Harnessing liquid biopsies to guide immune checkpoint inhibitor therapy

Immunotherapy (IO), involving the use of immune checkpoint inhibition, achieves improved response-rates and significant disease-free survival for some cancer patients. Despite these beneficial effects, there is poor predictability of response and substantial rates of innate or acquired resistance, resulting in heterogeneous responses among patients. In addition, patients can develop life-threatening adverse events, and while these generally occur in patients that also show a tumor response, these outcomes are not always congruent. Therefore, predicting a response to IO is of paramount importance. Traditionally, tumor tissue analysis has been used for this purpose. However, minimally invasive liquid biopsies that monitor changes in blood or other bodily fluid markers are emerging as a promising cost-effective alternative. Traditional biomarkers have limitations mainly due to difficulty in repeatedly obtaining tumor tissue confounded also by the spatial and temporal heterogeneity of tumours. Liquid biopsy has the potential to circumvent tumor heterogeneity and to help identifying patients who may respond to IO, to monitor the treatment dynamically, as well as to unravel the mechanisms of relapse. We present here a review of the current status of molecular markers for the prediction and monitoring of IO response, focusing on the detection of these markers in liquid biopsies. With the emerging improvements in the field of liquid biopsy, this approach has the capacity to identify IO-eligible patients and provide clinically relevant information to assist with their ongoing disease management

Quantum optical memory protocols in atomic ensembles

We review a series of quantum memory protocols designed to store the quantum information carried by light into atomic ensembles. In particular, we show how a simple semiclassical formalism allows to gain insight into various memory protocols and to highlight strong analogies between them. These analogies naturally lead to a classification of light storage protocols into two categories, namely photon echo and slow-light memories. We focus on the storage and retrieval dynamics as a key step to map the optical information into the atomic excitation. We finally review various criteria adapted for both continuous variables and photon-counting measurement techniques to certify the quantum nature of these memory protocols

Evaluating assumptions of scales for subjective assessment of thermal environments – Do laypersons perceive them the way, we researchers believe?

International audienc

Disclosure of cancer diagnosis and quality of life in cancer patients: should it be the same everywhere?

<p>Abstract</p> <p>Background</p> <p>Evidence suggests that truth telling and honest disclosure of cancer diagnosis could lead to improved outcomes in cancer patients. To examine such findings in Iran, this trial aimed to study the various dimensions of quality of life in patients with gastrointestinal cancer and to compare these variables among those who knew their diagnosis and those who did not.</p> <p>Methods</p> <p>A consecutive sample of patients with gastrointestinal cancer being treated in Cancer Institute in Tehran, Iran was prospectively evaluated. A psychologist interviewed patients using the Iranian version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Patients were categorized into two groups: those who knew their diagnosis and those who did not. Independent sample t-test was used for group comparisons.</p> <p>Results</p> <p>In all 142 patients were interviewed. A significant proportion (52%) of patients did not know their cancer diagnosis and 48% of patients were aware that they had cancer. They were quite similar in most characteristics. The comparison of quality of life between two groups indicated that those knew their diagnosis showed a significant lower degree of physical (P = 0.001), emotional (P = 0.01) and social functioning (P < 0.001), whereas the global quality of life and other functional scales including role functioning and cognitive functioning did not show significant result. There were no statistically significant differences between symptoms scores between two groups, except for fatigue suggesting a higher score in patients who knew their diagnosis (P = 0.01). The financial difficulties were also significantly higher in patients who knew their cancer diagnosis (P = 0.005). Performing analysis of variance while controlling for age, educational status, cancer site, and knowledge of cancer diagnosis, the results showed that the knowledge of cancer diagnosis independently still contributed to the significant differences observed between two groups.</p> <p>Conclusion</p> <p>Contrary to expectation the findings indicated that patients who did not know their cancer diagnosis had a better physical, social and emotional quality of life. It seems that due to cultural differences between countries cancer disclosure guidelines perhaps should be differing.</p

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Anxiety and depression in patients with gastrointestinal cancer: does knowledge of cancer diagnosis matter?

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal cancer is the first leading cause of cancer related deaths in men and the second among women in Iran. An investigation was carried out to examine anxiety and depression in this group of patients and to investigate whether the knowledge of cancer diagnosis affect their psychological distress.</p> <p>Methods</p> <p>This was a cross sectional study of anxiety and depression in patients with gastrointestinal cancer attending to the Tehran Cancer Institute. Anxiety and depression was measured using the Hospital Anxiety and Depression Scale (HADS). This is a widely used valid questionnaire to measure psychological distress in cancer patients. Demographic and clinical data also were collected to examine anxiety and depression in sub-group of patients especially in those who knew their cancer diagnosis and those who did not.</p> <p>Results</p> <p>In all 142 patients were studied. The mean age of patients was 54.1 (SD = 14.8), 56% were male, 52% did not know their cancer diagnosis, and their diagnosis was related to esophagus (29%), stomach (30%), small intestine (3%), colon (22%) and rectum (16%). The mean anxiety score was 7.6 (SD = 4.5) and for the depression this was 8.4 (SD = 3.8). Overall 47.2% and 57% of patients scored high on both anxiety and depression. There were no significant differences between gender, educational level, marital status, cancer site and anxiety and depression scores whereas those who knew their diagnosis showed a significant higher degree of psychological distress [mean (SD) anxiety score: knew diagnosis 9.1 (4.2) vs. 6.3 (4.4) did not know diagnosis, P < 0.001; mean (SD) depression score: knew diagnosis 9.1 (4.1) vs. 7.9 (3.6) did not know diagnosis, P = 0.05]. Performing logistic regression analysis while controlling for demographic and clinical variables studied the results indicated that those who knew their cancer diagnosis showed a significant higher risk of anxiety [OR: 2.7, 95% CI: 1.1–6.8] and depression [OR: 2.8, 95% CI: 1.1–7.2].</p> <p>Conclusion</p> <p>Psychological distress was higher in those who knew their cancer diagnosis. It seems that the cultural issues and the way we provide information for cancer patients play important role in their improved or decreased psychological well-being.</p

Relative motion of transmembrane segments S0 and S4 during voltage sensor activation in the human BKCa channel

Large-conductance voltage- and Ca2+-activated K+ (BKCa) channel α subunits possess a unique transmembrane helix referred to as S0 at their N terminus, which is absent in other members of the voltage-gated channel superfamily. Recently, S0 was found to pack close to transmembrane segments S3 and S4, which are important components of the BKCa voltage-sensing apparatus. To assess the role of S0 in voltage sensitivity, we optically tracked protein conformational rearrangements from its extracellular flank by site-specific labeling with an environment-sensitive fluorophore, tetramethylrhodamine maleimide (TMRM). The structural transitions resolved from the S0 region exhibited voltage dependence similar to that of charge-bearing transmembrane domains S2 and S4. The molecular determinant of the fluorescence changes was identified in W203 at the extracellular tip of S4: at hyperpolarized potential, W203 quenches the fluorescence of TMRM labeling positions at the N-terminal flank of S0. We provide evidence that upon depolarization, W203 (in S4) moves away from the extracellular region of S0, lifting its quenching effect on TMRM fluorescence. We suggest that S0 acts as a pivot component against which the voltage-sensitive S4 moves upon depolarization to facilitate channel activation

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance