Nevertheless, though EVs contained only a fraction of secreted miRNAs, these molecules were stable at 37 °C in a serum-containing environment, indicating that if sufficient miRNA loading is achieved, EVs can remain delivery-competent for a prolonged period of time. This study suggests that the passive endogenous EV loading strategy might be a relatively wasteful way of loading miRNA to EVs, and active miRNA loading approaches are needed for developing advanced EV miRNA therapies in the future.The extracellular environment consists of a plethora of molecules, including extracellular miRNA that can be secreted in association with extracellular vesicles (EVs) or soluble protein complexes (non-EVs). Yet, interest in therapeutic short RNA carriers lies mainly in EVs, the vehicles conveying the great majority of the biological activity. Here, by overexpressing miRNA and shRNA sequences in parent cells and using size exclusion liquid chromatography (SEC) to separate the secretome into EV and non-EV fractions, we saw that >98% of overexpressed miRNA was secreted within the non-EV fraction. Furthermore, small RNA sequencing studies of native miRNA transcripts revealed that although the abundance of miRNAs in EVs, non-EVs and parent cells correlated well (R The constant drive to find novel carriers of RNA therapeutics has witnessed a shift from artificial delivery systems to nature-inspired biomolecule carriers, such as extracellular vesicles (EVs). Owing to their pivotal role in cell-to-cell communication as well as the inherent ability to deliver bioactive lipids, proteins and nucleic acid, EVs hold a spotlight position in the research of advanced therapeutic carriers.EVs contain a wide range of nucleic acid molecules, and though stretches of up to several kilobases in size have been detected , the majIt is evident from previous studies that most of the extracellular nucleic acids are not associated with vesicular structures, being either bound to protein complexes or lipoprotein particles ,14,15,16The presence of a distinct miRNA per EV has been estimated to be approximately 1 in 100, or even 1 in 10,000 ,31,32, bHere, we analyze the relative abundance of a selection of miRNA molecules in the EV and non-EV secretory fractions at their basal level as well as after endogenous overexpression. These results are viewed in the global context of basal miRNA secretion to provide extended knowledge on extracellular sorting of short RNA sequences.® High-Fidelity DNA Polymerase  from the genomic DNA of human embryonic kidney (HEK293T) cells with the oligonucleotide primers  described by Diederichs et al. [To express precursors for the specific miRNAs, pri-miRNA sequences were amplified by PCR using Phusions et al. . The obts et al. . The shRFor miRNA overexpression studies, after 48 h of media conditioning, the EVs were isolated by using the Amicon Ultra-15 100 kDa molecular weight cut-off spin-filters , discriminating the retentate and flow-through/filtrate. Otherwise, the retentate was subjected to ultrafiltration SEC fractionation using the S-400 as described in   was used to prepare the samples for subsequent RNA extraction according to the manufacturer’s protocol. For miRNA validations, to account for the variation in RNA extraction efficiencies, 3 μL of a 5 μM synthetic miRNA oligonucleotide, cel-miR-39 (5′-UCACCGGGUGUAAAUCAGCUUG) , was added to each sample at the phenol extraction stage. In all cases, cDNA synthesis was performed by using the TaqMan MicroRNA Reverse Transcription Kit and the respective hairpin primers from TaqMan MicroRNA Assays . The quantification was performed by using the TaqMan Gene Expression Master Mix together with the respective TaqMan MicroRNA Assays on a Step-One Real-Time PCR instrument . shRNA detection was performed by synthesizing cDNA as above, but using custom-designed hairpin RT primers and amplified using KiCqStart SYBR Green qPCR Ready Mix  on a Step-One Real-Time PCR instrument, as above. The PCR efficiency of each reaction was calculated with the LinRegPCR program , while the Ct values were obtained from StepOne Software . In the case of overexpression studies, an equal amount of input RNA was used for reverse transcription. For miRNA validations, the total miRNA quantity was normalized to cel-miR-39 levels and further corrected for the volume of the respective fractions (Sepharose Fast Flow 4 fractionation volumes of 3–3.5 mL for EVs and 131–133 mL for non-EV (non-EV fraction + flow-through of 100 kDa ultrafiltration units)). For EV miRNA stability studies, EVs were isolated and purified as above, mixed with FBS at a final serum concentration of 10% and incubated at 37 °C for indicated times. At the end of each time point, Trizol LS was added to samples, and the RNA was extracted and analyzed by qPCR, as above. 2. A total of 6 × 106 cells were first seeded onto 15 cm cell culture dishes. After 24 h, cells were washed with 0.01 M phosphate-buffered saline (PBS) to ensure removal of any residual FBS before a media change to OptiMEM  supplemented with 1% Antibiotic Antimycotic Solution . The supernatant was collected after 48 h, and EVs were harvested by ultrafiltration SEC fractionation as described in [Neuro2a, hTERT-MSCs and HEK293T cell-lines were maintained in high-glucose (4.5 g/L) DMEM GlutaMax  or RPMI medium 1640  supplemented with 10% fetal bovine serum . All cells were cultured in a humidified atmosphere at 37 °C and 5% COribed in . To obtaNanoparticle tracking analysis (NTA) was performed by using the NanoSight NS500 nanoparticle analyzer  to measure the size distribution and concentration of the purified EVs. The EV samples were diluted in PBS, and the movement of the particles was recorded within five 30 s videos that were subsequently analyzed using the NTA Software 2.3  at detection threshold 5, camera gain 350 and shutter setting 800. Transmission Electron Microscopy (TEM) was performed as described in .TM BCA Protein Assay Kit , and an equal amount of protein (10 µg) of each sample was separated by SDS-PAGE by using NuPAGE Sample Reducing Agent  on precast NuPAGE 4–12% Bis-Tris Protein Gels . Thereafter the proteins were transferred to a polyvinylidene difluoride (PVDF) membrane and probed using anti-Calnexin , anti-Alix  or Syntenin-1  antibodies as per manufacturer’s instructions. The secondary antibodies used were goat anti-mouse IRDye 800 CW  or donkey anti-rabbit IRDye 800 CW . Precision Plus Protein Dual Color Standards ladder was utilized to determine the size of protein bands . The final blots were imaged using the LI-COR Odyssey CLx infrared imaging system .Sample protein concentrations were measured using the PierceLibrary preparation ,small RNA sequencing and data analysis regarding mapping and annotation were performed as outlined in . For datThe statistics for RNA stability experiments were performed using simple linear regression analysis using GraphPad Prism Version 6 . The same software (Version 7) was used for statistical fold enrichment analyses with two-way ANOVA, Tukey post-hoc correction. The statistics from the differential expression analysis of miRNAs were generated by the R software package DESeq2 .It is known that cells constantly release miRNA into the extracellular space. A fraction of secreted miRNA is encapsulated in EVs, yet according to previous reports ,13, a laIn the present study, SEC-purified EVs were characterized by Western blot, Nanoparticle Tracking Analysis (NTA) and transmission electron microscopy (TEM) . FurtherMany studies focusing on developing EVs as therapeutic RNA carriers aim to enrich miRNA in EVs by overexpressing the respective transcripts in EV-producing cells. Yet, little is known about miRNA secretion patterns when using such a strategy. To investigate to what extent miRNAs of interest get released to the extracellular space, we transiently overexpressed pri-let-7a and pri-let-7b in HEK cells ) and quantified the respective mature miRNA levels in the EV and non-EV portion of the secretome. This analysis revealed that the majority of overexpressed let-7a and let-7b mature transcripts were secreted in the non-EV fraction, whereas the EV fraction contained <2% of the respective miRNAs b. This sThe pri-miRNA expression vectors used above were driven by the CMV promoter, which is a Pol II promoter and requires full processing of the overexpressed pri-miRNA transcripts by respective miRNA processing machinery . However, in many cases, when aiming to load EVs with small RNA, the transcription is initiated using the U6 (Pol III) promoter. This promoter facilitates highly efficient RNA transcription with well-defined transcription start and termination sites, being extensively used for expressing short hairpin RNA (shRNA) for siRNA-like gene silencing purposes. Having this in mind, we also overexpressed a shRNA transcript  in HEK cells to measure its effect on shRNA secretion in EVs. Interestingly, even though the U6-promoter-driven shRNA expression level was significantly higher than the miRNA expression level using the CMV-driven pri-miRNA expression vectors (data not shown), we observed that the shRNA secretion profile remained unchanged. Namely, <2% of all secreted shLuc was found in the EVs, where similarly to naturally secreted miR-21 transcripts, it remained stable in a serum-containing environment for 24 h c,d. ThisAfter observing that the overexpressed miRNAs are predominantly secreted via a non-vesicular route, we were curious to investigate whether the same trend applies to miRNAs at their basal expression level. Hence, we sequenced RNA from cells, SEC-derived EV and non-EV fractions from HEK and MSC cell lines and focused on the analysis of reads between 17 and 35 nucleotides in length. This restriction was applied in order to not only cover full-length miRNAs but also to include other small RNAs (sRNA) and check for the presence of larger RNA fragments. The latter have been found in EVs by the authors and others even when focusing on short reads a 38,46,,46,47.Principal component analysis (PCA) showed a clear cluster separation between cell, EV and non-EV samples, indicating significant differences in their RNA composition b. Upon dOverall, it must be noted that the proportion of sRNA biotype sequences in the RNA secretome was relatively low . The majority of the annotated sequences were ‘rRNA’ and ‘otherRNA’ fragments , the latAfter observing that miRNA and piRNA were the most abundant sRNA species both in EVs and non-EVs, we next decided to analyze whether there are any differences in miRNA secretion between the different fractions. We observed that nearly all miRNA reads fell in the range of 20–24 nucleotides in length and represented mature miRNA sequences c. Intere2 = 0.78 and R2 = 0.84 for HEK and MSC samples, respectively; 2 = 0.86 and R2 = 0.87, respectively). The same was seen for the HEK non-EV sample (R2 = 0.78), yet miRNAs in HEK EVs showed a somewhat lower correspondence to their source cells (R2 = 0.69). Nevertheless, these generally high correlation coefficients indicate that, for most miRNAs, a significant contributor to their secretory level is their expression in the cells, suggesting an average stochastic secretion mechanism with evidence on selective sorting in the case of some miRNAs.The analysis of expression levels of individual miRNAs revealed a good correlation between the EV and non-EV samples  analysis of mature miRNA levels in both cell types by comparing miRNA expression between EVs and non-EVs as well as against their background level in their source cells. As a general observation, miRNAs that were enriched in EVs and in the non-EV fraction displayed low cellular expression levels a,b. In HFrom the sequencing results, we could conclude that the enrichment of short RNA sequences at their basal level into EVs can mainly be seen for lowly expressed sequences. To validate these findings with an alternative methodology, we focused on miRNAs with a wide basal expression range . We selected five miRNAs from our data set  and quantified their secretion level by RT-qPCR. As compared to source cells, the level of miR-100 and miR-21 was higher in the extracellular space for HEK cells and lower for MSCs , whereasThe extracellular space consists of multiple types of nucleic acid carriers, including EVs, which are one of the most actively studied fractions of the secretome. EVs contain many different types of RNA and, as widely demonstrated, transfer it in a biologically active form ,49,50. OThe study at hand employed a well-established secretome fractionation strategy, SEC, in order to explore the relative distribution of short RNA stretches in EVs and non-EV secretory fractions. Our preliminary observations in the RNA overexpression context showed that <2% of analyzed miRNAs were secreted via EVs, while the great majority of overexpressed miRNA ended up in the non-EV fraction, corroborating