Advances in digital health and digital learning are transforming the lives of patients, health care providers, and health professional students. In the interdisciplinary field of communication sciences and disorders (CSD), digital uptake and incorporation of digital topics and technologies into clinical training programs has lagged behind other medical fields. There is a need to understand professional and student experiences, opinions, and needs regarding digital health and learning topics so that effective strategies for implementation can be optimized.This cross-sectional survey study aims to interdisciplinarily investigate professional and student knowledge, use, attitudes, and preferences toward digital health and learning in the German-speaking population.German: Logopäd*innen), medical students, and speech-language pathology students. Differences in knowledge, use, attitudes, and preferences across profession, generation, and years of experience were analyzed.An open-ended, web-based survey was developed and conducted with professionals and students in CSD including phoniatricians and otolaryngologists, speech-language pathologists (P<.001), generation (P=.001), and years of experience (P<.001), which demonstrated that students and younger participants were less familiar with digital health terminology. Professional (P<.001) and generational differences were also found (P=.04) in knowledge of digital therapy tools, though no significant differences were found for digital learning tools. Participants primarily used computers, tablets, and mobile phones; non–eHealth-specific tools ; and digital formats such as videos, web courses, and apps. Many indicated a desire for more interactive platforms, such as virtual reality. Significant differences were found across generations for positive views toward digitalization (P<.001) and across profession for feelings of preparedness (P=.04). Interestingly, across profession (P=.03), generation (P=.006), and years of experience (P=.01), students and younger participants demonstrated greater support for medical certification. Commonly reported areas of concern included technical difficulties, quality and validity of digital materials, data privacy, and social presence. Respondents tended to prefer blended learning, a limited to moderate level of interactivity, and time and space–flexible learning environments , with a notable proportion still preferring traditional time and space–dependent learning .A total of 170 participants completed the survey. Respondents demonstrated greater familiarity with digital learning as opposed to eHealth concepts. Significant differences were noted across profession (This comprehensive investigation into the current state of CSD student and professional opinions and experiences has shown that incorporation of digital topics and skills into academic and professional development curricula will be crucial for ensuring that the field is prepared for the ever-digitalizing health care environment. Deeper empirical investigation into efficacy and acceptance of digital learning and practice strategies and systematic training and practical organizational supports must be planned to ensure adaptive education and practice. Telepractice wearable medical devices, medically certified apps through mobile health , health portals, and personalized medicine are among just a few of the many technologies that are increasingly affecting the health care sector. In this context, the multifaceted terms ll-being -4. Alonglearning -8. The ilearning ,10.However, research has demonstrated that digital skills and the use of digital tools are still not an integral component of professional health care education -14. SuggIn the field of communication sciences and disorders (CSD), investigation into such topics appears to fall behind other medical fields ,24. ProfGerman: Logopäd*innen), phoniatricians, and otolaryngologists among other professionals who work closely together to comprehensively treat communication disorders. Given that interdisciplinary education has been identified as a key component to future proofing the ever-digitalizing health care environment, it will be useful to investigate interprofessional perspectives and experiences [Importantly, CSD is an interdisciplinary field that includes speech-language pathologists  . In a seThe study was approved by the Ethics Committee of the Medical Faculty, RWTH Aachen University, and participation was voluntary, anonymous, and could be ended at any time. A convenience sample was collected through sharing an invitational letter and flyer containing a link to the open survey with professional regulating bodies and university clinical programs in speech-language pathology, phoniatrics, and otolaryngology, as well as open student and professional groups on Facebook. To partake in the survey, participants had to be one of the following: (1) physician in phoniatrics or otolaryngology, (2) SLP, (3) medical student, (4) speech-language pathology student (SLP student). Before beginning the survey, participants were prompted to read through the detailed study background, aims, procedures, anonymous data to be collected, and data protection policies; were given the information of relevant contact persons for the study such as the study organizer and data privacy office; and were required to give informed consent before proceeding. No personal information was collected other than demographic information including profession, years of experience, generation, and gender. No incentives were offered.The web-based survey was hosted on university licensed LimeSurvey version 4.3.14+200826, a web-based statistical survey web application that conforms with the required data security legislation as dictated by the German Federal Data Protection Act, the European Data Protection Directive 95/46/EC, and the European General Data Protection Regulation (GDPR) . Unique please choose one of the following answers, multiple answers may be chosen, and please rate the following statements), and technical terms were defined as appropriate to ensure common understanding of the topic or intention of the question. Array questions contained 2 to 5 interrelated statements, which participants rated on a 4-point Likert scale . An even-numbered scale was used to avoid central tendency bias, and positive versus negative statements were counterbalanced. Free-text entries were conditionally displayed and followed branching logic based on the preceding yes or no question; they allowed for expansion upon the chosen answer and additional comments. All questions except for free-text entries were mandatory for survey completion and submission. As previous literature has demonstrated no differences in missing data, internal consistency reliability, and mean scale scores across survey participant groups given and not given the option of forward and backward navigation [A semistructured anonymous questionnaire was designed, pretested, and cross-checked by an interdisciplinary team consisting of an SLP, a phoniatrician, and an instructional designer to ensure that questions were clearly formulated and targeted desired data. The survey contained 24 questions pertaining to knowledge, use, attitudes, and preferences regarding digital learning and health as well as sociodemographic information. In total, 1 to 4 questions were displayed per page depending on the question type. There were 12 screens, including the initial page with participant information on which the participant had to give consent before proceeding. The following question types were included in the survey: multiple choice with single fixed choice, multiple answers (with a free-text response option), arrays with Likert scale ratings, yes or no questions, and free-text entries. Directions were provided for each question to avoid confusion . A Bonferroni adjustment was applied for post hoc pairwise comparisons for all reported significant findings. Chi-square tests were implemented using an alpha level of P<.05 to determine whether there were associations among (1) profession, (2) generation, and (3) years of experience and knowledge of digital tools, associated concerns, and perceived benefits in terms of time and space . Cramer V coefficients provided insight into the strength of associations. Post hoc Z tests for significant chi-square tests were also performed.Data from the anonymous surveys were analyzed using the IBM SPSS version 27  to generThis study analyzed student and clinician knowledge, use, attitudes, and preferences regarding digital health and learning in CSD in German-speaking countries. Of the 213 unique survey visitors, 13 visited the start page (page containing study information and informed consent) but never began the survey and 29 began the survey though did not complete it. Thus, the participation rate was 93.9% (200/213) and the completion rate was 80.3% (171/213). Only completed questionnaires  were analyzed. One survey from a student in dentistry could not be used, and thus 170 total surveys were analyzed for the study. Participant characteristics are summarized in digital health and eHealth, of the total 170, 20 (11.8%) respondents had heard of the terms and felt confident in their knowledge, 88 (51.8%) respondents indicated having heard of the terms but having limited knowledge, 38 (22.3%) indicated having heard of the terms but being unsure of their meaning, and 24 (14.1%) indicated having never heard of the terms. Kruskal-Wallis tests further revealed significant effects of profession , generation , and years of professional experience  on the level of familiarity with these terms. Pairwise comparisons using the Bonferroni correction revealed that differences were particularly prominent between SLPs and SLP students (P=.013), physicians and SLP students (P<.001), physicians and medical students (P<.001), and physicians and SLPs (P=.03), with the former reporting greater familiarity with the terms than the latter. Pairwise comparisons also revealed that Generation X reported greater familiarity with the terms than generations Z (P=.002) and Y (P=.04). Respondents who were still studying (no experience) tended to be significantly less familiar with the terms digital health and eHealth than those who had 16 to 20 years of experience (P=.001) and those who had more than 20 years of experience (P=.005).Regarding knowledge of the terms digital learning or e-learning, 40.6% (69/170) respondents reporting being familiar with and feeling confident with the terms, 54.7% (93/170) indicated being familiar with the terms but having limited knowledge, and 4.7% (8/170) reported having heard of the terms but being unsure of their meaning. No significant differences were found across profession, generation, or years of experience. These terms were explicitly defined following these questions to ensure common understanding for the remainder of the survey. The terms digital health, and eHealth, were defined as the use of technology or digital media to promote, maintain, or manage a person's health. The terms digital learning and e-learning were defined as “all forms of learning in which electronic or digital media are used to support learning processes, in this case especially in the context of medical or clinical education.”In contrast, when respondents were asked about their familiarity with the terms digital therapy tool was broadly defined as “any electronic or digital media to be used for clinical or clinical research purposes .” Regarding familiarity with such tools, 61.8% (105/170) respondents indicated being familiar with such tools, whereas 38.2% (65/170) were not. Significant differences in knowledge of digital therapy tools were found across profession , with a moderate effect size . Post hoc Z tests demonstrated significant differences at the P=.05 level for physicians and medical students who were less familiar with digital therapy tools and for SLPs who were more familiar with digital therapy tools. Significant differences were also found across generations  with a moderate effect size . Generation Y  had more respondents who were familiar with digital therapy tools (P=.05). No significant differences were demonstrated across years of experience.To similarly establish common understanding, the term apps, computer programs, teletherapy platforms, and AAC devices), specific tools , and responses that included both general and specific tools . Notably, there were several digital health tools mentioned that were unrelated to communication disorders or used for personal use . The relevant digital therapy tools that were mentioned included therapy apps (n=77), AAC devices/software (n=13), computer learning software for children (n=19), teletherapy videoconferencing platforms (n=8), computer-based web programs (n=8), diagnostic apps or computer software (n=3), and other (n=4). The most frequently reported digital tools were the therapy apps Neolexon (n=26), Speech Care (n=11), Phonolo (n=6), and Lexico (n=5). Opinions regarding digital therapy tools were mixed. Positive reports included ease of access, increased practice opportunities for patients, user-friendliness of some applications, usefulness of complexity and stimuli settings, and the potential for increasing patient motivation through interactive activities. Reported negative opinions included associated costs, issues with navigation and user-friendliness , concerns of screen time and distractibility for children, and an emphasis on digital tools only as a supplement to traditional therapy. In relation to associated costs, one respondent added that paid apps often provided greater user support and features.A total of 50% (85/170) participants responded to the optional follow-up question “What digital therapy tools are you already familiar with? What did you think of these tools?” Free-text responses consisted of references to general digital therapy tools  respondents indicated that they were already familiar, whereas 39 (22.9%) were not. No significant differences were found across profession, generation, or years of experience. A total of 62.4% (106/170) participants responded to the optional follow-up question “What digital learning tools are you already familiar with for academic studies, teaching, or continuing education? What did you think of these tools?” Responses consisted of references to general digital learning tools , specific tools , and responses that included both general and specific tools . Digital learning tools fell into the general categories of learning management systems (n=50), videoconferencing systems (n=34), web courses or webinars (n=28), videos (n=26), web journals (n=18), apps (n=17), websites and search engines (n=17), learning platforms (n=15), podcasts (n=8), digital notecards (n=7), collaboration platforms (n=7), web polling (n=6), repositories of shared materials (n=5), forums (n=2), e-books (n=2), presentation software (n=3), clinical learning tools (n=2), and other (n=5). In terms of specific resources, the learning management system Moodle (n=33), the videoconferencing platforms Zoom (n=19) and Microsoft Teams (n=9), the video platform YouTube (n=9), and the web journal platform PubMed (n=5) were the most frequently mentioned. Notably, many of the mentioned digital learning tools were not specific to the field of CSD, but rather general to educational milieus. Opinions regarding digital learning tools were mixed. Reported benefits included flexibility and accessibility, usefulness for learning theoretical material, the variety of tools available, and multifunctionalities. In addition, 3 participants mentioned that experiences were often dependent on specific implementation by instructors. Frequently reported concerns included limited direct interaction and opportunity to ask follow-up questions, lack of user-friendliness, the learning curve to use digital tools, and concerns regarding the effectiveness for complex topics or practical skills.To ensure common understanding, the term devices referred to specific electronic equipment , software or digital system referred to commonly used clinical and non– clinical-specific tools or programs serving a specific purpose , and formats referred to the text-, visual-, or multimedia-based presentation of the information . Students and professionals in CSD appear to use computers (n=168), smartphones (n=137), and tablets (n=90) as their primary devices for digital therapy, teaching, and learning purposes. They also use e-Readers (n=7) and MP3 players (n=6). Other devices mentioned in the optional free response other category included electronic communication supports (n=1), laptops (n=2), and audio recorders (n=2). Only one respondent used game consoles or virtual reality (VR) equipment for therapy, teaching, or learning purposes. A summary of device use across profession, generation, and years of experience is shown in Digital tool use was primarily descriptively analyzed across devices, software or digital system, formats, and frequency of use. Whereas other category, respondents also reported using WhatsApp (n=1), cloud learning platform (n=1), test evaluation software (n=1), therapy apps (n=1), therapy material database (n=1), and handwritten digital notes via Notability (n=1). A summary of software or digital system used across profession, generation, and years of experience is shown in Word processing software (n=169), presentation software (n=141), videoconferencing software (n=141), research databases (n=115), and spreadsheet software (n=114) were the most frequently reported software or digital systems used among respondents. Respondents also reported using online appointment systems (n=70), electronic health records (n=60), statistics software (n=52), hospital information systems (n=38), e-billing ((n=28), hospital communication systems (n=18), e-prescriptions (n=15), and telemedicine (n=13). In the optional free response other category, respondents mentioned learning platforms such as Moodle and Ilias (n=1) and the Promethean ActivTable for Education. These results are shown in In terms of digital formats, respondents most frequently used videos (n=155), websites (n=150), web-based seminars or courses (n=130), and apps (n=100). When compared with digital formats that respondents could imagine themselves using in the future, many respondents reported also being open to other currently less frequently available digital formats including podcasts (n=112), 3D models (n=108), simulations (n=106), e-books (n=105), VR (n=72), serious games (n=66), and social networking (n=59). In the 3H=15.200; P=.002), generation , and years of experience  were found. Post hoc comparisons revealed that SLPs used digital therapy tools significantly more frequently than SLP students (P=.01) and medical students (P=.03). Across generations, Generation Z reportedly used digital therapy tools significantly less frequently than Generation Y (P=.01) and X (P=.03), which can likely be attributed to the fact that these individuals are typically still studying and thus have yet to implement these tools in practice. Finally, respondents with 1 to 5 years of experience were noted to use digital therapy tools at a significantly greater frequency than respondents who were still studying (P=.001).When asked about frequency of digital therapy tool use, of the total 170, 47 (27.6%) respondents indicated that they were still studying, 60 (35.3%) respondents indicated never using digital therapy tools, 27 (15.9%) indicated monthly, 26 (15.3%) indicated weekly, and 10 (5.9%) indicated daily. Significant effects for profession  and years of experience . Post hoc comparisons revealed that Generation Z used digital learning tools significantly more frequently than Generation Y. No significant differences were found across years of experience in post hoc analyses.Regarding digital learning tools, 18.8% (32/170) respondents never used such tools, 31.2% (53/170) used these monthly, 32.9% (56/170) used these weekly, and 17.1% (29/170) used digital learning tools daily. Significant differences were found across generation  with Generation Z viewing digitalization significantly more positively than Generation Y (P=.003), X (P=.02), and baby boomers (P=.008). When asked whether they felt prepared for the digital revolution, of the 170, only 29 (17.1%) respondents fully agreed, 78 (45.9%) somewhat agreed, 58 (34.1%) somewhat disagreed, and 5 (2.9%) fully disagreed. Significant differences were found across profession  specifically with SLPs reporting greater preparedness than SLP students (P=.04). In total, of the 170, 105 (61.8%) respondents fully agreed and 60 (35.3%) somewhat agreed that eHealth, digital tools, and competences needed to be integrated into future curricula, whereas 5 (2.9%) individuals somewhat disagreed.Most of the respondents held positive views regarding digitalization in medicine. When asked whether they viewed digitalization positively, of the total 170, 68 (40%) respondents agreed, 90 (52.9%) somewhat agreed, 11 (6.5%) somewhat disagreed, and 1 (0.6%) disagreed. Significant generational differences were found , generation , and years of experience . Pairwise comparisons revealed no significant differences across professions, though Generation Z tended to agree with using medically certified products more strongly than Generation X (P=.008). Across years of experience, respondents who were still studying agreed more with the use of medically certified products than their counterparts with ≥20 years of experience (P=.02). When asked whether they would pay a fair price for a digital therapy tool given good ratings, of the 170, 38 (22.4%) respondents agreed, 103 (60.6%) somewhat agreed, 26 (15.3%) somewhat disagreed, and 3 (1.8%) disagreed.Regarding digital therapy tool attitudes, of the 170, 91 (53.5%) respondents agreed, 74 (43.5%) somewhat agreed, and 5 (2.9%) somewhat disagreed to being open to the use of digital therapy tools. In total, 28.2% (48/170) respondents agreed and 41.2% (70/120) somewhat agreed that digital therapy tools offered more advantages than disadvantages with the potential to individualize therapy, whereas 28.8% (49/170) somewhat disagreed and 1.8% (3/170) disagreed. A total of 2.9% (5/170) respondents agreed and 32.4% (55/170) somewhat agreed that they doubted the quality or validity of digital therapy tools, whereas 53.5% (91/170) somewhat disagreed and 11.2% (19/170) fully disagreed. When asked whether they would be more likely to use a digital therapy tool if it were medically certified, of the 170 respondents, 87 (51.2%) respondents agreed, 63 (37.1%) somewhat agreed, 16 (9.4%) somewhat disagreed, and 4 (2.4%) disagreed. Significant differences were demonstrated across profession . In total, 2.9% (5/170) respondents emphasized problems with data privacy and security as an additional concern; 2.4% (4/170) respondents also emphasized the value of direct face-to-face therapy and human interactions, with 0.6% (1/170) respondent specifically citing the negative effects of screen time on child language and social development. Other notable concerns included legal repercussions in cases of medical malpractice and the current lack of sufficient knowledge and exchange regarding digital therapy tools.When asked about concerns regarding digital therapy tools, 8.8% (15/170) respondents indicated no concerns, 55.9% (95/170) indicated technical difficulties, 49.4% (84/170) indicated limited validity of digital therapy tools, and 72.9% (124/170) indicated insufficient diagnostic and therapeutic quality of tools. A total of 12.9% (22/170) participants indicated Regarding digital learning tool attitudes, of the 170, 125 (73.5%) respondents agreed, 42 (24.7%) somewhat agreed, and 3 (1.8%) somewhat disagreed to being open to the use of digital learning tools. In total, 41.8% (71/170) respondents agreed, 44.7% (76/170) somewhat agreed, 12.4% (21/170) somewhat disagreed, and 1.2% (2/170) disagreed that digital learning offered more advantages than disadvantages. Furthermore, 0.6% (1/170) respondent agreed, 7.1% (12/170) somewhat agreed, 62.9% (107/170) somewhat disagreed, and 29.4% (50/170) disagreed that they doubted the quality and validity of digital learning tools. Many respondents agreed  and somewhat agreed  that they would be more likely to use a digital learning tool if it were to be developed by an academic institution or professional regulating body, whereas 5.9% (10/170) somewhat disagreed and 3.5% (6/170) fully disagreed. In total, 21.8% (37/170) respondents agreed, 44.7% (76/170) somewhat agreed, 26.5% (45/170) somewhat