Next-generation proteomics for quantitative Jumbophage-bacteria interaction mapping.

Host-pathogen interactions are pivotal in regulating establishment, progression, and outcome of an infection. While affinity-purification mass spectrometry has become instrumental in characterizing such interactions, it suffers from limitations in scalability and biological authenticity. Here we present the use of co-fractionation mass spectrometry for high throughput analysis of host-pathogen interactions from native viral infections of two jumbophages (ϕKZ and ϕPA3) in Pseudomonas aeruginosa. This approach enabled the detection of > 6000 unique host-pathogen interactions for each phage, encompassing > 50% of their respective proteomes. This deep coverage provided evidence for interactions between KZ-like phage proteins and the host ribosome, and revealed protein complexes for previously undescribed phage ORFs, including a ϕPA3 complex showing strong structural and sequence similarity to ϕKZ non-virion RNA polymerase. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of host-pathogen interactomes and protein complex dynamics upon infection.

The ϕPA3 phage nucleus is enclosed by a self-assembling 2D crystalline lattice.

To protect themselves from host attack, numerous jumbo bacteriophages establish a phage nucleus-a micron-scale, proteinaceous structure encompassing the replicating phage DNA. Bacteriophage and host proteins associated with replication and transcription are concentrated inside the phage nucleus while other phage and host proteins are excluded, including CRISPR-Cas and restriction endonuclease host defense systems. Here, we show that nucleus fragments isolated from ϕPA3 infected Pseudomonas aeruginosa form a 2-dimensional lattice, having p2 or p4 symmetry. We further demonstrate that recombinantly purified primary Phage Nuclear Enclosure (PhuN) protein spontaneously assembles into similar 2D sheets with p2 and p4 symmetry. We resolve the dominant p2 symmetric state to 3.9 Šby cryo-EM. Our structure reveals a two-domain core, organized into quasi-symmetric tetramers. Flexible loops and termini mediate adaptable inter-tetramer contacts that drive subunit assembly into a lattice and enable the adoption of different symmetric states. While the interfaces between subunits are mostly well packed, two are open, forming channels that likely have functional implications for the transport of proteins, mRNA, and small molecules.

Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping.

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages ( ϕ KZ and ϕ PA3) in Pseudomonas aeruginosa . This enabled the detection > 6000 unique host-pathogen and > 200 pathogen-pathogen interactions for each phage, encompassing > 50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a ϕ PA3 complex showing strong structural and sequence similarity to ϕ KZ nvRNAp, suggesting ϕ PA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms.

Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

Interpretable survival prediction for colorectal cancer using deep learning.

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95% CI: 0.66-0.73) and 0.69 (95% CI: 0.64-0.72), and added significant predictive value to a set of nine clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R2 = 18% in both validation sets). Next, we generated human-interpretable histologic features by clustering embeddings from a deep-learning-based image-similarity model and showed that they explained the majority of the variance (R2 of 73-80%). Furthermore, the clustering-derived feature most strongly associated with high DLS scores was also highly prognostic in isolation. With a distinct visual appearance (poorly differentiated tumor cell clusters adjacent to adipose tissue), this feature was identified by annotators with 87.0-95.5% accuracy. Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading.

Background: Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication. Methods: In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5-25 years of follow-up (median: 13, interquartile range 9-17). Results: Here, we show that the A.I.'s risk scores produced a C-index of 0.84 (95% CI 0.80-0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78-0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.'s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71-0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01-0.15) and 0.07 (95% CI 0.00-0.14), respectively. Conclusions: Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Determining breast cancer biomarker status and associated morphological features using deep learning.

Background: Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results. Methods: We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch-level (>135 million patches, 181 slides) and slide-level (n = 3274 slides, 1249 cases, 37 sites). Interpretability analyses were performed using Testing with Concept Activation Vectors (TCAV), saliency analysis, and pathologist review of clustered patches. Results: The patch-level AUCs are 0.939 (95%CI 0.936-0.941), 0.938 (0.936-0.940), and 0.808 (0.802-0.813) for ER/PR/HER2, respectively. At the slide level, AUCs are 0.86 (95%CI 0.84-0.87), 0.75 (0.73-0.77), and 0.60 (0.56-0.64) for ER/PR/HER2, respectively. Interpretability analyses show known biomarker-histomorphology associations including associations of low-grade and lobular histology with ER/PR positivity, and increased inflammatory infiltrates with triple-negative staining. Conclusions: This study presents rapid breast cancer biomarker estimation from routine H&E slides and builds on prior advances by prioritizing interpretability of computationally learned features in the context of existing pathological knowledge.

Managing dissociative symptoms following the use of esketamine nasal spray: a case report.

Patients with treatment-resistant depression (TRD) treated with esketamine nasal spray commonly experience transient symptoms of dissociation. Manifestations of dissociation, such as feelings of detachment from the environment, can cause considerable anxiety for patients. Nonpharmacologic interventions may help clinicians to manage associated anxiety and confusion due to dissociation following administration of esketamine nasal spray. We present the case of a 64-year-old woman with major depressive disorder who participated in a clinical trial evaluating the efficacy and safety of esketamine nasal spray in conjunction with an oral antidepressant for TRD. The patient received flexible doses of esketamine nasal spray (56 or 84 mg) twice weekly for 4 weeks. On treatment day 1, the patient was administered 56 mg of esketamine nasal spray using two nasal spray devices (28 mg per device). Twenty minutes after the first esketamine nasal spray device was administered, the patient experienced a dissociative episode lasting 40 minutes that caused anxiety and confusion. The patient was encouraged to listen to music during treatment sessions, which resulted in notable improvement of her symptoms. Listening to music of choice immediately following esketamine nasal spray administration along with reassurance from staff may help manage confusion and anxiety associated with dissociation.

Optimizing Discharge Opioid Prescribing Practices After Mastectomy With Immediate Reconstruction.

A quality improvement initiative was conducted to provide guidelines for opioid prescribing following mastectomy with immediate reconstruction. Patients undergoing mastectomy with concurrent tissue expander reconstruction were surveyed at their first postoperative visit to determine use of pain medication, satisfaction, and refill rates. Opioid prescriptions were converted to total oral morphine milligram equivalents (MMEs). Guidelines for postdischarge prescriptions were developed. During phase I, 16 patients were surveyed to determine baseline prescribed MMEs and rate of satisfaction. A guideline was subsequently developed to standardize postdischarge prescribing (550 MMEs prescribed average risk vs 900 MMEs high risk), and the survey was repeated (phase II). Median 210 MMEs were used. Of the 23 patients, 1 required a refill, 83% were highly satisfied, and 77% of opioids were unused. Guidelines were further revised to limit prescribed opioids (290 MME average risk vs 450 MME high risk), and the survey was repeated (phase III). A median of 118 MMEs was used. Of the 22 patients, 5 required refills, 73% were highly satisfied, and 53% of opioids were unused. Phase IV included 27 patients. A median of 98 MMEs was used. Two patients required refills, 93% were highly satisfied, and 58% of opioids were unused. Our finding showed that there is significant overprescription of opioids after elective breast surgery. Practice guidelines can reduce the amount of opioids prescribed. Reducing excess opioids available in the community is a noble goal; however, it must be done cautiously, as decreased patient satisfaction can be an unintended consequence.

Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection.

BACKGROUND: Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially causing rescanning, and workflow delays. Although scan time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting only parts of a slide is impractical. METHODS: We developed a convolutional neural network (ConvFocus) to exhaustively localize and quantify the severity of OOF regions on digitized slides. ConvFocus was developed using our refined semi-synthetic OOF data generation process and evaluated using seven slides spanning three different tissue and three different stain types, each of which were digitized using two different whole-slide scanner models ConvFocus's predictions were compared with pathologist-annotated focus quality grades across 514 distinct regions representing 37,700 35 µm × 35 µm image patches, and 21 digitized "z-stack" WSIs that contain known OOF patterns. RESULTS: When compared to pathologist-graded focus quality, ConvFocus achieved Spearman rank coefficients of 0.81 and 0.94 on two scanners and reproduced the expected OOF patterns from z-stack scanning. We also evaluated the impact of OOF on the accuracy of a state-of-the-art metastatic breast cancer detector and saw a consistent decrease in performance with increasing OOF. CONCLUSIONS: Comprehensive whole-slide OOF categorization could enable rescans before pathologist review, potentially reducing the impact of digitization focus issues on the clinical workflow. We show that the algorithm trained on our semi-synthetic OOF data generalizes well to real OOF regions across tissue types, stains, and scanners. Finally, quantitative OOF maps can flag regions that might otherwise be misclassified by image analysis algorithms, preventing OOF-induced errors.

Vortioxetine versus placebo in major depressive disorder comorbid with social anxiety disorder.

BACKGROUND: Major Depressive Disorder (MDD) and Social Anxiety Disorder (SAD) are highly comorbid, yet the combined condition has not been subject to any placebo-controlled treatment trials. This study reports a trial of vortioxetine, an antidepressant that has also shown benefit in Generalized Anxiety Disorder (GAD), in patients meeting DSM-5 criteria for both MDD and SAD. METHODS: The study was a 12-week double-blind, placebo-controlled comparison of vortioxetine 10-20 mg/day or placebo administered on a 1:1 ratio. The study was designed to include 40 male or female outpatients aged 18-70 years. The primary endpoint was the "composite" Clinical Global Impression of Improvement (CGI-I) responder rate, factoring in improvement in both MDD and SAD features. Major secondary outcome measures were changes on the Montgomery Asberg Depression Rating Scale (MADRS) and Liebowitz Social Anxiety Scale (LSAS). RESULTS: On the composite CGI-I, 10 of 20 (50%) vortioxetine and six of 20 (30%) placebo-treated patients were rated as responders, a non-significant difference. However, vortioxetine-treated patients did show significantly greater improvement than those on placebo on both the MADRS (effect size 0.672) and LSAS (effect size 0.714). Efficacy in depression was seen before improvement in SAD. Adverse effects were similar to those previously reported. CONCLUSIONS: In this preliminary trial vortioxetine appears safe and effective for patients with MDD comorbid with SAD, with robust effect sizes on dimensional measures of both depression and social anxiety, but failure to separate from placebo on the primary outcome measure of composite responder rate. More studies of patients with comorbid conditions are needed, as this mirrors what is often seen in clinical practice.