with a recent study showing similar findings from virus-infected cells . MoreoveOwing to the insufficient systematic knowledge on how short RNA sequences are secreted in the global context on the basal level, it is difficult to design robust universal strategies for miRNA enrichment in EVs. Though a number of small RNA sequencing studies have been performed to define the exact nucleic acid composition of EVs, the majority thus far exploit UC-based EV enrichment protocols ,62,63,64To follow up on the aforementioned overexpression work, we continued to use SEC, separated the secretome of HEK cells and MSCs into the EV and non-EV fractions and aimed to characterize the global sRNA sorting at the basal expression level. The preliminary clustering analysis showed a clear separation between cellular, EV and non-EV samples. While this suggested global differences, the content of EVs and non-EVs appeared to be more similar to each other than to their source cells. A closer look at different short RNA biotypes revealed that the similarity might be attributed to piRNA sequences, of which the cells were relatively depleted, as well as to miRNAs, being highly prevalent in cells and less abundant in the secretory fractions. Nevertheless, the miRNA expression both in EVs and non-EVs correlated highly to that of their parent cells, suggesting that in general, the most important factor determining the level of secreted miRNA is its expression level in the source cells. This does not, however mean that miRNA sequences cannot be enriched in EVs, as certain proteins mediating miRNA sorting into EVs, such as hnRNP-Q, hnRNPA2B1 and major vault protein, have been suggested ,68,69. YImportantly, we must underline that even if a specific sequence was enriched in EVs versus non-EVs in relative terms, the total abundance of miRNAs in the non-EV fraction, in fact, greatly exceeded that of EVs. Though similar evidence has been reported previously by others, often by using UC for EV isolation ,15, we w


Physical activity has been associated with reduced risk of seven types of cancer. It remains unclear, however, whether muscle-strengthening activities also reduce cancer incidence and mortality.PubMed, Embase, Web of Science and Scopus were searched from inception to March 2020. Summary hazard ratio (HR) and 95% confidence intervals (CI) were estimated using random-effects models.I2 0%; 2 studies), but not with incidence of other 12 types of cancer. Muscle-strengthening activities were associated with lower total cancer mortality: HRs for high vs low levels of muscle-strengthening activities was 0.87 ; and HR for ≥2 times/week vs < 2 times/week of muscle-strengthening activities was 0.81 . Regarding the weekly duration of muscle-strengthening activities, HR for total cancer mortality were 0.91  for 1–59 min/week and 0.98  for ≥60 min/week vs none. Combined muscle-strengthening and aerobic activities (vs none) were associated with a 28% lower total cancer mortality .Twelve studies , 6 to 25 years of follow-up, including 1,297,620 participants, 32,196 cases and 31,939 deaths, met inclusion criteria. Muscle-strengthening activities were associated with a 26% lower incidence of kidney cancer (HR for high vs low levels of muscle-strengthening activities: 0.74; 95% CI 0.56 to 0.98; Muscle-strengthening activities were associated with reduced incidence of kidney cancer and total cancer mortality. Combined muscle-strengthening and aerobic activities may provide a greater reduction in total cancer mortality.The online version contains supplementary material available at 10.1186/s12966-021-01142-7. World Cancer Research Fund/American Institute of Cancer Research concluded that convincing evidence exist to support that aerobic, moderate to vigorous physical activity (MVPA) is associated with a reduced risk of colon cancer, and there is probable evidence for a reduction in the risk of breast cancer and endometrial cancer [American College of Sports Medicine [and the United States Department of Health and Human Services [Physical activity has received much attention in recent years for its potential to prevent some types of cancer –3. In 20Medicine  and the Services  have conServices .World Health Organization (WHO) physical activity recommendations for health included muscle-strengthening activities involving major muscle groups during at least twice weekly [Muscle-strengthening activities are activities that maintain or increase the physical fitness component of muscle strength, as well as body composition, balance and muscular endurance . The 202e weekly , due to e weekly , 9, typee weekly  and all-e weekly , and ince weekly  and kidne weekly , but note weekly , 13.Given these favorable results of muscle-strengthening activities in cancer prevention, we conducted a systematic review to assess the epidemiologic evidence on 1) the association of muscle-strengthening activities with cancer incidence and mortality by types of cancer; 2) the parameters  of muscle-strengthening activities needed to reduce cancer incidence and mortality; 3) the joint association of muscle-strengthening activities and aerobic MVPA with cancer incidence and mortality; and 4) the association of muscle-strengthening activities with cancer incidence and mortality according to potential effect modifiers.This systematic review was registered under PROSPERO (CRD42020153846) and the reporting followed the PRISMA 2020 checklist recommendations .We conducted a broad search, without publication date or language filters, in MEDLINE (via Pubmed), Embase, Web of Science and Scopus databases in March 2020, using search terms related to exposure (“strength training”) and outcome (“cancer”). Details on the search strategy can be found in the supplementary material.Our systematic review aimed to investigate whether muscle-strengthening activities (exposure) can reduce the risk of cancer incidence and mortality (outcome) in participants without cancer at baseline (population). Specifically, our research question addresses primary prevention: Do muscle-strengthening activities prevent cancer incidence and mortality in healthy populations? Observational studies (case-control and cohort) that evaluated the association between muscle-strengthening activities, alone or combined with aerobic MVPA, and cancer incidence or mortality, were eligible for inclusion. The studies should contain information on muscle-strengthening activities  in healthy adults (≥18 years) free of cancer at baseline (for cohort studies). Theoretically, randomized clinical trials were eligible for our analysis, but yielded no results, probably due to logistical and economic issues. Congress abstracts, narrative reviews and cross-sectional studies were ineligible. We excluded cohort studies that included participants with cancer at baseline as they address an entirely different research question – secondary/tertiary prevention: Do muscle-strengthening activities improve cancer survival  in populations with a diagnosis of cancer? Selection of articles first involved reading and evaluating titles and abstracts considering the scope of the systematic review. Then, the full texts were read for the final selection. In both stages, the articles were selected by two researchers (WN and GF) and compared; in cases of disagreement, a third researcher (LFMR) was asked to arbitrate.Data were extracted on study characteristics (year of publication and study design), participants (sample size and sociodemographic characteristics), exposure , muscles used, follow-up time (for cohort studies), outcomes  and results . When the information was unavailable in the study, we contacted the authors to complement data extraction.Two researchers (DHL and JPRL) independently assessed the risk of bias in each study using the ROBINS-I tool , which iI2 and Cochran’s Q statistics [Fixed and random effect meta-analyses were performed to estimate summary measures (HR and 95% CI) for the association between high vs low levels of muscle-strengthening activities and cancer incidence and mortality by types of cancer. “High” was defined as the group with the highest volume (or frequency) of muscle-strengthening activities, whereas “low” was the group that reported none or lower volume (or frequency) of muscle-strengthening activities. Subgroup analyses were performed with studies that used similar criteria for categorizing exposure . Whenever possible, a meta-analysis was conducted to estimate the summary HR and 95% CI for the joint association of muscle-strengthening activities  and aerobic MVPA  with cancer incidence and mortality. Heterogeneity between study results was quantified by atistics . The sysatistics  and sourSearch strategy was performed in four databases  and duplicates were removed, obtaining a total of 4958 articles. Of these, 7 met the eligibility criteria for the systematic review. An additional manual search from the lists of recent reviews and included studies, as well as the continuous update from Pubmed alerts yielded 5 eligible studies. Finally, the systematic review included 12 studies , 18–25   for bladder cancer, 0.74  for kidney cancer, 0.90  for lung cancer, 1.04  for pancreatic cancer, and 1.02  for prostate cancer     for 1–59 min/week and 0.98  for ≥60 min/week  Fig. A. Four s0%) Fig. B. Two steek Fig. C.Fig. 3I2 0%) for muscle-strengthening activities only, 0.92  for aerobic MVPA only, and 0.80  for muscle-strengthening activities plus aerobic MVPA  was associated with a lower incidence of kidney  and bladI2 21%) for muscle-strengthening activities only, 0.89  for aerobic MVPA only, and 0.72  for muscle-strengthening activities plus aerobic MVPA ; thus, we have summarized the results across studies using random-effect model, which provides a wider confidence interval of the summary HR and more conservative claims of statistical significance compared to fixed-effect models. We could not explore the sources of heterogeneity between studies due to limited number of studies. Third, studies were conducted in US, Australia and United Kingdom, while limiting generalizability, particularly to low- and middle-income countries. Finally, our risk of bias assessment suggests that most of the studies are prone to bias, particularly confounding bias and selective reporting bias. For instance, most studies did not adjust for dietary factors  that may play a role in cancer incidence and mortality. Residual confounding by smoking is also a concern in physical activity and cancer studies [Our systematic review has some limitations that warrant consideration. First, results from meta-analyses are prone to measurement errors inherent in the included studies. Measurement error in muscle-strengthening activities could have affected the magnitude of the associations. In this case, as most of the studies included in our systematic review were prospective cohort studies, we expect that measurement error of muscle-strengthening activities being non-differential in regard to the outcome ascertained; thus, non-differential measurement error of the exposure may have underestimated the magnitude of the associations of high versus low levels of muscle-strengthening activities with cancer incidence and mortality. All studies included in our systematic review were based on participants’ self-report, so the actual intensity or duration of the muscle-strengthening activities performed cannot be established. Second, the studies reviewed used different questions to assess participation in muscle-strengthening activities and different analytical categories. This may have increased the statistical heterogeneity between studies, e.g., moderate to high in the meta-analysis for total cancer mortality ( studies , 28. Sel studies . All stuIn conclusion, our systematic literature review suggests that muscle-strengthening activities might play a preventive role for incidence of kidney cancer. In addition, we found that muscle-strengthening activities might be a protective factor for total cancer mortality, and that performing both muscle-strengthening and aerobic activities may provide a larger reduction in total cancer mortality. The associations between muscle-strengthening and incidence of other types of cancer remain inconclusive due to the limited number of studies and moderate to high heterogeneity between results. Future prospective cohort studies with long-term follow-up, repeated-measures of muscle-strengthening activities over time and the intensity they are performed, and detailed covariates including aerobic MVPA should be conducted to provide a stronger evidence-base for synthesis. In addition, studies including more diverse populations with wide range of muscle-strengthening activities are needed to increase generalizability and to define dose-response relationship of muscle-strengthening activities with cancer incidence and mortality.Additional file 1.


Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 : 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion. The classification of RCC consists of three major histologic RCC subtypes. Clear cell renal cell carcinoma (ccRCC) is the most common subtype , papillary renal cell carcinoma (pRCC) accounts for about 15–20% of RCC, and chromophobe renal cell carcinoma (chRCC) makes up approximately 5% of RCC4. The classic morphologic features of ccRCC include compact, alveolar, tubulocystic or rarely papillary architecture of cells with clear cytoplasm and characteristic network of small, thin-walled vessels5. Papillae or tubulopapillary architecture with fibrovascular cores and frequently with foamy macrophages are identified pRCC6. Of note, although renal oncocytoma is the most common benign renal tumor type, it still remains difficult to distinguish clinically from renal cell carcinoma including chRCC7. There are well-documented disparities in histologic appearances of chRCC and renal oncocytoma: chRCC shows prominent cell border, raisinoid nuclei and perinuclear halo, while oncocytoma displays nested architecture with myxoid or hyalinized stroma and cells with eosinophilic or granular cytoplasm and small round nuclei. However, the eosinophilic variant of chRCC can mimic the histologic features of oncocytoma, given their similar histogenesis8.Kidney cancer is among the ten most common cancers worldwide11. Inspection and examination of complex RCC histologic patterns under the microscope, however, remain a time-consuming and demanding task for pathologists. The manual classification of RCC has shown a high rate of inter-observer and intra-observer variability12, as renal tumors can have varied appearances and combined morphologic features, making classification difficult. With the advent of whole-slide images in digital pathology, automated histopathologic image analysis systems have shown great promise for diagnostic purposes15. Computerized image analysis has the advantage of providing a more efficient, objective, and consistent evaluation to assist pathologists in their medical decision-making processes. In recent years, significant progress has been made in applying deep learning techniques, especially convolutional neural networks (CNNs), to a wide range of computer vision tasks as well as biomedical imaging analysis applications18. CNN-based models can automatically process digitized histopathology images and learn to extract cellular patterns associated with the presence of tumors21.Histological classification of RCC is of great importance in patient’s care, as RCC subtypes have significant implication in the prognosis and treatment of renal tumorsIn this study, we developed a CNN-based model for classification of renal cell carcinoma based on surgical resection slides. We used resection slides from our tertiary medical institution, Dartmouth-Hitchcock Medical Center (DHMC), which include rare subtypes, such as normal, renal oncocytomas, and chRCC, for the development of our model. We evaluated this model on 78 independent surgical resection slides from our institution and 917 surgical resection RCC slides from The Cancer Genome Atlas (TCGA) database. Furthermore, we evaluated this model for RCC classification on 79 biopsy slides from our institution. The study presented in this paper utilizes deep neural networks to automatically and accurately differentiate RCC from benign renal tumor cases and classify RCC subtypes on both surgical resection and biopsy slides.Table Of note, our pipeline is the same for resection and biopsy slides and processes each slide using the same method and parameters. Typical biopsy core specimens include 2 to 8 biopsy cores. In this study, the biopsy cores were analyzed and evaluated together at the slide level. Some biopsy cores may only consist of benign renal parenchyma, and some biopsy cores may contain an insufficient amount of lesional tissue; therefore, our approach considered all biopsy cores together at the level of slide.The confusion matrices for each of our three test sets are shown in Fig. 22.We visualize the patches on whole-slide images in our test sets with a color-coded scheme according to the classes predicted by our model. This visualization provides pathologists with insights into the major regions and features that contribute to the classification decisions of our method, to avoid the “black-box” approach toward the outputs. Figure 26. Of note, the most common benign renal tumor is oncocytoma  and is known for mimicking RCC on histology slides27. Therefore, it is very important to recognize different subtypes of RCC as well as benign renal neoplasms such as oncocytoma.Classification of renal cell carcinoma subtype is a clinically important task that enables clinicians to predict prognosis and to choose the optimal management for patients with RCC. Different RCC subtypes may have different prognosis, underlining the importance of differentiation of these subtypes. Clear cell RCC has a worse prognosis compared to chromophobe or papillary RCC at the same stageThis study proposed and evaluated a deep neural network model for automated renal cell carcinoma classification on both surgical resection and biopsy whole-slide images. We chose ResNet-18 architecture as the backbone of our pipeline, which involved a patch-prediction aggregation strategy. Our final model achieved an average F1-score of 0.92, 0.91, and 0.95 for independent resection whole-slide image test sets from DHMC and TCGA databases, and DHMC biopsy whole-slide images, respectively. This study is the first step toward utilizing deep learning methods to automatically classify RCC subtypes and oncocytoma on histopathology images.30. To distinguish between chRCC and oncocytoma classes, which are less common in public datasets, we used region-of-interest annotations to develop a highly accurate patch classifier. Recently, a combination of a convolutional neural network and directed cyclic graph-support vector machine (DAG-SVM) was used for the classification of three RCC subtypes using the TCGA dataset17. Another common method in digital pathology is using a weakly supervised approach with a multiple instance learning framework to train a diagnostic model without region-of-interest annotations32. Our study stands out from this previous study for several reasons: (1) our approach follows a more intuitive methodology based on patch-level confidence scores and achieved an average AUC of > 0.95 on the test sets; (2) our method was evaluated on both DHMC and TCGA datasets to show its generalizability on surgical resection whole-slide images, establishing a strong baseline for future studies in the classification of renal cell carcinoma subtypes; (3) our study includes identification of benign renal neoplasm, oncocytoma, in addition to all major RCC subtypes; and (4) we showed the application and generalizability of our model to a test set of biopsy whole-slide images, which also achieved promising results.Previous work on machine learning applications to kidney cancer histopathology is mostly focused on resection slides and three RCC subtypes, without the consideration of benign or oncocytoma classes, with validation on a single test setOf note, because the TCGA dataset is focused on malignant cancer cases, oncocytoma, a benign subtype, does not exist among the TCGA whole-slide images. Therefore, this subtype was not included in the surgical resection slides in our external test set. Additionally, chromophobe RCC makes up about 5% of RCC occurrences and we could only identify a few chromophobe biopsy slides at DHMC. Similarly, for clinical purposes, only a few normal biopsy slides are stored at DHMC, as more emphasis is put on renal tumor biopsy slides. Considering the prevalence and availability of chromophobe biopsy slides, and the availability of normal slides at our institution, we excluded chromophobe RCC and normal class from our biopsy test set, and evaluated our model on the two major RCC subtypes  and the major renal benign tumor type . Notably, the generalizability of our model to biopsy whole-slide images has a wide range of application, as it could assist clinicians with fast and reliable diagnoses and follow-up recommendations for patients.Manual histopathological analysis is a tedious and time-consuming task that could induce errors and variability among different pathologists. Our model addresses this limitation by providing a new technology that has the potential to help pathologists achieve a more efficient, objective, and accurate diagnosis and classification of renal cell carcinoma. In particular, our approach could provide clinical assistance and a second opinion to general surgical pathologists that are not specialized in genitourinary pathology.Our error analysis shown in Figure 34. Another direction of improvement is to utilize graph-based approaches, such as Slide Graph35, to capture the location information of features in whole-slide classification. Of note, our proposed whole-slide inference utilizes aggregation of patch-level predictions, and is permutation invariant and does not consider the location information of patches. Finally, we plan to implement a prospective clinical trial to validate this approach in clinical settings and quantify its impact on the efficiency and accuracy of pathologists' diagnosis of renal cancer.As a future direction, we plan to expand our dataset and test sets through external collaborations for a more robust and extensive evaluation of RCC subtypes. This extension will include rare subtypes and classes, such as clear-cell papillary renal cell carcinomas. In addition, Table 36. The distribution of whole-slide images that were used in this study is summarized in Table A total of 486 whole-slide images were collected from patients who underwent renal resection, including 30 normal slides with benign renal parenchyma and no renal neoplasm, from 2015 to 2019 from Dartmouth-Hitchcock Medical Center or DHMC, a tertiary medical institution in New Hampshire, USA. These hematoxylin and eosin (H&E) stained surgical resection slides were digitized by Aperio AT2 scanners  at 20× magnification (0.50 µm/pixel). We partitioned these slides into a training set of 385 slides, a development (dev) set of 23 slides, and a test set of 78 slides. Additionally, we collected 79 RCC biopsy slides from 2015 to 2017 from DHMC, as well as 917 whole-slide images of kidney cancer from TCGA for external validation. This study and the use of human participant data in this project were approved by the Dartmouth-Hitchcock Health Institutional Review Board (IRB) with a waiver of informed consent. The conducted research reported in this article is in accordance with