disagreed, and 7.1% (12/170) disagreed that they felt confident in their knowledge of digital learning tools.other concerns in the free response field. Of these 18 responses, 2 respondents’ answers expanded upon closed answer choices . Five respondents expanded upon concerns regarding the quality of learning, citing the difficulty to verify the quality of digital learning tools, compromised retention of knowledge, the questionable practical relevance of digital learning for the development of interpersonal skills, and fears regarding the potential depreciation of therapeutic competence. Other concerns mentioned included limited scope and specificity of learning materials, difficulty with tracking attendance and engagement, extensive preparation time for digital material, associated costs, and compromised data privacy.When asked about their concerns regarding digital learning tools, 11.8% (20/170) respondents reported no concerns, 52.4% (89/170) reported technical difficulties, 45.3% (77/170) reported questionable quality of learning material, 40% (68/170) reported difficulties with self-discipline and sufficient learning competence, and 52.4% (89/170) had concerns regarding reduced social interaction. A total of 10.6% (18/170) participants indicated Respondents were asked to indicate their preferred level of virtuality, interactivity, and flexibility in terms of time and space. Out of the 170 total respondents, 4 (2.4%) indicated that they preferred in-person learning, 39 (22.9%) preferred in-person learning with additional e-material, 105 (61.8%) respondents preferred blended learning, and 22 (12.9%) preferred the inverted classroom approach. In terms of interactivity, 21 (12.4%) respondents preferred a passive level, 69 (40.6%) preferred a limited level, 63 (37.1%) preferred a moderate or complex level, and 17 (10.0%) preferred an advanced or active level. No significant differences across profession, generation, or years of experience were found regarding the preferred level of virtuality and interactivity. In terms of time and space flexibility, 28.8% (49/170) respondents preferred traditional time and space–dependent learning environments, 3.5% (6/170) preferred time flexibility only, 30.6% (52/170) preferred space flexibility only, and 37.1% (63/170) respondents preferred time and space–flexible learning environments. As data did not meet chi-square test assumptions for the minimal number of observations per cell, significant findings across profession and years of experience are not reported.This cross-sectional survey study is, to the best of our knowledge, one of the first to investigate knowledge, use, attitudes, and preferences regarding digital health and learning topics interdisciplinarily across the fields of speech-language pathology, phoniatrics, and otolaryngology, which work collaboratively together to treat communication disorders. Although some previous studies have investigated some of these aspects within each respective field, it is useful to analyze across disciplines to identify knowledge gaps, areas for improvement, and collaborative problem-solving opportunities.digital health and eHealth, only 11.8% (20/170) of respondents indicated feeling confident in their knowledge of the terms. This was especially evident between students and working professionals, the latter of which indicated greater familiarity with terminology as they are more likely to encounter such concepts in their clinical practice. Moreover, the significant finding that older generations and professionals with more experience had greater familiarity of such terminology than their younger and less experienced counterparts highlights the urgency to make eHealth and digital skills an integral part of clinical curricula. In previous studies, health science students similarly reported uncertainty with information technology in health care settings, even if they were confident using technologies in everyday situations. An automatic transfer of digital skills to professional contexts cannot be assumed simply because students are digital natives [Digitale-Versorgung-Gesetz) in Germany has primarily been medicine-focused and has not equally engaged all clinical fields [Participants overall reported greater surface level knowledge regarding terminology and specific therapeutic and learning tools. When presented with the terms  natives ,15,57,58 natives . Althoug natives . Exploril fields . Given tdigital learning and e-learning. This is not surprising given that the term e-learning emerged in the 1990s and has since even spread to outside academic contexts, whereas the definition of the term eHealth has been debated since the early 2000s, and the debate continues today [digital health and eHealth have often been used interchangeably, the World Health Organization has suggested that digital health refers to the general use of technologies for health and can encompass eHealth, which refers to the application of health care information technologies  [In contrast, respondents reported being more knowledgeable regarding the terms es today -64. Althiptions) . Howeveriptions) . Learneriptions) .health-conscious generation [Most of the respondents indicated knowing digital therapy tools, especially SLPs when compared with physicians and medical students. Although this finding could be explained by different professional scopes of practice, it could also reveal a gap that interdisciplinary collaboration could help to close. Although there is existing literature regarding digital applications in otorhinolaryngology and phoniatrics in the German-speaking population ,69, thesneration -73. Respneration ,72,74. Fneration ,45,75,76Most respondents 131/170, 77.1%) reported that they were familiar with digital learning tools. General and specific tools were reported with learning management systems , videoconferencing systems , and web-based courses or webinars predominating, which may reflect the surge of these platforms during the ongoing COVID-19 pandemic .1% repor,78-82. Aother category across questions pertaining to use of digital technology, respondents often did not differentiate among devices, software or digital systems, and formats, indicating a pressing need for foundational digital literacies education [In line with commonly reported device use, professionals and students mostly used computers, smartphones, and tablets for learning, teaching, and professional purposes ,19,83,86ducation ,21,22. HIn terms of frequency of use, the largest proportion of respondents  surprisingly responded that they never used digital therapy tools, further pointing to the lag in digitalization implementation. Given that students are usually not working clinically, the significant differences observed between them and SLPs is unsurprising. However, the finding that professionals with 1-5 years of experience used digital therapy tools more indicates a growing acceptance of digital eHealth solutions ; it coulAttitudes toward digitalization were primarily positive, with Generation Z demonstrating significantly more positive views than their generational counterparts, as expected per previous research trends . ConsistMost respondents indicated being open to using digital therapy tools. Interestingly, Generation Z and respondents who were still studying reported being more likely to use digital tools with medical certification than their Generation X counterparts and those with ≥20 years of professional experience. To the best of our knowledge, this specific finding has not been previously reported in the literature. A study with French university students found that students were more comfortable with digital health interventions provided these would be promoted by institutional or official entities, though these opinions were not compared against those of seasoned professionals or older generations . As Geneother free response category, doubts regarding patient acceptance, secure data transfer and storage, and ethical consequences were mentioned concerns, which require co-operation with policy and governmental regulation for effective implementation [Intriguingly, a little under one-third of respondents demonstrated skepticism regarding whether such tools provided more advantages than disadvantages and indicated doubts regarding their quality and validity, and a little over half of respondents reported concerns with technical difficulties. These concerns urge for focused research on efficacy and barriers to digital technology use. Current and future physicians and clinicians should be increasingly engaged in systematically evaluating eHealth technologies through tools such as the Mobile App Rating Scale (MARS) . In the entation ,100,101.entation .In terms of digital learning, most respondents were open to its use, agreed that it offered more advantages than disadvantages, and felt mostly confident with their knowledge of digital learning tools. Interestingly, when reporting concerns, however, 45.3% (77/170) of respondents still questioned the quality of digital learning material, despite previous indications of not being doubtful of quality. This disconnect reveals hesitations in digital uptake and aligns with the reported need to find effective means of verifying the quality and validity of digital learning tools. Although technical standards exist through organizations such as the International Organization for Standardization (ISO) or the Institute of Electrical and Electronics Engineers, these are not immediately comprehensible for clinical instructors, and efforts are still being made to devise frameworks that fit within clinical professional responsibilities and standards -104.In the following study, no significant differences in preferred levels of virtuality, interactivity, and flexibility in terms of time and space were observed across profession, generation, or years of experience. The absence of generational differences aligns with several previous reports -107. MosCritically, this study must be interpreted in light of its limitations. First, this cross-sectional investigation was conducted in the German-speaking population and thus may have limited external validity to other cultural and geographical contexts. However, as digitalization is a global phenomenon, we hope that our findings shed some light on factors that could be playing into common trends, areas of opportunity, and barriers to the uptake of digital technologies. It is also important to note that the stark contrast in the number of female versus male participants can likely be attributed to the fact that the field of SLP is predominantly female (93%) and the number of female otorhinolaryngologists has been steadily rising (35.7% as of 2020) in Germany ,114. In adaptive education and practice. An incorporation of digital skills into clinical curricula will be necessary as the clinical landscape continues to evolve. It has been previously recommended, for example, that students begin to also receive formal coursework in data analytics, governance, and privacy as well as emerging models of care such as remote monitoring, self-management, and increasing telepractice [As a survey study, the following investigation analyzed perceptions regarding digital therapy and learning, though it would be important in future studies to also measure actual knowledge, implementation, and use. In moving forward toward an uncertain digital future in which the evidence base for new technologies has yet to be developed or may even be rendered obsolete before one can even be formed, it will be essential to engage in practice . Data vipractice ,21,67. Tpractice . As collOur study has demonstrated that students and professionals in CSD continue to feel uncertain regarding their knowledge of digital health and digital learning, are still using more traditional devices, software or systems, and formats, though are interested in exploring more interactive, digital means for teaching, learning, and practice. They have understandable concerns surrounding quality and validity of digital resources, data privacy, technical complexity, social engagement, and effective implementation. These concerns amidst the rapidly changing digital landscape urges for the expedited exploration of empirically substantiated, though flexible, solutions.


