Mood symptoms and gut function across the menstrual cycle in individuals with premenstrual syndrome.

Gastrointestinal (GI) symptoms such as bloating, constipation, and nausea are common in the days before menstruation, experienced by as many as 73 % of menstruating individuals. Mood may influence the link between menstrual cycle and GI symptoms, with prior studies indicating that even among healthy controls, GI symptoms worsen premenstrually and are associated with worsening mood. Associations between GI symptoms and mood are poorly understood among those with premenstrual syndrome (PMS), a cluster of mood and/or physical symptoms that occur in the week before menses affecting roughly 20 % of menstruators. Our primary aim was to examine associations between GI symptoms and mood symptoms across the menstrual cycle, in those who do and do not report PMS using a menstrual tracking app. We hypothesized that GI symptoms would be reported more frequently in the luteal phase than follicular phase, and that frequency of GI symptoms would be positively associated with mood symptoms in those with PMS. We analyzed data from 33,628 menstrual cycles across 32,241 participants, including n = 27,897 controls (29,137 menstrual cycles) and n = 4344 PMS participants (4491 menstrual cycles). GI symptoms were reported significantly more frequently in the luteal phase than the follicular phase in both control and PMS groups (p < 0.001). Mood symptoms were significantly positively associated with GI symptoms in both groups, in both follicular and luteal phases (p < 0.001). Results suggest that premenstrual GI symptoms are a common issue, and additional work is needed to explore associations between mood and GI symptoms in the context of the menstrual cycle.

Well-being in healthy Icelandic women varies with extreme seasonality in ambient light.

Seasonal variation in photoperiod may affect psychosocial and physical well-being in healthy persons. We tested this hypothesis in healthy pre-menopausal women, without a history of mood disorders, living year-round in Reykjavik, Iceland (64.1°N). Participants reported daily self-assessments of well-being throughout a complete ovulatory menstrual cycle in summer and/or winter (70% participated in both seasons). Scores for mood, cognitive acuity, social support, physical health and a composite of these four indicators were each significantly higher in summer than in winter (linear mixed effects models: p < .001 for each model); tiredness did not differ by season. The effect of season was not significantly changed by inclusion of body mass index and/or age as covariates. Some prior studies have been hampered by sparse time sampling, inattention to covariates and/or relying on recalled data. This is to our knowledge the first investigation to test the study hypothesis with daily real-time data spanning complete ovulatory menstrual cycles in each of two seasons. This dense sampling has revealed modest seasonal variation in well-being in healthy women. Daylength (sunlight exposure) is likely a major, but not necessarily sole, factor in these seasonal differences in well-being; temperature is likely less important given Iceland's relatively moderate (for its high latitude) seasonal temperature swings.

More than blood: app-tracking reveals variability in heavy menstrual bleeding construct.

BACKGROUND: Heavy menstrual bleeding (HMB) is associated with impaired quality of life and may signal serious health problems. Unresolved challenges in measuring menstrual bleeding and identifying HMB have hampered research and clinical care. Self-reported bleeding histories are commonly used but these may be influenced by recall bias, personal beliefs regarding "normal" flow volume, and the experience of other physical symptoms or disruptions to daily life. The potential usefulness of menstrual-tracking mobile applications, which allow real-time user-entered data recording, for assessing HMB has not been studied. We evaluated recall bias in reported period duration, the relationship of tracked period duration and daily flow volume to subsequently reported period heaviness, variation in quality of life associated with increasing period heaviness, and the advantages and limitations of using app-tracked data for clinical and research purposes. METHODS: An online questionnaire was distributed to current users of Clue, a commercially available menstrual health tracking app, asking them to characterize their last period. We compared responses to the user's corresponding Clue app-tracked data. The study sample comprised 6546 U.S.-based users (aged 18-45 years). RESULTS: Increasing reported heaviness was associated with increasing app-tracked period length and days of heavy flow, impaired quality-of-life (especially body pain severity), and disrupted activities. Of those reporting having had a heavy/very heavy period, ~ 18% had not tracked any heavy flow, but their period length and quality-of-life indicators were similar to those who had tracked heavy flow. Sexual/romantic activities were the most affected across all flow volumes. Compared to app-tracked data, 44% recalled their exact period length; 83% recalled within ± 1 day. Overestimation was more common than underestimation. However, those with longer app-tracked periods were more likely to underestimate period length by ≥ 2 days, a pattern which could contribute to under-diagnosis of HMB. CONCLUSION: Period heaviness is a complex construct that encapsulates flow volume and, for many, several other bleeding-associated experiences (period length, bodily impairments, disruptions of daily activities). Even very precise flow volume assessments cannot capture the multi-faceted nature of HMB as experienced by the individual. Real-time app-tracking facilitates quick daily recording of several aspects of bleeding-associated experiences. This more reliable and detailed characterization of bleeding patterns and experiences can potentially increase understanding of menstrual bleeding variability and, if needed, help to guide treatment.