this approved Dartmouth-Hitchcock Health IRB protocol and the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research involving Human Subjects37. Each ROI was associated and labeled as clear cell RCC, papillary RCC, chromophobe RCC, oncocytoma, or normal. All annotated ROIs were confirmed by one pathologist at a time before being broken into fixed-size patches for our model training and validation steps.Two pathologists (R.R. & B.R.) from the Department of Pathology and Laboratory Medicine at DHMC manually annotated the surgical resection whole-slide images in our training and development sets. In this annotation process, bounding boxes outlining regions of interest (ROIs) for each subtype were generated using Automated Slide Analysis Platform (ASAP), a fast viewer and annotation tool for high-resolution histopathology images38. These results are then aggregated through a confidence-based inference mechanism to classify the whole-slide images. As a result, this framework allows us to analyze a high-resolution, whole-slide image with a feasible memory requirement. Figure Given the large size of high-resolution histology images and the memory restrictions of currently available computer hardware, it is not feasible to analyze a whole-slide image all at once. Therefore, in this work, we use a computational framework developed by our group that relies on deep neural network image analysis on small fixed-size patches with an overlap of 1/3 from the whole-slide images38 on the annotated ROIs in our training and development sets to generate fixed-size  patches. To balance the dataset, we randomly selected the same number of 12,240 patches from the training set for each subtype. The distribution of this patch-level dataset is available in Table 39. These models used the multi-class cross entropy loss function and were trained for 40 epochs with an initial learning rate of 0.001. The learning rate was reduced by a factor of 0.9 every epoch during the training. The trained models assign a label with a confidence score  for each patch. We compared the trained models in our cross-validation process. Among the trained models, we selected a ResNet-18 model, which achieved the best average F1-score of 0.96 on the development set, for further whole-slide inference. The model’s performance on the development set is summarized in Table To do this, we utilized a sliding window approachFor whole-slide classification, our approach aggregated patch-level predictions based on their confidence scores. For each whole-slide image, we automatically processed the image by removing the white background, breaking down the remaining areas in each whole-slide image into fixed-size  patches, and feeding the patches to our trained deep neural network to generate a pool of patch-level predictions. Of note, to enhance the robustness of our method, we removed all low-confidence patches from this pool so that their confidence scores were less than the threshold of 0.9. We performed a grid search to find the best threshold for the patch-level confidence score on the development set.To aggregate the patch-level predictions, we computed the percentage of patches that belongs to each class in the pool of patches from a whole-slide image. We applied a grid-search optimization on patch-based statistics in the development set to build our inference criteria for whole-slide inference. In our whole-slide image inference criteria, if any of the renal subtypes  accounted for more than 5.0% of the total number of patches, we labeled the whole-slide image as an abnormal class with the greatest number of patches. Otherwise, we classified the whole-slide image as overall normal. The details of our grid search process are included in Supplementary Materials, Appendix To show the accuracy and generalizability of our approach, we evaluated our method on three different test sets: (1) 78 independent surgical resection whole-slide images from DHMC, (2) 917 surgical resection whole-slide images from the TCGA database, and (3) 79 biopsy whole-slide images from DHMC.40.In this evaluation, we establish the gold standard for each whole-slide image in our test sets based on the original institutional label and the verification of a pathologist (R.R.) involved in our study. If there is any disagreement, we send the cases to our senior pathologist (B.R.) to resolve the disagreement. For this multi-class classification, we used precision, recall, the F1-score, and the area under the curve (AUC), as well as confusion matrices to show the discriminating performance of our approach for renal cancer classification. In addition, 95% confidence intervals (95% CIs) were computed using the bootstrapping method with 10,000 iterations for all the metricsSupplementary Information.


In January 2020, an outbreak of atypical pneumonia caused by a novel coronavirus, SARS-CoV-2, was reported in Wuhan, China. On Jan 23, 2020, the Chinese government instituted mitigation strategies to control spread. Most modeling studies have focused on projecting epidemiological outcomes throughout the pandemic. However, the impact and optimal timing of different mitigation approaches have not been well-studied.We developed a mathematical model reflecting SARS-CoV-2 transmission dynamics in an age-stratified population. The model simulates health and economic outcomes from Dec 1, 2019 through Mar 31, 2020 for cities including Wuhan, Chongqing, Beijing, and Shanghai in China. We considered differences in timing and duration of three mitigation strategies in the early phase of the epidemic: city-wide quarantine on Wuhan, travel history screening and isolation of travelers from Wuhan to other Chinese cities, and general social distancing.Our model estimated that implementing all three mitigation strategies one week earlier would have averted 35% of deaths in Wuhan (50% in other cities) with a 7% increase in economic impacts (16-18% in other cities). One week’s delay in mitigation strategy initiation was estimated to decrease economic cost by the same amount, but with 35% more deaths in Wuhan and more than 80% more deaths in the other cities. Of the three mitigation approaches, infections and deaths increased most rapidly if initiation of social distancing was delayed. Furthermore, social distancing of working-age adults was most critical to reducing COVID-19 outcomes versus social distancing among children and/or the elderly.Optimizing the timing of epidemic mitigation strategies is paramount and involves weighing trade-offs between preventing infections and deaths and incurring immense economic impacts. City-wide quarantine was not as effective as city-wide social distancing due to its much higher daily cost than social distancing. Under typical economic evaluation standards, the optimal timing for the full set of control measures would have been much later than Jan 23, 2020 (status quo).The online version contains supplementary material available at (10.1186/s12889-022-12659-2). In January 2020, an outbreak of atypical pneumonia caused by a novel coronavirus, SARS-CoV-2, was reported in Wuhan, China. The outbreak quickly grew and expanded to other cities and countries. At the end of January, there were nearly 10,000 confirmed COVID-19 cases, and over 200 reported deaths in China [Despite the actions taken by the Chinese government, the number of confirmed COVID-19 cases grew to 45,000 in mid-February and 80,000 by early March , 4. By AIn responding to an infectious disease outbreak, governments are faced with questions of both when to enact control measures, such as mandated social distancing or city-wide quarantining, and for how long. Enacting measures early stems transmission and results in fewer cases and deaths; however, extreme measures come at substantial economic costs and disruption to daily life , 6. On tPrevious studies evaluating COVID-19 control measures have primarily focused on epidemiological outcomes only, with no consideration of economic impacts. For example, Wu et. al.  conducteIn this study, we use a mathematical model of COVID-19 to explore how differences in the timing and duration of mitigation strategies would have impacted both epidemiological and economic outcomes in Wuhan, China, the epicenter of the epidemic, as well as in three highly-connected Chinese cities . While our model specifically focuses on the situation in these four Chinese cities, our analysis provides insights into decision-making trade-offs in the context of the COVID-19 epidemic mitigation that are applicable to other localities.We developed a mathematical model to reflect SARS-CoV-2 transmission dynamics within and between four major Chinese cities: Wuhan, Chongqing, Beijing, and Shanghai. We modeled the outbreak as starting on Dec 1, 2019 with a zoonotic source in Wuhan. While there is some evidence that SARS-CoV-2 may have been circulating at low levels 1-2 months prior, sustained viral transmission was likely established in late November or early December, justifying our start date . We simuhttps://github.com/Anthony-zh-Zhang/COVID_19_model.We present the health state transition diagrams in Figs. SARS-CoV-2 transmission within each city was modeled following a Susceptible-Exposed-Infected-Recovered (SEIR) dynamic compartmental model, which is commonly used for COVID-19 modelling –9. The mThe proportion of susceptible individuals becoming infected at each time step in a given age category was calculated by multiplying the number of contacts of each age category, the age-specific prevalence of SARS-CoV-2 infection at that time step, and the per-contact transmission rate. The contact mixing matrix, which summarizes the number of daily contacts for each age category with every other age category, was estimated from an age-mixing study in Southern China .The daily per-contact transmission rate was estimated through calibration. We assumed that symptomatic and asymptomatic infections had the same per-contact probability of transmission due to similar levels of viral shedding regardless of symptom status , 20. We The model also included terms reflecting travel to and from Wuhan. Travel volumes were taken to be the same as those estimated by Wu et al. . All incThe model allowed for three different types of control measures (or mitigation strategies) to mitigate SARS-CoV-2 spread: 1) social distancing (either society-wide or among certain age groups); 2) travel history-based quarantining for those with recent travel to Wuhan , and 3) city-wide quarantine (applicable only to Wuhan). Social distancing was assumed to reduce the number of daily contacts by 50% among applicable age categories . Travel α of individuals newly arriving from Wuhan transition directly into the quarantined states. Varying