Background: Gastrointestinal cancers patients might experience multiple primary tumors in the digestive tract. Therefore, identifying potential biomarkers can help us better understand the underlying mechanism. From the GEO database, four profiles of gastrointestinal cancers were gathered for the screening process, and six hub genes were found by bioinformatics analysis. Collagen type I alpha 1 chain (COL1A1), one of the hub genes, is a component of the extracellular matrix and is critical for tumor microenvironment. However, the expression level, signaling pathway, prognostic prediction, and immunological value of COL1A1 in different cancers remain unclear.Methods: We comprehensively analyzed gene expression and genetic alteration patterns of COL1A1 among 33 types of malignancies from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression projects. Besides, we explored the correlation of COL1A1 with cancer prognosis, immune infiltrates, PD-L1, tumor mutational burden (TMB)/microsatellite instability status (MSI), and the pathway and drug sensitivity of co-expressed genes.Results: The results showed that COL1A1 was highly expressed and associated with poor prognosis in the majority of cancers. The most common alteration type of COL1A1 was missense mutation, and COL1A1 was associated with poor prognosis in KIRP, LGG, MESO, SKCM, and STAD. For the immunologic significance, COL1A1 expression was closely related to high TMB in THYM, LAML, ACC, KICH, PRAD, and LGG, and high MSI in TGCT, MESO, PRAD, COAD, SARC, and CESC. In addition, COL1A1 was positively correlated with the abundance of CAFs, macrophages, and tumor-infiltrating lymphocytes. However, it was negatively correlated with CD8+ T cells mainly in CESC, HNSC-HPV+, and SKCM. Besides, as a component of the extracellular matrix, COL1A1 was involved in the activation of epithelial-mesenchymal transition (EMT), and high expression of HTRA1 was resistant to multiple drugs.Conclusion:COL1A1 can serve as a prognostic and immunological biomarker in different cancers. Type I Alpha 1 Chain (COL1A1) of multiple primary tumors in the gastrointestinal tract based on bioinformatics technology, which might become potential diagnostic biomarkers of gastrointestinal cancers.Esophageal, gastric, and colorectal cancer are the most common types of gastrointestinal cancers. According to Global Cancer Statistics 2018, colon, gastric, rectal, and esophageal cancer have been ranked the fourth, sixth, eighth, and ninth, respectively, in the incidence of human malignant tumors, and their mortality rates were 5.8, 8.2, 3.2, and 5.3%, respectively . HoweverCOL1A1 is the gene which encodes the pro-alpha 1 chains of type I collagen whose triple helix comprises two alpha 1 chains and one alpha 2 chain  (We downloaded four gene expression profiles from the GEO database (gov/geo) : the GSEgov/geo)  was basegov/geo)  was basegov/geo)  was basehttp://www.ncbi.nlm.nih.gov/geo/geo2r) is a tool for analyzing differentially expressed genes in the GEO database, which can compare the expression of genes in tumor and normal samples. Adj. p < 0.05 and |logFC| > 1 were set as the cutoff criteria to select DEGs for these microarray, respectively. Then the overlapping DEGs among the four datasets were identified by the online Venn diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).GEO2R (https://david.ncifcrf.gov/) provides a comprehensive set of functional annotation tools to understand the biological meaning behind a large number of genes  (version 11.0) (The protein–protein interaction (PPI) network of differentially expressed genes was constructed based on the online website STRING   and was on 11.0) . The MCOhttps://www.oncomine.org) database is currently the world's largest cancer gene-chip database and an integrated data-mining platform, which can analyze differential gene expressions in normal and tumor tissues  (http://timer.cistrome.org/) was employed to analyze the differential expression of COL1A1 in different tumors and normal tissues    was usedhttps://www.cbioportal.org) is an online database which provides visualization, analysis, and the ability to download large-scale cancer genomics data  and disease-free survival (DFS) of COL1A1 expression and cancer-associated fibroblasts, CD8+ T cells, and macrophages. TISIDB (http://cis.hku.hk/TISIDB/index.php) is an integrated repository portal for tumor and immune system interaction, which integrates multiple heterogeneous data types (COL1A1 expression and tumor-infiltrating lymphocytes (TILs), immunoinhibitors, immunostimulators, and the MHC molecule.The occurrence and development of tumors are closely related to the tumor immune microenvironment. We used the “Immune-Gene” module of TIMER2 to analyze the relationship between ta types . We usedCOL1A1 expression and PD-L1 (CD274) among different cancers. RNA-seq data of 33 types of tumor were downloaded from the Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) data portal website . TMB is derived from the article “The Immune Landscape of Cancer” published by p-value <0.05 was considered statistically significant.PD-L1, TMB, and MSI are important predictive markers of immunotherapy. We used GEPIA to analysis the association of https://string-db.org/) (COL1A1 protein expression. The parameters were set as follows: a minimum required interaction score [“Low confidence (0.150)”], meaning of network edges (“evidence”), max number of interactors to show (“no more than 50 interactors” in first shell), and active interaction sources (“Experiments”). Moreover, we used the GEPIA2 database to obtain the top 100 genes most correlated to COL1A1. Then we drew the Venn diagram to obtain the overlapping genes by the online Venn diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/). Next, we used GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) (COL1A1 and the obtained genes.We used the STRING database (db.org/)  to obtaiCALite/)  to conduBased on the cutoff criteria, four mRNA expression profiles of esophageal squamous cell carcinoma, esophageal adenocarcinoma, gastric adenocarcinoma, and colorectal adenocarcinoma were gathered from the GEO database in order to screen out potential biomarkers of gastrointestinal cancers, including GSE20347, GSE92396, GSE103236, and GSE110224, respectively. After analysis using the GEO2R tool and Venn diagram, a total of 21 DEGs were identified: 19 genes were upregulated and 2 genes were downregulated. The Venn diagram is shown in For all DEGs, extracellular regions, cell surfaces, the proteinaceous extracellular matrix, and the collagen trimer were mostly enriched in cellular components (CC). The biological process (BP) mainly included the collagen catabolic process, extracellular matrix organization and disassembly, cell adhesion, cartilage development, and skeletal system development. As for molecular function (MF), the DEGs were related to extracellular matrix structural constituent, serine-type endopeptidase activity, metalloendopeptidase activity, protein binding, and extracellular matrix binding . FurtherSPP1, BGN, THBS2, MMP3, COL1A1, and TIMP1 , cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal adenocarcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), renal clear cell carcinoma (KIRC), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid adenocarcinoma (THCA), diffuse large B lymphoma (DLBC), testicular germ cell tumors (TGCT), and thymoma (THYM)  .COL1A1 in different tumor stages. The results showed that COL1A1 expression is closely related to the late stage of ACC, BCLA, ESCA, KICH, KIRP, STAD, and THCA . Breast invasive carcinoma had the highest amplification frequency (6%) and sarcoma had the highest fusion frequency of COL1A1. It is noteworthy that all mesothelioma cases had copy number amplification (4.6%) and all uveal melanoma had deletion of COL1A1 (1.25%) , progression-free survival (p = 0.0385), and disease-specific survival (p = 0.0361). Although these alternations showed a tendency of better disease-free survival, it was not statistically significant (p = 0.395) (We found  (1.25%) . The maimutation . In addi= 0.395) .COL1A1 expression level and DNA methylation, copy number, and clinical data in STAD . Furthermore, COL1A1 expression was closely associated with Barrett’s esophagus (p = 0.003), family history (p = 0.016), histology type (p = 0.048), and reflux history (p = 0.030). Copy number variation (CNV) showed no significance in STAD .Promoter methylation levels were significantly lower in primary tumors than in normal tissues in gastrointestinal cancers . The pro in STAD . The resCOL1A1 gene expression and prognosis, we used GEPIA2 to analyze the OS and DFS of COL1A1 among different cancers. The results indicated that high expression of COL1A1 is related to poor overall survival of KIRP , LGG , MESO , SKCM , and STAD  , COAD , ESCA , KIRP , LGG , and PRAD   . Moreove 0.0011) .COL1A1 expression and cancer-associated fibroblasts in most tumors. Moreover, BLCA, ESCA, HNSC, COAD, READ, and STAD showed a statistically positive correlation of COL1A1 expression and macrophages. However, CESC, HNSC-HPV+, and SKCM showed a negative relationship between COL1A1 expression and CD8+ T cells   , and higle drugs .Recently, several trials reported on the multiple primary tumors of the digestive tract. However, it is difficult to distinguish them from metastatic cancer, thus the diagnosis is relatively complicated. In this study, we screened out four chips of esophageal, gastric, and colorectal cancer from the GEO database and identified 21 DEGs by using the bioinformatics analysis. Furthermore, the PPI network analysis identified six hub genes. All of these genes were upregulated in gastrointestinal cancers. We utilized the methodology of bioinformatics in our previously published studies and successfully screened out some hub genes of certain types of cancer. In this study, we continue to use these same methods and referred to other methods based on some studies.COL1A1. It has been reported that the COL1A1 is highly expressed not only in gastrointestinal cancers but also in other cancers, involving tumorigenesis, metastasis, and immune infiltration. However, there is no pan-cancer research to comprehensively analyze COL1A1. Therefore, we expanded the research scope to pan-cancer and analyzed its gene expression level, mutation, DNA methylation, tumor prognosis, tumor immune microenvironment, and the association with predictive markers of immunotherapy, the pathway, and drug sensitivity in 33 kinds of tumors.Moreover, we analyzed one of these six genes, COL1A1 is the main member of the type I collagen family, and the protein is a component of the extracellular matrix. We found that COL1A1 was highly expressed in various cancers  are closely related to the tumor immune microenvironment and the expression levels of PD-L1, TMB, and MSI. An impressive finding of this study was that the expression of