Background: Preceding suicide attempts strongly predict future suicidal acts. However, whether attempting suicide per se increases the risk remains undetermined. We longitudinally investigated among patients with mood disorders whether after a suicide attempt future attempts occur during milder depressive states, indicating a possible lowered threshold for acting.Methods: We used 5-year follow-up data from 581 patients of the Jorvi Bipolar Study, Vantaa Depression Study, and Vantaa Primary Care Depression Study cohorts. Lifetime suicide attempts were investigated at baseline and during the follow-up. At follow-up interviews, life-chart data on the course of the mood disorder were generated and suicide attempts timed. By using individual-level data and multilevel modeling, we investigated at each incident attempt the association between the lifetime ordinal number of the attempt and the major depressive episode (MDE) status .Results: A total of 197 suicide attempts occurred among 90 patients, most during MDEs. When the dependencies between observations and individual liabilities were modeled, no association was found between the number of past suicide attempts at the time of each attempt and partial remissions. No association between adjusted inter-suicide attempt times and the number of past attempts emerged during follow-up. No indication for direct risk-increasing effects was found.Conclusion: Among mood disorder patients, repeated suicide attempts do not tend to occur during milder depressive states than in the preceding attempts. Previous suicide attempts may indicate underlying diathesis, future risk being principally set by the course of the disorder itself. Patients with depressive or bipolar disorder have a high risk for suicidal ideation, suicide attempts –3, and dTime plays a major role in suicidal behavior in major depressive disorder (MDD) and bipolar disorder (BD) . Over 70stress-diathesis model by van Heeringen and Mann  recognizes three main constructs, including dispositional vulnerability factors, cognitive processes associated with psychiatric disturbance, and cognitive processes associated with suicidal acts   model (Three-Step Theory (3ST)   postulatde (ITS) , a non-fV) model  hypothesry (3ST)  postulatA clear consensus exists of a suicide attempt being a major risk indicator for a subsequent attempt , 26. HowThe hypothesis of a suicide attempt directly increasing the likelihood of a subsequent suicide attempt has rarely been addressed in longitudinal clinical samples. Previous findings indicate that each past suicide attempt at baseline increases the risk for a next attempt by approximately a third –34. ThesBased on a large well-characterized prospective mood disorder cohort and life-chart methodology, we aim to examine within an individual whether sequential suicide attempts are associated with a lower level of depression severity. In addition, we explore whether an inverse association exists between the cumulative number of suicide attempts and the time between successive suicide attempts.Altogether 597 patients within three representative screening-based mood cohorts were followed for 5 years . The projects were collaborations between the University of Helsinki and Helsinki University Central Hospital, Department of Psychiatry; the Department of Public Health Solutions, Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland; and the Primary Health Care Organization of the City of Vantaa, Finland. The appropriate ethics committees approved the study design. The detailed methodologies of each cohort from baseline to the 5-year follow-up have been previously published –43.A total of 3,555 primary or secondary care patients showing symptoms of a mood episode were screened for either bipolar disorder (JoBS), MDD (VDS), or unipolar depressive disorder (PC-VDS). Informed consent was requested after full disclosure of the study protocols. The DSM-IV axis I diagnoses were made by structured clinical interview for DSM-IV disorders, SCID-I/P  in JoBS Patients were re-interviewed at 6, 18 months, and 5 years in JoBS (SCID-I/P) and VDS (SCAN 2.0 at 6 and 18 months and SCID-I/P at 5 years), and at 18 months and 5 years in PCD-VDS (SCID-I/P). In addition, patients were extensively assessed with rating scales on clinical symptoms and suicidal ideation. A total of 407 patients (68.2%) were interviewed at the 5-year follow-up. Detailed information on the study design and follow-up procedures is presented in A graphic life-chart was created at all interviews based on DSM-IV criteria to depict the course of the mood disorder throughout the follow-up. This graphic life-chart was created by combining information from all available sources including the diagnostic interviews, study rating scales, and medical and psychiatric records. In addition, information from scales used in routine clinical practice was variably available. During these extensive interviews, a