The integrative method combined with SVD leads showed the highest intra-subject reproducibility (p < 0.00001). No reproducible delay between heart rate changes and QRS-T angle changes was found. This was interpreted as a suggestion that the measurement of QRS-T angle might offer direct assessment of cardiac autonomic responsiveness at the ventricular level.Three-dimensional angle between the QRS complex and T wave vectors is a known powerful cardiovascular risk predictor. Nevertheless, several physiological properties of the angle are unknown or poorly understood. These include, among others, intra-subject profiles and stability of the angle relationship to heart rate, characteristics of angle/heart-rate hysteresis, and the changes of these characteristics with different modes of QRS-T angle calculation. These characteristics were investigated in long-term 12-lead Holter recordings of 523 healthy volunteers . Three different algorithmic methods for the angle computation were based on maximal vector magnitude of QRS and T wave loops, areas under the QRS complex and T wave curvatures in orthogonal leads, and weighted integration of all QRS and T wave vectors moving around the respective 3-dimensional loops. These methods were applied to orthogonal leads derived either by a uniform conversion matrix or by singular value decomposition (SVD) of the original 12-lead ECG, giving 6 possible ways of expressing the angle. Heart rate hysteresis was assessed using the exponential decay models. All these methods were used to measure the angle in 659,313 representative waveforms of individual 10-s ECG samples and in 7,350,733 individual beats contained in the same 10-s samples. With all measurement methods, the measured angles fitted second-degree polynomial regressions to the underlying heart rate. Independent of the measurement method, the angles were found significantly narrower in females ( The observations that the myocardial repolarisation sequence cannot follow the same spatial path as the preceding depolarisation sequence was made not long after the invention of electrocardiography. Already in 1934, In 2000, however, the seminal report by The risk-prediction properties of the QRS-T angle are thus now proven beyond any reasonable doubt. The strength of the QRS-T angle-based prediction of cardiovascular risk and/or of cardiovascular death has been shown comparable if not exceeding that of established risk factors including left ventricular ejection fraction, QRS complex width, and heart rate . NeverthConcentrating on these knowledge gaps, we utilised ECG measurements made in long-term 12-lead Holter recordings obtained in a large population of normal healthy volunteers who were investigated during clinical pharmacology studies. For the comparison of the possibilities of the angle calculations, we used three different methods, namely the measurements based on the so-called maximum and area calculations  as well The ECG recordings used in the study have been reported previously in studies that investigated very different electrophysiology factors . For theIn brief, clinical pharmacology studies were conducted at 3 different clinical research sites. These studies enrolled 523 healthy volunteers including 259 females. Before study enrolment, all the volunteers had a normal standard clinical ECG and normal clinical investigation. All the source studies used the same standard inclusion and exclusion criteria mandated for Phase I pharmacology investigations  includin2.Demographic data including age, sex, racial classification, body height, and body weight were collected. Lean body mass (LBM) was calculated as LBM = 0.29569*W+41.813*H-43.2933 for females and LBM = 0.3281*W+33.929*H-29.5336 for males, where W is body weight in kilograms and H is body height in metres ; it was In each study participant, repeated three to four long-term 12-lead Holter ECG recordings with Mason-Likar electrode positions were obtained during full day-time periods  while the subjects were not allowed to smoke and/or consume alcohol or caffeinated drinks and/or to sleep. No medication was administered during these recordings and, where appropriate, sufficient wash-out periods after previous treatment periods were used. The study protocols were mutually consistent in respect of the conduct during the drug-free baseline days. Since only drug-free data were used in the investigation reported here, further details of source studies are immaterial.As previously reported , multiplIn each of these ECG segments, QRS complex  and the T wave offset were identified following published procedures  that incUsing a previously proposed technique , QT inteSubsequently, Pearson correlation coefficients were calculated between the analysed beat and representative median waveform in (a) a window surrounding the QRS onset by ±40 ms and (b) a window surrounding the T wave offset by ±50 ms. The QRS-T pattern of the given beat was accepted for further analysis only if both these correlation coefficients exceeded 0.9. This assured that beats substantially distorted by pollution noise were excluded. No other restrictions were applied. This made the data selection/exclusion less stringent compared to previous investigations of beat-to-beat QT interval analyses .The source clinical studies included episodes of per-protocol changes of postural positions. This allowed to capture measurable ECG segments at broad heart rate ranges .For each analysed ECG segment, a 5-min history of preceding RR intervals was obtained. This was combined with the positions of the individual beats within the segment, allowing to obtain a beat-specific history of RR intervals in a 5-min history of each analysed beat. That is, the RR interval of the histories also included the positions of all QRS complexes regardless of whether these were or were not accepted for further analysis.The assessment of spatial QRS-T angle requires to derive 3 orthogonal leads from the 12-lead ECG signal. This was performed in two different ways.Firstly, previously published conversion matrix suitable for Mason-Likar electrode positions  was used1, S2, and S3 .Subsequently, the same 12-lead ECG signals  were processed by singular value decomposition  between Three principal algorithmic methods of deriving the 3-dimensional QRS-T angle were considered. The following notations is used in their descriptions:A triplet of values The area-based QRS-T angle method uses the integrals of the orthogonal ECG leads . That isThe maximum vector-based QRS-T angle calculation is based on the notion that QRS and T wave orientations are defined by the maxima of the vector magnitudes within the corresponding orthogonal loops . That isThe QRS-T angle is then computed as the spatial angle The integral-based QRS-T angle calculation is based on a weighted average of all angles between all points within the QRS loop and all points within the T wave loop, weighted by the product of the magnitudes of the vectors represented by these points. That is, within the orthogonal XYZ, MaximumXYZ, IntegralXYZ, AreaSVD, MaximumSVD, and IntegralSVD – all with obvious meanings. All the values of the angles were expressed in degrees with values between 0o and 180o.The two possibilities of obtaining orthogonal lead system and the three possibilities of computing the QRS-T angle led to six combinations that were investigated in this study. We shall term these combinations AreaFor each 10-s ECG segment analysed in the study, the six different computations of the QRS-T angle were applied to the median representative waveform and to each of the individual beats accepted for the analysis. For each of the six computations, the values obtained for the individual beats were also averaged.XYZ and AreaXYZ results, MaximumXYZ and IntegralXYZ results, and AreaXYZ and IntegralXYZ results. The same comparisons were also made for the three different measurements combined with the SVD-based orthogonal leads.Three different types of comparisons of different QRS-T angle expressions were made. Firstly, for each algorithmic method, the values obtained based on a combination with the XYZ conversion matrix were compared with the values obtained for the same ECG signal based on the combination of the SVD-based orthogonal leads. The comparison was made for both individual beats and representative median waveforms of ECG segments. Secondly, for each of the six QRS-T angle expressions, comparisons were made between the values derived from representative median waveforms of ECG segments and the averages of the values obtained from individual beats within the same ECG segments. Finally, for both the representative median waveforms and individual beats, comparisons were made between MaximumFor each of the comparisons, Bland-Altman-type  of scattTo allow statistical evaluation of these comparisons, in each of the analyses and in each of the study subjects, intra-subject mean of absolute differences of the compared QRS-T angle values and intra-subject standard deviation of these differences were also obtained.For an initial assessment, intra-subject relationships between measured QRS-T angles were investigated based on graphical representations. As an initial approximation of the dependency, second degree polynomial regression model between QRS-T angles and underlying heart rate  was used in subsequent investigations.To replicate the procedures that were previously used in the estimates of the QT/RR hysteresis , QRS-T aIn more detail, for each individual beat Where For this purpose, a geometric progression sequence of 50 values between 1 and 300 was used to vary the coefficient The analysis was subsequently repeated deriving the heart rate All these steps were performed for each study subject and for all six QRS-T angle expressions.The expressions of underlying heart rate based on a simple average of preceding RR intervals does not reflect gradual decrease of the influence of heart rate changes in more remote history. Therefore, exponential decay models were also used, in the same form as previously developed for the purposes of estimating QT/RR hysteresis .That means that the same form of polynomial regression analysis as described with the experiments of average estimates of rate hysteresis (see previous section) were used replacing the In more detail, if beat Two different exponential decay models were considered which differed in the definition of the weights While the model assuming the dependency on the absolute time preceding the beat While the parameter As with the experiments of average estimates of rate hysteresis, the search of optimal subject-specific hysteresis time constant started with a geometric progression sequence of 50 values specifying the hysteresis constant between 1 and 277 . Subsequently, standard golden search algorithm was used to determine the “optimal” hysteresis constant for each study subject.Physiologic characteristics of QRS-T angle in individual study subjects were investigated in the same way for all six QRS-T angle expressions. For each subject and for each angle expression, setting of the optimal hysteresis constant  was used for the assessment of physiologic characteristics. The QRS-T angle measurements at individual beats were therefore used.For each subject and for each QRS-T angle expression For each subject and for each QRS-T angle expression In addition, for each angle expression Were computed for The projections of the The dependency of QRS-T angle measurements on the underlying heart rate makes it non-sensical to investigate intra-subject reproducibility of the measurements without considering the relationship to heart rate. Therefore, to investigate the reproducibility of the different QRS-T angle expressions, residual errors In the higher degree polynomial regressions, coefficients The intra-subject degree of the polynomial curvature of the heart rate relationship was estimated by the differences Computation of the matrix-based conversion of 12-lead ECG signals to the orthogonal XYZ leads, the SVD of the signals, and the computation of the QRS-T angles utilised purpose developed software written in C++. Microsoft visual studio (version 16.11.8) implementation of Microsoft Visual C++ 2019 compiler (version 00435-60000-00000-AA114) was used.t-test assuming different variances of the compared samples. Intra-subject comparisons, e.g., the comparisons between t-test.Descriptive data are presented as means ± SD. Comparison between study groups, e.g., between female and male sub-populations, were tested by two-sample two-tail Pearson correlation coefficients and linear regressions were used to study the relationship between BMI and LBM and the QRS-T angle values. The linear regressions were computed together with their 95% confidence bands.p values below 0.05 were considered statistically significant. Because of interdependence between the different indices, no correction for multiplicity of statistical testing was made.Statistical tests used IBM SPSS package, version 27. 2, range 20.0–30.0 kg/m2) and males . As expected, lean body mass was lower in females (45.3 ± 5.0 kg) than in males . Among females, 38.6% and 56.7% of the subjects classified themselves as of African origin and of White Caucasian origin, respectively. Among males, these proportions were 51.3% and 42.8%, respectively. The remainder of the population classified themselves as of Asian, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, or Other.As stated, the source clinical pharmacology studies enrolled 523 healthy volunteers including 259 females. There were no statistical age differences between females  and males . Similarly, the body mass index was not different between females .Scatter diagrams of the differences between QRS-T angle measurements made in conversion matrix based orthogonal XYZ leads and in orthogonal SVD-based leads are shown in A and B of A of p < 0.00001). The sex difference was only significant for the Integral method applied to individual beats (p = 0.0011). The intra-subject standard deviations of the differences shown in B of p < 0.00001 for all). Sex differences seen in this panel of p < 0.0001) except for both cases of the Maximum method.This visual observation was confirmed statistically as shown in the top panels Similar visual comparisons between the methods are also seen in C and D of C of p < 0.0001 to p < 0.001). The results of the Maximum methods were also only those that showed no similar significant difference between females and males. The intra-subject standard deviations of the differences .Scatter diagrams of the comparison between QRS-T angle calculation methods are shown in The results of the experiments investigating interval-based QRS-T-angle/heart-rate hysteresis estimates are shown in XYZ and MaximumSVD angle expressions reach closes fit to heart rate measured approximately over preceding 20 s or a similar number of RR intervals. With the other angle expressions, the result is much less clear since heart rate measurements of intervals longer than 20 s or 20 RR intervals do not appear to make any difference in the closeness of fit until approximately rate measurements over the preceding 3 min.When tested considering only measurements preceded by variable heart rates, MaximumWhen testing the hysteresis effect in the complete data of each subject, the results are even less expected. When measuring the effect of heart rate assessed in an interval longer than approximately 30 s, the closeness of fit of the regressions between Area and Integral expressions and heart rate remains stable while the Maximum expressions show only minimal gradual increases of the regression residuals.The intra-subject optimisation of the heart rate measurement was similarly inconclusive since no clearly defined minima of regression residuals were found in substantial majority of study subjects.These rather unexpected observations were replicated in experiment with exponential decay optimisation as seen in Similar to the optimisation of heart rate measurements, no defined optimal hysteresis constants were found for almost all study subjects. With the Area and Integral expressions, the intra-subject golden cut searches converged to the maximum available scale of available data.XYZ, MaximumXYZ, IntegralXYZ, AreaSVD, MaximumSVD, and IntegralSVD, respectively. The corresponding proportions of The optimum degree of polynomial regression between QRS-T angle and underlying heart rate was found to be XYZ, MaximumXYZ, IntegralXYZ, AreaSVD, MaximumSVD, and IntegralSVD, respectively. Pooling female and male subjects together, the regression residuals of the second-degree polynomial regression between QRS-T angle and optimum (hysteresis-optimisation based) measurement of heart rate were 11.37 ± 3.26, 13.36 ± 5.55, 8.93 ± 2.38, 10.20 ± 2.71, 13.11 ± 7.28, and 7.97 ± 1.77° for AreaThe intra-subject curvatures of second-degree polynomial regressions between QRS-T angle expressions and the underlying heart rate were used to construct images in A and B of p < 0.00001 for all sex comparisons).Statistical evaluations of the sex differences are demonstrated in panels A of XYZSVDXYZSVDXYZSVD with all these differences between QRS-T angle expressions being highly statistically significant . At the heart rate of 120 bpm  which were, in turn, significantly smaller than the results by the other expressions (p < 0.00001). There were, however, no significant differences between the other angle expressions.Statistically significant differences were also noted between the different QRS-T angle expressions (corresponding to the measurement differences that were described previously). At the heart rate of 60 bpm  higher regression residuals, i.e., lower intra-subject reproducibility of the QRS-T angle values “corrected for the underlying heart rate” when the Area or Integral expressions were used. The sex difference for the MaximumXYZ expression did not reach statistical significance while the sex difference for the MaximumSVD expression showed the opposite sex difference (p = 0.015).Panel XYZ expression while the lowest residuals (7.82 ± 1.74°) were seen with the IntegralSVD expression. The averaged order of the residuals contrasted with the order measurement values since it was the opposite what was observed for the values measured at 60 bpm, that is the residuals were ordered MaximumXYZSVDXYZSVDXYZSVD with the individual step differences highly statistically significant (p < 0.00001) apart from the difference between MaximumXYZ and MaximumSVD which was only marginal (13.31 ± 5.64° vs. 13.11 ± 7.45°) and not statistically different.More importantly, there were substantial disagreements between the different QRS-T angle expressions. In the total population, the highest residuals (13.31 ± 5.64°) were seen with MaximumD of p < 0.00001).Panel C of E and F of the same Figure show regression residuals of second-degree (panel E) and first degree, i.e., linear (panel F) regression analysis relating the QRS-T angle expressions to heart rate calculated based on a simple average of preceding 1-min RR intervals. While there are numerically slight  increases in the displayed values from panel C to panel E as well as from panel E to panel F, the patterns are practically the same, highlighting the absence of any clearly detectable hysteresis-type delays between the changes of heart rate and of QRS-T angles.For comparison with the intra-subject reproducibility as illustrated in panel G of p-values between 0.01 and 0.001) for all angle expressions except of MaximumSVD.Panel H of G can be seen, although without statistical significances. The panel also shows that these residual differences were very tiny compared to those shown in panel G .Finally, panel None of the characteristics summarised in The race categories others that African or White Caucasian origin were too infrequent for any meaningful analysis. Therefore, the race comparison was only possible between subjects of African and White Caucasian origin. Neither statistically significant differences nor trends towards borderline statistical differences were found.p-values of the significance of the Pearson correlations ranged between 0.039 and 0.337) and were systematically significantly correlated with BMI in males . The corresponding scatter diagrams and linear regressions of the projected The projections of the No such significant or borderline significant correlations were found when studying the relationship to LBM .The study led to several observations of which three appeared of physiologic and practical importance.First, despite dealing with very substantial datasets, we were unable to determine any clear hysteresis-type delay between heart rate changes and QRS-T angle changes. The most likely explanation of this observation is that QRS-T angle does not react to heart rate  but that it is influenced directly by mechanisms and regulatory processes that simultaneously influence the heart rate. Second, we show important practical differences between the methods of the QRS-T angle measurement. Third, we confirm that under normal circumstances, female hearts show lesser differences between QRS and T wave loop orientations.Our first observation likely suggests that the QRS-T angle is directly influenced by cardiac autonomic and neurohumoral status  rather tIf this observation is confirmed in independent data, it would offer a substantial advance in the assessment of cardiac autonomic status at the level of ventricular myocardium, e.g., through the paraventricular ganglia , rather The possibility that the assessment of QRS-T angle might serve this purpose is supported by some of the previous observations. The predictive value of QRS-T angle was reported additive to HRV-based risk stratification  in a mulFurther studies are needed to verify our conjecture of the direct sympathetic influence on QRS-T angle. Among others, atrial pacing during electrophysiology studies with abrupt changes of the stimulation rate  would bXYZ and MaximumSVD results were also significantly larger compared to the other two methods and, in terms of the relationship to the underlying heart rate, the Maximum method also appeared to show significantly lower intra-subject reproducibility which is well known to be of substantial practical importance for any risk assessment method  when using SVD-based rather than conversion matrix-based XYZ orthogonal leads. This is not surprising since SVD leads to optimally constructed orthogonal leads for individual QRS-T patterns of each beat or each representative waveform. On the contrary, using the same conversion matrix for all ECG is likely to lead to some signal loss of the orthogonal components that might be of importance for the valid assessment of the angle.SVD is the optimum expression of the 3-dimensional QRS-T angle. It showed not only the tightest intra-subject reproducibility but also the closest correspondence between the averaged measurements of individual beats within a 10-s ECG segment and the measurement performed at the representative waveform of the same segment.Considering all this together, the study suggests that the IntegralThe six different QRS-T angle expressions that we have investigated are naturally not the only possibilities. In addition to simplistic measurements that can be performed “by hand” using standard 12-lead ECG images , differeThere is a principal difference between the orthogonal lead systems that we used for the conversion of the 12-lead ECGs into 3-dimensional representations in which the QRS-T spatial angles can be computed. The conversion to the XYZ leads was based on a published transformation matrix  that wasBecause of the transformation differences, the orthogonal system of leads S1, S2, and S3 cannot be considered as a simple rotation of the XYZ axes. Both the SVD and the XYZ conversion matrix utilise the information from all original ECG leads . However, since both transformations act differently, the 3-dimensional morphology of the QRS complex and T wave loops are not identical  and thus, the angles measured between these loops are not the same. Nevertheless, as shown in SVD method might suggest that, on average, the errors of the 3-dimensional XYZ reconstruction might be marginally larger than the errors due to the omission of the higher algebraic components derived by SVD.As already discussed, the results of the study give some preference to the SVD-based conversion to the orthogonal leads. This likely because both the XYZ matrix conversion and the SVD-based orthogonal representation involve certain level of numerical imprecision. The XYZ matric conversion applies the same transformation matrix to every ECG. Although the matrix was derived from a regression analysis involving more than 500 separate ECG signals , it cannWhile differences between sexes have been described before , the proSVD expression might be the optimum way of assessing the QRS-T angle in both females and males. With this expression, sex differences of around 10° might be expected.From a practical point of view, using the same normal value and/or the same dichotomy of the QRS-T angle for both females and males seems inappropriate for clinical risk assessment studies. Such studies either need to use different normality limits or, preferably, include sex of the