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Abstract Background Little is known about how use of chemotherapy has evolved in breast cancer patients. We therefore describe chemotherapy patterns for women with stage I-IIIA breast cancer in the Optimal Breast Cancer Chemotherapy Dosing (OBCD) Study using data from KPNC (Kaiser Permanente Northern California) and KPWA (Kaiser Permanente Washington). Findings Among 33,670 women, aged 18 + y, diagnosed with primary stage I-IIIA breast cancer at KPNC and KPWA from 2006 to 2019, we explored patterns of intravenous chemotherapy use, defined here as receipt of intravenous cytotoxic drugs and/or anti-HER2 therapies. We evaluated trends in chemotherapy receipt, duration over which chemotherapy was received, and number of associated infusion visits. In secondary analyses, we stratified by receipt of anti-HER2 therapies (trastuzumab and/or pertuzumab), given their longer duration. 38.9% received chemotherapy intravenously, declining from 40.2% in 2006 to 35.6% in 2019 (p-trend

Abstract Mobile phone applications (“apps”) are potentially an effective, low-burden method to collect patient-reported outcomes outside the clinical setting. Using such apps consistently and in a timely way is critical for complete and accurate data capture, but no studies of concurrent reporting by cancer patient–caregiver dyads have been published in the peer-reviewed literature. This study assessed app engagement, defined as adherence, timing, and attrition with two smartphone applications, one for adult cancer patients and one for their informal caregivers. This was a single-arm, pilot study in which adult cancer patients undergoing IV chemotherapy or immunotherapy used the DigiBioMarC app, and their caregivers used the TOGETHERCare app, for approximately one month to report weekly on the patients’ symptoms and wellbeing. Using app timestamp metadata, we assessed user adherence, overall and by participant characteristics. Fifty patient–caregiver dyads completed the study. Within the one-month study period, both adult cancer patients and their informal caregivers were highly adherent, with app activity completion at 86% for cancer patients and 84% for caregivers. Caregivers completed 86% of symptom reports, while cancer patients completed 89% of symptom reports. Cancer patients and their caregivers completed most activities within 48 h of availability on the app. These results suggest that the DigiBioMarC and TOGETHERCare apps can be used to collect patient- and caregiver-reported outcomes data during intensive treatment. From our research, we conclude that metadata from mobile apps can be used to inform clinical teams about study participants' engagement and wellbeing outside the clinical setting.

Abstract Background This study examines the impact that the COVID‐19 pandemic has had on computed tomography (CT)‐based oncologic imaging utilization. Methods We retrospectively analyzed cancer‐related CT scans during four time periods: pre‐COVID (1/5/20–3/14/20), COVID peak (3/15/20–5/2/20), post‐COVID peak (5/3/20–12/19/20), and vaccination period (12/20/20–10/30/21). We analyzed CTs by imaging indication, setting, and hospital type. Using percentage decrease computation and Student's t‐test, we calculated the change in mean number of weekly cancer‐related CTs for all periods compared to the baseline pre‐COVID period. This study was performed at a single academic medical center and three affiliated hospitals. Results During the COVID peak, mean CTs decreased (−43.0%, p

BackgroundTimely collection of patient-reported outcomes (PROs) decreases emergency department visits and hospitalizations and increases survival. However, little is known about the outcome predictivity of unpaid informal caregivers’ reporting using similar clinical outcome assessments. ObjectiveThe aim of this study is to assess whether caregivers and adults with cancer adhered to a planned schedule for electronically collecting patient-reported outcomes (PROs) and if PROs were associated with future clinical events. MethodsWe developed 2 iPhone apps to collect PROs, one for patients with cancer and another for caregivers. We enrolled 52 patient-caregiver dyads from Kaiser Permanente Northern California in a nonrandomized study. Participants used the apps independently for 4 weeks. Specific clinical events were obtained from the patients’ electronic health records up to 6 months following the study. We used logistic and quasi-Poisson regression analyses to test associations between PROs and clinical events. ResultsParticipants completed 97% (251/260) of the planned Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE) surveys and 98% (254/260) of the Patient-Reported Outcomes Measurement Information System (PROMIS) surveys. PRO-CTCAE surveys completed by caregivers were associated with patients’ hospitalizations or emergency department visits, grade 3-4 treatment-related adverse events, dose reductions (P

