A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease.

The application of single-cell technologies in clinical nephrology remains elusive. We generated an atlas of transcriptionally defined cell types and cell states of human kidney disease by integrating single-cell signatures reported in the literature with newly generated signatures obtained from 5 patients with acute kidney injury. We used this information to develop kidney-specific cell-level information ExtractoR (K-CLIER), a transfer learning approach specifically tailored to evaluate the role of cell types/states on bulk RNAseq data. We validated the K-CLIER as a reliable computational framework to obtain a dimensionality reduction and to link clinical data with single-cell signatures. By applying K-CLIER on cohorts of patients with different kidney diseases, we identified the most relevant cell types associated with fibrosis and disease progression. This analysis highlighted the central role of altered proximal tubule cells in chronic kidney disease. Our study introduces a new strategy to exploit the power of single-cell technologies toward clinical applications.

Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort.

This study aimed to assess the concordance of various psychometric scales in detecting Perinatal Depression (PND) risk and diagnosis. A cohort of 432 women was assessed at 10-15th and 23-25th gestational weeks, 33-40 days and 180-195 days after delivery using the Edinburgh Postnatal Depression Scale (EPDS), Visual Analogue Scale (VAS), Hamilton Depression Rating Scale (HDRS), Montgomery-Åsberg Depression Rating Scale (MADRS), and Mini International Neuropsychiatric Interview (MINI). Spearman's rank correlation coefficient was used to assess agreement across instruments, and multivariable classification models were developed to predict the values of a binary scale using the other scales. Moderate agreement was shown between the EPDS and VAS and between the HDRS and MADRS throughout the perinatal period. However, agreement between the EPDS and HDRS decreased postpartum. A well-performing model for the estimation of current depression risk (EPDS > 9) was obtained with the VAS and MADRS, and a less robust one for the estimation of current major depressive episode (MDE) diagnosis (MINI) with the VAS and HDRS. When the EPDS is not feasible, the VAS may be used for rapid and comprehensive postpartum screening with reliability. However, a thorough structured interview or clinical examination remains necessary to diagnose a MDE.

TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells.

Adoptive immunotherapy with T cells engineered with tumor-specific T cell receptors (TCRs) holds promise for cancer treatment. However, suppressive cues generated in the tumor microenvironment (TME) can hinder the efficacy of these therapies, prompting the search for strategies to overcome these detrimental conditions and improve cellular therapeutic approaches. CD1d-restricted invariant natural killer T (iNKT) cells actively participate in tumor immunosurveillance by restricting suppressive myeloid populations in the TME. Here, we showed that harnessing iNKT cells with a second TCR specific for a tumor-associated peptide generated bispecific effectors for CD1d- and major histocompatibility complex (MHC)-restricted antigens in vitro. Upon in vivo transfer, TCR-engineered iNKT (TCR-iNKT) cells showed the highest efficacy in restraining the progression of multiple tumors that expressed the cognate antigen compared with nontransduced iNKT cells or CD8+ T cells engineered with the same TCR. TCR-iNKT cells achieved robust cancer control by simultaneously modulating intratumoral suppressive myeloid populations and killing malignant cells. This dual antitumor function was further enhanced when the iNKT cell agonist α-galactosyl ceramide (α-GalCer) was administered as a therapeutic booster through a platform that ensured controlled delivery at the tumor site, named multistage vector (MSV). These preclinical results support the combination of tumor-redirected TCR-iNKT cells and local α-GalCer boosting as a potential therapy for patients with cancer.

Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito Aedes albopictus in Switzerland.

BACKGROUND: In Switzerland, Aedes albopictus is well established in Ticino, south of the Alps, where surveillance and control are implemented. The mosquito has also been observed in Swiss cities north of the Alps. Decision-making tools are urgently needed by the local authorities in order to optimize surveillance and control. METHODS: A regularized logistic regression was used to link the long-term dataset of Ae. albopictus occurrence in Ticino with socioenvironmental predictors. The probability of establishment of Ae. albopictus was extrapolated to Switzerland and more finely to the cities of Basel and Zurich. RESULTS: The model performed well, with an AUC of 0.86. Ten socio-environmental predictors were selected as informative, including the road-based distance in minutes of travel by car from the nearest cell established in the previous year. The risk maps showed high suitability for Ae. albopictus establishment in the Central Plateau, the area of Basel, and the lower Rhone Valley in the Canton of Valais. CONCLUSIONS: The areas identified as suitable for Ae. albopictus establishment are consistent with the actual current findings of tiger mosquito. Our approach provides a useful tool to prompt authorities' intervention in the areas where there is higher risk of introduction and establishment of Ae. albopictus.