patients as well as heart rates of the analysed QRS-T angle values in multivariable analyses. Our data also suggest that the IntegralSimilar to other electrocardiographic sex differences , it seemAlthough statistically significant in males and borderline significant in females, the decreases in the QRS-T angles with increasing BMI were only modest. Perhaps, this is partially because per protocol of the source clinical studies, only subjects with BMI between 20 and 30 were included. Still, two possible explanations for such a population trend might be proposed.First, even a marginal increase in BMI might signify modest thickening in the chest fatty layers that likely act as electrical capacitors with slight effects on the difference between epicardial potentials and surface ECG signals. Investigations of ECGs recorded in substantially obese subjects might be helpful to investigate this phenomenon further. Second, even within this narrow range of the BMI values, their differences might be related to the different levels of athletic training and physical activity that was not systematically assessed in the study subjects. Such differences might manifest by different levels of autonomic activity  as well When using the QRS-T angle assessment in future risk-prediction studies, relation to BMI might need to be considered, especially when dealing with very lean or markedly obese patients.Limitations of our study also need to be considered. Importantly, as our data were obtained in healthy volunteers participating at clinical pharmacology studies, we are not able to relate our QRS-T angle measurement to any cardiac risk and/or follow-up events. While the Maximum and Area method have previously been shown to be potent risk predictors, the Integral method still needs to be investigated in this way although the so-called TCRT method of the very first risk-studies of the angle is a combination of the integral approach applied to the QRS complex with the maximum approach applied to the T wave . The resDespite these limitations, the study shows that it is plausible to speculate that spatial QRS-T angle measurement might allow direct assessment of cardiac autonomic responsiveness at the ventricular level. Further evaluations and confirmations of this hypothesis are needed but if confirmed, it would not only explain the risk-prediction properties of the angle but also allow employing the angle measurement in focused profiling of cardiovascular risk. The study also shows that the newly proposed Integral measurement of the angle offers increased measurement stability especially if the measurement is performed using the SVD-derived orthogonal leads optimised for each analysed ECG recording. Finally, the study confirms sex differences in physiologic QRS-T angle measurement with values in females lower than values in males irrespective of the heart rate at which the measurement is performed.


The COVID-19 pandemic led to many colleges of pharmacy having to make major changes relating to their infrastructure and delivery of their curriculum within a very short time frame, including the transition of many components to an online setting. This scoping review sought to summarize what is known about the impact of COVID-19 on pharmacy education and the effectiveness of adaptation strategies which were put in place. PubMed, Web of Science, OVID Medline, and MedEdPortal were searched to identify pharmacy education-related articles published since the beginning of the COVID-19 pandemic. For article inclusion, the following criteria had to be met: described original research, related directly to PharmD or PharmBS education, related to the impact of COVID-19 on pharmacy education, and was available in English. Out of a total of 813 articles, 50 primary research articles were selected for inclusion. Our review of these identified four domains relating to the impact of COVID-19 on pharmacy education and/or effectiveness of adaptation strategies: (1) lab-based courses and activities , (2) experiential education, (3) didactic education, and (4) student well-being. The key research findings are summarized and discussed. While the COVID-19 pandemic has clearly brought many challenges to pharmacy education, it has also led to key improvements and innovations. The World Health Organization (WHO) officially characterized COVID-19 as a pandemic in March 2020 [The majority of pharmacy programs transitioned to offering at least some of their curriculum via online platforms as part of their COVID-19-driven adaptation strategy. Undoubtedly, pharmacy programs that already offered online classes were better positioned to transition more of their curricular components to an online setting during the COVID-19 pandemic based on their existing infrastructure and experience. Several educational research studies published before the COVID-19 pandemic have shown that online courses are generally well accepted by pharmacy students and that academic standards can be maintained when pharmacy courses are offered online ,3,4. ForThe purpose of this scoping review was to summarize evidence relating to the impact of the COVID-19 pandemic on pharmacy education, as well as to highlight COVID-19-driven improvements and innovations. A review of educational research data and reflection on how the COVID-19 pandemic has impacted pharmacy education can help guide the future development of pharmacy programs and improve pharmacy education outcomes, as well as help inform contingency planning measures. A scoping search of the literature to find relevant articles was conducted using the search engines PubMed, Web of Science, OVID Medline, and MedEdPortal. Search terms related to pharmacy education and COVID-19  relating to pharmacy education. Articles were then assigned to one or more of these domains. The following characteristics for each study were also captured: year of publication, country where the study was conducted, number and type of participants, and study type. http://www.prisma-statement.org/ (accessed on 10 January 2022)) flow chart for article inclusion. A total of 813 articles were identified from the literature search after removal of duplicates. After applying our inclusion strategy , this was reduced to 50 articles. It is noteworthy that we did not have to exclude any articles due to their not being available in English. We identified four main domains within the 50 articles selected for inclusion: impact of the COVID-19 pandemic on (1) lab-based courses and activities  (n = 17) (2) experiential education (n = 7) (3) didactic education (n = 17) and (4) student well-being (n = 14). Information for each of these studies, including year of publication, country where the study was conducted, number and type of participants, and study type, can be found in Approximately 34% (17 out of 50 articles) of the included studies focused on the impact of COVID-19 on lab-based courses and activities, including Interprofessional Education (IPE) lab-based courses and activities . Nine ofOur review of these studies determined that multiple different strategies were employed to allow for the continuation of lab-based classes during the pandemic, including the use of face-to-face real-time video-conferencing, the generation and use of recorded videos to demonstrate key skill sets, requiring students to submit videos of themselves describing the skill or using role-play with family members/friends who are acting as mock patients to demonstrate competency, and the usage of MyDispense software. Overall, data from these studies indicate that virtual lab-based classes were well received by students, although several challenges were noted, including increased stress levels and technical challenges. The administrators who were surveyed also noted key challenges, including the inability to assess certain skills in an online setting and increased instructor stress levels . Three oSeven of the studies specifically surveyed students’ opinions relating to in-person versus virtual skills-based labs ,19,20,23Two of the studies assessed the impact of offering virtual lab classes on course learning outcomes and scores by comparing student cohort performance in pre-pandemic in-person classes versus in virtual classes delivered during the pandemic classes ,16. ThesThree articles described the use of commercially available simulation platforms ,14,23. TTwo studies clearly showed benefit to using ‘MyDispense’ to help deliver lab-based classes and support its continued usage ,58. The Approximately 14% (7 out of the 50 articles) of studies focused on the impact of COVID-19 on experiential education . Five ofAll seven studies described the impact of offering virtual introductory pharmacy practice experience (IPPE) and APPE rotations and training. Overall, student experiences were positive and there was a high level of satisfaction with the materials presented. Most students felt that clear connections made between didactic knowledge and application of this knowledge within the training and that the virtual experiences helped them better understand relevance to pharmacy practice ,30,31,32Reynolds et al. reported improvements in students’ perceptions of their performance as they relate to knowledge, skills, and abilities and calculations using a pre- post-course survey assessment, but these self-reported improvements were not statistically significant . OverallIt is noteworthy that the study by Moreau et al. showed a telemedicine-based training experience that was well received by students . As partApproximately 34% (17 out of the 50 articles) of the included studies focused on the impact of COVID-19 on didactic classes . One stuOverall, based on data from these studies, the majority of pharmacy students appear to have had a positive experience with COVID-19-driven curricular changes relating to didactic classes (the most common change was transition to online delivery); however, there were some exceptions, and data from many studies indicate that students still prefer in-person classes. Frequently observed positive themes included ‘flexibility’ and ‘self-directed learning’. Frequently observed negative themes included ‘technology’, ‘study load’, and ‘well-being’. Only one study  sought to determine the impact of COVID-19-driven changes on student performance. In this study, Hussain et al. compared midterm and final grades for a communications course offered pre-COVID-19 (offered via in-person delivery) versus offered during COVID-19 (offered via online delivery). No significant change in performance was observed. An innovation by Almaghaslah et al. was to use social media (Twitter and Instagram) to support the teaching of didactic classes during the pandemic . PrimariTwo studies included college of pharmacy administrators or faculty members as administrators ,44. The Approximately 28% (14 out of the 50 articles) of the included studies focused on the impact of COVID-19 on student well-being . Of the While 13 of the 14 (92.3%) included studies reported some form of a negative impact of either COVID-19 or the adaptation of online learning such as increases in stress and anxiety levels, one study (0.07%) reported a decrease in student stress levels before exams. Specifically, Hettinger et al. reported on the impact of COVID-19 on pharmacy student stress during high-stakes performance-based assessments before and during COVID-19 . Based oThree studies (21.4%) analyzed the effect of COVID-19 on student quality of life, with specific references to stress, anxiety, and depression, while another three studies (21.4%) included coping strategies and resiliency mechanisms as part of the student survey instruments and measured the impact of coping strategies on emotional distress. Only one of the fourteen studies (0.07%), led by Moreno-Fernandez et al., included emotional intelligence as a part of the student response paradigm and found that inclusion of an emotional intelligence workshop alleviated self-reported academic burnout from 63.5% before to 31.1% after the emotional intelligence workshop . They alInterestingly, Atarabeen et al. included an analysis of pharmacy students in a program in the United States that employs flipped classroom pedagogy . This isThis scoping review summarizes data from research studies which sought to determine the impact of the COVID-19 pandemic on pharmacy education. Our review of these studies has identified common trends as well as areas which would benefit from further research. We also highlight key COVID-19-driven innovations and improvements which were found to be and/or perceived to be effective. The vast majority of studies focused on students