deletion . On the deletion , and macdeletion , which pdeletion  found thCOL1A1 was detected in these patients, we hypothesize that adding the COL1A1 inhibitor to ICIs plus chemotherapy would further raise the efficacy, and relevant clinical trials could be carried out to study its efficacy and toxicity in the future. Another phase II study compared the sequence of chemotherapy and anti–PD-1 antibody in neoadjuvant therapy for locally advanced esophageal squamous cell carcinoma. The results showed that immunotherapy given 2 days after chemotherapy had a higher pathological complete response rate (36.4 vs. 7.7%) compared with simultaneous administration  for the first-line treatment of advanced non-small cell lung cancer with 1–49% of the PD-L1 expression level . If highstration . This reCOL1A1 inhibitor were administrated might be worthy of further discussion.The combination of radiotherapy and immunotherapy might be another research highlight. It is believed that radiation could activate the immune system and reverse cold tumors into hot tumors, and thus improve the efficacy of immunotherapy. However, the results of the FORCE study showed that nivolumab combined with radiotherapy (5 × 4 Gy) did not increase the objective response rate of advanced non-small cell lung cancer compared to nivolumab alone . For theCOL1A1 inhibitor can overcome EGFR-TKI resistance.It was found that the efficacy of EGFR-TKI was not satisfied for NSCLC patients with a high level of PD-L1, and drug resistance may appear during the treatment . YamazakCOL1A1 inhibitors in different cancers, and it might be an optional treatment for patients with advanced tumors, just like pembrolizumab could be used in MSI-H patients.In the era of precision medicine, it would be exciting to carry out a biomarker-guided basket trial to study the potential role of COL1A1 across 33 tumors, this study still has some limitations. Firstly, the current study merely focused on COL1A1 at the transcriptional level, but lacked the description of its protein transcriptome and the post-translational level. Secondly, the results of our study lack external validation in other public datasets. Finally, the databases used in this study were mainly based on the gene-chip and sequencing data of tumor tissue from TCGA, so the cell-level analysis of immune cell markers could have introduced systematic bias. Future studies should focus more on single-cell sequencing technology, in vitro experiments, and translational research.Nevertheless, although we employed multiple bioinformatics databases to analyze the role of COL1A1 could be a marker for different cancers and is critical for tumor immune microenvironments (COL1A1 might provide additional information on precision oncology.In conclusion, we found that ronments . Further


Phosphatidylinositol-3-phosphate (PtdIns(3)P) is essential for cell survival, and its intracellular synthesis is spatially and temporally regulated. It has major roles in two distinctive cellular pathways, namely, the autophagy and endocytic pathways. PtdIns(3)P is synthesized from phosphatidylinositol (PtdIns) by PIK3C3C/VPS34 in mammals or Vps34 in yeast. Pathway-specific VPS34/Vps34 activity is the consequence of the enzyme being incorporated into two mutually exclusive complexes: complex I for autophagy, composed of VPS34/Vps34–Vps15/Vps15-Beclin 1/Vps30-ATG14L/Atg14 , and complex II for endocytic pathways, in which ATG14L/Atg14 is replaced with UVRAG/Vps38 . Because of its involvement in autophagy, defects in which are closely associated with human diseases such as cancer and neurodegenerative diseases, developing highly selective drugs that target specific VPS34/Vps34 complexes is an essential goal in the autophagy field. Recent studies on the activation mechanisms of VPS34/Vps34 complexes have revealed that a variety of factors, including conformational changes, lipid physicochemical parameters, upstream regulators, and downstream effectors, greatly influence the activity of these complexes. This review summarizes and highlights each of these influences as well as clarifying key questions remaining in the field and outlining future perspectives. PIK3CA, PIK3CB, PIK3CG and PIK3CD genes, respectively. Class I enzymes synthesize PtsInsP3 from PtdInsP2 and are mostly active on the plasma membrane (PM) . E. E225]. 2− flies . Similaraturated . The higPtdIns is also known to be abundant in yeast Atg9 vesicles, in which PtdIns makes up over 40% of the vesicle lipids . Both inBcPI-PLCH82A, and indirect monitoring of diacylglycerol (DAG), a converted product from PtdIns by PI-PLC on specified organelles. These studies agreed well on high PtdIns abundance in the Golgi and low abundance in the PM [BcPI-PLCH82A could not be found in either early or late endosomes [BcPI-PLCH82A could not detect PtdIns in late endosomes, there was an indication of PtdIns existing there [Except for the autophagosome and the ER, from which PtdIns is distributed throughout the cell, exact intracellular localization of PtdIns has been enigmatic. While subcellular fractionation followed by lipid mass spectrometry can provide quantitative information on lipid species, including acyl chain length and saturation, it lacks the topology information. Also, because organelle membranes are to some extent interconnective, it is technically difficult to resolve each organelle fraction and to fractionate membrane contact sites between organelles. Recently developed methods from two groups to detect intracellular PtdIns may complement these approaches ,224. Then the PM ,224. Intn the PM ,43,44,45ndosomes . Endosomndosomes , indicatndosomes ,234. An ndosomes , althougng there . Becauseng there , one posR172HTrp53) [In actual human bodies, lipid compositions differ between sexes and between aged and young populations . For exa53R172H)  increase53R172H) . Also, a53R172H) . These s53R172H) . Therefo53R172H) . Also, i53R172H) . These f2 with blades 5 and 6 [The PtdIns(3)P produced by complex I during autophagy recruits downstream effectors such as WIPIs and DFCP1, which leads to nucleation of phagophores ,245 Fig. The WD- 5 and 6 . For the 5 and 6 ,217. WIP 5 and 6 ,217,249  5 and 6 ,250 sepa 5 and 6 ,203. Fra 5 and 6 . AlthougUnlike autophagy, the endocytic pathway is branched into multiple pathways with various membranous compartments. These endosomal compartments are sorted in a variety of ways, and their membranes undergo a variety of membrane trafficking and deformations, including tubulation, fission/scission, tethering, and fusion. These are vital for receptor and ion channel recycling, formation of multivesicular bodies (MBVs), and endosome maturation from early to late endosomes ,253,254.As described in In endocytic pathways, there are a variety of PtdIns(3)P-binding proteins that typically carry the PX or FYVE domains ,260,261.A representative protein family for PX domain-carrying proteins is the large sorting nexins (SNXs) family . They ca2 [2. Because MTMRs and Fab1/PIKFyve are found mainly in late endosomes, PtdIns(3)P is less abundant there than in early endosomes, although it is still essential for the maintenance of late endosomes, since the lack of VPS34/Vps34 causes a loss of multivesicular bodies (MVBs) [The FYVE domain was named after the proteins Fab1, YOTB, Vac1, and EEA1. Compared with PX domains, it appears that FYVE domains are more specialized for PtdIns(3)P binding . FYVE-co2 ,283. This (MVBs) ,284,285 As mentioned above, interactions with Rab1a and Rab5a greatly increase the activities of complex I and complex II, respectively ,45. OtheIn addition, a variety of proteins, including Ambra1 , PAQR3 , GORASP2Recent progress in cryo-EM and cryotomography, and in other biophysical techniques such as HDX-MS and single molecule kinetics, has helped understand the detailed activation mechanisms of VPS34/Vps34 complexes. These approaches have revealed that the kinase domain of VPS34/Vps34 is not the sole activation determinant of activity. Instead, activity is greatly influenced by the binding subunits of the complexes, lipid environments, and accessory regulatory proteins. Therefore, in some human diseases, where the above influencing factors are altered, it might be possible that the VPS34 activity is significantly altered even though the genes encoding the VPS34 complex subunits are not mutated. To examine this, developing a quantitative, physiologically relevant, and handy method to measure VPS34 activity in vivo as well as in vitro is essential. In the cell, since the PtdIns(3)P-driven signalling in autophagy and endocytic pathways is regulated spatially and temporally, it is conceivable that VPS34/Vps34 complexes may interact with their binding proteins and membranes only transiently, as the cases of the complex I–Rab1a and complex II–Rab5a interactions. Methods to precisely screen these weak and transient interactions such as BioID will be powerful tools for the future proteomics of VPS34 complexes. Lastly, this knowledge on activation mechanisms can be applied to translational efforts to develop more specific activators and inhibitors for the treatment of human diseases.


Human ovarian folliculogenesis is a highly regulated and complex process. Characterization of follicular cell signatures during this dynamic process is important to understand follicle fate . The transcriptional signature of human oocytes and granulosa cells (GCs) in early-growing and ovulatory follicles have been previously described; however, that of oocytes with surrounding GCs in small antral follicles have not been studied yet. Here, we have generated a unique dataset of single-cell transcriptomics (SmartSeq2) consisting of the oocyte with surrounding GCs from several individual (non-dominant) small antral follicles isolated from adult human ovaries. We have identified two main types of  follicles, with a distinct oocyte and GC signature. Using the CellphoneDB algorithm, we then investigated the bi-directional ligand–receptor interactions regarding the transforming growth factor-β (TGFβ)/bone morphogenetic protein (BMP), wingless-type (MMTV)-integration site (WNT), NOTCH, and receptor tyrosine kinases (RTK) signaling pathways between oocyte and GCs within each antral follicle type. Our work not only revealed the diversity of small antral follicles, but also contributes to fill the gap in mapping the molecular landscape of human folliculogenesis and oogenesis. Human ovarian folliculogenesis represents one of the most complex biological processes within the body and protocols to recapitulate folliculogenesis in vitro in clinical settings are highly desirable for fertility preservation purposes ,2. Its cIn the human ovary, the generation of primordial ovarian follicles, consisting of an oocyte arrested at the diplotene stage (dictyate) of meiotic prophase I surrounded by a single layer of flat (or squamous) granulosa cells (GCs) occurs before birth ,5. SubseTwo main