The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.

The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index-ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a "good early outcome". A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an "excellent early outcome". The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months' follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

Artificial Intelligence in Thyroid Field-A Comprehensive Review.

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Profiling the Location and Extent of Musicians' Pain Using Digital Pain Drawings.

BACKGROUND AND AIMS: According to existing literature, musicians are at risk of experiencing a range of painful musculoskeletal conditions. Recently, a novel digital technology was developed to investigate pain location and pain extent. The aim of this study was to describe pain location and pain extent in musicians using a digital method for pain drawing (PD) analysis. Additionally, the association between PD variables and clinical features were explored in musicians with pain. METHODS: One hundred and fifty-eight musicians (90 women and 68 men; aged 22.4 ± 3.6 years) were recruited from Swiss and U.K. conservatories. Participants were asked to complete a survey including both background musical information and clinical features, the QuickDASH (QD) questionnaire, and the digital PDs. RESULTS: Of the 158 participants, 126 musicians (79.7%) reported having pain, with higher prevalence in the areas of the neck and shoulders, the lower back, and the right arm. The mean percentage of pain extent was 3.1% ± 6.5%. The mean QD score was higher for musicians with pain than for those without pain. Additionally, the results indicated a positive correlation between the QD score and pain extent, and there were significant correlations between age and pain intensity, as well as between pain extent and pain intensity. CONCLUSIONS: The high prevalence of pain among musicians has been confirmed using a digital technique for PD acquisition and analysis. In addition, positive correlations between pain extent and upper limb disability have been demonstrated. Our findings highlight the need for effective prevention and treatment strategies for musicians.

Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis.

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant CD5+ B lymphocytes in blood, bone marrow, and lymphoid organs. CD1d-restricted invariant natural killer T (iNKT) cells are innate-like T lymphocytes strongly implicated in tumor surveillance. We investigated the impact of iNKT cells in the natural history of the disease in the Eµ-Tcl1 (Tcl1) CLL mouse model and 68 CLL patients. We found that Tcl1-CLL cells express CD1d and that iNKT cells critically delay disease onset but become functionally impaired upon disease progression. In patients, disease progression correlates with high CD1d expression on CLL cells and impaired iNKT cells. Conversely, disease stability correlates with negative or low CD1d expression on CLL cells and normal iNKT cells, suggesting indirect leukemia control. iNKT cells indeed hinder CLL survival in vitro by restraining CD1d-expressing nurse-like cells, a relevant proleukemia macrophage population. Multivariable analysis identified iNKT cell frequency as an independent predictor of disease progression. Together, these results support the contribution of iNKT cells to CLL immune surveillance and highlight iNKT cell frequency as a prognostic marker for disease progression.

Computational study of the fluid-dynamics in carotids before and after endarterectomy.

In this work, we provide a computational study of the effects of carotid endarterectomy (CEA) on the fluid-dynamics at internal carotid bifurcations. We perform numerical simulations in real geometries of the same patients before and after CEA, using patient-specific boundary data obtained by Echo-Color Doppler measurements. We analyze four patients with a primary closure and other four where a patch was used to close arteriotomies. The results show that (i) CEA is able to restore physiological fluid-dynamic conditions; (ii) among the post-operative cases, the presence of patch leads to local hemodynamic conditions which might imply a higher risk of restenosis in comparison with the cases without patch.

Invariant NKT cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and correlates with remission state.

Immune reconstitution plays a crucial role on the outcome of patients given T cell-depleted HLA-haploidentical hematopoietic stem cell transplantation (hHSCT) for hematological malignancies. CD1d-restricted invariant NKT (iNKT) cells are innate-like, lipid-reactive T lymphocytes controlling infections, cancer, and autoimmunity. Adult mature iNKT cells are divided in two functionally distinct CD4(+) and CD4(-) subsets that express the NK receptor CD161 and derive from thymic CD4(+)CD161(-) precursors. We investigated iNKT cell reconstitution dynamics in 33 pediatric patients given hHSCT for hematological malignancies, with a follow-up reaching 6 y posttransplantation, and correlated their emergence with disease relapse. iNKT cells fully reconstitute and rapidly convert into IFN-γ-expressing effectors in the 25 patients maintaining remission. CD4(+) cells emerge earlier than the CD4(-) ones, both displaying CD161(-) immature phenotypes. CD4(-) cells expand more slowly than CD4(+) cells, though they mature with significantly faster kinetics, reaching full maturation by 18 mo post-hHSCT. Between 4 and 6 y post-hHSCT, mature CD4(-) iNKT cells undergo a substantial expansion burst, resulting in a CD4(+)