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Objective: The COVID-19 global pandemic has led to the death of millions around the world and impacted the overall health of many people. In this article we aim to compare reproductive health indicators in the first 6 months of 2020 to the prior year, as well as explore stress and quality of life during this time., Methods: This retrospective observational study examined the menstrual cycles of 1159 women who were using a fertility tracking device to record their menstrual cycle and BBT data. We utilised a supplemental mobile application to administer a supplemental survey to collect data on stress and quality of life. Descriptive analyses were conducted with t- tests for two-group comparisons., Results: Study participants from 15 countries contributed to a total of 13,194 cycles. 23.1% (268/1159) responded to the survey focussed on assessing psychosocial distress. 44.4% (119/268) of the study participants reported that they had noticed a change in their menstrual cycle, temperature curve, or menstruation in the past 12 months. Cycle analysis found the average cycle length and pre-ovulation phase length was longer in the first 6 months of 2019, while the average days of menstruation was slightly longer in 2020., Discussion: Our findings indicate that menstrual cycle indicators changed only slightly in the first 6 months of 2020 but were still statistically significant. We were also able to understand that these study participants had some level of awareness of changes to their menstrual health.

Objective: Fertility tracking devices offer women direct-to-user information about their fertility. The objective of this study is to understand how a fertility tracking device algorithm adjusts to changes of the individual menstrual cycle and under different conditions., Methods: A retrospective analysis was conducted on a cohort of women who were using the device between January 2004 and November 2014. Available temperature and menstruation inputs were processed through the Daysy 1.0.7 firmware to determine fertility outputs. Sensitivity analyses on temperature noise, skipped measurements, and various characteristics were conducted., Results: A cohort of 5328 women from Germany and Switzerland contributed 107,020 cycles. Mean age of the sample was 30.77 [SD 5.1] years, with a BMI of 22.07 kg/m^2 [SD 2.4]. The mean cycle length reported was 29.54 [SD 3.0] days. The majority of women were using the device 80-100% of the time during the cycle (53.1%). For this subset of women, the fertility device identified on average 41.4% [SD 6.4] possibly fertile (red) days, 42.4% [SD 8.7] infertile (green) days and 15.9% [SD 7.3] yellow days. The number of infertile (green) days decreases proportionally to the number of measured days, whereas the number of undefined (yellow) days increases., Conclusion: Overall, these results showed that the fertility tracker algorithm was able to distinguish biphasic cycles and provide personalised fertility statuses for users based on daily basal body temperature readings and menstruation data. We identified a direct linear relationship between the number of measurements and output of the fertility tracker.

Background: Daysy is a fertility monitor that uses the fertility awareness method by tracking and analyzing the individual menstrual cycle. In addition, Daysy can be connected to the application DaysyView to transfer stored personal data from Daysy to a smartphone or tablet (IOS, Android). This combination is interesting because as it is shown in various studies, the use of apps is increasing patients´ focus on their disease or their health behavior. The aim of this study was to investigate if by the additional use of an App and thereby improved usability of the medical device, it is possible to enhance the typical-use related as well as the method-related pregnancy rates., Result: In the resultant group of 125 women (2076 cycles in total), 2 women indicated that they had been unintentionally pregnant during the use of the device, giving a typical-use related Pearl-Index of 1.3. Counting only the pregnancies which occurred as a result of unprotected intercourse during the infertile (green) phase, we found 1 pregnancy, giving a method-related Pearl-Index of 0.6. Calculating the pregnancy rate resulting from continuous use and unprotected intercourse exclusively on green days, gives a perfect-use Pearl-Index of 0.8., Conclusion: It seems that combining a specific biosensor-embedded device (Daysy), which gives the method a very high repeatable accuracy, and a mobile application (DaysyView) which leads to higher user engagement, results in higher overall usability of the method.

Tumour-specific mutations are ideal targets for cancer immunotherapy as they lack expression in healthy tissues and can potentially be recognized as neo-antigens by the mature T-cell repertoire. Their systematic targeting by vaccine approaches, however, has been hampered by the fact that every patient's tumour possesses a unique set of mutations ('the mutanome') that must first be identified. Recently, we proposed a personalized immunotherapy approach to target the full spectrum of a patient's individual tumour-specific mutations. Here we show in three independent murine tumour models that a considerable fraction of non-synonymous cancer mutations is immunogenic and that, unexpectedly, the majority of the immunogenic mutanome is recognized by CD4(+) T cells. Vaccination with such CD4(+) immunogenic mutations confers strong antitumour activity. Encouraged by these findings, we established a process by which mutations identified by exome sequencing could be selected as vaccine targets solely through bioinformatic prioritization on the basis of their expression levels and major histocompatibility complex (MHC) class II-binding capacity for rapid production as synthetic poly-neo-epitope messenger RNA vaccines. We show that vaccination with such polytope mRNA vaccines induces potent tumour control and complete rejection of established aggressively growing tumours in mice. Moreover, we demonstrate that CD4(+) T cell neo-epitope vaccination reshapes the tumour microenvironment and induces cytotoxic T lymphocyte responses against an independent immunodominant antigen in mice, indicating orchestration of antigen spread. Finally, we demonstrate an abundance of mutations predicted to bind to MHC class II in human cancers as well by employing the same predictive algorithm on corresponding human cancer types. Thus, the tailored immunotherapy approach introduced here may be regarded as a universally applicable blueprint for comprehensive exploitation of the substantial neo-epitope target repertoire of cancers, enabling the effective targeting of every patient's tumour with vaccines produced 'just in time'.

Multiple genetic events and subsequent clonal evolution drive carcinogenesis, making disease elimination with single-targeted drugs difficult. The multiplicity of gene mutations derived from clonal heterogeneity therefore represents an ideal setting for multiepitope tumor vaccination. Here, we used next generation sequencing exome resequencing to identify 962 nonsynonymous somatic point mutations in B16F10 murine melanoma cells, with 563 of those mutations in expressed genes. Potential driver mutations occurred in classical tumor suppressor genes and genes involved in proto-oncogenic signaling pathways that control cell proliferation, adhesion, migration, and apoptosis. Aim1 and Trrap mutations known to be altered in human melanoma were included among those found. The immunogenicity and specificity of 50 validated mutations was determined by immunizing mice with long peptides encoding the mutated epitopes. One-third of these peptides were found to be immunogenic, with 60% in this group eliciting immune responses directed preferentially against the mutated sequence as compared with the wild-type sequence. In tumor transplant models, peptide immunization conferred in vivo tumor control in protective and therapeutic settings, thereby qualifying mutated epitopes that include single amino acid substitutions as effective vaccines. Together, our findings provide a comprehensive picture of the mutanome of B16F10 melanoma which is used widely in immunotherapy studies. In addition, they offer insight into the extent of the immunogenicity of nonsynonymous base substitution mutations. Lastly, they argue that the use of deep sequencing to systematically analyze immunogenicity mutations may pave the way for individualized immunotherapy of cancer patients.