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Optical Coherence Tomography (Oct) and Fundus Fluorescein Angiography (FFA) in Neuro-Ophthalmology
Published in Vivek Lal, A Clinical Approach to Neuro-Ophthalmic Disorders, 2023
Ramandeep Singh, Deeksha Katoch, Mohit Dogra, Basavaraj Tigari, Simar Rajan Singh, Sahil Jain, Bruttendu Moharana, Sabia Handa, Mangat R. Dogra
On fundus examination, both discs showed pallor of the temporal neuroretinal rim (Figure 3A.2a and b). Fluorescein angiography showed no delay in perfusion of the disc or non-perfusion of disc in both eyes (Figure 3A.2c–f). OCT-RNFL showed severe bilateral loss of nerve fiber layer (Figure 3A.2g and h). He could not perform visual fields due to poor vision.
Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features
Published in mAbs, 2023
Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei
Since the energetics-only model is independent of any sequence-specific information, we assessed the performance of this supervised model by constructing a receiver-operating-characteristic (ROC) curve derived from the prediction of the 70-up bin (Figure 3c). As we aim to identify thermostable sequences, the prediction accuracy of the 70-up bin is most important. We evaluated the ROC for four test datasets: two held-out (Sets P and Q) and two blind datasets representing a test scFv and an isolated scFv. The area under ROC is over 0.7, denoting a high classification accuracy. Figure 3d shows the Spearman correlation coefficient for all four test datasets, with the energetic-only, sequence-only and energetics + sequences models, respectively. On held-out datasets (Set P and Q), among the supervised models, the coefficients are over 0.5 for energetics-only model, with energetics + sequences model showing a slightly better performance. However, on blind datasets, the performance drops for energetics + sequences and sequences-only (coefficients under 0.1). The energetics-only model shows better correlation for the blind datasets (0.2 and 0.4 respectively) than the other supervised models. In comparison with the PTLM performance in Figure 2c-f, the AntiBERTy-finetuned model shows better correlation (average correlation of 0.52 versus 0.29 for SCNN trained on Sets A-Q and 0.4 for ensemble of SCNNs, see Sup. Fig. S6-S7). However, it is important to note that the language models are inherently skewed toward the experimental datasets.
Targeting stearoyl-coa desaturase enhances radiation induced ferroptosis and immunogenic cell death in esophageal squamous cell carcinoma
Published in OncoImmunology, 2022
Hui Luo, Xiaohui Wang, Shuai Song, Yunhan Wang, Qinfu Dan, Hong Ge
To further evaluated SCD1 in radiation resistance, we performed siRNA inference experiments. As presented in Figure 2c–f, knockdown of SCD1 caused minimal side effects on cell viability. In parallel with the above interpretation, knockdown of SCD1 attenuated survival fractions in both KYSE70 and KYSE410 cells when combined with RT (Figure 2g,h). In addition, we confirmed whether ferroptosis inducer works well to improve the effectiveness of RT. As expected, RSL3 significantly attenuated the surviving fraction of KYSE70 and KYSE410 cells when combined with RT (supplementary Figure S1e,f). Conversely, there was no synergistic effect between Fer-1 and RT, and pharmacological blockade of ferroptosis was even shown to increase cancer cell surviving fraction when combined with RT (Figure 2i,j).
Temporal dysregulation of hypothalamic integrative and metabolic nuclei in rats fed during the rest phase
Published in Chronobiology International, 2022
Oscar D. Ramirez-Plascencia, Nadia Saderi, Skarleth Cárdenas Romero, Omar Flores Sandoval, Adrián Báez-Ruiz, Herick Martínez Barajas, Roberto Salgado-Delgado
We hypothesized that the source of the hypocaloric non-photic input might be the NPY-innervation from the IGL and the Arcuate nucleus (ARC) of the hypothalamus, which have been shown to regulate SCN activity under negative metabolic conditions (Challet et al. 1996; Fernandez et al. 2020; Saderi et al. 2013). Food intake during the rest phase increases NPY expression in the ARC and other hypothalamic areas, such as the Lateral Hypothalamus (Ramirez-Plascencia et al. 2017). However, we did not observe any change neither in NPY-IR in the SCN nor in the IGL activation of DF rats when compared with AL and NF controls (Figure 2c-f). These differences might depend on the experimental procedure since we did not deprive animals of food for 48 h (Saderi et al. 2013) and DF perhaps does not challenge the IGL and ARC enough to trigger an NPY-orexigenic response. As an alternative to NPY input, the serotoninergic innervation from Medial Raphe nucleus (MR) might convey metabolic information to the SCN (Awasthia et al. 2020). An increase in the activity of Median Raphe (MR) has been reported in animals refed after 36 h of fasting (Wu et al. 2014) or subjected to food restriction (Takase and Nogueira 2008), suggesting that ascending projection from the MR might modulate the activity of the SNC in response to energy deficit.