or fungal ITS, the targeted genes are the basis for taxonomical assignment. The traditional bioinformatical procedure has for decades made use of a clustering protocol by which sequences are pooled into packages of shared percent identity, typically at 97%, to yield Operational Technical Units (OTUs). Progress in the data processing methods has however led to the possibility of minimizing technical sequencers errors, which were the main reason for the OTU choice, and to analyze instead the exact Amplicon Sequence Variants (ASV) which is a choice yielding much less agglomerated reads. We have tested the two procedures on the same 16S metabarcoded bacterial amplicons dataset encompassing a series of samples from 17 adjacent habitats, taken across a 700 meter-long transect of different ecological conditions unfolding in a gradient spanning from cropland, through meadows, forest and all successional transitions up to the seashore, within the same coastal area. This design allowed to scan a high biodiversity basin and to measure alpha, beta and gamma diversity of the area, to verify the effect of the bioinformatics on the same data as concerns the values of ten different ecological indexes and other parameters. Two levels of progressive OTUs clustering, (99% and 97%) were compared with the ASV data. The results showed that the OTUs clustering proportionally led to a marked underestimation of the ecological indicators values for species diversity and to a distorted behaviour of the dominance and evenness indexes with respect to the direct use of the ASV data. Multivariate ordination analyses resulted also sensitive in terms of tree topology and coherence. Overall, data support the view that reference-based OTU clustering carries several misleading disadvantageous biases, including the risk of missing novel taxa which are yet unreferenced in databases. Since its alternatives as de novo clustering have on the other hand drawbacks due to heavier computational demand and results comparability, especially for environmental studies which contain several yet uncharacterized species, the direct ASV based analysis, at least for prokaryotes, appears to warrant significand advantages in comparison to OTU clustering at every level of percent identity cutoff. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive decline in cognitive function, which significantly increases pain and social burden. However, few therapeutic interventions are effective in preventing or mitigating the progression of AD. An increasing number of recent studies support the hypothesis that the gut microbiome and its metabolites may be associated with upstream regulators of AD pathology. Methods: In this review, we comprehensively explore the potential mechanisms and currently available interventions targeting the microbiome for the improvement of AD. Our discussion is structured around modern research advancements in AD, the bidirectional communication between the gut and brain, the multi-target regulatory effects of microbial metabolites on AD, and therapeutic strategies aimed at modulating gut microbiota to manage AD. Results: The gut microbiota plays a crucial role in the pathogenesis of AD through continuous bidirectional communication via the microbiota-gut-brain axis. Among these, microbial metabolites such as lipids, amino acids, bile acids and neurotransmitters, especially sphingolipids and phospholipids, may serve as central components of the gut-brain axis, regulating AD-related pathogenic mechanisms including ß-amyloid metabolism, Tau protein phosphorylation, and neuroinflammation. Additionally, interventions such as probiotic administration, fecal microbiota transplantation, and antibiotic use have also provided evidence supporting the association between gut microbiota and AD. At the same time, we propose an innovative strategy for treating AD: a healthy lifestyle combined with targeted probiotics and other potential therapeutic interventions, aiming to restore intestinal ecology and microbiota balance. Conclusion: Despite previous efforts, the molecular mechanisms by which gut microbes act on AD have yet to be fully described. However, intestinal microorganisms may become an essential target for connecting the gut-brain axis and improving the symptoms of AD. At the same time, it requires joint exploration by multiple centers and multiple disciplines. [ABSTRACT FROM AUTHOR]

Gut microbiota (GM), together with its metabolites (such as SCFA, tryptophan, dopamine, GABA, etc.), plays an important role in the functioning of the central nervous system. Various neurological and psychiatric disorders are associated with changes in the composition of GM and their metabolites, which puts them in the foreground as a potential adjuvant therapy. However, the molecular mechanisms behind this relationship are not clear enough. Therefore, before considering beneficial microbes and/or their metabolites as potential therapeutics for brain disorders, the mechanisms underlying microbiota–host interactions must be identified and characterized in detail. In this review, we summarize the current knowledge of GM alterations observed in prevalent neurological and psychiatric disorders, multiple sclerosis, major depressive disorder, Alzheimer's disease, and autism spectrum disorders, together with experimental evidence of their potential to improve patients' quality of life. We further discuss the main obstacles in the study of GM–host interactions and describe the state-of-the-art solution and trends in this field, namely "culturomics" which enables the culture and identification of novel bacteria that inhabit the human gut, and models of the gut and blood–brain barrier as well as the gut–brain axis based on induced pluripotent stem cells (iPSCs) and iPSC derivatives, thus pursuing a personalized medicine agenda for neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Background: The gut microbiome plays a crucial role in human health, and recent research has highlighted its potential impact on ocular health through the gut–eye axis. Dysbiosis, or an imbalance in the gut microbiota, has been implicated in various ocular diseases. Methods: A comprehensive literature search was conducted using relevant keywords in major electronic databases, prioritizing recent peer-reviewed articles published in English. Results: The gut microbiota influences ocular health through immune modulation, maintenance of the blood–retinal barrier, and production of beneficial metabolites. Dysbiosis can disrupt these mechanisms, contributing to ocular inflammation, tissue damage, and disease progression in conditions such as uveitis, age-related macular degeneration, diabetic retinopathy, dry eye disease, and glaucoma. Therapeutic modulation of the gut microbiome through probiotics, prebiotics, synbiotics, and fecal microbiota transplantation shows promise in preclinical and preliminary human studies. Conclusions: The gut–eye axis represents a dynamic and complex interplay between the gut microbiome and ocular health. Targeting the gut microbiome through innovative therapeutic strategies holds potential for improving the prevention and management of various ocular diseases. [ABSTRACT FROM AUTHOR]