Adaptation of metabolism to multicellular aggregation, hypoxia and obese stromal cell incorporation as potential measure of survival of ovarian metastases.

Ovarian metastases exfoliate from the primary tumor and it is thought that aggregation supports their survival in the peritoneal cavity during dissemination but the underlying mechanisms are not clearly identified. We have previously shown that ovarian cancer cells acquire an increasingly glycolytic and metabolic flexible phenotype during progression. In the present study, we investigated how hypoxia, aggregation, and the incorporation of the obese stromal vascular fraction (SVF) affect cellular metabolism and the response to common anti-cancer and anti-diabetic drugs. Our results show a reduction of glucose uptake, lactate secretion, cellular respiration and ATP synthesis in response to hypoxia and aggregation, suggesting that the observed reduced proliferation of cells aggregated into spheroids is the result of a down-regulation of respiration. Recruitment of SVF to spheroids increased the spheroids invasive capacity but reduced respiration only in the most aggressive cells. Further, aggregation and hypoxia reduced the response to the metabolic drugs AICAR and metformin, and the chemotherapeutic agents cisplatin and paclitaxel. Our results suggest that the adaptation of cellular metabolism may contribute to enhanced survival under non-permissive conditions, and that these metabolic alterations may provide targets for future interventions that aim to enhance the survival of women with metastatic ovarian cancer.

Evaluation of Sodium Polystyrene Sulfonate Dosing Strategies in the Inpatient Management of Hyperkalemia.

BACKGROUND: Hyperkalemia occurs frequently in an inpatient setting, for which sodium polystyrene sulfonate (SPS) is a common treatment modality. Few studies have investigated the dose-response of SPS. OBJECTIVE: To quantify the change in serum potassium after 15-, 30-, and 60-g oral and 30-g rectal doses of SPS. Secondary objectives were to compare the proportion of patients attaining post-SPS dose normokalemia between dosing groups and to investigate the effect of certain characteristics on SPS dose-response. METHODS: The reduction in serum potassium after 15-, 30-, and 60-g oral and 30-g rectal doses of SPS administered to adult inpatients was evaluated through a retrospective chart review. Ottawa Hospital Research Ethics Board approval was obtained prior to data collection. RESULTS: A total of 118 patients were included in the analysis. Serum potassium levels were reduced by 0.39, 0.69, 0.91, and 0.22 mEq/L following 15-, 30-, and 60-g oral doses and a 30-g rectal dose of SPS, respectively. A greater proportion of patients (50% vs 23%) remained hyperkalemic in the 15-g versus the 60-g group (P = 0.018), and all patients in the rectal group remained hyperkalemic. No patient in any group experienced postdose hypokalemia. The influence of all studied interindividual characteristics on SPS dose-response was clinically nonsignificant. CONCLUSION: Mild hyperkalemia can be effectively treated with a single 60-g oral dose of SPS as monotherapy, with minimal risk of hypokalemia. Moderate to severe hyperkalemic episodes warrant alternative therapy. The potassium-lowering effect is correlated to SPS dose and is independent of interindividual characteristics.

Life and treatment goals of patients with advanced, incurable cancer.

PURPOSE: Goals of care conversations have been suggested as a strategy for helping patients with advanced cancer manage the uncertainty and distress associated with end-of-life care. However, knowledge deficits about patient goals limit the utility of such conversations. We described the life and treatment goals of patients with incurable cancers, including goal values and expectancies. We examined the associations between paramount goals and patient prognosis, performance status, and psychological adjustment. METHODS: Patients with advanced lung cancer, gastrointestinal cancer, or melanoma (N = 84) completed measures of prognosis for 12-month survival, hope, optimism, depression, and anxiety. Oncologists provided patient performance status and prognosis for 12-month survival. We conducted interviews with a subset of patients (N = 63), eliciting life and treatment goals, values, and expectancies. RESULTS: Patient life goals resembled goals among healthy populations; whereas, treatment goals were perceived as separate and more important. Cure and fight cancer emerged as the most important goals. Patients who valued cure the most had worse performance status (M = 1.46 vs. 0.78) and more depressive symptoms (M = 6.30 vs. 3.50). Patients who valued fight cancer the most had worse self-prognosis (M = 69.23 % vs. 86.11 %), fewer treatment goals (M = 2.08 vs. 3.16), and lower optimism (M = 15.00 vs. 18.32). CONCLUSIONS: Patients with advanced cancer perceive treatment goals as separate from and more important than life goals. They hold optimistic expectancies for achieving their goals and for survival. Valuing cure highly may put patients at risk for experiencing psychological maladjustment.

