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BackgroundThe response to COVID-19 catalyzed the adoption and integration of digital health tools into the health care delivery model for musculoskeletal patients. The change, suspension, or relaxation of Medicare and federal guidelines enabled the rapid implementation of these technologies. The expansion of payment models for virtual care facilitated its rapid adoption. The authors aim to provide several examples of digital health solutions utilized to manage orthopedic patients during the pandemic and discuss what features of these technologies are likely to continue to provide value to patients and clinicians following its resolution.ConclusionThe widespread adoption of new technologies enabling providers to care for patients remotely has the potential to permanently change the expectations of all stakeholders about the way care is provided in orthopedics. The new era of Digital Orthopaedics will see a gradual and nondisruptive integration of technologies that support the patient's journey through the successful management of their musculoskeletal disease.

Due to the high mortality rate, more effective non-invasive diagnostic methods are still needed for lung cancer, the most common cause of cancer-related death worldwide. In this study, the integration of Raman and Fourier-transform infrared spectroscopy with advanced data-fusion techniques is investigated to improve the detection of lung cancer from human blood plasma samples. A high statistical significance was found for important protein-related oscillations, which are crucial for differentiating between lung cancer patients and healthy controls. The use of low-level data fusion and feature selection significantly improved model accuracy and emphasizes the importance of structural protein changes in cancer detection. Although other biomolecules such as carbohydrates and nucleic acids also contributed, proteins proved to be the decisive markers found using this technique. This research highlights the power of these combined spectroscopic methods to develop a non-invasive diagnostic tool for discriminating lung cancer from healthy state, with the potential to extend such studies to a variety of other diseases. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite the continuous therapeutic efforts metastatic renal cell carcinoma (mRCC) is a dreadful disease, but the many options available provide an horizon of hope for these patients. Sequential therapy based on vascular endothelial growth factor-tyrosine kinase inhibitors (VEGFR-TKI) continues in use. We present a nomogram for a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) mRCC treated with nephrectomy and VEGFR-TK, based on four independent clinical predictors: Eastern Cooperative Oncology Group (ECOG) status; International Metastatic RCC Database Consortium (IMDC) score; Morphology, Attenuation, Size and Structure (MASS) and Response Evaluation Criteria in Solid Tumors (RECIST) response criteria. This tool may be useful to clinicians assessing risk and prognosis of patients with mRCC. (1) Objective: To develop a clinically useful nomogram that may provide a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) metastatic renal cell carcinoma (mRCC) treated with nephrectomy and vascular endothelial growth factor receptor–tyrosine kinase inhibitor (VEGFR-TKI)-based sequential therapy. (2) Methods: A prospectively maintained database of 145 patients with mRCC treated between 2008 and 2018 was analyzed to predict the CSS of patients receiving sunitinib and second- and third-line therapies according to current standards of practice. A nomogram based on four independent clinical predictors (Eastern Cooperative Oncology Group status, International Metastatic RCC Database Consortium score, the Morphology, Attenuation, Size and Structure criteria and Response Evaluation Criteria in Solid Tumors response criteria) was calculated. The corresponding 1- to 10-year CSS probabilities were then determined from the nomogram. (3) Results: The median age was 60 years (95% CI 57.9–61.4). The disease was metastatic at diagnosis in 59 (40.7%), and 86 (59.3%) developed metastasis during follow-up. Patients were followed for a median 48 (IQR 72; 95% CI 56–75.7) months after first-line VEGFR-TKI initiation. The concordance probability estimator value for the nomogram is 0.778 ± 0.02 (mean ± SE). (4) Conclusions: A nomogram to predict CSS in patients with CC mRCC that incorporates patient status, clinical risk classification and response criteria to first-line VEGFR-TKI at 3 months is presented. This new tool may be useful to clinicians assessing the risk and prognosis of patients with mRCC. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) is reprogrammed by cancer cells and participates in all stages of tumor progression. The contribution of stromal cells to the reprogramming of the TME is not well understood. Here, we provide evidence of the role of the cytokine oncostatin M (OSM) as central node for multicellular interactions between immune and nonimmune stromal cells and the epithelial cancer cell compartment. OSM receptor (OSMR) deletion in a multistage breast cancer model halted tumor progression. We ascribed causality to the stromal function of the OSM axis by demonstrating reduced tumor burden of syngeneic tumors implanted in mice lacking OSMR. Single-cell and bioinformatic analysis of murine and human breast tumors revealed that OSM expression was restricted to myeloid cells, whereas OSMR was detected predominantly in fibroblasts and, to a lower extent, cancer cells. Myeloid-derived OSM reprogrammed fibroblasts to a more contractile and tumorigenic phenotype and elicited the secretion of VEGF and proinflammatory chemokines CXCL1 and CXCL16, leading to increased myeloid cell recruitment. Collectively, our data support the notion that the stromal OSM/OSMR axis reprograms the immune and nonimmune microenvironment and plays a key role in breast cancer progression., Introduction The tumor microenvironment (TME), composed of different cell types (e.g., fibroblasts, adipocytes, endothelial and infiltrating immune cells), harbors complex cell interactions that are often manipulated and hijacked by tumor [...]