Background: Clinical observations indicate a correlation between the gut microbiota and overactive bladder (OAB) symptoms. Nevertheless, the causal relationship and mechanisms between gut microbiota and OAB symptoms remain elusive. Methods: Two-sample Mendelian randomization (MR) analyses were performed to assess the association between gut microbiota and OAB symptoms, including urinary incontinence (UI). Data were obtained from the MiBioGen International Consortium genome-wide association studies (GWAS) dataset and the IEU GWAS database. The inverse variance weighted method was used as the primary approach in the MR analysis, with the weighted median, MR-Egger, and weighted mode methods as supplementary approaches. Sensitivity analyses were employed to assess potential violations of the MR assumptions. Results: Our analysis identified seven gut bacterial taxa with a causal relationship to OAB and nine gut bacterial taxa associated with UI. Genera Eubacteriumfissicatenumgroup, LachnospiraceaeNK4A136group, and Romboutsia were identified as protective factors against OAB, while genera Barnesiella, FamilyXIIIAD3011group, Odoribacter, and RuminococcaceaeUCG005 were associated with an increased risk of OAB. A higher abundance of the genus Coprococcus3, order Burkholderiales, and phylum Verrucomicrobia predicted a lower risk of UI. Conversely, the class Mollicutes, genus Ruminococcus gauvreauii group, order Mollicutes RF9, and phylum Firmicutes and Tenericutes were positively correlated with UI risk. The sensitivity analysis excluded the influence of potential heterogeneity and horizontal pleiotropy. Conclusion: This study revealed a causal relationship between gut microbiota and OAB symptoms, providing new insights and a theoretical foundation to identify biomarkers and therapeutic targets for patients with OAB symptoms. [ABSTRACT FROM AUTHOR]

The incidence of breast implant illness (BII) and BII-related explant procedures has not decreased with current surgical and treatment techniques. It is speculated the main underlying cause of BII complications is the result of chronic, sub-clinical infections residing on and around the implant. The infection, and subsequent biofilm, produce antagonistic compounds that drive chronic inflammation and immune responses. In this study, the microbial communities in over 600 consecutive samples of infected explant capsules and tissues were identified via next-generation sequencing to identify any commonality between samples. The majority of the bacteria identified were Gram-positive, with Cutibacterium acnes and Staphylococcus epidermidis being the dominant organisms. No correlation between sample richness and implant filling was found. However, there was a significant correlation between sample richness and patient age. Due to the complex nature, breast augmentation failures may be better addressed from a holistic approach than one of limited scope. [ABSTRACT FROM AUTHOR]

The gut microbiota undergoes substantial development from birth, and its development in the initial years of life has a potentially lifelong effect on the health of the individual. However, various factors can disrupt the development of the gut microbiota, leading to a condition known as dysbiosis, particularly in preterm infants. Current studies involving adults have suggested that the gut microbiota not only influences the gut but also has multidimensional effects on remote organs; these pathways are often referred to as the gut–organ axis. Imbalance of the gut microbiota may lead to the development of multiple diseases. Recent studies have revealed that gut dysbiosis in preterm infants may cause several acute morbidities—such as necrotizing enterocolitis, late‐onset sepsis, bronchopulmonary dysplasia, and retinopathy of prematurity—and it may also influence long‐term outcomes including neurodevelopment and somatic growth. This review mainly presents the existing evidence regarding the relationships between the gut microbiota and these morbidities in preterm infants and explores the role of the gut–organ axis in these morbidities. This paper thus offers insights into the future perspectives on microbiota interventions for promoting the health of preterm infants. [ABSTRACT FROM AUTHOR]