α allows us to consider different levels of effectiveness of the travel screening efforts in identifying and reaching recent arrivals. Further details on these calculations are provided in Additional file To accommodate the potential policy of quarantining of travelers in Chongqing, Beijing, and Shanghai who have recently arrived from Wuhan, the models for these three cities include compartments stratified by travel history (either having recently arrived from Wuhan or having had no recent travel from Wuhan). When the travel-based quarantining policy is inactive, this stratification makes no difference to the model; however, when the policy is activated, a proportion of individuals with travel history from Wuhan are immediately moved to a set of quarantined states where contact is eliminated. Additionally, once the travel history-based quarantining strategy is introduced, a proportion The per-contact SARS-CoV-2 transmission rate and age-specific COVID-19 infection mortality rates were estimated through model calibration. We used Incremental Mixture Importance Sampling (IMIS) , 23, a BWe validated the calibrated model by comparing our simulated model outcome  under the status quo scenario (details below) with the actual epidemiological data in the four cities.Our status quo simulations captured the actual timing of control measures as they were announced and implemented. Thus, in the status quo, all three control measures were instituted on Jan 23, 2020 , 25. We We then used the model to simulate counterfactual scenarios by varying the start dates and duration of the different control measures, which we compare to the status quo . In one analysis we varied the implementation start date of all three mitigation strategies, while keeping the same end dates as the status quo. In another analysis, we considered different duration of the workplace and school closures in cities other than Wuhan while keeping Wuhan under quarantine and social distancing till the end of the simulation time horizon. We did not vary the end date of the lockdown of Wuhan, since ending the lockdown policy prior to March 31, 2020 seemed unlikely given its central role in the early COVID-19 epidemic in China.Health consisted of cumulative number of SARS-CoV-2 infections and COVID-19 deaths in each of the four cities over the simulated time horizon. To be comparable with economic and mitigation costs, we monetized health outcomes using a disability-adjusted life-year (DALY) and willingness-to-pay approach. We estimated the DALYs associated with a fatal case of COVID-19 by calculated the years of life lost using the average life expectancy for each of the three age groups. For non-fatal SARS-CoV-2 infections, we calculated DALYs using estimates from the 2003 SARS outbreak in China  as disabIn addition to health outcomes, we also considered direct health care costs associated with caring for hospitalized COVID-19 patients. The cost per hospitalized patient was estimated from the cost of a hospitalized SARS patient in the 2003 outbreak, inflated to 2020 USD  .Total disease burden for a given scenario was calculated as the sum of direct health care costs and monetized DALYs.To estimate the economic cost of city-wide quarantine (Wuhan) and social distancing measures (other cities), we divided the loss in Q1 2020 GDP (accounting for both GDP reductions and foregone projected growth relative to Q1 2019) in each city by the duration of the policy  to calculate the city-specific daily economic cost. For Wuhan, this resulted in a daily city-wide quarantine cost of 340 million 2020 USD; while social distancing incurred a daily cost of 232, 349, 377 million 2020 USD in Chongqing, Beijing, and Shanghai, respectively .Through model calibration, our model was able to closely match the number of (diagnosed) infections in the “Elderly” age category and the total number of deaths reported in Wuhan on Mar 9, 2020, by the Chinese Center for Disease Control and Prevention , Table 3Our model predicts that implementing all three mitigation strategies two weeks earlier  would have averted 47% (1525) of COVID-19 deaths in Wuhan and over 70% of deaths in the other three cities  Table . HoweverOverall, the model predicted that net economic cost (sum of economy losses and disease burden) would actually be lower in the four modeled cities if mitigation strategies had started later than the status quo. Economic costs would be minimized if mitigation strategies had been enacted on Feb 20, 2020 for Wuhan, and Feb 22, 2020 for three other cities respectively. In Wuhan, in particular, though substantial health benefits could be achieved through earlier mitigation , the economic loss of city-wide quarantining was so high that it outweighed these benefits  started to reopen workplaces on Feb 29, 2020, other cities and regions made different decisions. Some reopened workplaces, while schools remained closed. Others opted to extend school and workplace closures through mid-April ,32. We tComparing Scenario 1 and 2, ending social distancing practice for non-elderly age groups two weeks earlier would have nearly tripled (doubled) the number of infections (deaths) in each of the three cities Table . FurtherWe also compared results in Table In the sensitivity analysis, we first explored the differential impact of implementing the different mitigation strategies at different times. We then conducted one-way sensitivity analyses on assumed model parameters, including the effectiveness of travel history screening, contact rate increases during Chinese New Year, and contact rate reduction when practicing social distancing  in Technical Appendix — One- and two-way sensitivity analyses varying key model parameters are presented in the Appendix, Section 3. When we vary the contact rate during Chinese New Year (CNY)/Two-way sensitivity analyses , and three other highly-connected megacities: Chongqing, Beijing, and Shanghai. Our model predicted that instituting mitigation strategies earlier and extending them for longer reduces infections and deaths, but at a substantial economic cost. Of the three mitigation strategies considered, social distancing was the most effective, while the city-wide quarantine on Wuhan and travel history screening had only marginal impacts. When we considered age-targeted social distancing strategies, workplace closures to reduce contact among the “Adult” age group averted the greatest number of deaths, but also incurred the highest economic costs. Extending social distancing among “Youth” and “Elderly” age groups resulted in relatively few reductions in deaths.–By considering both health and economic impacts, our results highlight the trade-offs in deciding both when and for how long to institute mitigation strategies. Our model predicted that enacting mitigation strategies later than the status quo would have reduced economic costs. For Wuhan, this finding is largely driven by the extremely high cost of the city-wide quarantine, which had only a modest impact on preventing infections, especially when compared to social distancing. Thus, it is clear that China made large economic sacrifices to achieve control over the epidemic, perhaps even greater sacrifices than would be justified by the number of averted COVID-19 infections and deaths under typical economic evaluation standards . However34Our study has several limitations. First, our study was limited in scope, focusing on Wuhan as the epicenter epidemic and travelers out of Wuhan into three major cities. We did not consider inter-city travel beyond these cities, nor the possibility of infections being introduced from a city other than Wuhan. This assumption is most valid early on in the epidemic . Given tAdditional file 1Technical appendix — fig s2. Varying social distancing start date only, maintaining initiation of other control measures on Jan 23, 2020.Technical appendix — fig s3. Varying social distancing start date only, maintaining initiation of other control measures on Jan 23, 2020. Wuhan is excluded from the plot to show greater detail in the other three cities.Technical appendix — fig s4. Varying start date for all control measures and doubled mortality rate for all age groups, maintaining initiation of other control measures on Jan 23, 2020.Technical appendix — fig s5. Varying Wuhan travel history screening start date, maintaining initiation of other control measures on Jan 23, 2020. Wuhan is excluded from the plot to show greater detail in the other three cities.Technical appendix — fig s6. Varying Wuhan quarantine and Wuhan travel history screening start date, maintaining social distancing initiation on Jan 23, 2020. Wuhan is excluded from the plot to show greater detail in the other three cities.Technical appendix — fig s7. Varying Wuhan travel history screening and social distancing start date, maintaining Wuhan city-wide quarantine initiation on Jan 23, 2020. Wuhan is excluded from the plot to show greater detail in the other three cities.Technical appendix — fig s8. One-way sensitivity analysis on contact rate during Chinese New Year (CNY). In the base case, we assumed contacts rates increase by 20% for all age categories.Technical appendix — fig s9. One-way sensitivity analysis on the relative contact rate reduction when practicing social distancing. In the base case, this is assumed to be 0.50.Technical appendix — fig s10. One-way sensitivity analysis on travel history screening effectiveness, α. In the base case, α=1.0.Technical appendix — fig s11. Two-way sensitivity analysis (subplot 1) on contact rate during Chinese New Year (CNY) and travel history screening effectiveness, α. For this subplot, α=1.0.Technical appendix — fig s12. Two-way sensitivity analysis (subplot 1) on contact rate during Chinese New Year (CNY) and travel history screening effectiveness, α. For this subplot, α=0.75.Technical appendix — fig s13. Two-way sensitivity analysis (subplot 1) on contact rate during Chinese New Year (CNY) and travel history screening effectiveness, α. For this subplot, α=0.50.Technical appendix — fig s14. Two-way sensitivity analysis (subplot 1) on contact rate during Chinese New Year (CNY) and relative contact rate reduction when practicing social distancing. For this subplot, contact rate reduction is 25%.Technical appendix — fig s15. Two-way sensitivity analysis (subplot 1) on contact rate during Chinese New Year (CNY) and relative contact rate reduction when practicing social distancing. For this subplot, contact rate reduction is 50%.Technical appendix — fig s16. Two-way sensitivity analysis (subplot 1) on contact rate during Chinese New Year (CNY) and relative contact rate reduction when practicing social distancing. For this subplot, contact rate reduction is 75%.