rather than faculty members or administrators, and many were survey-based. Most focus was placed on assessing student perceptions of COVID-19-driven changes and their effectiveness. Only a few studies included an assessment of the impact of COVID-19-driven changes on student scores and grades. Implementation of more outcomes-based studies are needed to further establish the effectiveness of virtual classes and experiences and thereby help guide decisions regarding their continued usage beyond the pandemic. Typically, surveys comprised a combination of Likert score and open-ended questions; the latter allowed for thematic analysis and for the identification of key benefits as well as issues and concerns. Several study designs were employed for survey studies, including conducting a pre- post-test or survey within a given class or course or simply conducting an end of course survey. Several studies compared data collected prior to the pandemic with pandemic data, and while useful, these data should be interpreted with caution due to potential confounding effects. Only one study used a cross-over design which allowed for the intervention to be assessed within the same cohort. The most frequently described COVID-19-driven change was the transition of classes and experiences to an online setting, with many studies reporting the use of virtual platforms such as Zoom, BigBlueButtom, and Microsoft Teams. MyDispense software was found to be helpful for the delivery of lab-based classes and for rotations. Data from the majority of the studies indicate that most pharmacy students were satisfied with the online delivery of didactic classes, lab-based classes, and practice experiences, although most students stated they still preferred in-person delivery. This finding aligns with what has been reported by other medical education programs; for example, a survey study by Stoeher et al.  found that the majority of students were satisfied with the transition to online learning . Many of the 50 studies included in this scoping review conducted thematic analyses, and these identified ‘flexibility’ and ‘self-directed learning’ as benefits to online learning and identified ‘cheating’, ‘mental health’, ‘communication’, ‘patient care experience’, and ‘technical issues’ as key challenges. Increasing and supporting self-directed learning experiences aligns well with directives from pharmacy governing bodies such the Accreditation Council for Pharmacy Education (ACPE) and is a key competency for any healthcare professional including pharmacists. It is therefore encouraging that students identified this as a positive associated with online delivery, and in this regard, the data support the continued usage of online delivery for at least some components of pharmacy education. Cheating on exams is well known to be a challenge associated with online delivery of any higher education course or experience, and this has been reported in both pre- and post-COVID-19 studies ,7,8. NonThe successful use of online delivery of didactic classes pre-pandemic and during the pandemic is well described in the literature for many types of higher education, including pharmacy education, with many students indicating high levels of satisfaction with online education ,6,7,8,66The delivery of lab-based classes during the COVID-19 pandemic was extremely challenging for many reasons, including the need to deliver additional content relating to the COVID-19 pandemic-driven expansion in pharmacist scope of practice, as well as the difficulties associated with accommodating the meaningful assessment of lab-based skills in an online setting. A major benefit that was reported by several studies, and which would support continued usage of online learning activities, was their ability to help develop and support the use of skills relating to telemedicine, a field that is rapidly expanding. Similar findings have been reported within medical education ,69. AgaiThe continuation of IPPE and APPE rotations during the COVID-19 pandemic was also extremely challenging. As with the other curricular areas, a key strategy was the implementation of virtual IPPE and APPE rotations. The successful use of virtual clinical rotations during the COVID-19 pandemic has been reported by several medical schools ,71,72,73While a PRISMA-based strategy was used to identify the studies reported in this scoping review, it is possible that not all relevant studies were included. Many of the studies reported here have a relatively small sample size and not all studies include specific details regarding recruitment of participants. Based on this limitation, the presence of study bias cannot be ruled out. It is also noteworthy that many studies were more focused on proof of concept rather rigorous assessment, which is a reflection of the relativeness newness of this area of research. Pharmacy educators and administrators have gone above and beyond to help ensure student success throughout the COVID-19 pandemic, and many have leveraged this crisis to further improve pharmacy education. There is clearly a need for more focus on assessment of student performance outcomes in future research studies. In addition, more focus is needed on the impact and effectiveness of virtual experiential rotations on professional development and assessment of the impact of COVID-19 on pharmacy educators (only two of the fifty articles collected data from faculty members and/or administrators). The collective survey data indicate that the majority of students were satisfied with the COVID-19-driven strategies described; however, it is clear that contingency planning should place focus on student wellness and that there is a need to develop improved strategies to minimize cheating in online assessments. The development of infection control policies and procedures should also be considered within contingency planning to help enable more classes, particularly lab-based classes, to continue in person . Key improvements and innovations include increased usage of telemedicine and increased training that supports development of telemedicine-related skills. COVID-19 has served as a key disruptor to pharmacy education, and it is likely that COVID-19-driven improvements and innovations will continue to occur and be put into wider use based on the sharing and discussion of experiences that have been reported within the pharmacy education community and beyond.


Urinary tract infections (UTIs) are commonly suspected in nursing home (NH) residents, commonly resulting in antimicrobial prescriptions, even when symptoms are non-specific. To improve the diagnosis and management of suspected UTIs in NH residents, we conducted a pilot test of a paper-based clinical algorithm across NHs in the southern U.S. with ten advanced practice providers (APPs). The paper-based algorithm was modified based on the clinical care needs of our APPs and included antimicrobial treatment recommendations. The APPs found the UTI antimicrobial stewardship and clinical decision support acceptable. The educational sessions and algorithm improved baseline confidence toward UTI diagnosing and treatment. The APPs thought the algorithm was useful and did not negatively impact workload. Feedback from the pilot study will be used to improve the next iteration of the algorithm as we assess its impact on prescribing outcomes. Urinary tract infections (UTIs) are the most diagnosed nursing home (NHs) infections, accounting for significant overprescribing in this setting ,2. The CBased on feedback from our NH colleagues, we modified the published algorithm to highlight the multiple entry points into the clinical decision-making pathway and specified empiric antimicrobial treatment options  based onWe conducted a feasibility study on a group of 20 APPs providing NH primary care in a large NH chain across the southern U.S. Our goal was to assess the effect of a UTI educational session and the performance of the modified UTI algorithm. The first half of the training session consisted of a 30-min educational session on the current evidence for the diagnosis and management of UTIs in NH residents. We had 18 APPs complete both a pre-training and a post-training survey assessing attitudes toward UTIs and clinical decision support, confidence toward UTI diagnosis and treatment, and APP demographics. Half also were randomly selected to receive an additional 30-min training session on the paper-based version of the algorithm that explained the rationale and clinical decision support tool. Our original study called for us to examine actual antibiotic prescribing patterns between each half of the sample, but due to COVID, these data could not be collected. We collected their reported prescribing decisions based on the algorithm in the 10 APPs randomized to the intervention. The intervention APPs provided written feedback regarding algorithm usefulness and potential changes to the algorithm for each suspected UTI case. They also provided feedback regarding workload via the NASA Task Load Index (NASA-TLX) . After ot-test) to determine the significance of the surveys, and all between-group comparisons were nonsignificant.Quantitative survey data were analyzed using STATA and Microsoft Excel software. One-sample 95% confidence intervals were calculated for each question in the pre-training, post-training, and post-intervention surveys. Due to aggregated and de-identified data, we were unable to conduct within-group comparisons , with an average of 3–5 years in practice; 61% worked in an urban setting, and 100% were female. Advance practice providers’ attitudes toward UTI antimicrobial stewardship and clinical decision support were favorable for all questions in the pre-training survey. These attitudes remained favorable in both the post-training for all participants and the post-intervention survey for the ten advance practice providers after one month of using the algorithm. When asked in the post-intervention survey about willingness to try guideline-assisted prescribing even if it required a decision support aid , the mean response was 3.1 (95% CI 2.8–3.4) . When ast-test) to determine the significance of the surveys, and all between-group comparisons were nonsignificant. When asked their confidence in determining a UTI in a nursing home resident in the post-training survey, the mean response was 1.6 (95% CI 1–2.2), and their confidence in exploring diagnoses other than UTIs for non-specific signs and symptoms had a mean response of 1.8 (95% CI 1.4–2.1). When asked if it was appropriate to reduce unnecessary antimicrobial use for UTIs , the mean response was 6.6 (95% CI 6.2–7.1).The APPs were confident in their ability to diagnose and treat UTIs in both the pre- and post-training surveys and the post-intervention survey . Because we had aggregated and de-identified data, we were unable to conduct within-group comparisons  was 2.4. Seven changes to the algorithm were suggested, including additional diagnostic guidance for residents unable to report symptoms due to dementia and expanded antimicrobial options.To our knowledge, this was one of the first uses of a suspected UTI clinical decision support algorithm in real-time clinical care. Other UTI clinical decision support is being developed but has not been examined in clinical trials ,10. The While several UTI clinical decision tools have shown benefits, it is unclear what components contribute the most to their success . As oppoOur study was limited by several factors. Primarily, we were unable to look at actual antibiotic prescribing due to COVID-19 closing most nursing homes in the United States for research in 2020. We also only piloted the clinical decision support in one nursing home chain and in 10 advance practice providers, significantly limiting the generalizability. Once the clinical decision support tool is finalized, we will conduct a larger study powered to examine the efficacy of the tool. Future work will include adding decision support nudges into the intervention, antimicrobial tailoring for the empiric treatment options based on susceptibility rates of common UTI organisms per the NH’s antibiogram, if available, and noting other considerations when selecting antimicrobials such as dementia status, resident colonization, previous infections, and prior antimicrobial exposure in the previous three months.Results from a pilot study of a clinical algorithm for the diagnosis and treatment of suspected UTIs in nursing home residents show that it is feasible in clinical care. These findings are relevant to nursing home providers interested in building their antimicrobial stewardship efforts.