Menstrual Cycle Characteristics of U. S. Adolescents According to Gynecologic Age and Age at Menarche.

STUDY OBJECTIVE: To characterize typical menstrual cycle characteristics in adolescents and determine how these differ with age at menarche or years since menarche (gynecologic age). METHODS: We surveyed 13 to 18-year-old U.S. users of the Clue app (N = 6,486) and linked their responses to app-recorded cycle data (N=38,916 cycles). We analyzed cycle characteristics including cycle length, cycle variability, period length, experience of heavy flow, and dysmenorrhea in relation to gynecologic age and menarcheal age using mixed effects models. RESULTS: With increasing gynecologic age, we observed dose-dependent associations of lower odds of cycle irregularity (defined as cycles that were highly variable, short, or long) and higher odds of reporting ≥1 day of heavy flow. Individuals <1 year post-menarche had lower odds of heavy flow (Odds Ratio (OR) = 0.3; 95% confidence interval (CI): 0.1, 0.6), and increased odds of having a highly variable cycle (OR = 2.6; 95% CI: 1.3, 5.2) or short cycles (OR = 5.0; 95% CI: 2.3, 11.0) compared to those who were 6+ years post-menarche. We also found associations with early and late age at menarche. Compared to menarcheal age of 14+ years, menarcheal age ≤10 years was associated with shorter cycle length (ß = -1.63 days; 95% CI: -2.51, -0.75), increased odds of dysmenorrhea (OR = 3.2; 95% CI: 2.3, 4.6), and decreased odds of high cycle variability (OR = 0.8; 95% CI: 0.6, 1.0). CONCLUSION: Cycle characteristics in adolescence are associated with menarcheal age and gynecologic age. Notably, highly variable cycles are common, especially among those with younger gynecologic age or older menarcheal age.

Associations Among Menstrual Cycle Length, Coronavirus Disease 2019 (COVID-19), and Vaccination.

OBJECTIVE: To assess whether coronavirus disease 2019 (COVID-19) is associated with menstrual cycle length changes and, if so, how that compares with those undergoing vaccination or no event (control). METHODS: We conducted a retrospective cohort analysis in which we analyzed prospectively tracked cycle-length data from users of a period tracker application who also responded to a survey regarding COVID-19 symptoms and vaccination. We restricted our sample to users aged 16-45 years, with normal cycle lengths (24-38 days) and regular tracking behavior during the five cycles around COVID-19 symptoms or vaccination or a similar time period for those experiencing no event (control group). We calculated the within-user change in cycle length (days) from the three consecutive cycles preevent average (either vaccination, disease, or neither; cycles 1-3) to the event (cycle 4) and postevent (cycle 5) cycles. We used mixed-effects models to estimate the age- and country-adjusted difference in change in cycle length across the groups. RESULTS: We included 6,514 users from 110 countries representing 32,570 cycles (COVID-19 symptoms: 1,450; COVID-19 vaccination: 4,643; control: 421). The COVID-19 cohort experienced a 1.45-day adjusted increase in cycle length during cycle 4 (COVID-19) compared with their three preevent cycles (95% CI 0.86-2.04). The vaccinated group experienced a 1.14-day adjusted increase in cycle length during cycle 4 (COVID-19 vaccine) compared with their preevent average (95% CI 0.60-1.69). The control group (neither vaccine nor disease) experienced a 0.68-day decrease (95% CI -1.18 to -0.19) in a similar time period. Post hoc tests showed no significant differences in the magnitude of changes between the COVID-19 and vaccination cohorts. In both cohorts, cycle length changes disappeared in the postevent cycle. CONCLUSION: Experiencing COVID-19 is associated with a small change in cycle length similar to COVID-19 vaccination. These changes resolve quickly within the next cycle.