KRAS and BRAF oncogenes was significantly associated with resistance to chemotherapy and sensitivity to drugs targeting oncogene-activated pathways. We conclude that tumor cell-intrinsic signals link oncogene status, but not MMR status, to variation in therapy response in CRC.The large majority of patients with metastatic colorectal cancer (mCRC) receive chemotherapy with or without targeted therapy. Tumors with a deficient DNA mismatch repair (dMMR) system respond poorly to systemic therapy and are associated with poor prognosis. However, it is unclear whether dMMR causes therapy resistance in a tumor cell-intrinsic manner, or whether other mechanisms underlie this association. We address this issue by exposing a panel of MMR-deficient and -proficient Patient-Derived Organoids (PDOs) to a series of clinically relevant drugs. We show that MMR status did not correlate with the response of PDOs to any of the drugs tested. By contrast, the presence of activating mutations in the KRAS and BRAF oncogenes was significantly associated with resistance to chemotherapy and sensitivity to drugs targeting oncogene-activated pathways. We conclude that mutant KRAS and BRAF impact the intrinsic sensitivity of tumor cells to chemotherapy and targeted therapy. By contrast, tumor cell-extrinsic mechanisms—for instance signals derived from the microenvironment—must underlie the association of MMR status with therapy response. Future drug screens on rationally chosen cohorts of PDOs have great potential in developing tailored therapies for specific CRC subtypes including, but not restricted to, those defined by BRAF/KRAS and MMR status.DNA mismatch repair deficiency (dMMR) in metastatic colorectal cancer (mCRC) is associated with poor survival and a poor response to systemic treatment. However, it is unclear whether dMMR results in a tumor cell-intrinsic state of treatment resistance, or whether alternative mechanisms play a role. To address this, we generated a cohort of MMR-proficient and -deficient Patient-Derived Organoids (PDOs) and tested their response to commonly used drugs in the treatment of mCRC, including 5-fluorouracil (5-FU), oxaliplatin, SN-38, binimetinib, encorafenib, and cetuximab. MMR status did not correlate with the response of PDOs to any of the drugs tested. In contrast, the presence of activating mutations in the  A deficient DNA mismatch repair (dMMR) system underlies the formation of a specific subtype of colorectal cancer (CRC), accounting for approximately 15% of all cases of primary CRC. In general, dMMR is associated with a lower risk of distant metastasis formation and a relatively good prognosis ,3,4, whiImmune checkpoint inhibitors (ICI) have revolutionized the treatment of patients with dMMR mCRC, producing durable responses that result in a significantly improved survival . DespiteKRAS and BRAF.Organoid technology can be used to generate Patient-Derived tumor Organoid (PDO) cultures, in which the genetic and functional characteristics of the original tumor are maintained ,13,14. ITissue samples from CRC patients were collected during surgery, or by fine-needle biopsies, within the Biobanking protocol HUB-Cancer TCBIO #12-093, which was approved by the medical ethical committee of the University Medical Center Utrecht (UMCU). Written informed consent from the donors was obtained prior to acquisition of the specimen for research use in the present study.® , mixed with a CRC culture medium  by 15 min incubation at 37 °C. After washout of Dispase type II with PBS , the organoids were fixed in 4% formaldehyde solution at room temperature for 20 min. The formaldehyde solution was aspirated and 200 µL 2% Agar  solution was added and hardened on a pre-cooled dish. Subsequently, the agar-droplets containing PDOs were embedded in paraffin blocks. For assessment of mismatch-repair status, immunohistochemistry staining was performed on a BenchMark Ultra Autostainer . Briefly, paraffin sections were cut at 4 µm and deparaffinized in the instrument with EZ prep solution  at 75 °C for 8 min. Heat-induced antigen retrieval was carried out using Cell Conditioning 1  for 32 min at 100 °C. The following antibodies were used: anti-hMLH1 , anti-hPMS2 , anti-hMSH2 , and anti-hMSH6 . Slides were counterstained with Hematoxylin and Bluing Reagent .PDOs were harvested as described previously in 2.3. Total DNA was isolated using the DNeasy Mini Kit  according to the manufacturer’s instructions. Extracted genomic material concentration and quality was measured using NanoDrop 2000  and Bioanalyzer 2100 . Whole genome sequencing of the PDOs was performed on an Illumina NovaSeq 6000 (2 × 150 bp). Mapping and assessment of germline single-nucleotide-variation (SNV) and insertion and/or deletion variants was performed using the “Illumina Analysis Pipeline” of Utrecht Bioinformatics Expertise Core within the Center for Molecular Medicine at the UMC.All drug screens were performed twice on different days. PDOs were mechanically and enzymatically dissociated into single cells by incubating in TrypLE for 5–10 min and replated to allow for the formation of organoids over the course of 72 h. After 72 h, PDOs were collected, incubated with Dispase II  for 15 min at 37 °C to remove Matrigel, and counted using a hemocytometer and trypan blue. PDOs (50 organoids/μL) were resuspended in 13 mL CRC culture medium without NAC and with low EGF concentration (1 ng/mL) supplemented with 5% Matrigel. PDO suspension (40 μL/well) was dispensed in clear-bottomed, black-walled 384-well plates  using an automated Multidrop™ Combi Reagent Dispenser. We generated a twelve-step, threefold drug matrices of 5-FU, oxaliplatin, SN-38, cetuximab, encorafenib, and binimetinib, as described in Drug responses were calculated on the basis of normalized viability data as previously described , and werp-value using the response values in the drug matrix. Pre-specified CRC subgroups  were compared using a Mann–Whitney U-test or Kruskal–Wallis test. p-values of <0.05 were considered significant.One DRC parameter was analyzed: area under the curve (AUC), inferred by integrating fitted curves using Simpson’s rule. To analyze the reproducibility between drug screens, we calculated Pearson’s R and corresponding BRAF- and KRAS-mutational status (MSH2 (p.Glu483*), and inactivating mutations in MLH1 (p.I219V) and MSH3 (p.W1111R), indicating that these PDOs were derived from patients with Lynch syndrome  and l status . The MMRl status . All sixsyndrome .Pearson’ R = 0.73 − 0.76; p < 0.05, All PDOs were screened for their sensitivity to commonly used drugs in the treatment of metastatic CRC, including the chemotherapeutic drugs 5-FU, oxaliplatin and irinotecan (SN-38), the EGFR-targeting antibody cetuximab, the BRAF inhibitor encorafenib, and the MEK inhibitor binimetinib A. All 12To investigate whether MMR status correlated with the sensitivity of CRC PDOs to any of the tested drugs, we generated dose response curves (DRC) and calculated the area under the curve (AUC) values for each of the fitted curves. We found no significant differences in sensitivity to any of the drugs between the groups of dMMR and pMMR PDOs . Thus, aBRAF, KRAS). These analyses revealed that BRAF- and KRAS-mutant PDOs were significantly (p = 0.017 and p = 0.008) more resistant to 5-FU and oxaliplatin when compared to BRAF/KRAS wildtype PDOs . Likewise, tumor sidedness is a potentially important variable in determining treatment responses. Studies with specifically selected (or generated) PDO cohorts  are required to address these questions in the future.KRAS and BRAF) with resistance to chemotherapy. However, none of these are currently used to select patients for, or exclude them from, treatment with chemotherapy [BRAF tumors for treatment with BRAF inhibitor combination therapies, the selection of patients with dMMR tumors for immunotherapy, and the exclusion of patients with KRAS mutant tumors from treatment with EGFR-targeting therapy [BRAF and KRAS on treatment response, we found that within the PDO cohort used, the presence of these oncogenes was significantly associated with reduced sensitivity to 5-FU and oxaliplatin. An intrinsic (relative) resistance of tumor cells with mutant BRAF or KRAS to chemotherapy may contribute to the poor prognosis that is associated with the presence of these oncogenes [BRAF PDOs are more sensitive to inhibitors of BRAF and that KRAS and BRAF mutant PDOs are more sensitive to inhibition of the BRAF target MEK. The results therefore support the concept of targeted inhibition of oncogene-activated signaling pathways as a basis for future treatment strategies, involving the concomitant suppression of feedback pathways.Many studies have found correlations between the presence of specific genetic alterations (including but not limited to mutations in otherapy . By cont therapy . Althougncogenes ,32. The By using PDOs as a model system for evaluating clinically relevant drug responses, we conclude that KRAS and BRAF, but not MMR status, are dominant factors in determining the intrinsic sensitivity of tumor cells to chemotherapy and targeted therapy. Future drug screens on rationally chosen cohorts of PDOs have great potential in developing tailored therapies for specific CRC subtypes including, but not restricted to, those defined by BRAF/KRAS and MMR status.