As a hot topic today, ferroptosis is closely involved in the progression and treatment of cancer. Accordingly, we built a prognostic model around ferroptosis to predict the overall survival of OSCC patients. We used up to 6 datasets from 3 different databases to ensure the credibility of the model. Then, through differentially expressed, Univariate Cox, and Lasso regression analyses, a model composed of nine prognostic-related differently expressed ferroptosis-related genes  were constructed. Moreover, Kaplan–Meier curves, Receiver Operating Characteristic curves and principal component analysis used to verify the model's predictive ability showed the model's superiority. To deeply understand the mechanism of ferroptosis affecting the occurrence, development and prognosis of OSCC, we performed enrichment analysis in different risk groups identified by the model. The results showed that numerous TP53-related, immune-related and ferroptosis-related functions and pathways were enriched. Further immune microenvironment analysis and mutation analysis have once again revealed the correlation between risk score and immunity and TP53 mutation. Finally, the correlation between risk score and OSCC clinical treatment, as well as Nomogram show the brilliant clinical application prospects of the prognostic model. In 2020, there were more than 370,000 new cases of oral cancer (2.0%), and the death toll surpassed 170,000 (1.8%)2. In the past few decades, the number of young people suffering from OSCC has sharply increased year by year3. Alcohol abuse, smoking, betel nut chewing, and human papillomavirus (HPV) infection are the most common risk factors that lead to OSCC4. At present, surgery is the mainstay curative treatment for OSCC. The patient's clinical and pathological characteristics determine whether to perform the adjuvant treatment (radiotherapy or chemotherapy)5. If early OSCC can be detected in time and treated appropriately, the 5-year survival rate can reach 90%6. Regrettably, most oral cancers are discovered until the later stages, resulting in a poor prognosis for OSCC7, and more than half of the affected patients with OS  of less than 5 years8. Even though we have made progress in surgery and adjuvant chemotherapy because patients often have a regional recurrence and lymph node metastasis, the later prognosis is still poor3. In recent years, immune checkpoint inhibitors (ICIs) have been approved for OSCC, but not all patients can benefit from ICIs9. Hence, as the limitations of various treatments, combined with OSCC's intense aggressiveness and frequent metastasis and recurrence10, it is necessary to find a model that can be used to predict the prognosis and treatment effects of OSCC.Oral squamous cell carcinoma (OSCC) accounts for more than 95% of oral cancers11. Ferroptosis can be regulated by a gamut of small molecules, multiple signaling pathways, and organelles, inducing cancer cell death12. These facts confirm the possibility of cancer treatment with ferroptosis. More and more studies have demonstrated that ferroptosis plays an essential role in treating and prognosis of numerous diseases in recent years. Studies have shown that ferroptosis is closely related to cancer immunotherapy and tumor-infiltrating immune cells13. Lang et al. also found that radiation can induce tumor ferroptosis, indicating that radiation therapy can also achieve anti-tumor through ferroptosis Efficacy14. Collectively, ferroptosis plays a vital role in the oncogenesis and treatment of tumors and provides a new option for cancer prognosis prediction15.Ferroptosis is a newly recognized non-apoptotic programmed cell death process with unique morphological and biodynamic characteristics. Lipid peroxidation accumulation and iron dependence are the two main characteristics of ferroptosis17. Tumor protein 53 (TP53) is the first tumor suppressor gene to be identified. Its mutations are related to the initiation and progression of various human cancers18. Studies have revealed TP53 can inhibit tumor function by inducing ferroptosis19. The above studies suggested that we may explain the relationship between ferroptosis and the prognosis of OSCC from the perspectives of ICIs, TMB, TP53 mutation.ICIs have an excellent therapeutic effect on some patients with metastatic cancer, but they lack predictive biomarkers. Tumor mutation burden (TMB), defined as the total number of somatic/acquired mutations per coding area of a tumor genome (Mut/Mb) 22, has been demonstrated by many studies as a predictive biomarker potential for potential the identification of patients with cancerTaken together, the various treatment methods for OSCC have their shortcomings, and there is the absence of appropriate models to predict prognostic. To remedy the problems, we used prognostic-related differentially expressed ferroptosis-related genes (PR-DE-FRGs) obtained from five cohorts of the TCGA, GEO, and ICGC databases to construct a prognostic model and used 6 datasets from 3 databases to verify the model. We then explored the mechanism of ferroptosis in the initiation, progression, and prognosis of OSCC from multiple perspectives such as multiple signaling pathways, immunity, gene mutations, and cell stemness characteristics. Given the importance of ICIs to immunotherapy, we further explored the correlation between the risk score and the expression of ICIs-related and M6A-related genes. Finally, considering the relevance of ferroptosis and clinical treatment, we ran the correlation analysis between immunophenoscore (IPS) and the expression of 9 PR-DE-FRGs and between the expression of 9 PR-DE-FRGs and chemotherapeutics. And constructed a nomogram to predict the prognosis of the patient.http://cancergenome.nih.gov/) of head and neck squamous cell carcinoma (TCGA-HNSCC) Sample. And we extracted RNA sequencing of 363 samples of OSCC  and clinical data of 346 samples. At the same time, somatic gene mutation data has also been downloaded. Thereafter, on July 8, 2021, the RNA sequencing data of 229 samples  of the GSE30784 cohort, the RNA sequencing and clinical data of 97 OSCC samples from the GSE41613 cohort and another RNA sequencing and clinical data of 83 OSCC samples from the GSE65858 cohort. The above three external validation data cohorts were acquired from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). Not only that, on July 14, 2021, the RNA sequencing and clinical data of 28 oral cancer samples were downloaded from the ICGC website (https://dcc.icgc.org/releases/current/Projects/ORCA-IN). The last database was the FerrDb database (http://www.zhounan.org/ferrdb), which has 259 FRGs from the 784 articles on ferroptosis in the PubMed database. It is the source of the list of FRGs that we required for our research. Because these data are publicly available in TCGA, GEO, and ICGC databases, this study didn't require the approval of the local ethics committee. Extracted the gene expression quantification of the same FRGs between the list of FRGs and RNA sequencing data from 5 cohorts . In addition, we used the R package “limma” to perform differential expression analysis on OSCC samples and adjacent normal tissues from TCGA, with a false discovery rate (FDR) cut-off < 0.05, to obtain differentially expressed ferroptosis-related genes (DE-FRGs). A similar method was used to obtain DE-FRGs using RNA sequencing data of cancer, abnormal hyperplasia, and normal oral tissue in the GSE30784 cohort. Finally, the R “venn” package determined the overlapping genes among DE-FRGs obtained from the TCGA and GSE30784 cohort and FRGs obtained from the other three cohorts.All methods of this study were performed in accordance with relevant guidelines and regulations. The workflow of this research is shown in Fig. We collated the data of 5 cohorts to obtain samples with complete OS and RNA expression data. A univariate COX analysis was performed with a p-value cut-off of 0.05 to screen out prognosis related DE-FRGs (PR-DE-FRGs). The integrated samples from TCGA were randomly divided into the training set (n = 200) and the test set (n = 129) at a 6:4 ratio. The 13 PR-DE-FRGs from the training set were used to construct a lasso regression analysis to obtain highly relevant genes, thereby further improving the accuracy of predicting the clinical prognosis of OSCC patients. Finally, a prognostic model based on 9 PR-DE-FRGs was constructed by choosing the best Lasso penalization parameter (λ) determined by the smallest k-fold cross-validation with K = 10  composed of 13 PR-DE-FRGs was built with the minimum required interaction score was set to confidence score (0.150). The correlation analysis of 13 genes and the visualization of related networks are realized using R packages "reshape2", "psych", "RColorBrewer" and "igraph". In addition, we also divided all samples into high expression group and low expression group according to the best cutoff value of each PR-DE-FRGs expression value. Next, Kaplan–Meier survival curve was used to analyze the differences in survival between samples of high expression group and low expression group of these PR-DE-FRGs.To better evaluate the prognostic model's predictive ability, we used the training, test, and whole sets of TCGA, the GSE41613 and the GSE65858 sets of GEO, and the ORCA-IN set of ICGC at the same time. First, we divided the dataset samples into high-risk and low-risk groups based on the median value of the risk score of each case in the training, test, and whole sets respectively. We used the R packages "survival" and "survminer" to verify the performance of the Kaplan–Meier method to distinguish the OS of OSCC patients. Besides that, the Receiver Operating Characteristic (ROC) curves established based on six datasets were used to evaluate the model's accuracy in predicting prognosis. At the same time, the multivariate ROC curves established by the three sets of TCGA were used to verify the optimality of the model in predicting prognosis compared with other factors. Then, the "prcomp" function of the R package "stats" was used for the principal component analysis (PCA) of the expression of each group's genes. Finally, to further evaluate whether the risk score can be used as an independent prognostic indicator, we