subtypes of GCs are present in antral follicles: the cumulus GC (surround and provide support to the oocyte) and the mural GCs  ,12. AlthBased on the maturation state of the nucleus, oocytes can be classified into germinal vesicle (GV), metaphase I (MI), and metaphase II (MII). It is the peak of luteinizing hormone (LH), produced by the pituitary, that allows the GV oocyte (arrested in dictyate) to undergo GV breakdown, during which the nuclear envelope disintegrates. The oocyte then resumes meiosis, proceeds through the MI stage, extrudes the first polar body (PB) and arrests in the MII stage until fertilization takes place . StudiesRecently, we and others have used single-cell transcriptomics to reveal the molecular landscape of human ovarian follicular cells from different growing stages ,18,19,20n = 6) undergoing fertility preservation  and smooth muscle cell markers (TAGLN and RGS5), indicating that cells in CL2 may include endothelial and perivascular cells  and mural GCs  [Surprisingly, the GCs in our dataset (CL0) showed no separation between cumulus GCs (AKIRIN1) ,23 , presumably all in GV stage, as during collection no polar bodies were observed. To confirm the GV signature, we used two published single-cell transcriptomics datasets where human oocytes in GV, MI, and MII stages, after ovarian stimulation, were collected and sequenced ,25. In t, presumaNext, we performed unsupervised hierarchical clustering of the retained oocytes, including our nine  oocytes (AFOs), using the 100 most highly variable expressed genes, and obtained three main branches (OO.group): one with MII oocytes that underwent ovarian stimulation (MII group); one with the GV and MI oocytes that underwent ovarian stimulation as well as three AFOs (GV/MI group); and one with six AFOs (GrO group) A. SimilaPTEN, RRM2, WTAP, DNMT3A, SMAD2, DNAJA2, LHX8, and YBX2  and calculated the associated Gene Ontology terms . Genes hVCAN, FST, GJA1, TNNI3, GSTA1) and steroidogenic enzymes, such as CYP11A1, CYP19A1, and HSD3B2 (AR (Androgen receptor) , compelled us to investigate whether the matching GCs in the corresponding follicles also presented separate signatures. First, we calculated the mean gene expression of GCs (cells from CL0) per antral follicle and investigated the expression of known GC markers . The mead HSD3B2 A. The GCeceptor) A, which eceptor) .FCER1G, CD53, AIF1, and CX3CL1  and to distinguish GrO from GV   contained a GrO oocyte and GC2 GCs , type B (AF-B) contained a GrO oocyte and GC1 GCs (AF8.7), and type C (AF-C) contained a GV oocyte and GC1 GCs  C. To val, DPPA5) D. From t, DPPA5) E,F. Hencp < 0.05 and component genes expressed in at least 30% of the cells (oocytes or GCs) per AF group  in the GCs and high expression of BMP6, BMP15, GDF9 in oocytes, whereas Activin receptors were expressed in oocytes and GDF7, INHBB, and INHA in GCs (The transforming growth factor-β (TGF-β) superfamily have been shown to have an essential role in folliculogenesis ,36. We aA in GCs B. In agrA in GCs C and pSMA in GCs D. In agrJAG1 being expressed in GrO and GV oocytes, but NOTCH1 mostly expressed in GC1 GCs  mediated signaling, which involves growth factors, such as IGFs, FGFs, NRGs, VEGFs F. From t in AF-C F. Moreovollicles F,G. ConcOur work is the first to investigate the transcriptional signatures of oocytes and surrounding GCs in individual human small antral follicles. We have revealed that two types of oocytes (GV and GrO) could be found in small antral follicles, but also highlighted the heterogeneity of GCs surrounding the oocyte in the cumulus area. Previous studies on the molecular characterization of GCs from small antral follicles either pooled cells of the ovary or pooled cells of the same antral follicle ,20,42,43It is well established that a LH peak triggers resumption of meiosis in ovulatory follicles, but that the oocytes in the (adjacent) growing follicles present in the same ovary should remain in the GV stage (or undergo atresia). However, during the procedure of ovarian tissue cryopreservation (without ovarian stimulation) oocytes in MII stage have been found during tissue preparation in both prepubertal  and pubeOocytes in GV, MI, and MII stages can be distinguished by morphology (number of polar bodies), however in the data set analyzed, we and others  were unaThe small data size is the main limitation of our study. Nevertheless, we provided evidence for cellular heterogeneity in the GCs located in the region of the oocyte, reflecting ongoing differentiation to cumulus and mural GCs in small antral follicles. For example, the dynamic expression pattern of pKRT and CYP19A1 in GCs was associated with cumulus/mural differentiation in the region close to the oocyte. After we pooled the GCs by follicle, we identified two groups of GC signatures in healthy small antral follicles. However, to identify the subtypes of GCs in a systematic way, more GCs from a larger number of follicles is needed.From the antral follicles used for transcriptomics, we noticed that follicle type AF-A (GrO oocyte and GC2 GCs) only contained follicles from patient P7, which had sickle cell disease (SCD). Notably, the AF-A follicle (AF9.3b), identified by immunostaining, was collected from patient P9 that was also diagnosed with SCD. It is still unclear whether SCD results in sub-fertility; however, lower ovarian reserve and lower levels of AMH has been reported in the serum of SCD patients ,46. Our The bi-directional communication between oocyte and the surrounding GCs regulates both follicle growth and oocyte maturation . Other tn = 6) from P3, P8, P9, and P10 were used for immunostaining and a total of 14 antral follicles from P2, P7, and P8 were used for single-cell RNA-sequencing. For single-cell RNA-sequencing, the small antral follicles (1–8 mm) were collected in saline solution . The follicles were mechanically ruptured with tungsten needles and the region with the cumulus GCs and oocyte was trimmed and transferred into a clean dish. Individual follicular cells were manually picked up using a pulled glass-capillary under a stereo microscope . Oocytes were denuded by pipetting up and down. None of the collected oocytes contained a polar body. Each cell was transferred into a cold PCR tube contain 1.9 μL 0.2% TritonX-100 in DEPC water and 0.1 μL Recombinant RNase inhibitor , snap-frozen on dry ice, and stored at −80 °C.Small antral follicles used in this study were collected from 6 female patients of reproductive age , undergoing fertility preservation procedures. From 6 patients, 3 were oncological patients, 2 were diagnosed with sickle cell disease, and 1 with beta-thalassemia. Ovaries from patients P2, P8, and P10 were in the follicular phase, while from P3, P7, and P9 were in the luteal phase (contained a corpus luteum). Small antral follicles (Single-cell full-length cDNA libraries using the Smart-seq2 were prepared as previously described  and sequ6) from Seurat. Next, the top 1000 highly variable features (genes) were selected with FindVariableFeatures function. Principal Component Analysis (PCA) was performed using these top 1000 genes. The first 11 PCA dimensions were used to calculate cell clusters and project the cells on a two-dimensional plot using Uniform Manifold Approximation and Projection (UMAP) algorithm. After observing a batch effect on cell clustering and UMAP projection, the function fastMNN from batchelor package (v1.6.0), with adjusted parameters (k = 5) was used to correct it. New cell clustering and UMAP projection were calculated based on the batch corrected components. The count matrix was analyzed with a Seurat based workflow (v4.0.3)  using R For the hierarchical clustering, the top most variable genes were selected using R function rowVars from package genefilter (v1.72.1) . DiffereThe raw data (sequencing reads) of 7 GV, 7 MI, and 7 MII oocytes from Yu and colleagues  were dowp < 0.05, the retained receptor–ligand pairs with component genes expressed in more than 30% oocytes or GCs per antral follicle group. In brief, the receptor–ligand pairs selected for TGFβ/BMP signaling were filtered by key  gene symbols including ACVR, BMP, INHBA, INHBB, AMH, MIS, TGF, and GDF; NOTCH was used for NOTCH signaling; WNT, FZD, RSPO, LGR, LRP5, and LRP6 were used for WNT signaling; CSF1R, EGFR, EPHA2, ERBB2, ERBB3, ERBB4, FGFR, FGFR2, FGFR3, FGFR4, FLT1, FLT3, FLT4, IGF1R, INSR, KDR, KIT, MET, NGFR, NTRK1, NTRK2, PDGFRA, PDGFRB, and TEK were used for RTK signaling. The dot_plot function from CellphoneDB was applied to visualize the results.The CellphoneDB algorithm (v2.1.2)  was usedHuman ovarian tissue was fixed overnight in 4% paraformaldehyde (PFA) at 4 °C and then embedded in paraffin on a Shandon Excelsior tissue processor . Tissue sections (5 μm thickness) were deparaffinized in xylene and rehydrated in ethanol with a sequential dilution and distilled water, the sections were then transferred into 0.01 M sodium citrate buffer (pH 6.0) and heated for 12 min at 98 °C in a TissueWave 2 Microwave . To prevent non-specific antibody binding, tissue sections were treated with 1% BSA-0.05% Tween 20-PBS buffer for 1 h at room temperature (RT), followed by overnight incubation with primary antibodies at 4 °C. The primary antibodies used were mouse anti-PCNA , mouse anti-CD68 , rabbit anti-C1Q , goat anti-FOXL2 , mouse anti-StAR , rabbit anti-Collagen Type IV , mouse anti-CYP19A1 , mouse anti-Cytokeratin , mouse anti-Connexin-43/GJA1 , rabbit anti-S100B , mouse anti-β-Catenin/CTNNB1 , goat anti-DPPA5 , rabbit anti-ZO-1/TJP1 , mouse anti-AMH , mouse anti-N-Cadherin/CDH2 (GC-4) , rabbit anti-phospho-SMAD2 (Ser465/467) , rabbit anti-phospho-Smad1 (Ser463/465)/Smad5 (Ser463/465)/Smad9(Ser465/467) , rabbit anti-NOTCH2 , goat anti-JAG1 . Tissue sections were then washed 3 times (2 times with PBS and 1 time with 0.05% Tween 20-PBS) and incubated with secondary antibodies and DAPI  for 1 h at RT. The secondary antibodies used were Alexa Fluor 488 donkey anti-rabbit IgG , Alexa Fluor 594 donkey anti-mouse IgG , and Alexa Fluor 647 donkey anti-goat IgG . The In Situ Cell Death Detection Kit (FITC)  was used to stain for TUNEL according to the manufacturer’s instruction. ProLong Gold (Life Technologies) was used to mount the sections. Immunofluorescence was imaged on an inverted confocal microscope  with LAS software . ImageJ software [To calculate the DEGs for each cluster, we used the Wilcoxon rank sum test and p-value was adjusted with a Bonferroni correction (default setting from Seurat workflow). Statistics associated with the GO enrichment (using DESeq2) and the CellphoneDB analysis were integrated in the respective algorithms ,60.In conclusion, our transcriptional analysis on human small antral follicles highlighted the heterogeneity of GCs proximal to oocyte, the diversity of molecular signatures in different follicles, and the importance to study oocyte-granulosa crosstalk based on individual follicles.


