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The tumor microenvironment (TME) provides essential conditions for the occurrence, invasion, and spread of cancer cells. Initial research has uncovered immunosuppressive properties of the TME, which include low oxygen levels (hypoxia), acidic conditions (low pH), increased interstitial pressure, heightened permeability of tumor vasculature, and an inflammatory microenvironment. The presence of various immunosuppressive components leads to immune evasion and affects immunotherapy efficacy. This indicates the potential value of targeting the TME in cancer immunotherapy. Therefore, TME remodeling has become an effective method for enhancing host immune responses against tumors. In this study, we elaborate on the characteristics and composition of the TME and how it weakens immune surveillance and summarize targeted therapeutic strategies for regulating the TME.

PurposeFast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART.MethodsA conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics.ResultsThe average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU.ConclusionWe have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART.

BackgroundWe aimed to compare the detection rates of prostate cancer (PCa) and clinically significant prostate cancer(csPCa) by biparametric (bp-) and multiparameter magnetic resonance imaging (mpMRI).Materials and MethodsA total of 699 patients who underwent transperineal prostate biopsy in the Department of Urology, the Second Affiliated Hospital of Nantong University from January 2018 to December 2021 were retrospectively reviewed. Multivariate analysis was used to explore the influencing factors associated with the detection rates of PCa and csPCa. According to MRI examination before biopsy, the patients were divided into bpMRI group and mpMRI group. The detection rates of PCa and csPCa by bpMRI and mpMRI were compared. Furthermore, stratified analysis was performed for patients in these two groups to compare the detection rates of PCa and csPCa at different tPSA intervals, different prostate volume (PV) intervals and different PI-RADS V2 scores.ResultsA total of 571 patients were finally analyzed in this study after exclusion, and the overall detection rate of PCa was 54.5%. Multivariate analysis showed that patient age, tPSA level, prostate volume and PI-RADS V2 score were independent risk factors affecting the detection rates of PCa and csPCa. The detection rates of PCa and csPCa by bpMRI and mpMRI were comparable (51.3% vs. 57.9%, 44.0% vs. 48.0%, both P > 0.05), with no statistical significance. In the tPSA 10–20 ng/ml interval, the detection rates of PCa (59.72% vs. 40.35%, P = 0.011) and csPCa (51.39% vs. 28.82%, P = 0.005) by mpMRI were significantly higher than those by bpMRI, while in other tPSA interval (tPSA 60 ml) and different PI-RADS V2 scores (3, 4, and 5), the detection rates of PCa and csPCa were comparable between the two groups.ConclusionFor detecting PCa and csPCa, bpMRI and mpMRI had similar diagnostic efficacies, whereas mpMRI detected more PCa and csPCa in the tPSA interval of 10–20 ng/ml.

The emergence of chimeric antigen receptor (CAR) T cell therapy has shifted the paradigm of malignant tumor treatment, especially the advent of CD19-directed CAR-T cell therapy for the treatment of relapsed/refractory (R/R) B-cell malignancies. Although CAR-T cell therapy has promising effects, some patients are resistant to this treatment, leaving them with limited options. Therefore, strategies to overcome resistance to CAR-T cell therapy are needed. We retrospectively studied three R/R diffuse large B-cell lymphoma patients who were resistant to CAR-T cell therapy and whose disease was controlled after receiving pembrolizumab, 21D4 CAR-T cells, or ibrutinib and venetoclax. Some promising prevention and treatment strategies to overcome treatment resistance are also discussed.

Lonicerae Japonicae Flos (LJF) is commonly used in Chinese herbal medicines and exhibits anti-viral, anti-oxidative, and anti-inflammatory properties. The reciprocal relationship between sleep, the immune system and the central nervous system is well-established in the animal models. In this study, we used the mouse model to analyze the beneficial effects of the LJF on the dysregulated sleep-wakefulness cycle in response to acute sleep deprivation and lipopolysaccharide (LPS)-induced inflammation and the potential underlying mechanisms. Polysomnography data showed that LJF increased the time spent in non-rapid eye movement (NREM) sleep during the day under basal conditions. Furthermore, latency to sleep was reduced and the time spent in rapid eye movement (REM) sleep was increased during recovery from acute sleep deprivation. Furthermore, LJF-treated mice showed increased REM sleep and altered electroencephalogram (EEG) power spectrum in response to intra-peritoneal injection of LPS. LJF significantly reduced the levels of proinflammatory cytokines such as IL-6, TNF-α, and IL-1β in the blood serum as well as hippocampus, and medial prefrontal cortex (mPFC) tissues in the LPS-challenged mice by inhibiting microglial activation. Moreover, LJF increased the time spent in REM sleep in the LPS-challenged mice compared to the control mice. These results suggested that LJF stimulated the sleep drive in response to acute sleep deprivation and LPS-induced inflammation, thereby increasing REM sleep for recovery and neuroprotection. In conclusion, our findings demonstrate that the clinical potential of LJF in treating sleep disorders related to sleep deprivation and neuro-inflammation.