Rationale: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing timely and effective intervention before cancer development. Objectives: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. Methods: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. Results: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. Conclusions: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, together with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Human Leukocyte Antigen (HLA) is like a device that monitors the internal environment of the body. T lymphocytes immediately recognize the HLA molecules that are expressed on the surface of the cells of the different individual, attacking it defeats microorganisms that is one of the causes of rejection in organ transplants performed between people with unmatched HLA types. Over 2850 and 3580 different polymorphisms have been reported for HLA-A and HLA-B respectively, around the world. HLA genes are associated with the risk of developing a variety of diseases, including autoimmune diseases, and play an important role in pathological conditions. By using a deep learning method called multi-task learning to simultaneously predict the gene sequences of multiple HLA genes, it is possible to improve accuracy and shorten execution time. Some new systems use a model called convolutional neural network (CNNs) in deep learning, which uses neural networks consisting of many layers and can learn complex correlations between SNP information and HLA gene sequences based on reference data for HLA imputation, which serves as training data. The learned model can output predicted values of HLA gene sequences with high accuracy using SNP information as input. To investigate which part of the input information surrounding the HLA gene is used to make learning predictions, predictions were made using not only a small number of nearby SNP information but also many SNP information distributed over a wider area by visualizing the learning information of the model. While conventional methods are strong at learning using nearly SNP information and not good at learning using SNP information located at distant locations, some new systems are thought that prediction accuracy may have improved because this problem was overcome. HLA genes are involved in the onset of a variety of diseases and are attracting attention. As an important area from the perspective of elucidating pathological conditions and realizing personalized medicine. The applied multi-task learning to two different HLA imputation reference panels—a Japanese panel (n = 1118) and type I diabetes genetics consortium panel (n = 5122). Through 10-fold cross-validation on these panels, the multi-task learning achieved higher imputation accuracy than conventional methods, especially for imputing low-frequency and rare HLA alleles. The increased prediction accuracy of HLA gene sequences is expected to increase the reliability of HLA analysis, including integrated analysis between different racial populations, and is expected to greatly contribute to the identification of HLA gene sequences associated with diseases and further elucidation of pathological conditions. [ABSTRACT FROM AUTHOR]

The incidence of duodenal tumors (DTs) is increasing. However, the mechanisms underlying its development remain unclear. Environmental factors, including the microbiome and bile acids (BAs), are believed to influence tumor development. Therefore, we conducted a single-center, prospective, observational study to investigate the potential differences between patients with DTs and healthy controls (HCs) based on these factors. In addition, the BAs in the duodenal fluid were measured using liquid chromatography-tandem mass spectrometry. We recruited 41 patients and performed 16S rRNA-seq. There was no difference in the observed ASVs or PCoA plot of Bray–Curtis dissimilarity between the DTs and HCs. The lithocholic acid concentration was significantly lower in the DT group than in the control group. The ratio of CDCA to LCA was significantly higher in patients with DTs. No significant differences in microbiota were observed between DTs and HCs. In patients with DTs, the lithocholic acid concentration in duodenal was significantly lower than in HCs. [ABSTRACT FROM AUTHOR]

In environmental health, the specific molecular mechanisms connecting a chemical exposure to an adverse endpoint are often unknown, reflecting knowledge gaps. At the public Comparative Toxicogenomics Database (CTD; https://ctdbase.org/), we integrate manually curated, literature-based interactions from CTD to compute four-unit blocks of information organized as a potential step-wise molecular mechanism, known as "CGPD-tetramers," wherein a chemical interacts with a gene product to trigger a phenotype which can be linked to a disease. These computationally derived datasets can be used to fill the gaps and offer testable mechanistic information. Users can generate CGPD-tetramers for any combination of chemical, gene, phenotype, and/or disease of interest at CTD; however, such queries typically result in the generation of thousands of CGPD-tetramers. Here, we describe a novel approach to transform these large datasets into user-friendly chord diagrams using R. This visualization process is straightforward, simple to implement, and accessible to inexperienced users that have never used R before. Combining CGPD-tetramers into a single chord diagram helps identify potential key chemicals, genes, phenotypes, and diseases. This visualization allows users to more readily analyze computational datasets that can fill the exposure knowledge gaps in the environmental health continuum. [ABSTRACT FROM AUTHOR]

Background: The alternations of brain responses to a strong desire to void were unclear, and the gender differences under the strong desire to void remain controversial. The present study aims to identify the functional brain network's topologic property changes evoked by a strong desire to void in healthy male and female adults with synchronous urodynamics using a graph theory analysis. Methods: The bladders of eleven healthy males and eleven females were filled via a catheter using a specific infusion and withdrawal pattern. A resting-state functional magnetic resonance imaging (fMRI) was performed on the enrolled subjects, scanning under both the empty bladder and strong desire to void states. An automated anatomical labeling (AAL) atlas was used to identify the ninety cortical and subcortical regions. Pearson's correlation calculations were performed to establish a brain connection matrix. A paired t-test (p < 0.05) and Bonferroni correction were applied to identify the significant statistical differences in topological properties between the two states, including small-world network property parameters [gamma (γ) and lambda (λ)], characteristic path length (Lp), clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), and regional nodal efficiency (Enodal). Results: The final data suggested that females and males had different brain response patterns to a strong desire to void, compared with an empty bladder state. Conclusions: More brain regions involving emotion, cognition, and social work were active in females, and males might obtain a better urinary continence via a compensatory mechanism. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the most common cause of mortality and morbidity in Western countries, thus representing a global health concern. CVDs show different patterns in terms of the prevalence and presentation in men and women. The role of sex hormones has been extensively implicated in these sex-specific differences, due to the presence of the menstrual cycle and menopause in women. Moreover, the gut microbiota (GM) has been implicated in cardiovascular health, considering the growing evidence that it is involved in determining the development of specific diseases. In particular, gut-derived metabolites have been linked to CVDs and kidney disorders, which can in turn promote the progression of CVDs. Considering the differences in the composition of GM between men and women, it is possible that gut microbiota act as a mediator in regard to the sex disparities in CVDs. This narrative review aims to comprehensively review the interplay between sex, GM, and CVDs, discussing potential mechanisms and therapeutic options. [ABSTRACT FROM AUTHOR]