Abstract Purpose During catheter directed intraarterial therapy for liver lesions, challenging hepatic vascular anatomy can sometimes prevent selective administration of treatment delivery to liver tumors leading to increased toxicity to normal liver parenchyma. The objective of this study is to describe a variation of the double balloon technique that isolates the feeding artery to liver tumors proximally and distally to provide treatment delivery in lesions that cannot be otherwise selected. Materials and methods An IRB-approved retrospective review of 7 patients who had undergone either radioembolization, chemoembolization, or bland embolization and the double balloon technique was employed. The devices used for flow augmentation were two 2.1 French balloon microcatheters (Sniper™, Embolx). One balloon was inflated distal to target vessel and the second was inflated proximal to protect from reflux. Results DEB-TACE was performed in 3 cases, 90Y was performed in 4, and bland embolization was performed in the last patient. There were no adverse effects from the procedure or clinically evident effects from non-target embolization. Mean follow up time was 286.4 +/− 200.1 days. Six of the 7 patients are alive. One patient passed away on post-procedure day 121 from septic shock unrelated to the procedure. One patient was bridged to transplant with an additional TACE of a separate lesion. Conclusion Double-balloon technique for patients undergoing 90Y or chemoembolization is a safe adjunctive technique for super selective treatment of hepatic lesions where direct selection via catheter is not feasible. This may increase the range of lesions that can be both safely and effectively treated by catheter directed therapies.

Background By eliminating the requirement for participants to make frequent visits to research sites, mobile phone applications (“apps”) may help to decentralize clinical trials. Apps may also be an effective mechanism for capturing patient-reported outcomes and other endpoints, helping to optimize patient care during and outside of clinical trials. Objectives We report on the usability of Digital BioMarkers for Clinical Impact (DigiBioMarC™ (DBM)), a novel smartphone-based app used by cancer patients in conjunction with a wearable device (Apple Watch®). DBM is designed to collect patient-reported outcomes and record physical functions. Methods In a fully decentralized “bring-your-own-device” smartphone study, we enrolled 54 cancer patient and caregiver dyads from Kaiser Permanente Northern California (KPNC) from October 2020 through March 2021. Patients used the app for at least 28 days, completed weekly questionnaires about their symptoms, physical functions, and mood, and performed timed physical tasks. Usability was determined through a subset of the Mobile App Rating Scale (MARS), the full System Usability Scale (SUS), the Net Promoter Score (NPS), and semi-structured interviews. Results We obtained usability survey data from 50 of 54 patients. Median responses to the selected MARS questions and the mean SUS scores indicated above average usability. The NPS from the semi-structured interviews at the end of the study was 24, indicating a favorable score. Conclusions Cancer patients reported above average usability for the DBM app. Qualitative analyses indicated that the app was easy to use and helpful. Future work will emphasize implementing further patient recommendations and evaluating the app's clinical efficacy in multiple settings.

Semantic change is attested commonly in the historical development of lexicons across the world's languages. Extensive research has sought to characterize regularity in semantic change, but existing studies have typically relied on manual approaches or the analysis of a restricted set of languages. We present a large-scale computational analysis to explore regular patterns in word meaning change shared across many languages. We focus on two levels of analysis: (1) regularity in directionality, which we explore by inferring the historical direction of semantic change between a source meaning and a target meaning; (2) regularity in source-target mapping, which we explore by inferring the target meaning given a source meaning. We work with DatSemShift, the world's largest public database of semantic change that records thousands of meaning changes from over hundreds of languages. For directionality inference, we find that concreteness explains directionality in more than 70% of the attested cases of semantic change and is the strongest predictor among the alternatives including frequency and valence. For target inference, we find that a parallelogram-style analogy model based on contextual embeddings predicts the attested source-target mappings substantially better than chance and similarity-based models. Clustering the meaning pairs of semantic change reveals regular meaning shiftings between domains, such as body parts to geological formations. Our study provides an automated approach and large-scale evidence for multifaceted regularity in semantic change across languages.

Drp1 is the master regulator of mitochondrial fission, which has important impact on cellular functions. Here, Yang et al identified evolutionarily conserved proteins Clueless and its homolog CLUH as key regulators of Drp1 that function via translation of Drp1 receptors MiD49 and Mff.

