Machine learning-based phenogroups and prediction model in patients with functional gastrointestinal disorders to reveal distinct disease subsets associated with gas production.

Background and Objectives: Symptom-based subtyping for functional gastrointestinal disorders (FGIDs) has limited value in identifying underlying mechanisms and guiding therapeutic strategies. Small intestinal dysbiosis is implicated in the development of FGIDs. We tested if machine learning (ML) algorithms utilizing both gastrointestinal (GI) symptom characteristics and lactulose breath tests could provide distinct clusters. Materials and Methods: This was a prospective cohort study. We performed lactulose hydrogen methane breath tests and hydrogen sulfide breath tests in 508 patients with GI symptoms. An unsupervised ML algorithm was used to categorize subjects by integrating GI symptoms and breath gas characteristics. Generalized Estimating Equation (GEE) models were used to examine the longitudinal associations between cluster patterns and breath gas time profiles. An ML-based prediction model for identifying excessive gas production in FGIDs patients was developed and internal validation was performed. Results: FGIDs were confirmed in 300 patients. K-means clustering identified 4 distinct clusters. Cluster 2, 3, and 4 showed enrichments for abdominal distention and diarrhea with a high proportion of excessive gas production, whereas Cluster 1 was characterized by moderate lower abdominal discomforts with the most psychological complaints and the lowest proportion of excessive gas production. GEE models showed that breath gas concentrations varied among different clusters over time. We further sought to develop an ML-based prediction model to determine excessive gas production. The model exhibited good predictive capabilities. Conclusion: ML-based phenogroups and prediction model approaches could provide distinct FGIDs subsets and efficiently determine FGIDs subsets with greater gas production, thereby facilitating clinical decision-making and guiding treatment.

Duration of N1 sleep is a factor for excessive daytime sleepiness in epilepsy patients with interictal epileptiform discharges: A polysomnographic study.

Purpose: This study aimed to identify the occurrence of excessive daytime sleepiness (EDS) in epilepsy patients with interictal epileptiform discharges and to explore the impact of interictal sleep architecture and sleep-related events on EDS. Methods: This study included 101 epilepsy patients with interictal epileptiform discharges (IED) and 100 control patients who underwent simultaneous polysomnography and video ambulatory electroencephalography for >7 h throughout a single night. Multiple sleep latency tests were used to assess EDS. Comorbid EDS was present in 25 and 11 patients in the IED epilepsy and control groups, respectively. In addition, univariate and multivariate logistic regression analyses were performed to explore the factors influencing EDS. Results: The epilepsy group had a higher prevalence of comorbid EDS and shorter R sleep duration. Univariate logistic regression analysis indicated that an increased risk of EDS may be associated with prolonged N1 sleep duration, higher arousal index, lower mean saturation (mSaO2), higher oxygen desaturation index (ODI), and duration of wake after sleep onset (WASO). Multivariate logistic regression analysis revealed that N1 sleep duration was significantly correlated with EDS. Conclusion: In epilepsy patients with IED, the arousal index, mSaO2, ODI, and duration of WASO were weakly correlated with EDS, and the duration of N1 sleep demonstrated a significant positive correlation with EDS, which requires further research.

A polysomnographic study of slow-wave sleep loss in elderly patients with epilepsy.

Objective: The primary objective is to explore what causes slow-wave sleep loss in elderly patients with epilepsy. The secondary objective is to identify the PSG characteristics in elderly patients with epilepsy. The clinical demographics, sleep architecture, sleep-related events, and interictal epileptiform discharges are to be evaluated in the objectives. Methods: The video electroencephalography (VEEG) and polysomnogram (PSG) data from 44 elderly patients with epilepsy and 52 elderly patients with sleep disorders but without definite central nervous system diseases were analysed. This was a case-control study. The differences in the PSG sleep architecture parameters (total sleep time (TST), sleep efficiency, wake after sleep onset, etc.) and sleep-related events (apnea hypopnea index, oxygen desaturation index (ODI), periodic limb movement index, etc.) between the epilepsy and control groups. As Additionally, these parameters were assessed within the elderly patients with epilepsy, comparing the slow-wave sleep existence and slow-wave sleep loss groups, using VEEG and PSG. Results: The epileptic group exhibited significantly lower TST (343.477 ± 96.3046min vs 389.115 ± 61.5727min, p < 0.05), rapid eye movement (%) (13.011 ± 7.5384 vs 16.992 ± 6.7025, p < 0.05), non-rapid eye movement stage 3 (%) (1.35[0,7.225] vs 3.65[0.425,13.75], p < 0.05), and sleep efficiency (%) (69.482 ± 14.1771% vs 77.242 ± 10.6171%, p < 0.05). Conversely, the ODI (25.6[9.825,51.775] events/hour vs 16.85[5.3,30.425] events/hour, p < 0.05) and spontaneous arousal index (4.0455[2.1805,6.9609] events/hour vs 2.9709[1.4747,5.0554] events/hour, p < 0.05) were significantly higher in elderly patients with epilepsy. The prevalence of obstructive sleep apnea-hypopnea syndrome (OSAHS) was significantly higher in the slow-wave sleep loss group than in the slow-wave sleep existence group (100% vs 77.8%, p < 0.05). The incidence of slow-wave sleep loss was lower in patients with epilepsy aged between 75 and 85 years compared to those aged between 65 and 75 years. Conclusion: Elderly patients with epilepsy exhibit higher levels of ODI and spontaneous arousal index. Our findings indicate that OSAHS could be a contributing factor to slow-wave sleep loss in this population. The incidence of slow-wave sleep loss was lower in patients aged above 75 years among elderly patients with epilepsy.

