Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding

Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients' clinical record data or biological and imaging data. In practice, experienced clinicians can have a preliminary assessment of patients' health status based on patients' observable physical appearances, which are mainly facial features. However, such assessment is highly subjective. In this work, the efficacy of objectively capturing and using prognostic information contained in conventional portrait photographs using deep learning for survival predication purposes is investigated for the first time. A pre-trained StyleGAN2 model is fine-tuned on a custom dataset of our cancer patients' photos to empower its generator with generative ability suitable for patients' photos. The StyleGAN2 is then used to embed the photographs to its highly expressive latent space. Utilizing the state-of-the-art survival analysis models and based on StyleGAN's latent space photo embeddings, this approach achieved a C-index of 0.677, which is notably higher than chance and evidencing the prognostic value embedded in simple 2D facial images. In addition, thanks to StyleGAN's interpretable latent space, our survival prediction model can be validated for relying on essential facial features, eliminating any biases from extraneous information like clothing or background. Moreover, a health attribute is obtained from regression coefficients, which has important potential value for patient care

Distinct roles of DBHS family members in the circadian transcriptional feedback loop

Factors interacting with core circadian clock components are essential to achieve transcriptional feedback necessary for metazoan clocks. Here we show that all three members of the Drosophila Behavior Human Splicing (DBHS) family of RNA-binding proteins play a role in the mammalian circadian oscillator, abrogating or altering clock function when overexpressed or depleted in cells. Although these proteins are members of so-called nuclear paraspeckles, depletion of paraspeckles themselves via silencing of the structural non-coding RNA (ncRNA) Neat1 did not affect overall clock function, suggesting that paraspeckles are not required for DBHS-mediated circadian effects. Instead, we show that the proteins bound to circadian promoter DNA in a fashion that required the PERIOD (PER) proteins, and potently repressed E box-mediated transcription but not CMV promoter-mediated transcription when exogenously recruited. Nevertheless, mice with one or both copies of these genes deleted show only small changes in period length or clock gene expression in vivo. Data from transient transfections show that each of these proteins can either repress or activate depending on the context. Taken together, our data suggest that all of the DBHS family members serve overlapping or redundant roles as transcriptional cofactors at circadian clock-regulated genes

Generation of Human CRY1 and CRY2 Knockout Cells Using Duplex CRISPR/Cas9 Technology

Circadian clocks are endogenous oscillators essential for orchestrating daily rhythms in physiology, metabolism and behavior. While mouse models have been instrumental to elucidate the molecular mechanism of circadian rhythm generation, our knowledge about the molecular makeup of circadian oscillators in humans is still limited. Here, we used duplex CRISPR/Cas9 technology to generate three cellular models for studying human circadian clocks: CRY1 knockout cells, CRY2 knockout cells as well as CRY1/CRY2 double knockout cells. Duplex CRISPR/Cas9 technology efficiently removed whole exons of CRY genes by using two guide RNAs targeting exon-flanking intron regions of human osteosarcoma cells (U-2 OS). Resulting cell clones did not express CRY proteins and showed short period, low-amplitude rhythms (for CRY1 knockout), long period rhythms (for CRY2 knockout) or were arrhythmic (for CRY1/CRY2 double knockout) similar to circadian phenotypes of cells derived from classical knockout mouse models

Nanostructured amorphous gallium phosphide on silica for nonlinear and ultrafast nanophotonics

Nanophotonics based on high refractive index dielectrics relies on appreciable contrast between the indices of designed nanostructures and their immediate surrounding, which can be achieved by the growth of thin films on low-index substrates. Here we propose the use of high index amorphous gallium phosphide (a-GaP), fabricated by radio-frequency sputter deposition, on top of a low refractive index glass substrate and thoroughly examine its nanophotonic properties. Spectral ellipsometry of the amorphous material demonstrates the optical properties to be considerably close to crystalline gallium phosphide (c-GaP), with low-loss transparency for wavelengths longer than 650 nm. When nanostructured into nanopatches, the second harmonic (SH) response of an individual a-GaP patch is characterized to be more than two orders of magnitude larger than the as-deposited unstructured film, with an anapole-like resonant behavior. Numerical simulations are in good agreement with the experimental results over a large spectral and geometrical range. Furthermore, by studying individual a-GaP nanopatches through non-degenerate pump-probe spectroscopy with sub-10 fs pulses, we find a more than 5% ultrafast modulation of the reflectivity that is accompanied by a slower decaying free carrier contribution, caused by absorption. Our investigations reveal a potential for a-GaP as an adequate inexpensive and CMOS-compatible material for nonlinear nanophotonic applications as well as for photocatalysis.Fil: Tilmann, Benjamin. Ludwig Maximilians Universitat; AlemaniaFil: Grinblat, Gustavo Sergio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Berté, Rodrigo. Ludwig Maximilians Universitat; AlemaniaFil: Özcan, Mehmet. Ludwig Maximilians Universitat; AlemaniaFil: Kunzelmann, Viktoria F.. Technische Universitat München; AlemaniaFil: Nickel, Bert. Ludwig Maximilians Universitat; AlemaniaFil: Sharp, Ian D.. Ludwig Maximilians Universitat; AlemaniaFil: Cortés, Emiliano. Ludwig Maximilians Universitat; AlemaniaFil: Maier, Stefan A.. Ludwig Maximilians Universitat; AlemaniaFil: Li, Yi. Southern University Of Science And Technology; Chin

Protein phosphatase 4 controls circadian clock dynamics by modulating CLOCK/BMAL1 activity

In all organisms with circadian clocks, post-translational modifications of clock proteins control the dynamics of circadian rhythms, with phosphorylation playing a dominant role. All major clock proteins are highly phosphorylated, and many kinases have been described to be responsible. In contrast, it is largely unclear whether and to what extent their counterparts, the phosphatases, play an equally crucial role. To investigate this, we performed a systematic RNAi screen in human cells and identified protein phosphatase 4 (PPP4) with its regulatory subunit PPP4R2 as critical components of the circadian system in both mammals an

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data

Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as false positive metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Our proposed VSS loss improves the sensitivity of brain metastasis detection for DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the false positive metastases are reduced in the high sensitivity model and the precision reaches 99.6% for the high specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high sensitivity and high specificity models, on average only 1.5 false positive metastases per patient needs further check, while the majority of true positive metastases are confirmed. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice.Comment: Implementation is available to public at https://github.com/YixingHuang/DeepMedicPlu

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this work, we evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology using the 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal gray zone cases. For the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 63.65% and 74.57%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4's strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates good knowledge of statistics, CNS & eye, pediatrics, biology, and physics but has limitations in bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs well in diagnosis, prognosis, and toxicity but lacks proficiency in topics related to brachytherapy and dosimetry, as well as in-depth questions from clinical trials. For the gray zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Most importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts. Both evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Because of the risk of hallucination, facts provided by ChatGPT always need to be verified