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Purpose: Electronic (e) patient-reported outcomes (PROs) have been shown to improve the quality of life and survival in chemotherapy treated advanced cancer patients. We hypothesized that multidimensional ePRO centered approach could improve symptom management, streamline patient flow, and optimize the use of healthcare resources. Methods: In this multicenter trial (NCT04081558), colorectal cancer (CRC) patients receiving oxaliplatin-based chemotherapy as adjuvant or in the first- or second-line setting in advanced disease were included in the prospective ePRO cohort, while a comparative retrospective cohort was collected from the same institutes. The investigated tool consisted of a weekly e-symptom questionnaire integrated to an urgency algorithm and laboratory value interface, which generated semi-automated decision support for chemotherapy cycle prescription and individualized symptom management. Results: Recruitment to the ePRO cohort occurred 1/2019–1/2021 (n = 43). The comparator group (n = 194) consisted of patients treated in the same institutes 1–7/2017. The analysis was limited to adjuvant treated (n = 36 and n = 35). The feasibility of the ePRO follow-up was good with 98% reporting easy usage and 86% improved care, while health care personnel valued the easy use and logical workflow. In the ePRO cohort, 42% needed a phone call before planned chemotherapy cycles, while this was 100% in the retrospective cohort (p = 1.4e−8). Peripheral sensory neuropathy was detected significantly earlier with ePRO followed (p = 1e−5) but did not translate to earlier dose reduction, delays, or unplanned therapy termination compared to the retrospective cohort. Conclusion: The results suggest that the investigated approach is feasible and streamlines workflow. Earlier symptom detection may improve the quality in cancer care.

ICIs are a standard of care in several malignancies; however, according to overall response rate (ORR), only a subset of eligible patients benefits from ICIs. Thus, an ability to predict ORR could enable more rational use. In this study a ML-based ORR prediction model was built, with patient-reported symptom data and other clinical data as inputs, using the extreme gradient boosting technique (XGBoost). Prediction performance for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), and the performance was evaluated with accuracy, AUC (area under curve), F1 score, and MCC (Matthew’s correlation coefficient). The ORR prediction model had a promising LOOCV performance with all four metrics: accuracy (75%), AUC (0.71), F1 score (0.58), and MCC (0.4). A rather good sensitivity (0.58) and high specificity (0.82) of the model were seen in the confusion matrix for all 63 LOOCV ORR predictions. The two most important symptoms for predicting the ORR were itching and fatigue. The results show that it is possible to predict ORR for patients with multiple advanced cancers undergoing ICI therapies with a ML model combining clinical, routine laboratory, and patient-reported data even with a limited size cohort.

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL–Data Driven Network Loading; DNL–Dynamic Network Loading; DTA–Dynamic Traffic Assignment; ITS–Intelligent Transportation Systems; OD–Origin Destination; TTF–Travel Time Function; LTT–Linear Travel Time; DL–Demand level.

Purpose: Patient-reported outcome (PRO) follow-up has been shown to improve quality of life (QoL) and survival of cancer patients receiving chemotherapy. Kaiku Health application is a web-based electronic PRO (ePRO) tool which is designed for follow-up of cancer patients receiving immune checkpoint inhibitors (ICI). Purpose of the current study is to investigate whether symptoms collected by Kaiku Health ePRO tool on cancer patients receiving immune checkpoint inhibitors (ICI) follows to symptoms reported in clinical trials and whether coupling of specific symptoms does occur. Methods: We retrospectively collected data on symptom timing and severity, and QoL of patients followed with Kaiku Health IO module in two Finnish cancer centers between 2017 and 2018. Kaiku Health IO module consists of 18 adaptive questions, which assess the presence and severity of symptoms. Patients were requested (via e-mail) to fill online symptom questionnaires with 3–7 day interval and QoL questionnaires (QLQ-C30) with 1–2 month interval. Results: The IO module was used to follow 37 patients who had filled in total 559 symptom questionnaires. There was good adherence to ePRO follow-up with a median of 11 questionnaires filled per patient. The reported symptoms and their severity follow closely what has been seen in clinical trials investigating ICIs. Correlation analysis of the symptoms showed the strongest positive correlations between itching and rash; nausea and vomiting, decreased appetite, or stomach pain; cough and shortness of breath. Conclusions: The results of the current study suggest that real-world symptom data collected through the ePRO application on cancer patients receiving ICI therapy aligns with the data from clinical trials. Correlations between different symptoms occur, which might reflect therapeutic efficiency, side effects, or tumor progression. These correlations should be further investigated with data coupled to clinical outcomes.