A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity.diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes  by obesity status.We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and 160 013  hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized  than among those diagnosed with COVID-19 . In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity.We included 627 044  We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies. Obesity is associated with increased mortality and is a well-known risk factor for chronic conditions, such as diabetes, hypertension, cardiovascular disease, and cancer , 2. Due A review and meta-analysis of 75 studies reported that obesity is a risk factor for testing positive for SARS-CoV-2, for severe COVID-19 and for COVID-19 related mortality . A largeIn this study, we aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of COVID-19 patients living without obesity, in inpatient or outpatient settings.https://www.ohdsi.org/wp-content/uploads/2020/07/Protocol_COVID-19-Charybdis-Characterisation_V5.docx) designed by the Observational Health Data Sciences and Informatics (OHDSI) community. All data were standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) [We conducted a multinational cohort study using routinely collected healthcare data from January to June 2020 from Spain, the United Kingdom (UK), and the United States (US). This study was part of the “Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS)” study (protocol available for download at el (CDM) . Data reWe included primary, outpatient and inpatient care data from electronic health records (EHRs) and health insurance claims data from six databases. Data from Spain included the Information System for Research in Primary Care (SIDIAP), which includes primary linked to inpatient care data covering approximately 80% of the population in Catalonia, Spain . The UK diagnosed with COVID-19 , and (2) all patients hospitalized with a COVID-19 diagnosis. We considered clinical diagnoses in the definition of COVID-19 cases due to testing restrictions during the first months of the pandemic  [hospitalized with COVID-19 were identified as those having a hospitalization episode along with a clinical diagnosis or positive SARS-CoV-2 test within a time window from 21 days prior to admission up to the end of their hospitalization. We chose this time window to include patients with a diagnosis prior to hospitalization and to allow for a record delay in test results or diagnoses [diagnosed cohort, the index date was defined as the date of the COVID-19 clinical diagnosis or the earliest test day registered within seven days of a first positive test, whichever occurred first. In the hospitalized cohort, the index date was the day of hospitalization. Patients were followed from the index date to the earliest of death, end of the observation period, or 30 days [We included two non-mutually exclusive cohorts of patients: (1) all patients n Spain) . The diaiagnoses . We incl 30 days .diagnosed and hospitalized COVID-19 cohorts were stratified by obesity status: PLWO vs patients living without obesity . Obesity was defined as having an ever-recorded obesity diagnosis (Supplementary Appendix 3) and/or a body mass index (BMI) measurement between 30 and 60 kg/m2 and/or a bodyweight measurement between 120 and 200 kg prior or at index date. We included upper cut-off thresholds to discard implausible observations. Patients without obesity were those who did not fulfill the obesity definition.Both the Demographics (sex and age) were obtained at the index date. More than 15 000 medical conditions from up to one year prior to the index date were identified based on the Systematized Nomenclature of Medicine (SNOMED) hierarchy, with all descendant codes included .diagnosed cohort were hospitalization and fatality. For the hospitalized cohort, the 30-days outcomes were a requirement of intensive services (IS) , respiratory, cardiovascular, thromboembolic, and other events and fatality.Our 30-day outcomes of interest for the We described the number of patients included and the prevalence of obesity in each database as well as the demographics, comorbidities, and outcomes as proportions  with their respective 95% confidence intervals (CIs) for each database, by obesity status. To calculate these proportions in each database, we established a minimum count required , to minimize the risk of re-identification of patients. To compare medical conditions across groups, we calculated standardized mean differences (SMDs) , which whttps://github.com/ohdsi-studies/Covid19CharacterizationCharybdis, and was run locally in each database. Individual-level data remained within host institutions and only aggregate results from each database were provided to the research team and publicly shared. All the results reported in this paper and additional data are available for consultation at a dynamic and interactive website, which changes over time as new databases are added and/or results are updated to CHARYBDIS (https://data.ohdsi.org/Covid19CharacterizationCharybdis/).To ensure data privacy at all times, we employed a federated analysis approach . FollowiWe used R version 3.6 for data visualization. All the data partners obtained Institutional Review Board (IRB) approval or exemption to conduct this descriptive study.diagnosed and 160 013 hospitalized patients with COVID-19 (Table diagnosed cohort consisted of 122 058 patients from Spain (SIDIAP), 2336 from the UK (CPRD), and 502 650 from the US . The hospitalized cohort included 18 197 patients from Spain (SIDIAP) and 141 816 from the US . Among diagnosed and hospitalized patients, 207 859  and 63 866  had obesity, respectively. In all databases, the prevalence of obesity was lower among diagnosed patients than among those hospitalized, with differences ranging from 5 (IQVIA-OpenClaims) to 16% (SIDIAP).We included 627 044 diagnosed cohort, patients with and without obesity were mostly female. The proportion of females was higher among PLWO compared to patients without obesity in SIDIAP (63% vs 56%), CUIMC (61% vs 56%), and IQVIA-OpenClaims (61% vs 52%), while in VA-OMOP the opposite was observed (13% vs 19%). No differences were observed in CPRD and STARR-OMOP. In the hospitalized cohort, patients without obesity were predominantly male  but PLWO still were more commonly female in all databases aside from VA-OMOP . Differences in the proportion of females between PLWO and patients without obesity ranged from 3 (VA-OMOP) to 15% (CUIMC).The sex distribution (proportions and 95% CIs) of the patients are reported in Table diagnosed cohort, PLWO were slightly older than those without obesity . This was particularly marked in SIDIAP, where 40% of the PLWO were aged above 65 years and only 20% were so without obesity. Hospitalized patients were older than those diagnosed. In the hospitalized cohorts, PLWO were fairly consistently younger than those without obesity (except for SIDIAP). The proportion of patients aged above 65 ranged from 36 to 63% for PLWO and from 43 to 73% for those without obesity.The age distribution in each database is summarized in Table diagnosed and hospitalized cohorts using SMDs, which are summarized in Fig. diagnosed cohort, and 529 (STARR-OMOP) to 5240 (IQVIA-OpenClaims) in the hospitalized cohort. In both cohorts, medical conditions were largely more frequent among PLWO than patients without obesity.We compared baseline medical conditions of PLWO to those of patients without obesity in the diagnosed cohorts, PLWO consistently had a higher prevalence of comorbidities compared to those without obesity; these differences were meaningful  for the majority of comorbidities across databases. For example, while the prevalence of hypertension for PLWO ranged from 30 to 32% in Europe (SIDIAP and CPRD) and from 55 to 81% in the US, in those without obesity it ranged from 12 to 16% and from 26 to 53%, respectively. The SMD for hypertension was above 0.1 in all databases. As in the diagnosed cohort, PLWO hospitalized with COVID-19 had a higher prevalence of comorbidities than those without obesity, and these differences were meaningful for the majority of comorbidities. However, the differences between groups were less obvious. For example, heart disease differed by 20% among those diagnosed in VA-OMOP  and by 9% among those hospitalized ; although the SMD was still above 0.1 in all databases.The distribution of the selected key comorbidities is shown in Fig. diagnosed cohorts, hospitalization rates were higher among PLWO than among those without obesity in all databases. For example, in SIDIAP the proportion of patients hospitalized was 20% for PLWO and 10% for patients without obesity. However, these differences were meaningful (SMD > 0.1) only in three databases: SIDIAP, CUIMC, and STARR-OMOP. In PLWO, fatality ranged from 5 to 12% and was higher than in patients without obesity in SIDIAP and CUIMC , while in CPRD and VA-OMOP it was similar in both groups. SIDIAP was the only database with a meaningful difference in the proportion of fatality.The distribution of 30-days outcomes is shown in Fig. hospitalized cohort, PLWO more frequently had adverse events occurring in the 30 days after the index date than patients without obesity. For example, PLWO required IS and presented with ARDS more frequently than patients without obesity in the largest databases: IQVIA-OpenClaims  and VA-OMOP , whereas in CUIMC and STARR-OMOP percentages were similar. VA-OMOP was the only database with a meaningful difference in the proportion of IS. Similarly, heart failure was also more frequent among PLWO than among patients without obesity in CUIMC: 7% vs 3%, IQVIA-OpenClaims: 7% vs 5%, STARR-OMOP: 16% vs 9%, and VA-OMOP: 23% vs 17%), these differences were meaningful in CUIMC and STARR-OMOP. Sepsis, cardiac arrhythmia, and cardiovascular disease events were slightly more frequent among PLWO, although SMDs were below 0.1 in all databases. Acute kidney injury was the only outcome that was more frequent among patients without obesity; however, this difference was not meaningful in any database. As for fatality, there were no consistent nor meaningful differences between PLWO and patients without obesity in the hospitalized cohort: while it was higher for PLWO in SIDIAP (14% vs 11%), there were no differences in CUIMC (20% vs 21%) nor in VA-OMOP (16% vs 18%).Overall, in the hospitalized (40%) compared to those diagnosed (31%). PLWO diagnosed and hospitalized with COVID-19 were more commonly female, and those hospitalized were younger than patients without obesity. The extraction of more than 15 000 medical conditions revealed PLWO were not only more prone to have obesity-related comorbidities, such as hypertension, heart disease, and type 2 diabetes but also to more than a thousand different health conditions. After 30-days of follow-up, PLWO presented with higher hospitalization rates and intensive services requirements, although these differences were only meaningful in some databases.In this large cohort study including 627 044 COVID-19 patients from Spain, the UK, and the US, we found that the prevalence of obesity was higher among COVID-19 patients Our study has several strengths, such as its large amount of data. By bringing together harmonized data using a federated approach, we have conducted a large-scale study while respecting the confidentiality of patient records. The international approach of this study is a strong asset given that we are investigating the intersection of two major global threats, namely the obesity epidemic and the COVID-19 pandemic. The former, together with the diverse healthcare settings and populations described in this study, increase the generalizability of our findings. Further, we provide a wide overview of the characteristics and outcomes of patients with and without obesity, using data visualization tools to summarize large amounts of medical data. This exhaustive characterization goes far beyond prior studies reporting few comorbidities and supports the generation of new hypotheses that can be tested in future studies. In addition, for the sake of transparency and reproducibility, we have made methods, tools, and all results publicly available. As CHARYBDIS is an ongoing study, results (included longer follow-up time) will be updated and new studies focussing on obesity could be conducted. All of the above has been accomplished through the coordinated efforts of the OHDSI community to provide a rapid response to the COVID-19 pandemic.Our study also has limitations. First, we cannot exclude a selection bias of COVID-19 cases due to underreporting in the context of testing restrictions and asymptomatic or paucisymptomatic cases that usually do not seek medical care. Additionally, testing policies have varied across countries and time depending on the course of the pandemic. Nevertheless, the inclusion of patients clinically diagnosed (not tested) in different settings likely provided consistency to our data, although it might have incurred in false positives. Second, we did not have information on BMI as a continuous variable, which prevented us from investigating the impact of different categories of obesity in COVID-19 outcomes. This might explain the higher proportion of comorbidities and outcomes observed in the US databases, as PLWO from the US might have higher BMIs than those from Europe . In addiGiven the prevalence of obesity in Spain 24%), the UK 27%), and the US (37%), a high proportion of PLWO among COVID-19 cases was expected . However%, and th4%, the UWomen predominated among hospitalized patients with obesity, even though obesity rates are similar in both sexes in the three countries . Althoughospitalized PLWO were younger than those without obesity. Although younger individuals have less risk of infections and complications than older people due to having fewer comorbidities and a stronger immune system, this is not the case for those with obesity [We also found that  obesity , 30–32.  obesity . PLWO al obesity , 34, 35. obesity .2 or ≥40 kg/m2) [Finally, PLWO experienced adverse events more frequently than those without obesity, particularly hospitalization and the requirement of intensive services. Certainly, our results must be interpreted carefully considering the differences in demographics and comorbidities between these groups. Interestingly, in patients hospitalized, we did not observe clear differences in fatality between patients with and without obesity. While two meta-analyses reported that obesity is associated with a higher risk of COVID-19 related mortality; other large observational studies from the US and the UK using finer categories of BMI only found an association with mortality for morbid obesity (BMIs ≥ 35 kg/m0 kg/m2) , 29, 34.In this large international cohort, we showed that among COVID-19 cases, PLWO were more likely to be female, have more comorbidities, and worse outcomes than patients without obesity. The prevalence of obesity was higher among hospitalized patients with COVID-19 compared to patients diagnosed with COVID-19. Our results may be useful in guiding clinical practice and aid future preventative strategies for patients living with obesity, as well as providing useful data to support subsequent etiological studies focussed on obesity and COVID-19.Supplementary Material