The association of COVID-19 vaccination and menstrual health: A period-tracking app-based cohort study.

Background: In initial COVID-19 clinical trials, menstrual health was not formally monitored, yet anecdotal reports of menstruation changes surfaced on social media. This study aims to assess the association between COVID-19 vaccines and menstruation using Clue, a period-tracking application. Study design: A survey assessing demographics, menstrual health, stress levels, and COVID-19 vaccination was sent to Clue users between 12/7/2021 and 2/9/2022. Inclusion criteria were (1) 18 years or older (2) currently menstruating (3) not pregnant or breastfeeding since 1/2020. Menstrual data was collected for each participant. Users with cycle lengths more than 90 days were excluded. Cycle lengths were calculated for the 6-month average pre-vaccination (PRIOR), the cycle during which vaccination was administered (DURING), the cycle following DURING (AFTER1), and the cycle following AFTER1 (AFTER2). For periods, individuals were stratified based on whether vaccination was received during their menstrual period (DURING). Period lengths were additionally calculated for the 6-month average pre-vaccination (PRIOR), the first period following vaccination (AFTER1), and the period following AFTER1 (AFTER2). For unvaccinated participants, an index date (4/1/2022) was used to similarly designate menstrual cycles and periods. For each participant, cycle length changes for DURING, AFTER1, and AFTER2 compared to PRIOR were determined. Student's t-test compared the mean of these changes between vaccinated and unvaccinated groups. Results: Of 7,559 participants, 6,897 (91 %) were vaccinated. Compared to PRIOR, individuals vaccinated during their menstrual period demonstrated a statistically significant increase in the DURING period length, but not AFTER1 (p = 0.463) and AFTER2 (p = 0.692). No statistically significant changes were observed in period lengths of those vaccinated in between periods or in cycle lengths overall. Conclusion: A small but statistically significant change in period length was observed only in individuals vaccinated for COVID-19 during their menstrual period. Providers can better counsel menstruating individuals to reduce vaccine misinformation.

Intratumoral radiation dose heterogeneity augments antitumor immunity in mice and primes responses to checkpoint blockade.

Radiation therapy (RT) activates multiple immunologic effects in the tumor microenvironment (TME), with diverse dose-response relationships observed. We hypothesized that, in contrast with homogeneous RT, a heterogeneous RT dose would simultaneously optimize activation of multiple immunogenic effects in a single TME, resulting in a more effective antitumor immune response. Using high-dose-rate brachytherapy, we treated mice bearing syngeneic tumors with a single fraction of heterogeneous RT at a dose ranging from 2 to 30 gray. When combined with dual immune checkpoint inhibition in murine models, heterogeneous RT generated more potent antitumor responses in distant, nonirradiated tumors compared with any homogeneous dose. The antitumor effect after heterogeneous RT required CD4 and CD8 T cells and low-dose RT to a portion of the tumor. At the 3-day post-RT time point, dose heterogeneity imprinted the targeted TME with spatial differences in immune-related gene expression, antigen presentation, and susceptibility of tumor cells to immune-mediated destruction. At a later 10-day post-RT time point, high-, moderate-, or low-RT-dose regions demonstrated distinct infiltrating immune cell populations. This was associated with an increase in the expression of effector-associated cytokines in circulating CD8 T cells. Consistent with enhanced adaptive immune priming, heterogeneous RT promoted clonal expansion of effector CD8 T cells. These findings illuminate the breadth of dose-dependent effects of RT on the TME and the capacity of heterogeneous RT to promote antitumor immunity when combined with immune checkpoint inhibitors.

Effects of clinically relevant radionuclides on the activation of a type I interferon response by radiopharmaceuticals in syngeneic murine tumor models.

Radiopharmaceutical therapies (RPT) activate a type I interferon (IFN1) response in tumor cells. We hypothesized that the timing and amplitude of this response varies by isotope. We compared equal doses delivered by 90 Y, 177 Lu, and 225 Ac in vitro as unbound radionuclides and in vivo when chelated to NM600, a tumor-selective alkylphosphocholine. Response in murine MOC2 head and neck carcinoma and B78 melanoma was evaluated by qPCR and flow cytometry. Therapeutic response to 225 Ac-NM600+anti-CTLA4+anti-PD-L1 immune checkpoint inhibition (ICI) was evaluated in wild-type and stimulator of interferon genes knockout (STING KO) B78. The timing and magnitude of IFN1 response correlated with radionuclide half-life and linear energy transfer. CD8 + /Treg ratios increased in tumors 7 days after 90 Y- and 177 Lu-NM600 and day 21 after 225 Ac-NM600. 225 Ac-NM600+ICI improved survival in mice with WT but not with STING KO tumors, relative to monotherapies. Immunomodulatory effects of RPT vary with radioisotope and promote STING-dependent enhanced response to ICIs in murine models. Teaser: This study describes the time course and nature of tumor immunomodulation by radiopharmaceuticals with differing physical properties.