Insulin resistance-related features are associated with cognitive decline: a cross-sectional study in adult patients with type 1 diabetes.

BACKGROUND: To investigate the associations between insulin resistance (IR)-related features and cognitive function in type 1 diabetes (T1D). METHODS: A total of 117 adult patients with T1D were recruited in this cross-sectional study. IR-related features include overweight/obesity/central obesity, hypertension, atherogenic dyslipidemia, and decreased estimated insulin sensitivity (eIS). The Wechsler Memory Scale-Chinese Revision, Wisconsin Card Sorting Test, and Sustained Attention to Response Task was used to assess memory, executive function and sustained attention, respectively. A z-score was generated from each test, and a composite measure of global cognitive performance was calculated by averaging the z-scores of all tests. Cognitive differences were measured between T1D patients with and without IR-related features. The associations between IR-related features and and cognitive performance were analyzed using: logistic regression, partial correlation, and multivariate linear regression analysis. RESULTS: A total of 53 (45.3%) T1D patients were defined as having IR-related features. Individuals with IR-related features displayed worse overall cognitive scores compared to those without and had a 4-fold increase in the risk for having global cognitive z-score < 0. Among the IR-related features, higher triglyceride (TG) and lower eIS showed linear correlation with lower global cognitive performance. And the subsequent regression analysis identified eIS as the factor independently associated with global cognitive performance. CONCLUSIONS: We have provided evidence linking IR-related features to deteriorated cognitive function in adult patients with T1D. And eIS showed an independent positive correlation with global cognitive performance. Although no causal relationship can be drawn, IR emerges as an important factor reflecting cognitive function. TRIAL REGISTRATION: ClinicalTrials.gov NCT03610984.

Immunomodulatory Function of Pien Tze Huang in T Cell-Mediated Anti-tumor Activity against B16-F10, MC38 and Hep1-6 Tumor Models.

OBJECTIVE: To investigate the anti-tumor effects of Pien Tze Huang (PZH) in mouse models of B16-F10 melanoma, MC38 colorectal cancer, Hep1-6 hepatocellular carcinoma and chemically induced hepatocellular carcinoma model. METHODS: Various tumor models, including B16-F10, MC38 and Hep1-6 tumor hypodermic inoculation models, B16-F10 and Hep1-6 pulmonary metastasis models, Hep1-6 orthotopic implantation model, and chemically induced hepatocellular carcinoma model, were utilized to evaluate the anti-tumor function of PZH. Tumor growth was assessed by measuring tumor size and weight of solid tumors isolated from C57BL/6 mice. For cell proliferation and death of tumor cells in vitro, as well as T cell activation markers, cytokine production and immune checkpoints analysis, single-cell suspensions were prepared from mouse spleen, lymph nodes, and tumors after PZH treatment. RESULTS: PZH demonstrated significant therapeutic efficacy in inhibiting tumor growth (P<0.01). Treatment with PZH resulted in a reduction in tumor size in subcutaneous MC38 colon adenocarcinoma and B16-F10 melanoma models, and decreased pulmonary metastasis of B16-F10 melanoma and Hep1-6 hepatoma (P<0.01). However, in vitro experiments showed that PZH only had slight impact on the cell proliferation and survival of tumor cells (P>0.05). Nevertheless, PZH exhibited a remarkable ability to enhance T cell activation and the production of interferon gamma, tumor necrosis factor alpha, and interleukin 2 in CD4+ T cells in vitro (P<0.01 or P<0.05). Importantly, PZH substantially inhibited T cell exhaustion and boosted cytokine production by tumor-infiltrating CD8+ T cells (P<0.01 or P<0.05). CONCLUSION: This study has confirmed a novel immunomodulatory function of PZH in T cell-mediated anti-tumor immunity, indicating that PZH holds promise as a potential therapeutic agent for cancer treatment.