The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships  between depressive symptom severity and phone-measured mobility have yet to be fully explored.We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time.Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse–Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants’ location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility.P=.01), Location Entropy (time distribution on different locations) , and Residential Location Count (reflecting traveling)  were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected  the subsequent periodicity of mobility.This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants  years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings. Depression is a prevalent and serious mental health disorder that is a leading cause of disability worldwide . It can We focused on exploring how phone-collected location data could link individuals’ mobility and depression. Past survey-based studies found that mobility is significantly and negatively associated with depression -11. SeveIn recent years, there have been several studies -22 exploWe used a large longitudinal data set of an EU research program called Remote Assessment of Disease and Relapse–Major Depressive Disorder, which explored the utility of remote measurement technologies in long-term (up to 2 years) depression monitoring . We firsAll participants in the study had at least one diagnosis of depression in the most recent 2 years and were recruited from 3 countries ; additional details descriptions are reported in . ParticiFundacio Sant Joan de Deu Clinical Research Ethics Committee (CI: PIC-128-17) in London, and from the Medische Ethische Toetsingscommissie VUms (2018.012–NL63557.029.17) in the Netherlands.Ethical approval was obtained from the Camberwell St. Giles Research Ethics Committee (17/LO/1154) in London, from the We focused on phone location data and data from the 8-item Patient Health Questionnaire (PHQ-8) . The pasSeveral factors may affect our analysis, such as the COVID-19 pandemic, location data accuracy, and missing data. Notably, the COVID-19 pandemic and related lockdown policies greatly impacted European people’s mobility behaviors . TherefoWe calculated the distances between consecutive location records and the instantaneous speeds at all location records. The distance between 2 consecutive location records was computed by using the Haversine formula . The insThe second procedure was location clustering. Since the density-based spatial clustering of applications with noise method  can treaWe extracted 11 mobility features  from locVar(Lon)+Var(Lat)), where log is the logarithm, and Var(Lon) and Var(Lat) represent the variances of the longitude and latitude coordinates, respectively, in one PHQ-8 interval.The Location Variance represented the variability of each participant’s locations  and was The Moving Time represented the percentage of time that a participant spent in moving in one PHQ-8 interval . The feaThe Moving Distance was adjusted by dividing the total distance by the available time (in hours) in one PHQ-8 interval. In previous studies ,19, the The number of the unique location clusters that a participant visited in one PHQ-8 interval was calculated using density-based spatial clustering of applications with noise .Location Entropy represented the distribution of time spent by a participant at different location clusters in one PHQ-8 interval  and was pi is the percentage of time spent at location cluster i, thus the greater the average time, the higher the Location Entropy and vice versa [where ce versa .Normalized Entropy = Location Entropy / log (Number of Clusters)Because the number of location clusters varies across participants and the number of clusters is positively correlated with Location Entropy ,16,19, wIn previous studies ,18,19,21This new feature represented the number of residential locations. Since temporary home locations could reflect traveling , we usedPeople’s life rhythms  are related to depression . We proplonPSD and latPSD represent the power spectral density of longitude and latitude, respectively, and LF, MF, HF, and Total are the low frequency, middle frequency, high frequency, and total spectral power, respectively. If the individuals’ mobility is regular, the Long-term Rhythm or Circadian Rhythm will be high, otherwise, Short-term Rhythm will be high.where t and the immediately subsequent (2 weeks later) time point (t + 1).We used dynamic structural equation modeling to explore the relationships and the direction of relationships between mobility features and PHQ-8 scores over time. Dynamic structural equation modeling is a broad integrated framework that blends multilevel, time-series, and structural equation modeling ,38,39 anP values of coefficients in vector autoregressive models and correlations were adjusted using the Benjamini-Hochberg method [P value <.05. Vector autoregressive models were implemented in Mplus (version 8) [We built a vector autoregressive model with each mobility feature and PHQ-8 score as outcome variables and used age, gender, and work status as covariates -42 at thg method  for multrsion 8)  and multThe 2341 PHQ-8 intervals of 290 participants collected between November 2017 and February 2020 were included in our analysis. The sample had a median age of 50.0  years, with 215 (74.14%) female participants and 149 (51.38%) employed participants, with a median of 10  PHQ-8 scores and a median of 8.0  PHQ-8 intervals for each participant. The pairwise Spearman correlations between all 11 mobility features are presented in P=.11), all mobility features were significantly correlated with the PHQ-8 score at the within-individual level (P<.001) and Short-term Rhythm  were positively correlated, while other mobility features were negatively correlated. Between individuals, Location Variance  and Moving Distance  were significantly and negatively correlated with PHQ-8 scores.Except for Moving Time  and mobility features  (P<.001), while Location Entropy , Homestay , and Residential Location Count  were significantly correlated with subsequent PHQ-8 scores.There were significant and positive lagged effects exist in both PHQ-8 scores  . For croP=.01), Location Entropy , and Residential Location Count , while work status was significantly correlated with most mobility features . For lagged effects, older participants had significantly lower lagged effects on Moving Distance  and Homestay  than younger participants. Female participants had significantly lower lagged effects on Location Entropy  and Residential Location Count  than male participants. Compared with unemployed participants, employed participants have significantly lower lagged effects on the PHQ-8 score  and significantly higher lagged effects on Normalized Entropy . For cross-lagged effects, age was significantly and negatively correlated with the φ3 coefficient of Circadian Rhythm  in the corresponding vector autoregressive model.Older and employed participants had significantly lower intercepts of the PHQ-8 score than younger and unemployed participants . For mobThis study provides a comprehensive understanding of the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time by using dynamic structural equation modeling on a large longitudinal data set and considering correlations at both individual and population levels, lagged effects (the autoregressive nature over time), cross-lagged effects (direction of the relationships over time), and the influences of individual differences (demographic characteristics).P=.04) and Moving Distance  were still significantly correlated with PHQ-8 score at the between-individual level, which indicated these features are relatively robust for reflecting depressive symptom severity in the whole population. Compared with the results of previous phone-based studies, our results showed that population diversity affects correlations between mobility features and the depression score. Most mobility features were significantly correlated with depression scores in student-based studies [Most mobility features extracted in this paper were significantly correlated with the PHQ-8 score at the within-individual level , which i studies ,18, whil studies . These fP<.001) with the subsequent circadian rhythm measured by location data. The findings of a recent study [P=.02) with the subsequent PHQ-8 score (P=.001) at the within-individual level  in our population, possibly due to all participants in our study having at least one diagnosis of depression in recent 2 years [P=.004), indicating that the mobility rhythm of the older participants is affected by depressive symptoms for a shorter period than the young participants.For influences of individual differences on the levels of depressive symptom severity and mobility, we found that PHQ-8 scores tended to be lower in participants who are older or have jobs, which can be expected because previous survey-based studies have shown that depression is negatively correlated with age, and the unemployment rate in the depressed population is high -42. Gend 2 years , which mP<.001) among these 3 features, and it had significant cross-lagged effect  with the preceding PHQ-8 score. These findings demonstrated that the frequency-domain of location data can provide some additional information for evaluating depressive symptom severity in future research.We proposed 3 frequency-domain features to reflect the periodic characteristics of individuals’ mobility . They weWe obfuscated the raw location data due to privacy issues. Therefore, we did not have access to contextual information, which may mean some information was lost. Another limitation is that we only used the Lag-1 vector autoregressive models. We did not use high-order vector autoregressive models because we wanted to make our preliminary model simple to allow easier explanation and to avoid convergence problems in the procedure of coefficient estimations. We will attempt high-order vector autoregressive models in future research when we have more data without the impact of the COVID-19.We chose to build 11 dynamic structural equation modeling models, one for each mobility feature. Since each mobility feature has a specific meaning, the bivariate model can better explain changes of the feature before and after the changes in PHQ-8 scores indicating the longitudinal relationships. We attempted multivariate dynamic structural equation modeling with all mobility features, but the model failed to converge, possibly due to the multicollinearity between mobility features and complexity of the model. As all mobility features were devised for describing characteristics of individuals’ mobility, there were high correlations between mobility features . In futuThis study provides initial evidence of the relationship and the direction of the relationship between depressive symptom severity and phone-measured mobility over time. We found several mobility features affected depressive symptom severity, while changes in the depression score were associated with the subsequent periodic rhythm of mobility. These mobility features have the potential to be used as indicators for assessing depression risk in future clinical applications, which could provide timely suggestions for both people with depression risk  and physicians . This work may provide support for remote mental health monitoring practice in real-world settings.