Assessment of App-Based Versus Conventional Survey Modalities for Reproductive Health Research in India, South Africa, and the United States: Comparative Cross-Sectional Study.

BACKGROUND: There is a widely acknowledged global need for more research on reproductive health (including contraception, menstrual health, sexuality, and maternal morbidities) and its impact on overall well-being. However, several factors-notably, high costs, considerable effort, and the sensitivity of these topics-impede the collection of the necessary data, especially in less accessible and lower-income populations. The burgeoning ownership of smartphones and growing use of menstrual tracking apps (MTAs) may present an opportunity to conduct reproductive health research with fewer impediments than those associated with conventional survey methods. OBJECTIVE: The main objective was to ascertain the feasibility, potential usefulness, and limitations of conducting reproductive health research using a mainstream MTA. METHODS: In each of the 3 countries, we evaluated questionnaire responses from (1) current users of an MTA (Clue) and (2) participants surveyed using conventional survey modalities (in-person interviews, SMS text messaging, and web-based questionnaires). We compared these responses with published data collected from large nationally representative benchmark samples (the United States Census and the Demographic and Health Surveys for South Africa and India). RESULTS: Given a sufficiently large user base, app-distributed surveys were able to quickly capture large samples on par with other methods and at low cost, with the additional advantage of being able to deploy remotely and simultaneously across countries. In each country, neither the app nor the conventional modality sample emerged as a consistently closer match to the distributions of the demographic attributes and the patterns of contraceptive use reported for the respective benchmark sample. Despite efforts to obtain representative samples, the conventional modality samples sometimes over- and other times underrepresented some subgroups (eg, underrepresentation of married persons in the United States and overrepresentation of rural residents in India). In all 3 countries, app users were younger, more educated, more likely to be urban residents, and more likely to use nonhormonal rather than hormonal contraceptive methods compared with the respective national benchmark. App users, compared with the conventional modality samples, consistently reported being more comfortable discussing their menstrual periods with other persons (eg, family, friends, and health care providers), suggesting that MTA users may be more likely to respond truthfully to questions on sensitive or taboo health topics. The app samples' consistency across countries regarding users' demographic profiles, contraceptive choices, and personal attitudes toward menstruation supports the validity of making cross-country comparisons of survey findings for a given app's users. CONCLUSIONS: MTAs such as Clue can provide a quick, scalable, and cost-effective method for collecting health data, including on sensitive topics, across a wide variety of settings and countries. With expanding global access to technology and the increasing use of these tools, consumer MTAs can be a viable survey modality to strengthen reproductive health research.

The Link Between Age and Partner Preferences in a Large, International Sample of Single Women.

Women's capacity to reproduce varies over the life span, and developmental goals such as family formation are age-graded and shaped by social norms about the appropriate age for completing specific developmental tasks. Thus, a woman's age may be linked to her ideas about what an ideal partner should be like. With the goals of replicating and extending prior research, in this study we examined the role of age in women's partner preferences across the globe. We investigated associations of age with ideal long-term partner preferences in a cross-cultural sample of 17,254 single (i.e., unpartnered) heterosexual women, ages 18 to 67, from 147 countries. Data were collected via an online questionnaire, the Ideal Partner Survey. Confirming our preregistered hypotheses, we found no or only negligible age effects on preferences for kindness-supportiveness, attractiveness, financial security-successfulness, or education-intelligence. Age was, however, positively associated with preferences for confidence-assertiveness. Consistent with family formation goals, age was associated with an ideal partner's parenting intentions (high until approximately age 30, then decreasing afterward). Age range deemed acceptable (and in particular, the discrepancy between one's own age and the minimum ideal age of a partner) increased with age. This latter pattern also replicated in exploratory analyses based on subsamples of lesbian and bisexual women. In summary, age has a limited impact on partner preferences. Of the attributes investigated, only preference for confidence-assertiveness was linked with age. However, age range deemed acceptable and an ideal partner's parenting intention, a dimension mostly neglected in earlier research, substantially vary with age.

