Multi-Omics Profiling Suggesting Intratumoral Mast Cells as Predictive Index of Breast Cancer Lung Metastasis

Breast cancer lung metastasis has a high mortality rate and lacks effective treatments, for the factors that determine breast cancer lung metastasis are not yet well understood. In this study, data from 1067 primary tumors in four public datasets revealed the distinct microenvironments and immune composition among patients with or without lung metastasis. We used multi-omics data of the TCGA cohort to emphasize the following characteristics that may lead to lung metastasis: more aggressive tumor malignant behaviors, severer genomic instability, higher immunogenicity but showed generalized inhibition of effector functions of immune cells. Furthermore, we found that mast cell fraction can be used as an index for individual lung metastasis status prediction and verified in the 20 human breast cancer samples. The lower mast cell infiltrations correlated with tumors that were more malignant and prone to have lung metastasis. This study is the first comprehensive analysis of the molecular and cellular characteristics and mutation profiles of breast cancer lung metastasis, which may be applicable for prognostic prediction and aid in choosing appropriate medical examinations and therapeutic regimens.

Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS).Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15–20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury.Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01).Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions.

A Simple Predictive Index of the Abdominal Shape for Postoperative Complications After Laparoscopy-Assisted Distal Gastrectomy for Gastric Cancer

Background: The purpose of this study was to explore the effect of abdominal shape on the short-term surgical outcomes.Methods: This was a retrospective study that included 425 patients undergoing laparoscopic distal gastrectomy plus D2 lymph node dissection (LADG) from January 2013 to January 2021. The abdominal parameters, including the shortest distance of the pancreas from the anterior abdominal skin (PAAD), the lower sternum angle (LSA), the thickness of the subcutaneous fat at the navel level (SFT), the anteroposterior diameters (APD) and the left-right diameters (LRD) at the navel level, the distance from the xiphoid process to the navel (XND) and the distance from the xiphoid process to the pubis (XBD), were calculated by preoperative abdominal computed tomography (CT) imaging. The parameters and short-term surgical outcomes were analyzed.Results: In males, the number of retrieved lymph nodes was significantly higher in patients with a lower APD group (p = 0.031). The operation time was significantly shorter in the lower body mass index (BMI) (p = 0.007), lower LSA (p = 0.035), lower PAAD (p = 0.000), lower SFT (p = 0.004), lower APD (p = 0.000) and lower LRD (p = 0.014) groups. The estimated blood loss was significantly less in the lower BMI (p = 0.035), lower LSA (p = 0.001), lower PAAD (p = 0.012), lower SFT (p = 0.003), lower APD (p = 0.000) and lower LRD (p = 0.005) groups. The complications were fewer in the lower LSA (p = 0.012), lower APD (p = 0.043) and lower LRD (p = 0.023) groups. In females, the postoperative hospital stay was shorter in the lower PAAD (p = 0.027) and lower SFT (p = 0.004) groups, and the lower SFT group had fewer complications (p = 0.020). Furthermore, in multivariate analysis, higher PAAD (p = 0.037, odds ratio = 1.030, 95% CI = 1.002–1.059) was an independent factor for predicting postoperative complications in males.Conclusion: Various abdominal shapes can affect the difficulty of LADG. Higher PAAD is a simple independent index for predicting postoperative complications in males.

Opisthenar microvessel area as a sensitive predictive index of arterial stiffness in hypertensive patients

We aimed to analyze whether opisthenar microvessel area (OMA, measured with Optical Coherence Tomography (OCT) angiography) was associated with blood pressure (BP), arterial stiffness and whether OMA can predict arterial stiffness in hypertensive (HTN) patients. Results from 90 participants showed that BP, brachial-ankle pulse wave velocity (baPWV) and ankle brachial index (ABI) were significantly higher but OMA (in control, with cold- and warm-stimulation, NT, CST, HST and the differences, CSD, HSD) were significantly reduced in HTN group (n = 36) compared to non-HTN (n = 54). NT, CST, HST and HSD showed negative correlations with baPWV and ABI in all participants, female (n = 47) and male group (n = 43), but the correlation was absent when the participants were divided into HTN and non-HTN. Logistic Regression analysis showed that only baPWV was a significant risk factor for HSD (OR 19.7, 95%CI 4.959–78.733, p < 0.0001) but not the age, BMI, smoking, drinking or exercise status (p > 0.05). Receiver Operating Characteristics analysis for HSD was 0.781, 0.804, 0.770, respectively. HSD < 9439.5 μm2 predicted high BP and arterial stiffness (95% CI in all participants: baPWV, 0.681–0.881, SBP, 0.709–0.900, DBP, 0.672–0.867, p < 0.001). These results suggest that OMA is a sensitive index to predict arterial stiffness in HTN population.