performed univariate and multivariate Cox regression analysis on the three sets of TCGA factors .https://www.proteinatlas.org/) to further confirm the differential expression of 9 modeled genes between OSCC tissue and normal oral tissue. Finally, the IHC images of 7 PR-DE-FRGs protein expression in OSCC and normal oral tissues were obtained.We used the results of immunohistochemistry (IHC) staining from the human protein atlas database , but also obtained the written informed consent of all patients.Sangon Biotech provided primers for detection genes for us  was used to perform Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on risk group and gene expression matrix data. "c2.cp.kegg.v7.4.symbols.gmt" and "c5.go.v7.4.symbols.gmt" were selected as the gene set database. Normalized enrichment score (NES) was calculated by setting the permutation value to 1000, and significant enrichment pathways and functions were screened using a P-value < 0.05The R package "estimate" was used to calculate each sample's immune score and stromal score. To quantify the score of 16 immune cells and 13 immune functions, we used a single-sample gene set enrichment analysis (ssGSEA) based on the R packages "GSEABase" and "gsva". We not only compared the differences between the high-risk and the low-risk group in the immune score and stromal score, but also analyzed the correlation between risk score and immune score or stromal score using Spearman correlation test. Then a heatmap was used to display the distribution differences of 16 immune cells and 13 immune functions of each sample with a different risk score. The Spearman correlation test was again used to evaluate the relevance between the risk score and each immune cell or immune function score. Finally, we drew boxplots to show the differences in immune cells and immune functions score between different risk groups.21 and then the R package "GenVisR" was used to visualize the 20 most frequently mutated genes in different risk groups. The R package "maftools" was used for calculating TMB, and Kaplan–Meier methods were used to compare the OS difference between the high TMB and low TMB group to explore the influence of TMB on the prognosis of patients. According to the mutation status of TP53, TCGA samples were divided into wild and mutation group. Then the difference in risk score between the two groups was analyzed to show the correlation between TP53 mutation and ferroptosis in OSCC. Kaplan–Meier method was performed to compare the OS difference between the TP53 mutant and the wild group. Based on RNA sequencing, to further explore the relationship between TP53 mutation status and immune infiltrating cells, the deconvolution algorithm CIBERSORT22 was performed with p-value < 0.05, and finally obtained proportion matrix data of 22 immune cells in each tumor sample. The R package "Limma" was used to compare the differences in the content of infiltrating immune cells between the two groups, and the R package "Vioplot" was used to visualize the results.In the enrichment analysis, the high-risk group is enriched for many P53-related biological processes. We therefore further performed a TP53 mutation-related analysis. Somatic gene mutations in OSCC samples and corresponding clinical data were downloaded from TCGA database and then used to evaluate the relationship between mutations and prognostic models. We first used VarScan to detect the MAF files of somatic mutations23. OCLR-based transcriptomic and epigenetic signatures were applied to TCGA datasets to calculate the mRNAsi and mDNAsi23. Each mDNAsi/mRNAsi ranged from low (zero) to high (one)23. We analyzed the difference of mRNAsi /mRNAs between OSCC and adjacent normal tissues samples. Then, Spearman correlation analysis was used to determine the correlation between risk score and mRNAsi /mRNAsi, and Wilcoxon rank-sum test was used to compare the difference of mRNAsi/mRNAsi between high-risk and low-risk groups. Finally, the Wilcoxon rank-sum test was performed to gain insight into the differences in mRNAsi /mRNAsi between different subgroups of each clinicopathological feature.We downloaded the accurately calculated tumor cell stemness index (mDNAsi and mRNAsi) from related research. The stemness index in the research was calculated based on the OCLR algorithm trained on the types of stem cells  and their differentiated ectoderm, mesoderm and endoderm progenitor cellsGiven that immune check-point inhibitors (ICIs) play a significant role in immunotherapy, we performed the Spearman correlation test to explore the correlation between the risk score and the expression of ICIs-related genes. In addition, to verify the correlation results, we also compared the differences in ICIs-related gene expression between the high-risk and low-risk groups. Similar methods have also been applied to explore the correlation between M6A-related gene expression and prognostic models.https://tcia.at/home). The patient's IPS was obtained by evaluating the gene expression of the four cell types  that determine immunogenicity24. The immunogenicity increases as the IPS score increases24. Spearman correlation analysis was used to evaluate the correlation between the expression of 4 types of IPS and 9 types of PR-DE-FRGs to explore the the potential of these PR-DE-FRGs to predict the efficacy of immunotherapy. The NCI-60 database containing 60 different cancer cell lines from 9 different types of tumors was accessed through the CellMiner interface (https://discover.nci.nih.gov/cellminer)25. The Pearson correlation analysis was run to explore the relationship between the expression of 9 prognostic genes and 263 drugs approved by the FDA or clinical trials.The IPS of OSCC patients was available through The Cancer Immunome Atlas (TCIA) database  and SPSS Statistics .Depending on the features of the distribution, differences between continuous variables were classified by the Student's t-test or Mann–Whitney test and differences between categorical variables were classified by chi-square test or Fisher's exact test determined. To build a risk model based on the PR-DE-FRGs, we successively used univariate Cox regression analysis and lasso regression. The COX multivariate regression method was used to construct the Nomogram. The OS between groups was distinguished by Kaplan–Meier curve of log-rank test. The predictive power of different factors on the prognosis was judged by ROC curve. Risk score for an independent predictor of survival plays a useful role, we used univariate and multivariate Cox regression analysis to determine. The correlation between the variables was confirmed by Spearman or Pearson correlation analysis. It is stated that all analysis techniques were provided by the R programming language  protein expression in cancerous and normal oral tissues Fig. , we founFrom paired differential comparisons, we found that the relative mRNA expression of MAP1LC3A in OSCC was lower than in normal oral tissues, which was consistent with the shared data analysis results Fig. E. UnfortThe heat map roughly showed the distribution of clinical features of each sample Fig. A. Next, The results of GO and KEGG enrichment were shown in Fig. Since GO enrichment analysis found that the low-risk group was enriched with numerous immune-related biological functions and pathways, we conducted an in-depth analysis to further understand whether the risk score is related to the immune microenvironment. First, the correlation between overall immune cells or stromal cells and risk scores were analyzed. The results showed that the risk score was significantly negatively correlated with the immune score or stromal score  Fig. A. We con26. For example, Liu et al. implied that the identified ferroptosis-related genes were significantly correlated with glioma progression27. Mou et al. demonstrated the expression level of FRGs was significantly associated with the OS of clear cell renal cell carcinoma28. Similar results have been reported in lung adenocarcinoma30. We checked the literature of the PubMed database and found extraordinarily rare studies using FRGs to predict the prognosis of OSCC patients and only involve the TCGA database. There were scarce discussions on the mechanism of FRGs affecting the occurrence, development, and prognosis of OSCC. So, it is worthy of insight. In this study, the data of the five cohorts obtained by the crossover of TCGA, GEO, ICGC and FerrDb database were collated, and 13 PR-DE-FRGs were indicated through the Venn diagram and Univariate COX regression analysis. Further LASSO regression was performed on the training set to obtain nine highly correlated PR-DE-FRGs to construct a prognostic model with superior performance. To further improve the credibility of the conclusion, we used three databases to create six datasets to verify the results. The prognostic analysis results all showed a significant negative correlation between risk score and OS. In-depth Univariate and Multivariate Cox Regression Analyses found that risk score was the best independent predictor of OS. The six sets' ROC curves subsequently confirmed our results, indicating that the model has the best predictive effect. In the enrichment analysis, the high-risk group is enriched for many P53-related biological processes, while the low-risk group is enriched for a large number of immune-related cellular components, molecular functions, and biological processes. It is based on these enrichment results as a guide that we further conduct TME, TP53 Mutation analysis. And the final results showed that TME, Mutation analysis, and cell stemness were all linked to risk scores.Nowadays, some studies discussing the influence of FRGs expression on the occurrence of ferroptosis and in-depth analysis of the relationship between ferroptosis and the clinical characteristics of cancer have conducted a systematic analysis of ferroptosis and FRGs. Those results demonstrated that the promising potential of ferroptosis for cancer treatment31. But compared to this study, our study shows many advantages. First, the study by He et al. only used the TCGA HNSCC dataset for analysis, while we used both TCGA and GSE30784 cohorts to screen common DE-FRGs. In the performance verification of the