Obesity modulates the cellular and molecular microenvironment in the peritoneal cavity: implication for ovarian cancer risk.

Introduction: Abdominal obesity increases the risk of developing ovarian cancer but the molecular mechanisms of how obesity supports ovarian cancer development remain unknown. Here we investigated the impact of obesity on the immune cell and gene expression profiles of distinct abdominal tissues, focusing on the peritoneal serous fluid (PSF) and the omental fat band (OFB) as critical determinants for the dissemination of ovarian metastases and early metastatic events within the peritoneal cavity. Methods: Female C57BL/6 mice were fed a low-fat (LFD) or a high-fat diet (HFD) for 12 weeks until the body weights in the HFD group were significantly higher and the mice displayed an impaired glucose tolerance. Then the mice were injected with the murine ovarian cancer cells (MOSE-LTICv) while remaining on their diets. After 21 days, the mice were sacrificed, tumor burden was evaluated and tissues were harvested. The immune cell composition of abdominal tissues and changes in gene expression in the PSF and OFB were evaluated by flow cytometry and qPCR RT2-profiler PCR arrays and confirmed by qRT-PCR, respectively. Other peritoneal adipose tissues including parametrial and retroperitoneal white adipose tissues as well as blood were also investigated. Results: While limited effects were observed in the other peritoneal adipose tissues, feeding mice the HFD led to distinct changes in the immune cell composition in the PSF and the OFB: a depletion of B cells but an increase in myeloid-derived suppressor cells (MDSC) and mono/granulocytes, generating pro-inflammatory environments with increased expression of cyto- and chemokines, and genes supporting adhesion, survival, and growth, as well as suppression of apoptosis. This was associated with a higher peritoneal tumor burden compared to mice fed a LFD. Changes in cellular and genetic profiles were often exacerbated by the HFD. There was a large overlap in genes that were modulated by both the HFD and the cancer cells, suggesting that this 'genetic fingerprint' is important for ovarian metastases to the OFB. Discussion: In accordance with the 'seed and soil' theory, our studies show that obesity contributes to the generation of a pro-inflammatory peritoneal environment that supports the survival of disseminating ovarian cancer cells in the PSF and the OFB and enhances the early metastatic adhesion events in the OFB through an increase in extracellular matrix proteins and modulators such as fibronectin 1 and collagen I expression as well as in genes supporting growth and invasion such as Tenacin C. The identified genes could potentially be used as targets for prevention strategies to lower the ovarian cancer risk in women with obesity.

Comparative Study of the Effect of Radiation Delivered by Lutetium-177 or Actinium-225 on Anti-GD2 Chimeric Antigen Receptor T Cell Viability and Functions.

BACKGROUND AND PURPOSE: Chimeric antigen receptor (CAR) T cells have been relatively ineffective against solid tumors. Low-dose radiation which can be delivered to multiple sites of metastases by targeted radionuclide therapy (TRT) can elicit immunostimulatory effects. However, TRT has never been combined with CAR T cells against solid tumors in a clinical setting. This study investigated the effects of radiation delivered by Lutetium-177 (177Lu) and Actinium-225 (225Ac) on the viability and effector function of CAR T cells in vitro to evaluate the feasibility of such therapeutic combinations. After the irradiation of anti-GD2 CAR T cells with various doses of radiation delivered by 177Lu or 225Ac, their viability and cytotoxic activity against GD2-expressing human CHLA-20 neuroblastoma and melanoma M21 cells were determined by flow cytometry. The expression of the exhaustion marker PD-1, activation marker CD69 and the activating receptor NKG2D was measured on the irradiated anti-GD2 CAR T cells. Both 177Lu and 225Ac displayed a dose-dependent toxicity on anti-GD2 CAR T cells. However, radiation enhanced the cytotoxic activity of these CAR T cells against CHLA-20 and M21 irrespective of the dose tested and the type of radionuclide. No significant changes in the expression of PD-1, CD69 and NKG2D was noted on the CAR T cells following irradiation. Given a lower CAR T cell viability at equal doses and an enhancement of cytotoxic activity irrespective of the radionuclide type, 177Lu-based TRT may be preferred over 225Ac-based TRT when evaluating a potential synergism between these therapies in vivo against solid tumors.