Introduction: Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs. Methods: Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment. Results: The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200–2.03%; training=100–1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%). Conclusions: The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis. [ABSTRACT FROM AUTHOR]

Acupuncture, an important green and side effect-free therapy in traditional Chinese medicine, is widely use both domestically and internationally. Acupuncture can interact with the gut microbiota and influence various diseases, including metabolic diseases, gastrointestinal diseases, mental disorders, nervous system diseases, and other diseases. This review presents a thorough analysis of these interactions and their impacts and examines the alterations in the gut microbiota and the potential clinical outcomes following acupuncture intervention to establish a basis for the future utilization of acupuncture in clinical treatments. [ABSTRACT FROM AUTHOR]

The objective of the medical image analysis is to increase the effectiveness of the diagnosis options. The Coevolution Neural Network (CNN) is the predominant neural network architecture used in Deep Learning (DL) for medical image analysis. Recently, various innovative technics of DL such as different activation functions, optimization technics, and loss functions have enhanced the performance of CNNs. The Deep Learning CNN (DL-CNN) assists as valuable tool to assist radiologist in diagnosis and improves efficiency and accuracy. Numerous DL-CNN methods have been published to analyze medical images. This paper compiles the performance metrics of DL-CNN, as presented by various authors. This paper reviews the image analysis of six different diseases, viz., lung cancer, colorectal cancer, liver cancer, stomach cancer, breast cancer, and brain tumors. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a chronic, autoimmune rheumatic disease characterized by synovial joint inflammation with subsequent destruction as well as systemic manifestation, leading to impaired mobility and impaired quality of life. The etiopathogenesis of RA is still unknown, with genetic, epigenetic and environmental factors (incl. tobacco smoking) contributing to disease susceptibility. The link between genetic factors like "shared epitope alleles" and the development of RA is well known. However, why only some carriers have a break in self-tolerance and develop autoimmunity still needs to be clarified. The presence of autoantibodies in patients' serum months to years prior to the onset of clinical manifestations of RA has moved the focus to possible epigenetic factors, including environmental triggers that could contribute to the initiation and perpetuation of the inflammatory reaction in RA. Over the past several years, the role of microorganisms at mucosal sites (i.e., microbiome) has emerged as an essential mediator of inflammation in RA. An increasing number of studies have revealed the microbial role in the immunopathogenesis of autoimmune rheumatic diseases. Interaction between the host immune system and microbiota initiates loss of immunological tolerance and autoimmunity. The alteration in microbiome composition, the so-called dysbiosis, is associated with an increasing number of diseases. Immune dysfunction caused by dysbiosis triggers and sustains chronic inflammation. This review aims to provide a critical summary of the literature findings related to the hypothesis of a reciprocal relation between the microbiome and the immune system. Available data from studies reveal the pivotal role of the microbiome in RA pathogenesis. [ABSTRACT FROM AUTHOR]

The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology. [ABSTRACT FROM AUTHOR]

Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an advanced lung carcinoma prediction and risk screening model using transfer learning is proposed. Our proposed model initially preprocesses lung computed tomography images for noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement. The next phase segments the preprocessed images using the modified Bates distribution coati optimization (B-RGS) algorithm to extract key features. The PResNet classifier then categorizes the cancer as normal or abnormal. For abnormal cases, further risk screening determines whether the risk is low or high. Experimental results depict that our proposed model performs at levels similar to other state-of-the-art models, achieving enhanced accuracy, precision, and recall rates of 98.21%, 98.71%, and 97.46%, respectively. These results validate the efficiency and effectiveness of our suggested methodology in early lung carcinoma prediction and risk assessment. [ABSTRACT FROM AUTHOR]

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Background: Human breast milk (HBM) is a contributing factor in modulating the infant's gut microbiota, as it contains bacteria that are directly transferred to the infant during breastfeeding. It has been shown that children of women diagnosed with gestational diabetes mellitus (GDM) have a different gut microbiota compared to children of women without GDM. Our hypothesis is therefore that women with GDM have a different HBM microbiota, which may influence the metabolic function and capacity of the child later in life. The aim of this study was to investigate whether women with GDM have a different breast milk microbiota 1–3 weeks postpartum compared to women without GDM. Methods: In this case-control study, a total of 45 women were included: 18 women with GDM and 27 women without GDM. A milk sample was collected from each participant 1 to 3 weeks postpartum and the bacterial composition was examined by 16 S rRNA gene sequencing targeting the V4 region. Results: High relative abundances of Streptococcus and Staphylococcus were present in samples from both women with and without GDM. No difference could be seen in either alpha diversity, beta diversity, or specific taxa between groups. Conclusion: Our results did not support the existence of a GDM-associated breast milk microbiota at 1–3 weeks postpartum. Further research is needed to fully understand the development of the gut microbiota of infants born to mothers with GDM. [ABSTRACT FROM AUTHOR]