Performance of Three Asthma Predictive Tools in a Cohort of Infants Hospitalized With Severe Bronchiolitis

Childhood asthma develops in 30–40% of children with severe bronchiolitis but accurate prediction remains challenging. In a severe bronchiolitis cohort, we applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) to predict asthma at age 5 years. We applied the API, mAPI, and PARS to the 17-center cohort of infants hospitalized with severe bronchiolitis during 2011–2014 (35th Multicenter Airway Research Collaboration, MARC-35). We used data from the first 3 years of life including parent interviews, chart review, and specific IgE testing to predict asthma at age 5 years, defined as parent report of clinician-diagnosed asthma. Among 875/921 (95%) children with outcome data, parent-reported asthma was 294/875 (34%). In MARC-35, a positive index/score for stringent and loose API, mAPI, and PARS were 24, 68, 6, and 55%, respectively. The prediction tools' AUCs (95%CI) ranged from 0.57 (95%CI 0.54–0.59) to 0.68 (95%CI 0.65–0.71). The positive likelihood ratios were lower in MARC-35 compared to the published results from the original cohorts. In this high-risk population of infants hospitalized with severe bronchiolitis, API, mAPI, and PARS had sub-optimal performance (AUC &lt;0.8). Highly accurate (AUC &gt;0.8) asthma prediction tools are desired in infants hospitalized with severe bronchiolitis.

PaO2/FiO2 ratio forecasts COVID-19 patients’ outcome regardless of age: a cross-sectional, monocentric study

We studied the predictive value of the PaO2/FiO2 ratio for classifying COVID-19-positive patients who will develop severe clinical outcomes. One hundred fifty patients were recruited and categorized into two distinct populations (“A” and “B”), according to the indications given by the World Health Organization. Patients belonging the population “A” presented with mild disease not requiring oxygen support, whereas population “B” presented with a severe disease needing oxygen support. The AUC curve of PaO2/FiO2 in the discovery cohort was 0.838 (95% CI 0.771–0.908). The optimal cut-off value for distinguishing population “A” from the “B” one, calculated by Youden’s index, with sensitivity of 71.79% and specificity 85.25%, LR+4.866, LR−0.339, was < 274 mmHg. The AUC in the validation cohort of 170 patients overlapped the previous one, i.e., 0.826 (95% CI 0.760–0.891). PaO2/FiO2 ratio < 274 mmHg was a good predictive index test to forecast the development of a severe respiratory failure in SARS-CoV-2-infected patients. Moreover, our work highlights that PaO2/FiO2 ratio, compared to inflammatory scores (hs-CRP, NLR, PLR and LDH) indicated to be useful in clinical managements, results to be the most reliable parameter to identify patients who require closer respiratory monitoring and more aggressive supportive therapies. Clinical trial registration: Prognostic Score in COVID-19, prot. NCT04780373 https://clinicaltrials.gov/ct2/show/NCT04780373 (retrospectively registered).

Validation of fatty liver index as a marker for metabolic dysfunction-associated fatty liver disease

Aims Metabolic dysfunction-associated fatty liver disease (MAFLD) is a new nomenclature for nonalcoholic fatty liver. Along with obesity, fatty liver associated with metabolic dysfunction is increasing and has become a serious socioeconomic problem. Non-invasive testing for the confirmation of MAFLD, including the fatty liver index (FLI), can be used as an alternative method for diagnosing steatosis when imaging modalities are not available. To date, few studies have examined the effectiveness and validity of FLI for diagnosing MAFLD. Therefore, this study analyzed the effectiveness and validity of FLI for diagnosing MAFLD. Methods Medical records of men and women aged ≥ 19 years who underwent abdominal computed tomography (CT) examination at our facility between March 2012 and October 2019 were retrospectively reviewed. A comparative analysis between non-continuous variables was performed using the chi-squared test. The area under receiver operating characteristic (AUROC) curve was used to verify the effectiveness of FLI as a predictive index for MAFLD. Results Analysis of the association between MAFLD and abdominal CT revealed that the sensitivity and specificity of FLI for diagnosing MAFLD were 0.712 and 0.713, respectively. The AUROC of FLI for predicting MAFLD was 0.776. Conclusions Our study verified the accuracy of FLI for predicting MAFLD using CT. The FLI can be used as a simple and cost-effective tool for screening MAFLD in clinical settings.