Acrostalagmus, and its species A. luteoalbus as the major constituents of the mixed microbiota in the wet cork liner from an outdoor wall. Representatives of the genus were also present in the settled dust in offices where occupants suffered from symptoms related to the indoor air. One strain, POB8, was identified as A. luteoalbus by ITS sequencing. The strain produced the immunosuppressive and cytotoxic melinacidins II, III, and IV, as evidenced by mass spectrometry analysis. In addition, the classical toxigenic species indicating water damage, mycoparasitic Trichoderma, Aspergillus section Versicolores, Aspergillus section Circumdati, Aspergillus section Nigri, and Chaetomium spp., were detected in the wet outdoor wall and settled dust from the problematic rooms. The offices exhibited no visible signs of microbial growth, and the airborne load of microbial conidia was too low to explain the reported symptoms. In conclusion, we suggest the possible migration of microbial bioactive metabolites from the wet outdoor wall into indoor spaces as a plausible explanation for the reported complaints.Occupants may complain about indoor air quality in closed spaces where the officially approved standard methods for indoor air quality risk assessment fail to reveal the cause of the problem. This study describes a rare genus not previously detected in Finnish buildings,  Aspergillus, Penicillium, Trichoderma, Fusarium, Chaetomium, Streptomyces, Bacillus, and Nocardiopsis species, which produce bioreactive metabolites such as mycotoxins [The term “microbiota” refers to the microbial community in a defined environment. The term “microbiome” refers to the total genome of such microbiota. In buildings, the colonizing microbiota and the microbiome are global and uniform compared to those outdoors, which are local and diverse ,2. In urcotoxins ,13,14,15cotoxins ,17,18, mcotoxins ,31,32,33The loss of biological diversity promotes dysbiosis, imbalances in the microbial ecosystem on or within the body, and loss of tolerance to environmental microbes ,41,42,43Recently, new indoor toxigenic fungi and their toxins have been identified using toxicity screening of culturable indoor fungal isolates ,65,66. MOccupants may complain about the indoor air quality in closed spaces where there are no visible signs of microbial growth and the airborne load of microbial particles is too low to explain the reported symptoms ,73. ThisAspergillus, Chaetomium, and Trichoderma and an unrecognized fungus, as shown in Four office rooms in a public building, 131a, 131b, 335, and 145b, , were asThe massive microbial growths cultivated on pieces of the moist cork liner  and mineral wool (sample 3MW) collected from outside the problematic rooms are shown in Acrostalagmus-like conidiophores (MT1), tree Aspergillus morphotypes differing in fluorescence emission and toxic response , two Trichoderma morphotypes differing in conidia size , and one toxigenic Penicillium morphotype (MT7). The final morphotype was obtained by microscopic inspection of the cork liner, representing a potentially toxic ascomata-producing Chaetomium-like morphotype (MT11). The major bacterial colonizers were spore-forming actinobacteria that were toxic in either BSMI or ICP or both (MT8-MT10).The diversity of the major fungal isolates was characterized as follows: biomass lysates of five colonies from each plate were tested for toxic responses by two rapid screening bioassays, boar sperm motility inhibition (BSMI) assay and inhibition of cell proliferation (ICP), and fluorescence emission. These bioassays showed that more than 70% of the tested colonies were toxic. Conidiophores and conidia/spores were inspected using a phase contrast microscope. The toxigenic colonies from plates A to K  were groAcrostalagmus luteoalbus (MT1), Trichoderma atroviride (MT5), Trichoderma trixiae (MT6), and Penicillium expansum (MT7). The isolates assigned to morphotypes MT2, MT3, and MT4 were identified as belonging to Aspergillus section Versicolores, section Circumdati, and section Nigri, respectively, based on similarity to the reference strains SL/3, PP2, and HAMBI-1271. The spore-forming actinobacteria were morphologically identical to each other but were separated into three morphotypes, MT8-MT10, based on their toxic response in the bioassays.Representatives of morphotypes MT1 to MT7 were identified by ITS sequencing as Acrostalagmus, Aspergillus, Trichoderma, Penicillium, and Chaetomium, in settled dust collected from problematic rooms 131a, 131b, 335, and 145b, from nonproblematic rooms 134 and 223, and from room 146, where the occupant did not complain but had an air cleaner installed. The plates containing cultured settled dust are shown in We looked for representatives of the genera colonizing the cork liner and mineral wool, Acrostalagmus (MT1); Aspergillus section Versicolores (MT2), Circumdati (MT3), and Nigri (MT4); mycoparasitic isolates similar to T. trixiae (MT5) and T. atroviride (MT6); and P. expansum (MT7) and Chaetomium isolates (MT11). One isolate, MH52, was identified by ITS sequencing as Chaetomium globosum. The results in Chaetomium-like colonies. Settled dust from rooms 223 and 134, located farther from the water-damaged site of the wall, contained mainly nontoxic Rhizopus; none of the 40 tested biomass dispersals were toxic in the bioassays, and the occurrence of toxic colonies was <5%.A majority (>70%) of the tested colonies in dust from the problematic rooms and room 146 showed a toxic response in the bioassays. The results in A. luteoalbus strain POB8 shown in The strains representing morphotype MT1, similar to Ethanol extracts prepared from plate-grown biomass of strains POB8, A1/K, A2/K, A3/K, A4/K, and POB1 exhibited similar blue fluorescence to the biomass dispersal shown in Acrostalagmus sp. strains compared with Acrostalagmus luteoalbus POB8 isolated from the cork liners and indoor dust, they were tested with four complementary bioassays. The tests are described and referenced in 50 concentrations for the toxicity endpoints from the assays measuring different biological activities. A comparison of the EC50 concentrations obtained in the four bioassays revealed a characteristic and uniform toxicity profile for the five Acrostalagmus sp. isolates and A. luteoalbus strain POB8. For the six Acrostalagmus strains, the toxic endpoints in terms of EC50 concentrations were similar in the three assays, around 10 µg mL−1, indicating that the Acrostalagmus extracts, in contrast to the extracts of reference strains, inhibited sperm motility and cell proliferation at the same concentrations after 1 and 3 d, respectively. The low toxic endpoints in the SMID assay also indicated a rapid lethal effect in sperm cells exposed at 37 °C. Briefly, the toxicity profile revealed by the bioassays indicated that the Acrostalagmus extracts exhibited lethal and cytostatic toxicity when exposed to cells at 37 °C and a motility-inhibiting effect when exposed to cells at 22 °C for 1 d. None of the Acrostalagmus strains exhibited a rapid motility-inhibiting effect after 20 min of exposure at 22 °C. This uniform toxicity profile of the six Acrostalagmus sp. isolates  and A. luteoalbus POB8 separated them from the reference strains. This indicates that the strains may have produced the same bioactive metabolites, strengthening the hypothetical identity of the five Acrostalagmus sp. strains as A. luteoalbus.To reveal the diversity of the five A. luteoalbus strain POB8  , III (712 g/mol), and II (696 g/mol), respectively, reported earlier [The ethanol extract of -ESI-MS) A–D. On tz 1479.0 B. Compouz 1447.0 C. Compou17.7 min D. The ob earlier ,75. 1–3 were similar to those reported for the dimeric epipolythiodioxopiperazines [1 were identical to the reported MS/MS spectrum of melinacidin IV [1, 2, and 3 of A. luteoalbus were identified as melinacidin IV, III, and II, respectively. The amounts of melinacidin IV (230 µg mL−1), III (290 µg mL−1), and II (120 µg mL−1) in the ethanol extract of A. luteoalbus were calculated from the total absorbance (220 nm) of the HPLC-UV chromatograms.The obtained MS/MS data of compounds erazines ,77,78. Tcidin IV . FurtherAcrostalagmus luteoalbus, was detected as building-associated mold for the first time. This study illustrated that the mixed mycobiota cultivated from the wet wall and from settled indoor dust in problematic rooms seemed to contain the same major fungal species and genera. This indicates a possible connection between outdoor structures and indoor spaces  was introduced as a generic distinction between the former sections Verticillium and Nigrescentia [Verticillium in building materials and cork has been described, but no isolates have been identified as Acrostalagmus species [A. luteoalbus isolate has been shown [Interestingly, a rare mold genus identified as e strain ,66,79,80escentia ,82,83,84 species ,85. The en shown ,86. The A. luteoalbus strain POB8 produces melinacidins II, III, and IV. Melinacidin derivatives were previously reported from a variety of fungi including Acrostalagmus luteoalbus (syn. A. cinnabarinus) [Aspergillus fumigatus. Epipolythiodioxopiperazines have antiproliferative, cytotoxic, immunomodulatory, antiviral, and antimicrobial activity in vitro [This study presents the mass spectrometry data characteristics of melinacidins and shows that barinus) ,86. Meliin vitro ,87 and hin vitro ,88,89,90in vitro . This ma50 concentration in the BMSI and ICP assays for the tested melinacidin mixture was calculated as 0.3 to 0.6 µg mL−1. Based on a similar toxicity profile and blue fluorescence, it is possible, but not proven, that the liquid exudates secreted from the growing biomass of POB8  close to the wet wall, but not from dust from the two more remote nonproblematic rooms. The fungi representing the genera aterials ,79,92,93aterials ,95.−3) was too low to explain the reported symptoms. However, the negative pressure of 3–4 Pa and a sealing repair that was performed led to the suspicion of air leakage through the building structure [Occupants in the rooms complained about indoor air quality even though no signs of microbial growth or water damage were detected, and the airborne load of microbial particles  were detected, but the indoor air quality complaints continued. Samples had been taken from the rooms by methods approved for official use in Finland for health risk assessment of indoor air, i.e., VOC and airborne microbes with the Andersen impactor [A new survey of rooms 131a and 131b early in 2013 revealed a negative pressure of 3–4 Pa in relation to outdoor pressure, so sealing repair was performed. No elevated concentrations of airborne microbes  and on tryptic soy agar plates . Settled dust collected on cotton swabs was streaked on malt extract agar. Plates were incubated for 4 weeks at 22–24 °C.The methods used for cultivating the mold colonies were described in . Pieces A scheme illustrating the experimental design for tracking the diversity of major microbial constituents in building material and dust is shown in ® standard v. 11.0.06, 2012, Olympus Soft Imaging Solutions GmbH, Münster, Germany) and compared to reference strains according to Samson [Separation of the isolates into morphotypes (MTs) was based on the toxicity profile and morphology of conidiophores, conidia, ascomata, and ascospores obtained with a phase-contrast microscope  and image recording software /ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) and EF595F (5′-CGTGACTTCATCAAGAAGATG-3′)/EF1160R (5′-CCGATCTTGTAGACGTCCTG-3′), respectively [Selected representatives of the morphotypes were identified in previous studies by ITS or ectively .Rapid screening tests applied directly to the primary sampling plates to measure (a) the toxins affecting the cellular energy metabolism, mitochondria, and ion