Young onset breast cancer (YOBC) is an increasing demographic with unique biology, limited screening, and poor outcomes. Further, women with postpartum breast cancers (PPBCs), cancers occurring up to 10 years after childbirth, have worse outcomes than other young breast cancer patients matched for tumor stage and subtype. Early-stage detection of YOBC is critical for improving outcomes. However, most young women (under 45) do not meet current age guidelines for routine mammographic screening and are thus an underserved population. Other challenges to early detection in this population include reduced performance of standard of care mammography and reduced awareness. Women often face significant barriers in accessing health care during the postpartum period and disadvantaged communities face compounding barriers due to systemic health care inequities. Blood tests and liquid biopsies targeting early detection may provide an attractive option to help address these challenges. Test development in this area includes understanding of the unique biology involved in YOBC and in particular PPBCs that tend to be more aggressive and deadly. In this review, we will present the status of breast cancer screening and detection in young women, provide a summary of some unique biological features of YOBC, and discuss the potential for blood tests and liquid biopsy platforms to address current shortcomings in timely, equitable detection. [ABSTRACT FROM AUTHOR]

This article discusses the validation of predictive models for identifying clinical deterioration in acute care cardiology wards. The study evaluates the performance of a predictive analytics tool called CoMET, which was developed before the COVID-19 pandemic and tested during the pandemic. The results show that the models had good performance and accurately predicted clinical deterioration events. The study also found that the models performed better than other existing models and showed differences in performance based on biological sex. The authors suggest further research is needed to determine the impact of these models on clinical care. [Extracted from the article]

Systemic inflammation and neuroinflammation affect the natural course of the sporadic form of Alzheimer's disease (AD), as supported by epidemiological and preclinical data, and several epidemiological studies indicate a higher prevalence of AD in patients with inflammatory bowel disease. In this study, we explored whether colitis induced by dextran sulfate sodium (DSS) in young, presymptomatic/preplaque mice worsens and/or anticipates age-dependent cognitive impairment in Tg2576, a widely used mouse model of AD. We demonstrated that DSS colitis induced in young Tg2576 mice anticipates the onset age of learning and memory deficit in the Morris water maze test. To explore potential mechanisms behind the acceleration of cognitive decline in Tg2576 mice by DSS colitis, we focused on gut microbiota, systemic inflammation and neuroinflammation markers. We observed a Firmicutes/Bacteroidetes ratio change in Tg2576 DSS animals comparable to that of elderly Tg2576 mice, suggesting accelerated microbiota aging in Tg2576 DSS mice, a change not observed in C57BL6 DSS mice. We also observed substantial differences between Tg2576 and WT mice in several inflammation and neuroinflammation-related parameters as early as 3 months of age, well before plaque deposition, a picture which evolved rapidly (between 3 and 5.5 months of age) in contrast to Tg2576 and WT littermates not treated with DSS. In detail, following induction of DSS colitis, WT and Tg2576 mice exhibited contrasting features in the expression level of inflammation-evoked astrocyte-associated genes in the hippocampus. No changes in microglial features occurred in the hippocampus between the experimental groups, whereas a reduced glial fibrillary acidic protein immunoreactivity was observed in Tg2576 vs. WT mice. This finding may reflect an atrophic, "loss-of-function" profile, further exacerbated by DSS where a decreased of GFAP mRNA expression level was detected. In conclusion, we suggest that as-yet unidentified peripheral mediators evoked by DSS colitis and involving the gut-brain axis emphasize an astrocyte "loss-of-function" profile present in young Tg2576 mice, leading to impaired synaptic morphological and functional integrity as a very early sign of AD. [ABSTRACT FROM AUTHOR]

This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum. [ABSTRACT FROM AUTHOR]

Objective of this study is to identify factors associated with falls in MS patients in order to increase awareness and reduce the risk of falls. This scoping review used specific Mesh terms to formulate the literature search around falls and MS using Medline, Google Scholar, Scopus, and Embase search engines. English papers published between 2012 and 2022, studies with a clear definition of falls, McDonald's diagnostic criteria for MS, and those with Expanded Disability Status Scale (EDSS) or Patient Determined Disease Steps (PDDS) scores were included. Critical data from the selected articles were extracted and classified according to the different factors associated with falls in MS patients. Eighteen articles were included in this review. The most important factors associated with falls in MS patients identified were the severity and progression of the disease, mobility and balance problems, bladder dysfunction, fear of falling, fatigue, and cognitive dysfunction. In conclusion, this scoping review yielded the most common factors associated with falls in patients with MS. Study findings can be used to develop future interventions focusing on improving mobility, proprioception, and balance to decrease fall risk and injury amongst MS patients. [ABSTRACT FROM AUTHOR]

The authors discuss articles which investigated the utility of artificial intelligence (AI)-assisted methodology in enhancing diagnostic accuracy in lung cancer, pulmonary hypertension, and acute respiratory distress syndrome (ARDS). Topics include a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography and computational platform for doctor-AI cooperation in pulmonary arterial hypertension prognostication.