model, compared with the only external verification cohort (E-MTAB-8588 cohort) in the study of He et al., our research also used the GSE41613, GSE65858 and ICGC-ORCA-IN cohorts to externally verify the excellent performance of the model. These all reflect the higher accuracy and stability of our results. In the clinical application part, the study by He et al. only explored the guiding value of the model in chemotherapy, while our study also explored the guiding value of the model in immunotherapy. In the part of immune infiltration, He et al.'s research only explored the relationship between 22 kinds of immune cells and the model. Our study also explored the relationship between the model and 13 immune functions. In addition, the correlation analysis of mutation signature and stem cell signature is also not available in the study of He et al.It is worth mentioning that He et al. have identified the ferroptosis-related gene signature in HNSCC, which coincides with our research11. These 9 PR-DE-FRGs can be roughly divided into four categories: (Anti-)Oxidant Metabolism , Glutathione metabolism (BECN1), iron metabolism , p53-Mediated Regulation (DDIT4)32. CISD2 inhibition overcomes head and neck cancer resistance to ferroptotic cell death induced by sulfasalazine via increased accumulation of mitochondrial ferrous iron and lipid reactive oxygen species (ROS)33. Cheng et al. implied that Up-regulation of DDIT4 predicts poor prognosis in acute myeloid leukaemia34. DDIT4  regulates p53/TP53 mediator apoptosis in response to DNA damage and regulates cell growth, proliferation and survival by inhibiting the activity of mammalian target of rapamycin complex 1 (mTORC1)37. Previous literature has revealed that DDIT4 expression was much higher in nasopharyngeal carcinoma (NPC) and HNSCC tissues than in adjacent tissues39. Zhao et al. found that DDIT4 overexpression promotes the proliferation, migration, invasion and inhibits the apoptosis of NPC cells by partially activating the mTOR signaling pathway39. Song et al. associated increased tumor level of CA9 mRNA or protein with shorter survival times of patients with pancreatic, kidney, or lung cancers40. CA9 plays a role in equilibrating among hypoxia, iron metabolism and redox regulation in Malignant mesothelioma cells41. ALOX15 is one of the key genes that cause ferroptosis42, by mediating the production of Lipid ROS in various types of tissues and tumors43. Both ATG5 and BECN1 are components of the autophagy machinery contribute to ferroptotic cell death44. Previous studies revealed that, knockdown or knockout of ATG5 inhibited the BAY-stimulated autophagosome formation, and limited erastin-induced ferroptosis with decreased intracellular ferrous iron levels, and increase cellular ROS47. BECN1 can promote ferroptosis through the regulation of activity of the cysteine and glutamate antiporter system xc– in cancer cells48. Cells transfected with BNIP3 exhibit yielding a morphotype that is typical of necrosis accompanied by rapid and profound mitochondrial dysfunction, one of them characterized by increased reactive oxygen species production49. Previous studies have confirmed that PRDX6 can reduce peroxidized cell membrane phospholipids and plays a major role in repairing peroxidized cell membranes. The knockdown of intracellular PRDX6 significantly enhances LOOH and ferroptotic cell death triggered by ferroptosis inducers (Erastin and RSL-3)51. Giatromanolaki et al. found that cytoplasmic LC3A  expression was the autophagy-related parameter strongly and independently linked with poor prognosis in gastric cancer52. Due to the limitations of the resource, we did not conduct further basic experiments for verification, so we verified the correctness of the gene differential expression analysis by obtaining immunohistochemical staining images of PR-DE-FRGs. Stem cell characteristics reveal new drug targets for anti-cancer therapy23. Cell stemness usually show high levels of iron in the cells53. Our OSCC stem cell characteristics analysis found that CSCs score and risk score showed a significant positive correlation. In the high-risk group, stage T4 and N3 groups had significantly higher mDNAsi. This is consistent with our current knowledge. This conclusion indicated that cancerous cells in our high-risk group are more aggressive and can invade surrounding tissues, which is also reflected in the study of glioblastoma55.Univariate Cox analysis of the 9 PR-DE-FRGs used to construct the prognostic model revealed eight genes  were identified as prognostic risk factors, and MAP1LC3A was identified as prognostic protective factors. Similarly, the Kaplan–Meier test found that except for MAP1LC3A, the low expression of the other eight genes was significantly related to better OS. Therefore, the prognostic model constructed using these 9 PR-DE-FRGs has a solid ability to predict the prognosis of OSCC patients. Ferroptosis is a type of iron-dependent and lipid peroxidation-dependent nonapoptotic cancer cell death and is closely related to glutathione metabolism56, lung squamous cell carcinoma57, breast cancer58, bone and soft tissue sarcomas59. P53 suppresses tumor mutations by destroying cells and preventing the proliferation of abnormal genes after DNA damage, but mutated P53 will lose its function60. In addition, recent studies have also found that P53 can also exert its tumor suppressor function by inducing apoptosis and ferroptosis19. These findings explain the worse OS of patients in the TP53 mutation group, which may be because the TP53 mutation inhibits ferroptosis, which promotes the progression of OSCC and leads to a worse prognosis for patients. Consistent with the findings of Lyu et al., we observed that the content of CD8 T cells were higher in the wild TP53 group compared to mutated TP5361. Recent studies have implied that CD8 T cells activated by immunotherapy can promote tumor cell lipid peroxidation and ferroptosis by releasing interferon-gamma (IFNγ), thereby enhancing the anti-tumor efficacy of immunotherapy13. These findings explain the worse OS of patients in the TP53 mutation group, which may be because the TP53 mutation inhibits ferroptosis, which promotes the progression of OSCC and leads to a worse prognosis for patients. The low-risk group was enriched in a large number of immune-related cell components, molecular function, and biological process. Therefore, we further analyzed the OSCC immune microenvironment. The results showed that whether the immune score or stromal score, the score of the low-risk group was higher. In addition to aDCs, NK cells, and Th1 cells, the enrichment of 13 other immune cells in the low-risk group was significantly higher than that in the high-risk group. First, higher T-cell co stimulation score was observed in the low-risk group. Previous works of literature have proven that HNSCC patients with high CD8+ T cell penetration have a better prognosis62. And a study has shown a significant correlation between increased tumor penetration of CD8+ T cells and their tissue localization in OSCC63. Wang et al. also found that CD8+ T cells activated by immunotherapy can make tumors sensitive to ferroptosis by promoting tumor cell lipid peroxidation and IFN+13. The beneficial effect of numerous CD20+ B cells in the metastatic lymph nodes of HNSCC64. Dendritic cells (DC) play a crucial role in anti-tumor immunity, indicating a good prognosis in OSCC65. The high density of tumor-associated macrophages seems to have a better survival rate in colorectal cancer66. Tumor-associated mast cells are associated with a good prognosis in esophageal squamous cell carcinoma67, ovarian cancer68 and diffuse large B-cell lymphoma69. In ovarian cancer, the accumulation of tumor-infiltrating lymphocytes (TIL) is a good prognostic factor and can improve its OS70. And neutrophils induce iron-dependent accumulation of lipid peroxide in tumor cells by transferring bone marrow-containing oxidase particles into tumor cells, leading to ferroptosis71. Combined with the immune cell changes related to the TP53 mutation above, we were surprised to find that CD8 T cells infiltrated higher in the TP53 wild group, indicating that TP53 mutations may inhibit the proliferation of CD8 T cells and cause their content in OSCC to decrease. In the low-risk group, the Type II IFN Response score is higher, which in turn implies that it may be because of the reduction of CD8 T cells that the interferon-gamma (IFNγ) response is also reduced, thereby inhibiting the occurrence of ferroptosis, and ultimately leading to OSCC has a worse prognosis. Besides, low-risk group had higher fractions of CCR, Check-point, HLA, T cell co-stimulation, T cell co-inhibition, and Type II IFN Response scores. These results suggest that the poor prognosis of patients in the high-risk group is likely to be closely related to decreased anti-tumor immunity. Further KEGG pathway analysis showed numerous ferroptosis-related pathways that further revealed that the occurrence, development, and prognosis of OSCC are related to ferroptosis.The above was primarily the study of 9 PR-DE-FRGs in other diseases. To deeply analyze the biological processes and pathways that these 9 genes may participate in OSCC, we conducted GSEA. Interestingly, the high-risk group was enriched in many P53-related biological processes. And our research shows that the most frequently mutated gene in both the high-risk and low-risk groups is the TP53 gene. Naturally, we further conducted an analysis related to TP53. TP53 was related to the occurrence, development, and prognosis of a variety of cancers, such as metastatic head and neck cancer72. The results of our research on M6A-related genes again suggest the prospect of the prognostic model in clinical treatment applications. The above results demonstrated that our model could predict the efficacy of immunotherapy and chemotherapy to minimize patients' adverse reactions. Providing accurate and reliable prediction methods for the prognosis of cancer patients to optimize clinical treatment strategies and personalized treatment is the direction that many scholars have been working hard73. Therefore, we established a Nomogram based on seven factors to predict the OS of OSCC patients at 1, 