Adoptive cell therapy with chimeric antigen receptor (CAR) immunotherapy has made tremendous progress with five CAR T therapies approved by the US Food and Drug Administration for hematological malignancies. However, CAR immunotherapy in solid tumors lags significantly behind. Some of the major hurdles for CAR immunotherapy in solid tumors include CAR T cell manufacturing, lack of tumor-specific antigens, inefficient CAR T cell trafficking and infiltration into tumor sites, immunosuppressive tumor microenvironment (TME), therapy-associated toxicity, and antigen escape. CAR Natural Killer (NK) cells have several advantages over CAR T cells as the NK cells can be manufactured from pre-existing cell lines or allogeneic NK cells with unmatched major histocompatibility complex (MHC); can kill cancer cells through both CAR-dependent and CAR-independent pathways; and have less toxicity, especially cytokine-release syndrome and neurotoxicity. At least one clinical trial showed the efficacy and tolerability of CAR NK cell therapy. Macrophages can efficiently infiltrate into tumors, are major immune regulators and abundantly present in TME. The immunosuppressive M2 macrophages are at least as efficient as the proinflammatory M1 macrophages in phagocytosis of target cells; and M2 macrophages can be induced to differentiate to the M1 phenotype. Consequently, there is significant interest in developing CAR macrophages for cancer immunotherapy to overcome some major hurdles associated with CAR T/NK therapy, especially in solid tumors. Nevertheless, both CAR NK and CAR macrophages have their own limitations. This comprehensive review article will discuss the current status and the major hurdles associated with CAR T and CAR NK therapy, followed by the structure and cutting-edge research of developing CAR macrophages as cancer-specific phagocytes, antigen presenters, immunostimulators, and TME modifiers.

The established urinary antibiotic nitroxoline has recently regained considerable attention, due to its potent activities in inhibiting angiogenesis, inducing apoptosis and blocking cancer cell invasion. These features make nitroxoline an excellent candidate for anticancer drug repurposing. To rapidly advance nitroxoline repurposing into clinical trials, the present study performed systemic preclinical pharmacodynamic evaluation of its anticancer activity, including a methyl thiazolyl tetrazolium assay in vitro and an orthotopic urological tumor assay in vivo. The current study determined that nitroxoline exhibits dose-dependent anti-cancer activity in vitro and in urological tumor orthotopic mouse models. In addition, it was demonstrated that the routine nitroxoline administration regimen used for urinary tract infections was effective and sufficient for urological cancer treatment, and 2 to 4-fold higher doses resulted in obvious enhancement of anticancer efficacy without corresponding increases in toxicity. Furthermore, nitroxoline sulfate, one of the most common metabolites of nitroxoline in the urine, effectively inhibited cancer cell proliferation. This finding increases the feasibility of nitroxoline repurposing for urological cancer treatment. Due to the excellent anticancer activity demonstrated in the present study, and its well-known safety profile and pharmacokinetic properties, nitroxoline has been approved to enter into a phase II clinical trial in China for non-muscle invasive bladder cancer treatment (registration no. CTR20131716).

We found clear disparities in chemotherapy treatment and survival with respect to socioeconomic and marital status. Future studies should explore the possible reasons why patients with low socioeconomic status and who are unmarried are less likely to have chemotherapy.

Atopic dermatitis (AD), also known as eczema, is one of the most common chronic skin diseases. AD severity is primarily evaluated based on visual inspections by clinicians, but is subjective and has large inter-and intra-observer variability in many clinical study settings. To aid the standardisation and automating the evaluation of AD severity, this paper introduces a CNN computer vision pipeline, EczemaNet, that first detects areas of AD from photographs and then makes probabilistic predictions on the severity of the disease. EczemaNet combines transfer and multitask learning, ordinal classification, and ensembling over crops to make its final predictions. We test EczemaNet using a set of images acquired in a published clinical trial, and demonstrate low RMSE with well-calibrated prediction intervals. We show the effectiveness of using CNNs for non-neoplastic dermatological diseases with a medium-size dataset, and their potential for more efficiently and objectively evaluating AD severity, which has greater clinical relevance than mere classification.

Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals, and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (“segment”) AD lesions before assessing lesional severity, and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images.Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intra-class correlation coefficients (ICCs) at the pixel-level and the area-levels for different resolutions of the images. The average ICC was 0.45 (SE = 0.04) corresponding to a “poor” agreement between raters, while the degree of agreement for AD segmentation varied from image to image.The AD segmentation in digital images is highly rater-dependent even between dermatologists. Such limitations need to be taken into consideration when the AD segmentation data are used to train machine learning algorithms that assess eczema severity.