Climate warming and landscape fragmentation are both factors well known to threaten biodiversity and to generate species responses and adaptation. However, the impact of warming and fragmentation interplay on organismal responses remains largely under-explored, especially when it comes to gut symbionts, which may play a key role in essential host functions and traits by extending its functional and genetic repertoire. Here, we experimentally examined the combined effects of climate warming and habitat connectivity on the gut bacterial communities of the common lizard (Zootoca vivipara) over three years. While the strength of effects varied over the years, we found that a 2°C warmer climate decreases lizard gut microbiome diversity in isolated habitats. However, enabling connectivity among habitats with warmer and cooler climates offset or even reversed warming effects. The warming effects and the association between host dispersal behaviour and microbiome diversity appear to be a potential driver of this interplay. This study suggests that preserving habitat connectivity will play a key role in mitigating climate change impacts, including the diversity of the gut microbiome, and calls for more studies combining multiple anthropogenic stressors when predicting the persistence of species and communities through global changes. [ABSTRACT FROM AUTHOR]

Methotrexate (MTX) is an essential part of therapy in the treatment of acute lymphoblastic leukemia (ALL) in children, and inferior intellectual outcomes have been reported in children who are leukemia survivors. Although several studies have demonstrated that the interaction between gut microbiota changes and the brain plays a vital role in the pathogenesis of chemotherapy-induced brain injury, preexisting studies on the effect of MTX on gut microbiota changes focused on gastrointestinal toxicity only. Based on our previous studies, which revealed that MTX treatment resulted in inferior neurocognitive function in developing young rats, we built a young rat model mimicking MTX treatment in a child ALL protocol, trying to investigate the interactions between the gut and brain in response to MTX treatment. We found an association between gut microbiota changes and neurogenesis/repair processes in response to MTX treatment, which suggest that MTX treatment results in gut dysbiosis, which is considered to be related to MTX neurotoxicity through an alteration in gut–brain axis communication. [ABSTRACT FROM AUTHOR]

The human microbiome has a crucial role in the homeostasis and health of the host. These microorganisms along with their genes are involved in various processes, among these are neurological signaling, the maturation of the immune system, and the inhibition of opportunistic pathogens. In this sense, it has been shown that a healthy ocular microbiota acts as a barrier against the entry of pathogens, contributing to the prevention of infections. In recent years, a relationship has been suggested between microbiota dysbiosis and the development of neurodegenerative diseases. In patients with glaucoma, it has been observed that the microbiota of the ocular surface, intraocular cavity, oral cavity, stomach, and gut differ from those observed in healthy patients, which may suggest a role in pathology development, although the evidence remains limited. The mechanisms involved in the relationship of the human microbiome and this neurodegenerative disease remain largely unknown. For this reason, the present review aims to show a broad overview of the influence of the structure and composition of the human oral and gut microbiota and relate its dysbiosis to neurodegenerative diseases, especially glaucoma. [ABSTRACT FROM AUTHOR]

Introduction: Vaginal estrogen is a treatment for genitourinary symptoms of menopause (GSM), which comprises vaginal atrophy and urinary dysfunction, including incontinence. Previous studies show that estrogen therapy promotes lactobacilli abundance and is associated with reduced GSM symptoms, including reduction of stress incontinence. However, detailed longitudinal studies that characterize how the microbiome changes in response to estrogen are scarce. We aimed to compare the vaginalmicrobiota of postmenopausal women, before and 12 weeks after vaginal estrogen cream. Methods: A total of 44 paired samples from 22 postmenopausal women with vaginal atrophy and stress incontinence were collected pre-vaginal estrogens and were compared to 12 weeks post-vaginal estrogen. Microbiota was characterized by 16S rRNA amplicon sequencing and biodiversity was investigated by comparing the alpha- and beta-diversity and potential markers were identified using differential abundance analysis. Results: Vaginal estrogen treatment was associated with a reduction in vaginal pH and corresponded with a significant reduction in alpha diversity of the microbiota. Healthy vaginal community state type was associated with lower mean pH 4.89 (SD = 0.6), in contrast to dysbiotic state which had a higher mean pH6.4 (SD=0.74). Women with lactobacilli dominant community pre-treatment, showed stable microbiota and minimal change in their pH. Women with lactobacilli deficient microbiome pre-treatment improved markedly (p = 0.004) with decrease in pH -1.31 and change to heathier community state types. Conclusion: In postmenopausal women with stress incontinence, vaginal estrogen promotes Lactobacillus and Bifidobacterium growth and lowers vaginal pH. Maximum response is seen in those with a dysbiotic vaginal microbiota pre-treatment. [ABSTRACT FROM AUTHOR]