2, and 3 years and verified it through various methods. The results implied that our Nomogram has good performance.They were based on exploring the correlation between ferroptosis and cancer immunotherapy and adjuvant therapy and analyzing whether the prognostic model can predict its efficacy. The results of IPS demonstrated that the expression levels of three genes in nine PR-DE-FRGs could predict the efficacy of ICIs. In particular, the drugs susceptibility analysis showed that all genes significantly correlate with the latest NCCN recommended drugs. Studies have shown that m6A is involved in the regulation of some tumor-targeted therapeutic genesIn this study, we constructed a model with a good predictive capability based on 9 PR-DE-FRGs and used six datasets to verify its performance in multifaceted directions. And we also discussed the role of ferroptosis in the initiation, progression and prognosis of OSCC from multiple perspectives such as immunity, mutation, stem cell characteristics and clinical application. But there were still some limitations. First of all, this study has not yet performed qRT-PCR to verify the differential expression of 9 PR-DE-FRGs from the RNA transcript levels. Secondly, the immunohistochemical image had 5 PR-DE-FRGs differentially expressed at the protein expression level that has not been verified. Due to limited data sources, we also failed to account for the interference of treatments on the estimated prognostic performance of our prognostic model. And due to various limitations, in reality, it is difficult for us to combine clinical specimens and experiments to verify the relevant conclusions of the mechanism reasoning and clinical application sections. Finally, some conclusions obtained by this study were only reasonable inferences based on the previous research conclusions and our results and only provided a novel perspective for future research.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions  of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry. Physical activity (PA) of any intensity is associated with reduced all-cause mortality ,2 and reDuring the past decade there has been an exponential rise in the consumer wearables market, whereby 21% of US adults now own a wearable device with the capability of monitoring physical activity . A recent systematic review showed that wearing a consumer physical activity device is effective at increasing physical activity . These mAlongside the growth in use of commercial and research-grade wrist-worn accelerometers, numerous methods exist for deriving step counts from wrist-worn accelerometers, most of which remain proprietary and vary widely in accuracy. Toth et al.  reportedData from three trials were used in this analysis to ensure representation of age, PA levels and accelerometer brand.SMART Work and Life: A total of 756 desk-based workers were recruited from six councils in the UK . To be eEquivalency: A sample of 56 adults was recruited from two UK universities (University of Leicester and Loughborough University) to wear multiple different brands of accelerometer on the dominant and non-dominant wrists, and the thigh to assess changes in PA metrics between wear location and brands . We usedSTAND UP: A total of 60 participants aged ≥60 and 65–79 years who engaged in ≤75 min of self-reported vigorous-intensity PA per week and were able to walk unassisted were eligible . We used free-living data from both the activPAL and GENEActiv monitors in this analysis. Ethical approval was provided from an NHS Research Ethics Committee .Self-reported demographic data were obtained during baseline visits. Participant sex, ethnicity, date of birth , height and body mass were collected. Height and weight were used to calculate body mass index (BMI) for descriptive purposes.Wrist-worn accelerometers: The Axivity was worn on the non-dominant wrist in SMART Work and Life and Wrist equivalency studies. The GENEActiv was worn on the non-dominant wrist in STAND UP study. Across all studies, the accelerometers were set up to record at a sampling frequency of 100 Hz and with a dynamic sampling range of ±8 g. Devices were worn continuously for 24 h/day on the non-dominant wrist for 7 days, except for SMART Work and Life which measured behaviors over 8 days.Thigh-worn accelerometer: The activPAL (model: activPAL3 micro) was used in all three studies, recording accelerations at a sampling frequency of 20 Hz and with a dynamic range of ±2 g. The device was attached to the right thigh and worn for 7 days, except for SMART Work and Life, in which it was worn for 8 days. Participants wore the monitor continuously 24 h/day, only removing it to take part in water-based activities or to re-attach the device with a fresh dressing. Steps per day determined from the activPAL was used to assess concurrent validity of the Verisense algorithm. The activPAL was the reference measure as it has demonstrated high accuracy in detecting step count and stepping cadence during controlled laboratory and free-living conditions ,27,28The algorithm chosen  for this analysis was based on initial work estimating steps using acceleration patterns from smartphones . This alwww.cran.r-project.org, accessed on 23 March 2022) using R-package GGIR version 2.6-0 [All devices were initialized for data collection and downloaded, following wear, using the relevant manufacturers’ software. Axivity devices were initialized and raw data downloaded in raw.cwa format using OmGui . GENEActivs were initialized, and raw data downloaded in .bin format using GENEActiv PC (version 3.1). The activPAL devices were initialized and raw data downloaded in .csv format using PALanalysis . Raw activPAL data were processed in GGIR to make use of the non-wear detection algorithms within this process to ensure that only days with synonymous 24 h wear for both monitors were included in the analysis. Raw data for the Axivity, GENEActiv and activPAL were processed within open source software R . The PAL batch was used to generate daily summaries for time spent sitting, standing and stepping, and the number of steps achieved.Continuous participant characteristics were calculated as mean ± standard deviation (SD) and categorical data as the number (percentage). Bland–Altman validation analysis was used to compare the number of steps and MVPA steps between the activPAL and the Verisense algorithms for each dataset and the datasets combined. Mean bias and limits of agreement (LoA) were generated to describe the level of agreement . All anaIn total, 713 (86%) participants across the three studies had data from both the wrist accelerometer and activPAL on ≥1 day of 24 h wear. This consisted of 640 (85%), 46 (82%) and 27 (90%) from the SMART Work and Life, Equivalency and STAND UP studies, respectively. The Bland–Altman results for each study, and overall, are shown in When assessing the samples separately, the overestimation of steps was largest in the least active sample, STAND UP, for both the Verisense 1 and Verisense 2 algorithms. despite the mean bias between the Verisense 1 and Verisense 2 being reduced by a third . In the The Bland–Altman results for the number of steps at a MVPA intensity are shown in This study examined the performance of an open-source algorithm to estimate daily stepping-based metrics from wrist-worn accelerometer data, before and after optimization of the parameter thresholds on a subset of data to improve estimation accuracy. Our results showed that the step count from a wrist-worn accelerometer resulted in mean overestimation of 913 (9.7%) steps/day, compared with the activPAL worn on the thigh. However, with the optimized algorithm, the overestimation reduced to 742 (7.9%) steps/day, whilst greatly reducing the proportional bias observed. In contrast to the overestimation of total steps, MVPA steps (>100 steps/min) were underestimated. This is consistent with the underestimation of cadence observed during higher walking speeds and running . ImportaOur finding that the total daily step count estimates were higher when derived from the wrist compared with the thigh is consistent with similar research investigating differences in behavioral metrics based on accelerometer wear location. Using activities of daily living, previous analyses have shown that estimates of step count were ~1200 steps/day higher when derived from consumer-grade wrist-worn devices. compared with a pedometer worn at the waist ,35 and dWhen comparing the number of MVPA steps estimated by both versions of the Verisense algorithm, we saw a marked improvement in the underestimation between algorithms compared with the comparator, equivalent to ~50% reduction in the bias in the Verisense 2 algorithm compared with Verisense 1. As the algorithm was optimized to reduce the total step count bias, it is possible that the parameters could be altered further to perform better in detecting MVPA steps in the future. However, the underestimation of MVPA steps within each sample did improve substantially following optimization This is consistent with improved performance of the optimized algorithm observed during walking and running . UnderstThe main strengths of this study were the large sample size with concurrent wrist and thigh accelerometry free-living data across three diverse datasets covering young, middle-aged and older adults. Despite the sample mostly consisting of office workers, there was a wide range of physical activity within the overall sample which assisted in the optimization of parameters for the Verisense 2 algorithm, evidenced by the reduction in total and MVPA stepping bias, and, in particular, removing the proportional bias observed for total step counts in Verisense 1. An additional strength was that the algorithm is open-access  and workIn conclusion, this study found that an optimized version of the open-source Verisense step-count algorithm was more accurate than the original version in detecting total and MVPA steps in free-living data. compared to step count from a thigh-mounted activPAL. These findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal, when investigating stepping metrics with markers of health. The refined Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry.