Cancer immunotherapy by chimeric antigen receptor-modified T (CAR-T) cells has shown exhilarative clinical efficacy for hematological malignancies. Recently two CAR-T cell based therapeutics, Kymriah (Tisagenlecleucel) and Yescarta (Axicabtagene ciloleucel) approved by US FDA (US Food and Drug Administration) are now used for treatment of B cell acute lymphoblastic leukemia (B-ALL) and diffuse large B-cell lymphoma (DLBCL) respectively in the US. Despite the progresses made in treating hematological malignancies, challenges still remain for use of CAR-T cell therapy to treat solid tumors. In this landscape, most studies have primarily focused on improving CAR-T cells and overcoming the unfavorable effects of tumor microenvironment on solid tumors. To further understand the current status and trend for developing CAR-T cell based therapies for various solid tumors, this review emphasizes on CAR-T techniques, current obstacles, and strategies for application, as well as necessary companion diagnostics for treatment of solid tumors with CAR-T cells.

Graphical abstract

We studied the structures of the cerebellar cortex of young adult and old cats for age-related changes, which were statistically analysed. Nissl staining was used to visualize the cortical neurons. The immunohistochemical method was used to display glial fibrillary acidic protein (GFAP)-immunoreactive (IR) astrocytes and neurofilament-immunoreactive (NF-IR) neurons. Under the microscope, the thickness of the cerebellar cortex was measured; and the density of neurons in all the layers as well as that of GFAP-IR cells in the granular layer was analysed. Compared with young adult cats, the thickness of the molecular layer and total cerebellar cortex was significantly decreased in old cats, and that of the granular layer increased. The density of neurons in each layer was significantly lower in old cats than in young adult ones. Astrocytes in old cats were significantly denser than in young adult ones, and accom-panied by evident hypertrophy of the cell bodies and enhanced immunoreaction of GFAP substance. Purkinje cells (PCs) in old cats showed much fewer NF-IR dendrites than those in young adults. The above findings indicate a loss of neurons and decrease in the number of dendrites of the PCs in the aged cerebellar cortex, which might underlie the functional decline of afferent efficacy and information integration in the senescent cerebellum. An age-dependent enhancement of activity of the astrocytes may exert a protective effect on neurons in the aged cerebellum.

The subthalamic nucleus (STN) is an effective therapeutic target for deep brain stimulation (DBS) for Parkinson's disease (PD), and histamine levels are elevated in the basal ganglia in PD patients. However, the effect of endogenous histaminergic modulation on STN neuronal activities and the neuronal mechanism underlying STN-DBS are unknown. Here, we report that STN neuronal firing patterns are more crucial than firing rates for motor control. Histamine excited STN neurons, but paradoxically ameliorated parkinsonian motor deficits, which we attributed to regularizing firing patterns of STN neurons via the hyperpolarization-activated cyclic nucleotide-gated channel 2 (HCN2) channel coupled to the H2 receptor. Intriguingly, DBS increased histamine release in the STN and regularized STN neuronal firing patterns under parkinsonian conditions. HCN2 contributed to the DBS-induced regularization of neuronal firing patterns, suppression of excessive β oscillations, and alleviation of motor deficits in PD. The results reveal an indispensable role for regularizing STN neuronal firing patterns in amelioration of parkinsonian motor dysfunction and a functional compensation for histamine in parkinsonian basal ganglia circuitry. The findings provide insights into mechanisms of STN-DBS as well as potential therapeutic targets and STN-DBS strategies for PD.

Background and purpose: The worldwide pandemic of coronavirus disease 2019 greatly challenges public medical systems. With limited medical resources, the treatment priority is determined by the severity of patients. However, many mild outpatients quickly deteriorate into severe/critical stage. It is crucial to early identify them and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression.Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at the severe/critical stage of the two groups were compared. Both multivariate logistic regression and deep learning-based methods were used to build the prediction models, with their area under ROC curves (AUC) compared.Results: Multivariate logistic regression depicted 6 risk factors for malignant progression: age >55years (OR 5.334, 95%CI 1.8-15.803), comorbid with hypertension (OR 5.093, 95%CI 1.236-20.986), a decrease of albumin (OR 4.01, 95%CI 1.216-13.223), a decrease of lymphocyte (OR 3.459, 95%CI 1.067-11.209), the progressive consolidation from CT1 to CTsevere (OR 1.235, 95%CI 1.018-1.498), and elevated HCRP (OR 1.015, 95%CI 1.002-1.029); and one protective factor: the presence of fibrosis at CT1 (OR 0.656, 95%CI 0.473-0.91). By combining the clinical data and the temporal information of the CT data, our deep learning-based models achieved the best AUC of 0.954, which outperformed logistic regression (AUC: 0.893), Conclusions: Our deep learning-based methods can identify the mild patients who are easy to deteriorate into severe/critical cases efficiently and accurately, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.