Informal caregivers are a critical source of support for cancer patients. However, their perspectives are not routinely collected, despite health impacts related to the burden of caregiving. We created the TOGETHERCare smartphone application (app) to collect observer-reported outcomes regarding the cancer patient's health and caregiver's perceptions of their own mental and physical health, and to provide tips and resources for self-care and patient care. We enrolled 54 caregivers between October 2020 and March 2021 from Kaiser Permanente Northern California (KPNC), an integrated healthcare system. Fifty caregivers used the app for approximately 28 days. Usability and acceptability were assessed using questions from the Mobile App Rating Scale (MARS), the System Usability Scale (SUS), the Net Promoter Score (NPS), and semi-structured interviews. The caregivers' mean age was 54.4 years, 38% were female and 36% were non-White. The SUS total mean score was 83.4 (SD = 14.2), for a percentile rank of 90-95 ("excellent"). Median MARS responses to the functionality questions were also high. The NPS score of 30 at the end of the study indicated that most caregivers would recommend the app. Themes from semi-structured interviews were consistent across the study period and indicated that the app was easy to use and helpful. Caregivers indicated a need for feedback from the app, suggested some changes to the wording of questions, the app's visuals, and timing of notifications. This study demonstrated that caregivers are willing to complete frequent surveys about themselves and their patients. The app is unique because it provides a remote method to collect caregivers' observations about the patient that may be useful for clinical care. To our knowledge, TOGETHERCare is the first mobile app developed specifically to capture adult cancer patient symptoms from the informal caregiver's perspective. Future research will examine whether use of this app can help improve patient outcomes.

Image or feature matching-based indoor localization still faces many technical challenges. Image-tag-based schemes using pose estimation are accurate and robust, but they still cannot be deployed widely because their performance degrades significantly when the tag-camera distance is large, which requires densely distributed tags, and the designed system generally is specific to some special tags and lenses. Also, the lens distortion degrades the performance appreciably and is difficult to correct, especially for the wide-angle lenses. This article develops an image-tag-based indoor localization system using end-to-end learning to overcome these issues. It is a deep learning–based system that can learn the mapping from the original tag image to the final 2D location directly from training examples through self-learned features. It achieves consistent performance even when the tag-camera distance is large or when the image has a low resolution. The mapping learned by the deep learning model factors in all kinds of distortions without requiring any distortion estimation. The tag design is based on shape features to make it robust to lighting changes. The system can be easily adapted to new lenses/cameras and/or new tags. Thus, it facilitates easy and rapid deployment without requiring knowledge from domain experts. A drawback of the general deep learning model is its high computational requirements. We discuss practical solutions to enable real-time applications of the proposed scheme even when it is running on a mobile or embedded device. The performance of the proposed scheme is evaluated via a set of experiments in a real setting and has achieved less than 20 cm of positioning errors.

Cells are exposed to frequent mechanical and/or chemical stressors that can compromise the integrity of the plasma membrane and underlying cortical cytoskeleton. The molecular mechanisms driving the immediate repair response launched to restore the cell cortex and circumvent cell death are largely unknown. Using microarrays and drug-inhibition studies to assess gene expression, we find that initiation of cell wound repair in the Drosophila model is dependent on translation, whereas transcription is required for subsequent steps. We identified 253 genes whose expression is up-regulated (80) or down-regulated (173) in response to laser wounding. A subset of these genes were validated using RNAi knockdowns and exhibit aberrant actomyosin ring assembly and/or actin remodeling defects. Strikingly, we find that the canonical insulin signaling pathway controls actin dynamics through the actin regulators Girdin and Chickadee (profilin), and its disruption leads to abnormal wound repair. Our results provide new insight for understanding how cell wound repair proceeds in healthy individuals and those with diseases involving wound healing deficiencies.

We introduce a new estimate of mutual information between a dataset and a target variable that can be maximised analytically and has broad applicability in the field of machine learning and statistical pattern recognition. This estimate has previously been employed implicitly as an approximation to quadratic mutual information. In this paper we will study the properties of these estimates of mutual information in more detail, and provide a derivation from a perspective of pairwise interactions. From this perspective, we will show a connection between our proposed estimate and Laplacian eigenmaps, which so far has not been shown to be related to mutual information. Compared with other popular measures of mutual information, which can only be maximised through an iterative process, ours can be maximised much more efficiently and reliably via closed-form eigendecomposition.