The gut mucosal epithelium is one of the largest organs in the body and plays a critical role in regulating the crosstalk between the resident microbiome and the host. To this effect, the tight control of what is permitted through this barrier is of high importance. There should be restricted passage of harmful microorganisms and antigens while at the same time allowing the absorption of nutrients and water. An increased gut permeability, or "leaky gut", has been associated with a variety of diseases ranging from infections, metabolic diseases, and inflammatory and autoimmune diseases to neurological conditions. Several factors can affect gut permeability, including cytokines, dietary components, and the gut microbiome. Here, we discuss how the gut microbiome impacts the permeability of the gut epithelial barrier and how this can be harnessed for therapeutic purposes. [ABSTRACT FROM AUTHOR]

Quantitative real-time PCR of phylogenetic and functional marker genes is among the most commonly used techniques to quantify the abundance of microbial taxa in environmental samples. However, in most environmental applications, the approach is a rough assessment of population abundance rather than an exact absolute quantification method because of PCR-based estimation biases caused by multiple factors. Previous studies on these technical issues have focused on primer or template sequence features or PCR reaction conditions. However, how target gene sequence characteristics (e.g., evenness and dominance) in environmental samples affect qPCR quantifications has not been well studied. Here, we compared three primer sets targeting the beta subunit of the dissimilatory sulfite reductase (dsrB) to investigate qPCR quantification performance under different target gene sequence evenness and dominance conditions using artificial gBlock template mixtures designed accordingly. Our results suggested that the qPCR quantification performance of all tested primer sets was determined by the comprehensive effect of the target gene sequence evenness and dominance in environmental samples. Generally, highly degenerate primer sets have equivalent or better qPCR quantification results than a more target-specific primer set. Low template concentration in this study (~105 copies/L) will exaggerate the qPCR quantification results difference among tested primer sets. Improvements to the accuracy and reproducibility of qPCR assays for gene copy number quantification in environmental microbiology and microbial ecology studies should be based on prior knowledge of target gene sequence information acquired by metagenomic analysis or other approaches, careful selection of primer sets, and proper reaction conditions optimization. [ABSTRACT FROM AUTHOR]

For a long time, urine has been considered sterile in physiological conditions, thanks to the particular structure of the urinary tract and the production of uromodulin or Tamm–Horsfall protein (THP) by it. More recently, thanks to the development and use of new technologies, i.e., next-generation sequencing and expanded urine culture, the identification of a microbial community in the urine, the so-called urobiota, became possible. Major phyla detected in the urine are represented by Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Particularly, the female urobiota is largely represented by Lactobacillus spp., which are very active against urinary pathogenic Escherichia (E.) coli (UPEC) strains via the generation of lactic acid and hydrogen peroxide. Gut dysbiosis accounts for recurrent urinary tract infections (UTIs), so-called gut–bladder axis syndrome with the formation of intracellular bacterial communities in the course of acute cystitis. However, other chronic urinary tract infections are caused by bacterial strains of intestinal derivation. Monomicrobial and polymicrobial infections account for the outcome of acute and chronic UTIs, even including prostatitis and chronic pelvic pain. E. coli isolates have been shown to be more invasive and resistant to antibiotics. Probiotics, fecal microbial transplantation, phage therapy, antimicrobial peptides, and immune-mediated therapies, even including vaccines for the treatment of UTIs, will be described. [ABSTRACT FROM AUTHOR]

Kidney stone disease (KSD) is one of the most common urological diseases. The incidence of kidney stones has increased dramatically in the last few decades. Kidney stones are mineral deposits in the calyces or the pelvis, free or attached to the renal papillae. They contain crystals and organic components, and they are made when urine is supersaturated with minerals. Calcium-containing stones are the most common, with calcium oxalate as the main component of most stones. However, many of these form on a calcium phosphate matrix called Randall's plaque, which is found on the surface of the kidney papilla. The etiology is multifactorial, and the recurrence rate is as high as 50% within 5 years after the first stone onset. There is a great need for recurrence prevention that requires a better understanding of the mechanisms involved in stone formation to facilitate the development of more effective drugs. This review aims to understand the pathophysiology and the main molecular mechanisms known to date to prevent recurrences, which requires behavioral and nutritional interventions, as well as pharmacological treatments that are specific to the type of stone. [ABSTRACT FROM AUTHOR]