Evidence-based reviews of drugs causing medication-induced salivary gland dysfunction, such as xerostomia (sensation of oral dryness) and subjective sialorrhea are lacking. To compile a list of medicaments that influence salivary gland function, electronic databases were searched for relevant articles published up to June 2013. A total of 269 papers out of 3,867 records located satisfied the inclusion criteria (relevance, quality of methodology, strength of evidence). A total of 56 active substances with a higher level of evidence and 50 active substances with a moderate level of evidence of causing salivary gland dysfunction are described in this article. While xerostomia was a commonly reported outcome, the objective effect on salivary secretion was rarely measured. Xerostomia was, moreover, mostly reported as a negative side effect instead of the intended effect of that drug. A comprehensive list of medications having documented effects on salivary gland function or symptoms was compiled, which may assist practitioners in assessing patients who complain of dry mouth while taking medications.

Saliva is a complex fluid produced by three pairs of major salivary glands and by hundreds of minor salivary glands. It comprises a large variety of constituents and physicochemical properties, which are important for the maintenance of oral health. Saliva not only protects the teeth and the oropharyngeal mucosa, it also facilitates articulation of speech, and is imperative for mastication and swallowing. Furthermore, saliva plays an important role in maintaining a balanced microbiota. Thus, the multiple functions provided by saliva are essential for proper protection and functioning of the body as a whole and for the general health. A large number of diseases and medications can affect salivary secretion through different mechanisms, leading to salivary gland dysfunction and associated oral problems including xerostomia, dental caries and fungal infections. The first part of this review article provides an updated insight into our understanding of salivary gland structure, the neural regulation of salivary gland secretion, the mechanisms underlying the formation of saliva, the various functions of saliva and factors that influence salivary secretion under normal physiological conditions. The second part focuses on how various diseases and medical treatment including commonly prescribed medications and cancer therapies can affect salivary gland structure and function. We also provide a brief insight into how to diagnose salivary gland dysfunction. This article is protected by copyright. All rights reserved.

Sardinian alcohol-preferring (sP) and Sardinian alcohol-non preferring (sNP) rats have been selectively bred for opposite alcohol preference and consumption. Aiming to verify possible differences at the proteomics level between sP and sNP rats, we investigated the salivary proteome by a a liquid chromatography-mass spectrometry top-down-bottom-up integrated approach. For this purpose, submandibular saliva was collected from alcohol-naive sP and sNP rats under isoprenaline stimulation. A total of 200 peptides and proteins were detected and quantified in the two rat lines, 149 of which were characterized in their naturally occurring structure. The data are available via ProteomeXchange with identifier PXD006997. Surprisingly, sP rats exhibited marked quantitative and qualitative differences with respect to sNP rats, namely higher levels of proteoforms originating from submandibular gland protein C, and from submandibular rat protein 2, as well as those of several unidentified peptides and proteins. sP rats expressed some proteins not detectable in sNP rats such as the glutamine and glutamic acid-rich protein (GRP)-CB. The isoform GRP-B, detectable in both rat lines, was more abundant in sNP rats. The submandibular saliva of sNP rats was also characterized by very high levels of GRP-B proteolytic peptides and rat salivary protein 1. Whether these differences could contribute to the opposite alcohol preference and consumption of sP and sNP rats is currently unknown and requires further investigation.