Digital Global Recruitment for Women's Health Research: Cross-sectional Study.

BACKGROUND: With the increased popularity of mobile menstrual tracking apps and boosted Facebook posts, there is a unique opportunity to recruit research study participants from across the globe via these modalities to evaluate women's health. However, no studies to date have assessed the feasibility of using these recruitment sources for epidemiological research on ovulation and menstruation. OBJECTIVE: The objective of this study was to assess the feasibility of recruiting a diverse sample of women to an epidemiological study of ovulation and menstruation (OM) health (OM Global Health Study) using digital recruitment sources. The feasibility and diversity were assessed via click and participation rates, geographic location, BMI, smoking status, and other demographic information. METHODS: Participants were actively recruited via in-app messages using the menstrual tracking app Clue (BioWink GmbH) and a boosted Facebook post by DivaCup (Diva International Inc.). Other passive recruitment methods also took place throughout the recruitment period (eg, email communications, blogs, other social media). The proportion of participants who visited the study website after viewing and clicking the hypertext link (click rates) in the in-app messages and boosted Facebook post and the proportion of participants who completed the surveys per the number of completed consent and eligibility screeners (participation rates) were used to quantify the success of recruiting participants to the study website and study survey completion, respectively. Survey completion was defined as finishing the pregnancy and birth history section of the OM Global Health Study questionnaire. RESULTS: The recruitment period was from February 27, 2018, through January 24, 2020. In-app messages and the boosted Facebook post were seen by 104,000 and 21,400 people, respectively. Overall, 215 participants started the OM Global Health Study survey, of which 140 (65.1%), 39 (18.1%), and 36 (16.8%) participants were recruited via the app, the boosted Facebook post, and other passive recruitment methods, respectively. The click rate via the app was 18.9% (19,700 clicks/104,000 ad views) and 1.6% via the boosted Facebook post (340 clicks/21,400 ad views.) The overall participation rate was 44.6% (198/444), and the average participant age was 21.8 (SD 6.1) years. In terms of geographic and racial/ethnic diversity, 91 (44.2%) of the participants resided outside the United States and 147 (70.7%) identified as non-Hispanic White. In-app recruitment produced the most geographically diverse stream, with 44 (32.8%) of the 134 participants in Europe, 77 (57.5%) in North America, and 13 (9.8%) in other parts of the world. Both human error and nonhuman procedural breakdowns occurred during the recruitment process, including a computer programming error related to age eligibility and a hacking attempt by an internet bot. CONCLUSIONS: In-app messages using the menstrual tracking app Clue were the most successful method for recruiting participants from many geographic regions and producing the greatest numbers of started and completed surveys. This study demonstrates the utility of digital recruitment to enroll participants from diverse geographic locations and provides some lessons to avoid technical recruitment errors in future digital recruitment strategies for epidemiological research.

A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction.

We explore how to quantify uncertainty when designing predictive models for healthcare to provide well-calibrated results. Uncertainty quantification and calibration are critical in medicine, as one must not only accommodate the variability of the underlying physiology, but adjust to the uncertain data collection and reporting process. This occurs not only on the context of electronic health records (i.e., the clinical documentation process), but on mobile health as well (i.e., user specific self-tracking patterns must be accounted for). In this work, we show that accurate uncertainty estimation is directly relevant to an important health application: the prediction of menstrual cycle length, based on self-tracked information. We take advantage of a flexible generative model that accommodates under-dispersed distributions via two degrees of freedom to fit the mean and variance of the observed cycle lengths. From a machine learning perspective, our work showcases how flexible generative models can not only provide state-of-the art predictive accuracy, but enable well-calibrated predictions. From a healthcare perspective, we demonstrate that with flexible generative models, not only can we accommodate the idiosyncrasies of mobile health data, but we can also adjust the predictive uncertainty to per-user cycle length patterns. We evaluate the proposed model in real-world cycle length data collected by one of the most popular menstrual trackers worldwide, and demonstrate how the proposed generative model provides accurate and well-calibrated cycle length predictions. Providing meaningful, less uncertain cycle length predictions is beneficial for menstrual health researchers, mobile health users and developers, as it may help design more usable mobile health solutions.

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking.

OBJECTIVE: The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models. MATERIALS AND METHODS: We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information. RESULTS: Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities. DISCUSSION: Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure. CONCLUSIONS: Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.

Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data.

The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink GmbH of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms, or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.