Urinary tract infections (UTIs) are a common health issue affecting individuals worldwide. Recurrent urinary tract infections (rUTI) pose a significant clinical challenge, with limited understanding of the underlying mechanisms. Recent research suggests that the urobiome, the microbial community residing in the urinary tract, may play a crucial role in the development and recurrence of urinary tract infections. However, the specific virulence factor genes (VFGs) driven by urobiome contributing to infection recurrence remain poorly understood. Our study aimed to investigate the relationship between urobiome driven VFGs and recurrent urinary tract infections. By analyzing the VFGs composition of the urinary microbiome in patients with rUTI compared to a control group, we found higher alpha diversity in rUTI patients compared with healthy control. And then, we sought to identify specific VFGs features associated with infection recurrence. Specifically, we observed an increased abundance of certain VGFs in the recurrent infection group. We also associated VFGs and clinical data. We then developed a diagnostic model based on the levels of these VFGs using random forest and support vector machine analysis to distinguish healthy control and rUIT, rUTI relapse and rUTI remission. The diagnostic accuracy of the model was assessed using receiver operating characteristic curve analysis, and the area under the ROC curve were 0.83 and 0.75. These findings provide valuable insights into the complex interplay between the VFGs of urobiome and recurrent urinary tract infections, highlighting potential targets for therapeutic interventions to prevent infection recurrence. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains. [ABSTRACT FROM AUTHOR]

Urinary tract infection (UTI) caused by spinal cord injury (SCI) can have significant morbidity. There is currently a lack of relevant data in China. This study explores incidence and risk factors of UTI in hospitalized patients with SCI in China, and will help healthcare professionals to make informed clinical decisions to reduce the incidence of UTI. This retrospective study analyzed the medical records of patients with SCI who were hospitalized at three campuses of a hospital in central China between August 2014 and August 2023. The files of patients with SCI were reviewed for demographics and clinical characteristics. Logistic regression analysis was performed to identify risk factors associated with UTI. A total of 538 patients were included in this study. The incidence of UTI was 49.8%. Sex, hypoproteinemia, urinary incontinence, bladder irrigation, timing of rehabilitation, duration of indwelling urinary catheter were risk factors of UTI. The implementation of specific preventive measures is anticipated to result in a decrease in the occurrence of UTI among individuals with SCI, consequently enhancing their overall quality of life and prognosis. [ABSTRACT FROM AUTHOR]

Objectives: Caffeine's potential benefits on multiple sclerosis (MS), as well as on the ambulatory performance of non-MS populations, prompted us to evaluate its potential effects on balance, mobility, and health-related quality of life (HR-QoL) of persons with MS (PwMS). Methods: This single-arm pilot clinical trial consisted of a 2-week placebo run-in and a 12-week caffeine treatment (200 mg/day) stage. The changes in outcome measures during the study period (weeks 0, 2, 4, 8, and 12) were evaluated using the Generalized Estimation Equation (GEE). The outcome measures were the 12-item Multiple Sclerosis Walking Scale (MSWS-12) for self-reported ambulatory disability, Berg Balance Scale (BBS) for static and dynamic balance, Timed Up and Go (TUG) for dynamic balance and functional mobility, Multiple Sclerosis Impact Scale (MSIS-29) for patient's perspective on MS-related QoL (MS-QoL), and Patients' Global Impression of Change (PGIC) for subjective assessment of treatment efficacy. GEE was also used to evaluate age and sex effect on the outcome measures over time. (Iranian Registry of Clinical Trials, IRCT2017012332142N1). Results: Thirty PwMS were included (age: 38.89 ± 9.85, female: 76.7%). Daily caffeine consumption significantly improved the objective measures of balance and functional mobility (BBS; P-value<0.001, and TUG; P-value = 0.002) at each study time point, and the subjective measure of MS-related QoL (MSIS-29; P-value = 0.005) two weeks after the intervention. Subjective measures of ambulatory disability (MSWS-12) and treatment efficacy (PGIC) did not significantly change. The effect of age and sex on the outcome measures were also assessed; significant sex-time interaction effects were found for MSWS-12 (P-value = 0.001) and PGIC (P-value<0.001). The impact of age on BBS scores increased as time progressed (P-value = 0.006). Conclusions: Caffeine may enhance balance, functional mobility, and QoL in PwMS. Being male was associated with a sharper increase in self-reported ambulatory disability over time. The effects of aging on balance get more pronounced over time. Trial registration: This study was registered with the Iranian Registry of Clinical Trials (Registration number: IRCT2017012332142N1), a Primary Registry in the WHO Registry Network. [ABSTRACT FROM AUTHOR]

Bladder cancer (BC) constitutes one of the most diagnosed types of cancer worldwide. Advancements in and new methodologies for DNA sequencing, leading to high-throughput microbiota testing, have pinpointed discrepancies in urinary microbial fingerprints between healthy individuals and patients with BC. Although several studies suggest an involvement of microbiota dysbiosis in the pathogenesis, progression, and therapeutic response to bladder cancer, an established direct causal relationship remains to be elucidated due to the lack of standardized methodologies associated with such studies. This review compiles an overview of the microbiota of the human urinary tract in healthy and diseased individuals and discusses the evidence to date on microbiome involvement and potential mechanisms by which the microbiota may contribute to the development of BC. We also explore the potential profiling of urinary microbiota as a biomarker for risk stratification, as well as the prediction of the response to intravesical therapies and immunotherapy in BC patients. Further investigation into the urinary microbiome of BC patients is imperative to unravel the complexities of the role played by host–microbe interactions in shaping wellness or disease and yield valuable insights into and strategies for the prevention and personalized treatment of BC. [ABSTRACT FROM AUTHOR]