Background: Monochromatic blue light (MBL), with a wavelength between 400–490 nm, can regulate non-image-forming (NIF) functions of light in the central nervous system. The suprachiasmatic nucleus (SCN) in the brain is involved in the arousal-promoting response to blue light in mice. Animal and human studies showed that the responsiveness of the brain to visual stimuli is partly preserved under general anesthesia. Therefore, this study aimed to investigate whether MBL promotes arousal from sevoflurane anesthesia via activation of the SCN in mice.Methods: The induction and emergence time of sevoflurane anesthesia under MBL (460 nm and 800 lux) exposure was measured. Cortical electroencephalograms (EEGs) were recorded and the burst-suppression ratio (BSR) was calculated under MBL during sevoflurane anesthesia. The EEGs and local field potential (LFP) recordings with or without locally electrolytic ablated bilateral SCN were used to further explore the role of SCN in the arousal-promoting effect of MBL under sevoflurane anesthesia. Immunofluorescent staining of c-Fos was conducted to reveal the possible downstream mechanism of SCN activation.Results: Unlike the lack of effect on the induction time, MBL shortened the emergence time and the EEG recordings showed cortical arousal during the recovery period. MBL resulted in a significant decrease in BSR and a marked increase in EEG power at all frequency bands except for the spindle band during 2.5% sevoflurane anesthesia. MBL exposure under sevoflurane anesthesia enhances the neuronal activity of the SCN. These responses to MBL were abolished in SCN lesioned (SCNx) mice. MBL evoked a high level of c-Fos expression in the prefrontal cortex (PFC) and lateral hypothalamus (LH) compared to polychromatic white light (PWL) under sevoflurane anesthesia, while it exerted no effect on c-Fos expression in the ventrolateral preoptic area (VLPO) and locus coeruleus (LC) c-Fos expression.Conclusions: MBL promotes behavioral and electroencephalographic arousal from sevoflurane anesthesia via the activation of the SCN and its associated downstream wake-related nuclei. The clinical implications of this study warrant further study.

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent cancers worldwide. Recent studies have shown that cancer stem cells (CSCs) are present in ESCC, are thought to lead to aggressive tumor behavior and the prognosis. The CXC chemokine receptor 4 (CXCR4), is regarded as a putative CSCs marker in various malignancies. Here, we demonstrate that CXCR4 played a key role in ESCC progression and CXCR4 positive ESCC cells possessed stem-like properties. Furthermore, the anti-malarial agent chloroquine (CQ) targeted CXCR4-positive ESCC cells via STAT3 pathway. Therefore, CQ with anti-CSCs effects may be an effective adjunct to current ESCC chemotherapy regimens.

Studying animal behavior in the lab requires reliable delivering stimulations and monitoring responses. We constructed a comprehensive behavioral platform (ArControl: Arduino Control Platform) that was an affordable, easy-to-use, high-performance solution combined software and hardware components. The hardware component was consisted of an Arduino UNO board and a simple drive circuit. As for software, the ArControl provided a stand-alone and intuitive GUI (graphical user interface) application that did not require users to master scripts. The experiment data were automatically recorded with the built in DAQ (data acquisition) function. The ArControl also allowed the behavioral schedule to be entirely stored in and operated on the Arduino chip. This made the ArControl a genuine, real-time system with high temporal resolution (

Purpose: Fast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART. Methods: A conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics. Results: The average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU. Conclusion: We have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART. [ABSTRACT FROM AUTHOR]

A large body of literature has examined the relationship between social isolation and mental health in older adults. However, only a few studies have examined the mediating effects of aging attitudes on this relationship. This study investigated the impact of objective isolation (family isolation, friend isolation, and community isolation), and subjective social isolation (perceived isolation) on the mental health of Chinese older adults, and the mediating effect of aging attitudes. Mental health was assessed through depressive symptoms, using the Epidemiological Studies Depression Scale. The research sample comprising 7,024 elderly adults (60 years old), was obtained from the nationally representative 2014 Chinese Longitudinal Aging Social Survey. The regression analysis indicated that objective social isolation and subjective social isolation are independently related to mental health among older adults. Furthermore, in the mediation analysis, aging attitude was found to play a significant mediating role between social isolation and mental health. Our study concludes that though, objective and subjective social isolation are issues affecting mental health in older people, however, aging attitude also needs to be factored in that relationship as we have shown that there is a significant mediating effect.

Despite the flourishing of positive education, understanding of whether different character strengths have different predictive effects on academic achievement/well-being and the mechanisms of actions between character strengths are limited. Specifically, this study adopted strength use as a mediator to understand how character strength (assessed by caring, inquisitiveness, and self-control) is associated with students’ end-of-year academic achievements and eudaimonic well-being. Survey data from 349 adolescents from three different schools showed that three factors of character strengths have positive correlations with academic achievements and eudaimonic well-being. Regression models indicated that inquisitiveness and self-control predicted academic achievements, while caring, inquisitiveness, and self-control predicted eudaimonic well-being, with the foremost as the strongest predictor. Mediation analyses indicated that (1) strengths use fully mediated the relationship between inquisitiveness, self-control, and academic achievements/eudaimonic well-being, while (2) caring had a direct effect on eudaimonic well-being. These findings provided possible explanations on how character strengths could affect students’ academic achievements or eudaimonic well-being and theoretical and empirical evidence for practices that aim to enhance students’ academic achievements and positive developments via interventions based on character strengths.