homeostasis based on inhibition of boar spermatozoa motility (BSMI), and (b) the toxins affecting macromolecular synthesis and cytostatic activity based on inhibition of the proliferation of somatic cell line PK-15 (ICP) were previously described in detail ,79. In t50 concentrations, i.e., the lowest concentration of ethanol-soluble dry substances per mL of target cell suspension causing an adverse (toxic) effect in 50% of exposed cells. The test procedures and calculation of EC50 for the ethanol-dry substances and pure mycotoxins in the BSMI, SMID, and ICP assays were described previously [M and BSMIR assays measured motility after exposure of motile and resting spermatozoa, respectively. The SMID assay measured lethal toxicity as loss of plasma membrane integrity. The ICP assay measured cytostatic toxicity and cell death as the loss of the proliferating ability of growing somatic cells, which may be caused by inhibition of macromolecular synthesis and/or induction of necrotic or apoptotic cell death [Toxicity assays involving the ethanol extraction of lipophilic bioactive peptides and mycotoxins ,66,80,93eviously ,80,112. ll death ,66,73,80A. luteoalbus strain POB8 was harvested from a malt extract agar (MEA) plate incubated at room temperature for 10 days. The collected biomass, about 200–400 mg wet weight of A. luteoalbus, was extracted with ethanol, and the toxic ethanolic extract was analyzed by high-performance liquid chromatography–ion trap mass spectrometry (HPLC-IT-MS) as described by Salo et al. [m/z 50–2000. ESI source parameters used for analysis were: nebulizer gas pressure, 35 psi; drying gas flow rate, 8 L min−1; drying gas temperature, 350 °C; and capillary voltage, 5000 V. The column used was a SunFire C18, 2.1 × 50 mm, 2.5 μm . Separation of compounds from the ethanol extract of biomass of A. luteoalbus strain was done using an isocratic method of solution A, H2O with 0.1% (v/v) formic acid, and B, methanol in a ratio of 40/60 (v/v), for 15 min and a gradient of 100% B for 50 min at a flow rate of 0.2 mL min−1. Identification of compounds of the toxic ethanolic extract was done by HPLC-IT-MS and MS/MS analysis. The amounts of identified compounds were calculated from total absorbance (220 nm) of the HPLC-UV chromatogram of the A. luteoalbus POB8 ethanol-extracted biomass.The biomass of o et al. . HPLC–el


Cladonia metacorallifera var. reagens KoLRI002260 is a rare lichen known to produce phenolic compounds, such as rhodocladonic, thamnolic, and didymic acids. However, these metabolites have not been detected in isolated mycobionts. We investigated the effects of six carbon sources on metabolite biosynthesis in the C. metacorallifera mycobiont. Red pigments appeared only in Lilly and Barnett’s media with fructose at 15 °C after 3 weeks of culture and decreased after 6 weeks. We purified these red pigments using preparative-scale high performance liquid chromatography and analyzed them via nuclear magnetic resonance. Results indicated that 1% fructose-induced cristazarin and 6-methylcristazarin production under light conditions. In total, 27 out of 30 putative polyketide synthase genes were differentially expressed after 3 weeks of culture, implying that these genes may be required for cristazarin production in C. metacorallifera. Moreover, the white collar genes Cmwc-1 and Cmwc-2 were highly upregulated at all times under light conditions, indicating a possible correlation between cristazarin production and gene expression. The cancer cell lines AGS, CT26, and B16F1 were sensitive to cristazarin, with IC50 values of 18.2, 26.1, and 30.9 μg/mL, respectively, which highlights the value of cristazarin. Overall, our results suggest that 1% fructose under light conditions is required for cristazarin production by C. metacorallifera mycobionts, and cristazarin could be a good bioactive compound.Lichens are a natural source of bioactive compounds.  Lichens are symbiotic organisms composed of a lichen-forming fungus (the mycobiont) and an alga, a cyanobacterium, or both (the photobiont). Lichens produce various characteristic secondary metabolites, such as depsides, depsidones, dibenzofurans, pulvinates, chromones, and quinones. Notably, these metabolites, which have been detected in extracts of lichen thalli, are also produced when isolated mycobionts are cultured without their algal partners ,2,3,4. HCladonia cristatella mycobiont ), as per Culberson’s improved standardized method [v/v/v]). Analyses were monitored using a photodiode array detector  with a range of 190–800 nm throughout the HPLC run. The observed peaks were scanned between 190 nm and 400 nm. The sample injection volume was of 10 μL. The standards that were used were obtained acetone extracts from the following sources: rhodocladonic acid (retention time [tR] = 2.6 min) and didymic acid (tR = 10.7 min) isolated from the lichen thallus of Cladonia macilenta, thamnolic acid (tR = 3.3 min) from the lichen thallus of Thamnolia vermicularis, and cristazarin (tR = 3.4 min) and 6-methylcristazarin (tR = 8.1 min) from liquid cultures of C. cristatella Tuck.Secondary metabolites were analyzed via thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC). Prior to the TLC analysis, lichen thalli and cultured isolated fungal mycelia were soaked in 1 mL acetone for 5 min, and 50 µL of the concentrated extract was spotted around 2 mm in diameter on Silica gel 60 Fd method , was usetR = 3.4 min), putative cristazarin was purified using preparative HPLC (prep-HPLC). A total of 50 μL acetone extract was injected at a time. Following the purification of the 3.4 min tR peak (putative cristazarin), the purified metabolite was concentrated after ethyl acetate extraction and then subjected to a nuclear magnetic resonance (NMR) analysis.To confirm the presence of cristazarin based on the observed HPLC peak  with a Bruker AMX-500 spectrometer  at 500 MHz for 1H NMR spectra and at 125 MHz for 13C NMR spectra. Chemical shifts were calculated using tetramethylsilane as an internal standard. 1H and 13C NMR assignments were supported by 1H−1H correlation spectroscopy, heteronuclear multiple-quantum coherence, and heteronuclear multiple-bond correlation experiments.The chemical structures of the isolated compounds were determined by 1- and 2-dimensional NMR spectroscopies. Genome sequencing information was obtained from NCBI (accession number AXCT02000000) . The ann–ΔΔCt _ENREF_53 . T. TC. metic acids c. There c acids) . Based oC. metacorallifera mycobiont showed a different pigmentation on each of the six media, except for the control, which contained no carbon source  was analyzed to reveal a possible correlation between the expression of PKS genes in C. metacorallifera and cristazarin production.The expression of putative PKS genes a under cC. metacorallifera under non-treated fructose medium as a control.To select the most stable reference gene under the conditions that were assessed, we evaluated six candidate genes: the β-tubulin, α-tubulin, elongation factor 1β, ubiquitin extension protein, actin-2, and glyceraldehyde-3-phosphate dehydrogenase genes. Using the GeNorm software , we evalCmPKS genes . A total of 25 genes were classified into three groups according to their gene expression pattern  and white collar-2 (wc-2) genes are essential for most of the light-mediated processes in several fungal species [C. metacorallifera needed time and light to produce cristazarin. Furthermore, wc-1 and wc-2 orthologs in C. metacorallifera were identified according to the InterPro classification [Cmwc-1 and Cmwc-2.Previous studies have revealed that the transcription factors white collar-1 .In order to investigate the biological activity of cristazarin produced by C. metacorallifera, we determined the cytotoxicity of cristazarin in six cancer cell lines and one non-cancer cell line, namely HaCaT (human keratinocytes), by exposing these cells to different concentrations of cristazarin (concentration range: 0.78–100 μg/mL). The ICll lines . The AGSTrebouxia spp., Myrmecia spp., and Coccomyxa spp., sorbitol in Hyalococcus spp., and erythritol in Trentepohlia spp. [Nostoc spp., Scytonema spp., and Rhizonema spp. [C. metacorallifera is Coccomyxa viridis, which produces ribitol. Ribitol is a crystalline sugar alcohol (C5H12O5) that is formed by the reduction of ribose and converted into mannitol by the mycobiont via the pentose phosphate pathway [C. metacorallifera produces unique phenolic substances such as didymic, squamatic, barbatic, rhodocladonic, and usnic acids, which are synthesized via the polyketide pathway by an acetyl-CoA carboxylase that produces malonyl-CoA [Lichen photobionts are green algae or cyanobacteria that produce different types of carbohydrates via photosynthesis. Green algae produce polyols, such as ribitol in lia spp. . Cyanobaema spp. ,47. The  pathway ,49,50. C pathway . In natuonyl-CoA ,52. C. cristatella mycobionts in MY extract medium without an additional carbon source [C. metacorallifera mycobionts only in the fructose-supplemented medium under fluorescent light conditions, and this substance was revealed to be composed of cristazarin and 6-methylcristazarin. It is reported that some lichen substances have been detected only in symbiosis, but others have produced their compounds cultured only in cultured mycobionts [According to Yamamoto, Matsubara, Kinoshita, Kinoshita, Koyama, Takahashi, Ahmadjiam, Kurokawa and Yoshimura , cristazn source . HoweverC. metacorallifera produced cristazarin only in the medium with 1% fructose, but not in the media supplemented with glucose, sucrose, ribitol, mannitol, or sorbitol. Specifically, unlike the three kinds of sugar alcohols, pale red pigments were observed in media supplemented with glucose and sucrose. These sugar alcohols and glucose are commonly present in the thalli of lichens that have green algae as photobionts. Thus, sugar alcohols and glucose, which are already familiar with symbiotic mycobionts, can be difficult to become stressors or inducers that express genes related to secondary metabolites. The secondary metabolite production of lichen mycobionts is influenced by the carbon source [The mycobiont of n source ,51. ConvC. cristatella has also highlighted that sucrose promotes pigment production more than sugar alcohols do, and 4% is the optimal concentration in an LB medium [One of the main differences between aldoses and ketoses is that the carbonyl group in aldoses is present at the end of the carbon chain, while that in ketoses is present in the middle of the carbon chain. Moreover, the three sugar alcohols are also derived from the reduction of an aldose . Among the carbohydrates used in the present study, fructose was the only ketohexose with six carbon sources and the only carbon source that stimulated the production of cristazarin. Based on these results, it is expected that cristazarin will also be produced from sucrose, which is a mixture of fructose and glucose, and actually pale red pigment was shown. A previous study on B medium . Lichens produce many phenolic compounds, such as depsides, depsidones, dibenzofurans, pulvinates, chromones, and quinones. Furthermore, without symbiotic photobionts, mycobionts produce anthraquinones and cristazarin ,6. Some CmPKS3, CmPKS4, CmPKS9, and CmPKS15, which belong to group I-2, were highly expressed. This group presumably includes the PKS domain that synthesizes single aromatic ring polyketides, such as orcinol (orsellinic acid). In addition to group I-2, most of the other gene groups were also expressed after 3 weeks of culture, a time which corresponds with the appearance of red pigments. Furthermore, MTT assay of cristazarin clearly showed the potential anticancer drug  and Staphylococcus aureus (20 μg/mL), as well as antitumor activity (100 μM) [Overall, we showed that (100 μM) , it coul