The gastrointestinal tract harbours the largest population of mast cells in the body; this highly specialised leukocyte cell type is able to adapt its phenotype and function to the microenvironment in which it resides. Mast cells react to external and internal stimuli thanks to the variety of receptors they express, and carry out effector and regulatory tasks by means of the mediators of different natures they produce. Mast cells are fundamental elements of the intestinal barrier as they regulate epithelial function and integrity, modulate both innate and adaptive mucosal immunity, and maintain neuro-immune interactions, which are key to functioning of the gut. Disruption of the intestinal barrier is associated with increased passage of luminal antigens into the mucosa, which further facilitates mucosal mast cell activation, inflammatory responses, and altered mast cell–enteric nerve interaction. Despite intensive research showing gut dysfunction to be associated with increased intestinal permeability and mucosal mast cell activation, the specific mechanisms linking mast cell activity with altered intestinal barrier in human disease remain unclear. This review describes the role played by mast cells in control of the intestinal mucosal barrier and their contribution to digestive diseases.

Background: Irritable bowel syndrome (IBS) is a chronic disorder of gut-brain interaction frequently accompanied by mental conditions, including depression and anxiety. Despite showing substantial heritability and being partly determined by a genetic component, the genetic underpinnings explaining the high rates of comorbidity remain largely unclear and there are no conclusive data on the temporal relationship between them. Exploring the overlapping genetic architecture between IBS and mental conditions may help to identify novel genetic loci and biological mechanisms underlying IBS and causal relationships between them. Methods: We quantified the genetic overlap between IBS, neuroticism, depression and anxiety, conducted a multi-trait genome-wide association study (GWAS) considering these traits and investigated causal relationships between them by using the largest GWAS to date. Results: IBS showed to be a highly polygenic disorder with extensive genetic sharing with mental conditions. Multi-trait analysis of IBS and neuroticism, depression and anxiety identified 42 genome-wide significant variants for IBS, of which 38 are novel. Fine-mapping risk loci highlighted 289 genes enriched in genes upregulated during early embryonic brain development and gene-sets related with psychiatric, digestive and autoimmune disorders. IBS-associated genes were enriched for target genes of anti-inflammatory and antirheumatic drugs, anesthetics and opioid dependence pharmacological treatment. Mendelian-randomization analysis accounting for correlated pleiotropy identified bidirectional causal effects between IBS and neuroticism and depression and causal effects of the genetic liability of IBS on anxiety. Conclusions: These findings provide evidence of the polygenic architecture of IBS, identify novel genome-wide significant variants for IBS and extend previous knowledge on the genetic overlap and relationship between gastrointestinal and mental disorders.

This study examines neuropsychological impairments associated with chronic fatigue syndrome (CFS) and explores their association with related clinical factors. Sixty-eight women with CFS were assessed with a neuropsychological battery. Raw scores were adjusted for age and gender and were converted to T scores according to normative data extracted from a local sample of 250 healthy subjects. Neuropsychological dysfunction was calculated using summary impairment indexes (proportion of test scores outside normal limits—T score <40—for each cognitive domain). Finally, a linear regression was calculated to identify predictors of cognitive deficit, including intrinsic factors of the disease (level of fatigue and length of illness) and extrinsic factors (emotional factors, age, and education). Approximately 50% of scores showed impairment in attention and motor functioning, and nearly 40% showed impairment in speed information processing and executive functioning. Fatigue predicted attention and executive functioning impairment, and emotional factors predicted verbal memory dysfunction. According to our findings, cognitive dysfunction in CFS could be explained by pathophysiological processes of the disease. One implication of this would be the need to identify homogeneous subgroups of patients with CFS by taking into account common factors, which, in turn, would help to identify more specific cognitive profiles, which could then serve to implement appropriate therapeutic measures accordingly.

Background: Despite the high prevalence and significant burden of depression, underdiagnosis remains a persistent challenge. Automatic speech analysis (ASA) has emerged as a promising method for depression assessment. However, a comprehensive quantitative synthesis evaluating its diagnostic accuracy is still lacking. Objective: This systematic review and meta-analysis aimed to assess the diagnostic performance of ASA in detecting depression, considering both machine learning and deep learning approaches. Methods: We conducted a systematic search across 8 databases, including MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar from January 2013 to April 1, 2025. We included studies published in English that evaluated the accuracy of ASA for detecting depression, and reported performance metrics such as accuracy, sensitivity, specificity, precision, or confusion matrices. Study quality was assessed using a modified version of the Quality Assessment of Studies of Diagnostic Accuracy-Revised. A 3-level meta-analysis was performed to estimate the pooled highest and lowest accuracy, sensitivity, specificity, and precision. Meta-regressions and subgroup analyses were performed to explore heterogeneity across various factors, including type of publication, artificial intelligence algorithms, speech features, speech-eliciting tasks, ground truth assessment, validation approach, dataset, dataset language, participants’ mean age, and sample size. Results: Of the 1345 records identified, 105 studies met the inclusion criteria. The pooled mean of the highest accuracy, sensitivity, specificity, and precision were 0.81 (95% CI 0.79 to 0.83), 0.84 (95% CI 0.81 to 0.86), 0.83 (95% CI 0.79 to 0.86), and 0.81 (95% CI 0.77 to 0.84), respectively, whereas the pooled mean of the lowest accuracy, sensitivity, specificity, and precision were 0.66 (95% CI 0.63 to 0.69), 0.63 (95% CI 0.58 to 0.68), 0.60 (95% CI 0.55 to 0.66), and 0.64 (95% CI 0.58 to 0.70), respectively. Conclusions: ASA shows promise as a method for detecting depression, though its readiness for clinical application as a standalone tool remains limited. At present, it should be regarded as a complementary method, with potential applications across diverse contexts. Further high-quality, peer-reviewed studies are needed to support the development of robust, generalizable models and to advance this emerging field. Trial Registration: PROSPERO CRD42023444431; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023444431