Radionecrosis mimicking pseudo­progression in a patient with lung cancer and brain metastasis following the combination of anti­PD­1 therapy and stereotactic radiosurgery: A case report.

Brain metastases (BMs) usually develop in patients with non-small cell lung cancer. In addition to systemic therapy, radiation therapy and surgery, anti-programmed cell death-ligand 1 (PD-L1) therapy is another promising clinical anticancer treatment modality. However, the optimal timing and drug-drug interactions of anti-PD-L1 therapy with other combined treatments remain to be elucidated. Treatment with anti-PD-L1 therapy is associated with an increased risk of radionecrosis (RN) regardless of tumor histology. The present study described a case of RN in a patient with lung adenocarcinoma and with BM who received anti-PD-L1 therapy. Before anti-PD-L1 treatment, the patient received whole brain radiotherapy. During durvalumab treatment, the intracranial metastases regressed. The progression of intracranial lesions 9 months later prompted a second-line of therapy with PD-L1 inhibitor durvalumab and stereotactic radiotherapy (SRT). Despite stereotactic irradiation, the lesions progressed further, leading to surgical resection. On examination, RN was detected, but there was no evidence of metastatic lung cancer. The aim of the present study was to present the longitudinal change in magnetic resonance imaging in RN following STR and anti-PD-L1 combined therapy. The atypical image of RN is conditionally important for making an accurate preoperative diagnosis.

Fat-to-muscle ratio is associated with insulin resistance and cardiometabolic disorders in adults with type 1 diabetes mellitus.

AIMS: This study aimed to investigate the correlation of the fat-to-muscle ratio (FMR) with insulin resistance (IR) and cardiometabolic disorders (CMD) in patients with type 1 diabetes mellitus (T1DM). MATERIALS AND METHODS: We retrospectively recruited 420 adults with T1DM [52.6% men, median age 32.4 (24.5, 43.0) years]. Body composition was assessed by bioelectrical impedance analysis and FMR was calculated. The characteristics of the overall participants were compared between tertiles of FMR. Logistic regression analyses were performed to assess the association of FMR tertiles with IR and cardiometabolic risk factors. RESULTS: Median age and median haemoglobin A1c of all participants were 32.4 (24.5, 43.0) years and 7.4 (6.5, 8.7)%, respectively. The prevalence of IR and CMD was 18% and 38.6%. The FMR significantly differed between men and women [0.39 (0.31, 0.53) vs. 0.74 (0.63, 0.92), respectively, p < .001]. The proportion of IR and CMD gradually increased as the FMR increased. The multivariable-adjusted odd ratios for IR and CMD in FMR tertile 3 compared with tertile 1 were 4.8 [95% confidence interval (CI): (1.9, 12.1)] and 9.7 (95% CI: 4.2, 22.3), respectively, in men. For women, the corresponding odd ratios were 4.0 (95% CI: 1.2, 12.9) for IR and 5.8 (95% CI: 2.4, 13.6) for CMD. CONCLUSIONS: FMR is associated with IR and CMD in adults with T1DM and could be used as a promising parameter for targeting treatment in T1DM.

Biology of breast cancer brain metastases and novel therapies targeting the blood brain barrier: an updated review.

Brain metastasis (BM) is a critical cause of morbidity and mortality in patients with breast cancer (BC). Compared with other cancer cells, BC cells (BCs) exhibit special features in the metastatic process. However, the underlying mechanisms are still unclear, especially the crosstalk between tumour cells and the microenvironment. To date, novel therapies for BM, including targeted therapy and antibody‒drug conjugates, have been developed. Due to an improved understanding of the blood‒brain barrier (BBB) and blood-tumour barrier (BTB), the development and testing of therapeutic agents in clinical phases have substantially increased. However, these therapies face a major challenge due to the low penetration of the BBB or BTB. As a result, researchers have increasingly focused on finding ways to promote drug penetration through these barriers. This review provides an updated overview of breast cancer brain metastases (BCBM) and summarizes the newly developed therapies for BCBM, especially drugs targeting the BBB or BTB.