Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR.

Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.

An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.

Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient's admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.

A case report of ChAdOx1 nCoV-19 vaccine-associated encephalitis.

BACKGROUND: Vaccination against COVID-19 continues apace, but side-effects, both common and severe, continue to be reported. We report here the first published case of COVID-19 vaccine-related encephalitis. CASE PRESENTATION: A young woman presented with acute neuropsychiatric symptoms following recent ChAdOx1 nCoV-19 vaccination. Extensive investigation did not identify alternative causes. CONCLUSIONS: This difficult case is here described, including presentation, investigation, and management. Further study on neuropsychiatric side-effects of COVID-19 vaccination, including investigation as to whether there may be a causal link, is required.

Differentiating benign from malignant cardiac tumors with cardiac magnetic resonance imaging.

BACKGROUND: The purpose of this analysis is to describe the differences in cardiac magnetic resonance characteristics between benign and malignant tumors, which would be helpful for surgical planning. METHODS: This was a prospective cohort study of 130 patients who underwent cardiac magnetic resonance imaging for evaluation of a suspected cardiac mass. After excluding thrombi and tumors without definitive diagnosis, 66 tumors were evaluated for morphologic features and tissue composition. RESULTS: Of the 66 patients, 39 (59.0%) had malignant tumors and 27 (41.0%) had benign tumors. Patients with malignant tumors were younger when compared with those with benign tumors (age 51 years [42.8-60.0] vs 65 years [60.0-71.0] median). Malignant tumors more often demonstrated tumor invasion (69% vs 0% P < .001) and were more often associated with pericardial effusion (41% vs 7.4% P = .004). Presence of first-pass perfusion (100% vs 33% P < .001) and late gadolinium enhancement (100% vs 59.2%, P < .001) were significantly higher in malignant tumors. In logistic regression modeling, tumor invasion (P < .001) and first-pass perfusion (P < .001) were independently associated with malignancy. Furthermore, using classification and regression tree analysis, we developed a decision tree algorithm to help differentiate benign from malignant tumors (diagnostic accuracy ∼90%). The algorithm-weighted cost of misclassifying a malignant tumor as benign was twice that of classifying a benign tumor as malignant. CONCLUSIONS: Our study demonstrates that cardiac magnetic resonance imaging is a useful noninvasive method for differentiating malignant from benign cardiac tumors. Tumor size, invasion, and first-pass perfusion were useful imaging characteristics in differentiating benign from malignant tumors.

Beauty and the beholder: the role of visual sensitivity in visual preference.

For centuries, the essence of aesthetic experience has remained one of the most intriguing mysteries for philosophers, artists, art historians and scientists alike. Recently, views emphasizing the link between aesthetics, perception and brain function have become increasingly prevalent (Ramachandran and Hirstein, 1999; Zeki, 1999; Livingstone, 2002; Ishizu and Zeki, 2013). The link between art and the fractal-like structure of natural images has also been highlighted (Spehar et al., 2003; Graham and Field, 2007; Graham and Redies, 2010). Motivated by these claims and our previous findings that humans display a consistent preference across various images with fractal-like statistics, here we explore the possibility that observers' preference for visual patterns might be related to their sensitivity for such patterns. We measure sensitivity to simple visual patterns (sine-wave gratings varying in spatial frequency and random textures with varying scaling exponent) and find that they are highly correlated with visual preferences exhibited by the same observers. Although we do not attempt to offer a comprehensive neural model of aesthetic experience, we demonstrate a strong relationship between visual sensitivity and preference for simple visual patterns. Broadly speaking, our results support assertions that there is a close relationship between aesthetic experience and the sensory coding of natural stimuli.

Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases.

A new type of signaling network element, called cancer signaling bridges (CSB), has been shown to have the potential for systematic and fast-tracked drug repositioning. On the basis of CSBs, we developed a computational model to derive specific downstream signaling pathways that reveal previously unknown target-disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available patient gene expression data. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed specific CSBs for each metastasis that satisfy (i) CSB proteins are activated by the maximal number of enriched signaling pathways specific to a given metastasis, and (ii) CSB proteins are involved in the most differential expressed coding genes specific to each breast cancer metastasis. The identified signaling networks for the three types of breast cancer metastases contain 31, 15, and 18 proteins and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases. We conducted both in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Of special note, we found that the Food and Drug Administration-approved drugs, sunitinib and dasatinib, prohibit brain metastases derived from breast cancer, addressing one particularly challenging aspect of this disease.