judgement was made of the course of the mood disorder during the follow-up. Changes in mood episodes or phases were timed both by combining all aforementioned information and by using probes of important life events to enhance the accuracy of the chart. The phases of depressive symptoms were classified based on DSM-IV criteria as (1) remission (no MDE criteria symptoms), (2) partial remission (one to four of the nine symptoms), and (3) full MDE (at least five criteria symptoms). In JoBS, in addition hypomanic, manic, mixed, mixed depressive, cyclothymic, or substance-induced phases and subsyndromal states of hypomania of BD were recorded. However, the minimum duration for hypomania was two instead of 4 days, and mixed depressive states were included.A suicide attempt, by definition, was specified as self-injurious behavior involving at least some degree of intention to die . At baseline, we examined the lifetime number of suicide attempts by combining the information from both interviews and psychiatric records. At follow-ups, the occurrence of suicide attempts was investigated by patient interviews and psychiatric and medical records. Mood episodes and their timing were assessed first and independently from suicide attempts to limit circumventing the evaluation of a mood state to be based on suicidal behavior, and vice versa.We investigated the association between the number of previous suicide attempts and clinical MDE status at the time of a new monitored attempt. The number of previous suicide attempts at the time of an attempt formed a count variable, which can be modeled using Poisson regression . Note thi at attempt j isSpecifically, the multilevel Poisson regression model assumes that the logarithm of the expected number of suicide attempts (Y) preceding the current one for patient 0 is the baseline rate (those in full remission), xij is a dummy indicator for partial remission, and β1 is the associated effect difference to full remission, fij is a dummy indicator for full MDE and β2 is its associated effect difference to full remission, and ηi is the patient-specific liability of patient i. If many preceding suicide attempts at the time of a new attempt are associated with higher proportions of partial remission and remission stages, negative β1 and β2 coefficients are expected. The null hypothesis is β1 = β2 = 0. Substantive interpretation of the random intercept (ηi) is complicated by the fact that it reflects both the deterministic condition that a second suicide attempt during the follow-up is preceded by n + 1 attempts if the first attempt was preceded by n attempts (etc.), and the stochastic condition of the overall higher n values in those with repeated attempts.where βWe studied our power to detect different effect sizes using a simulation procedure typical in multilevel modeling . Poissonn = 22, having 99 repeat attempts), subtracted within-patient averages from time intervals of successive attempts, and investigated the association between the cumulative number of repeated suicide attempts and the time since a previous attempt. We estimated both ordinary correlation coefficients and a linear mixed model with patient-specific random intercepts, plus scatterplots.When studying cycle length acceleration in MDEs, over-representation of highly and consistently recurrent individuals among those with short inter-morbid intervals could result in the appearance of shortening of intervals between MDEs . To assen = 15) or medication-induced hypomania (n = 1) during the follow-up were excluded. Of the remaining 581 patients included here, altogether 90 had a total of 197 suicide attempts during the follow-up . These attempts represent our sample of interest in this study. Of the 597 patients included at the baseline, individuals with a suicide attempt during a mixed-state MDE  = 0.382, p = 0.821], but is subjected to bias due to dependent observations and the possible dependence of MDE status on time since the MDD-based recruitment to study (baseline). The latter remains an unlikely confounder because the time from baseline to an attempt and MDE status at the time of an attempt were uncorrelated . To model observation dependencies (trait-like differences in liability), we implemented a multilevel Poisson model predicting the number of previous suicide attempts at the time of a new attempt with (dummy) indicators of MDE status. Statistical power of the approach was first investigated.In terms of an average MDE status , the data showed no clear association between the within-individual cumulative number of suicide attempts and the clinical status of depression at the time of each attempt. Instead, most attempts occurred in full MDE . An ordiηi in Equation 1) with standard deviations 0, 1, 2, and 4 were tested, corresponding to RRs of 1, 2.7, 7.4, and 54.6, respectively. The RR value of 54.6 represents a case of large between-individual differences in liability. With the smaller between-individual differences, we had a good power to detect 3-fold numbers of suicide attempts associated with a one-step decrease in MDE status; with large between-individual differences, only strong (5-fold) effects could be reliably detected. However, model estimates indicated that the data were in the smaller range of between-individual differences  was statistically non-significant . When considering observation dependencies by logistic multilevel regression, MDE status was still unassociated with an observed suicide attempt being either first or next in order [p = 0.559].Altogether 31 first attempts occurred during the follow-up period. Contrary to the general supposition, on average first attempts occurred in less severe MDEs  than repeat attempts . By omitting observation dependencies, the difference  nor fixed effect of a linear mixed model  was significant when looking for an association between attempt number and time between attempts. Although repeat attempts beyond 13 were lumped together in these data, their removal did not influence the conclusion .The above analyses argued against the idea that the risk of a new suicide attempt grows with each attempt because repeat attempts occur in progressively less severe depression. The risk of repeating a suicide attempt could still grow by each attempt because depression or other triggers of suicidal behavior occur more often, in accelerating rapidity. Considering the unadjusted time intervals between successive suicide attempts, it may seem like the interval lengths are negatively associated with the cumulative number of repeat attempts during the follow-up . HoweverWe used longitudinal one-of-a-kind individual-level data to investigate the widely held hypothesis that a suicide attempt directly increasing the risk for a reattempt. Specifically, we prospectively and by individual-level analyses examined whether successive suicide attempts occurred among patients with MDD or BD at increasingly lower levels of depression. The influence of the course and severity of depressive morbidity was considered using life-chart data. The main finding, with both theoretical and clinical implications, is that the data provided no evidence for a suicide attempt directly increasing the risk for a reattempt.To our understanding, the study hypothesis was tested in a unique study design. The major strength was the availability of the longitudinal data on the course of the mood episodes by life-chart methodology, combined with the accurate timing of suicide attempts. Individual-level data enabled the modeling of within-individual differences in clinical status at the time of each sequentially monitored suicide attempt. The screening-based sampling yielded previously undiagnosed patients, assuring representativeness. Pooling data from the three representative cohorts to increase patient numbers was possible due to similar study methodologies. The study diagnostics were based on semi-structured interviews  with high inter-rater agreement. The drop-out rate during long-term follow-up was relatively limited and only a few patients missed all follow-ups. Although we could have had a better statistical power for small effect sizes, the power was still good for moderate effect sizes. Data of this kind are rare, and we did not observe any indication that the null hypothesis should be rejected.Although at the follow-ups, extensive information was gathered by interviews and scales, the main limitation of the study is the depiction of life-charts by three-step ordinary scales of remission, partial remission, and full MDE. This restriction may have resulted in ceiling effects when severity above the diagnostic threshold at each attempt was neither measured nor modeled. However, this is a likely limitation for all prospective clinical studies until more detailed data on a daily basis become available, for example, through ecological momentary assessments. The intensity of suicidal ideation may have varied at the time of the attempt. Ambiguities remain pertinent to underlying the specific constructs of psychological theories of suicidal behavior, which remained unassessed with actual instruments. However, suicidal ideation was, by definition, present at the time of each attempt irrespective of possible theoretical constructs driving its formation. The constructs involving engaging in suicidal acts were implicitly modeled by examining the probability to attempt suicide one more time either at lower levels of depression, or at an accelerating rate from the previous attempt. Both outcomes are clearly defined and are of clinical and theoretical interest. Some circularity between the timing of suicide attempts and the course and severity of MDEs may be unavoidable. To minimize confounding, the life-charts were reconstructed first and independently from the timing of suicide attempts, and information on both was complemented with medical records. In addition, attempting suicide seldom determines whether the diagnostic criteria for an MDE are met . Recall Patients with depressive or bipolar disorder are a high-risk group for suicidal behavior, accounting for over half of all suicides  and admiprior to the first attempt and remained thereafter. Accordingly, childhood adverse experiences may be associated with specific current and definable clinical characteristics, such as borderline personality disorder traits, which may mediate their effects on predisposition to attempting suicide . The patients/participants provided their written informed consent to participate in this study.KA reviewed the literature, drafted the initial version, and prepared the manuscript. IH, MH, PJ, SP, KR, KS, and MV were active researchers in the respective study cohorts. TR provided statistical expertise and conducted the analyses. EI created the project as the principal investigator. All authors participated in the study concept and design, critical interpretation of data, revisions of the paper, and approval of the final version for submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The whole world is fighting with the COVID-19 pandemic, which traps people home, causing high business and economic losses, and above all, leads to very serious deaths. The lack of a valid, accepted treatment protocol and vaccine that leads to continued treatment searches. Leng et al published their article in the Aging and Disease journal, which demonstrates that mesenchymal stem cells (MSCs) can be used for COVID-19 treatment. Adipose tissue is one of the most important MSCs sources in the body, and adipose derived stromal cells (ADSCs) from adipose tissue are also one of the most valuable components of stromal vascular fraction (SVF). Finally, Gentile and Sterodimas, have also published their article for the potential use of SVF in COVID-19 treatment in Aging and Disease journal. Their publication has been a guide in many ways. Adipose tissue-derived stromal cells have three main features: Immunomodulatory, anti-inflammatory and regenerative. Immunomodulator effects are used as a preventive in patients prone to disease; its anti-inflammatory effects may allow them to be used as a therapeutic during active disease period and finally regenerative effects to repair post-disease sequale. Those cells can be obtained not only enzymatically, but also mechanically with very benefits. They can be delivered not only systemically through the IV route but also to the target organ with a carrier. While suggesting any adipose tissue-derived treatment method possibility, the relation of adipose tissue COVID-19 should not be ignored. Because, COVID-19 shows its effect through ACE-2 and adipose tissue is very rich and important tissue in terms of ACE-2. This situation changed the balance of the whole world; in addition to three hundred thousands of deaths, it created a global economic loss of $ 3.5 trillion and fear of infection that affects all people . This is the biggest crisis that human beings experienced in the 21st century. Big crises, such as war or this kind of pandemics, can provide new treatment hopes or advance existing treatment methods. One of the best examples of this idea is that Morrestin experiences which promoting the healing in WW1, by transferring the fat tissue particles into the wounds of the injured soldiers [et al. in 2001 [According to the latest data, the COVID-19 pandemic, which affects 210 countries in the world, has infected approximately 8.6 million people as of June 17 and caused nearly 457.000 registered deaths  caused by COVID-19 [et al. presented clinical trials recorded in www.clinicaltrials.gov databases about stem cells and Covid-19 infection [th of March 2020 in their study, but as of June 19, 2020, this number was 49. In other words, the number of registered studies increased by approximately 500% over a period of 1 month. This is an indication that stem cell therapy will find a good place in the future . Ozturk et al. concluded that good outcomes from MSCs in COVID-19 infection due to their close intersections regarding pathogenesis of the disease and mechanism of action of MSCs [Similarly, stem cell applications, which are accepted as the treatment of the near future, have become increasingly important in the treatment of COVID-19. This topic was discussed in the medical literature started with the work of Leng et al in Aging & Disease journal . They puCOVID-19 . They stCOVID-19 . SimilarCOVID-19 . Also Oznfection . There w of MSCs . Finally of MSCs  .With thThere are two types of cells in all organs that make up our body—parenchymal cells responsible for the function of the organ and stromal cells that support them. Stromal cells provide tissue repair and renewal following stress, injury, illness, or aging, that is, from both intrinsic and extrinsic causes . MesenchMSCs attract particular attention due to their broad pharmacological effects, including anti-inflammatory, immunomodulatory, regenerative, pro-angiogenic and anti-fibrotic properties . MSCs arAdipose tissue is a very important mesenchymal stromal cell source, and stromal cells derived from adipose tissue are called ADSC. ADSCs are unique from bone marrow-derived MSCs (BM-MSCs) by the expression of the fatty acid translocase marker CD36 and the lack of expression of cell adhesion marker CD106 . Adiposein vivo [Adipose tissue is the largest reservoir in the body for MSCs and enables quality stromal cell production. Obtaining ADSC is much easier and cheaper than any other MSC. It does not cause any problems for both the machine and the experienced team. A ready-to-use, fresh, autologous stromal cell is obtained at the bedside in a very short time. This feature is very important in COVID-19 treatment because clinical trials almost universally use freshly thawed MSC stocks; however, cryo-preservation appears to impair the immunosuppressive properties of MSCs and shorten their persistence in vivo . There iin vivo . AlthougThe immunomodulatory effects of stromal cells have been primarily characterized as immunosuppressive, anti-inflammatory and tolerogenic. These 3 main features allow the use of stromal cells for 3 different periods: 1. Pre-infection period: Prevention, 2. Infection Period: Active disease treatment 3. Post-infection period: repair sequels and regeneration of organs affected. Basically, these effects are performed as an immunomodulatory as propagated by resident cells such as macrophages, T cells, and stromal cells.The best way to prevent COVID-19 infection is undoubtedly the vaccine. Unfortunately, however, still no vaccine has been found. A balance between pro- and anti-inflammatory mechanisms is critical in maintaining lung tissue homeostasis, therefore preventive topical/systemic stromal cell applications might be beneficial. A recent report from Wuhan suggests that SARS-CoV-2 induces a dysregulated immune response in severely ill COVID-19 subjects, characterized by reduced numbers of circulating memory T lymphocytes, as well as reduced helper / suppressor T cell and T Reg subtypes. It is tempting to speculate whether already dysfunctional immune responses in subjects with obesity may accentuate this SARS-CoV-2 effect on T cell function.  ADSCs fuγ-induced protein 10 (IP10), monocyte chemoattractant protein-1 (MCP1), macrophage inflammatory protein (MIP1A) and tumor necrosis factor-alpha (TNF-α) [Neighboring cells from oxidative stress and can support the viability and activity of neutrophils. Thus, ADSCs may help promote early microbial clearance while protecting native tissues. Interestingly, exogenously delivered ADSCs are known to express MCP-1, suggesting a potential mechanism for improved healing by supporting monocyte recruitment to the wound bed . Adipose (TNF-α) . The the (TNF-α) . A growi (TNF-α) . MSCs al (TNF-α) . Adminis (TNF-α) .The regenerative approaches have included extrinsic cell therapy such as the infusion of exogenous stem cells to repair the damaged structure of the respiratory system and intrinsic cell therapy such as the administration of small molecules to stimulate the endogenous lung stem/ progenitor cells for regeneration and replacement of the damaged structures . AdiposeOne of the key benefits of MSC-based therapies is their ability to preferentially home damaged tissues. It is worth elaborating on what exactly we mean by homing, as the term is often used vaguely in the literature. Following the recommendations of previous reviews, we take homing to encompass both non-systemic and systemic homing. In non-systemic homing, MSCs are transplanted locally at the target tissue and are then guided to the site of injury via a chemokine gradient. In systemic homing, MSCs are administered or endogenously recruited into the bloodstream and then must undergo a multi- step process to exit circulation and migrate to the injury site . Two priThe SARS-CoV-2 virus uses as a cellular entry the angiotensin-converting enzyme II (ACE2) receptor , which is widely distributed on the alveolar type II cells and capillary endothelium of the lungs, as well as in many other organs, including the cardiovascular, liver, kidney and gastrointestinal tract . For thiACE and RAS system are of great importance in the formation of hypertrophic adipocytes. The classical RAS has been found to be over-activated during the AT enlargement, thus elevated generation of angiotensin II (Ang II) may contribute to the obesity pathogenesis . TherefoAdipose-derived mesenchymal stromal cells can potentially be used not only for the treatment of COVID-19 active disease, but also for pre-disease protection or for regeneration of the affected organ after disease. SVF or TOST is a perfect combination of mesenchymal stromal cells. The use of this tissue cocktail, rather than mesenchymal stem cells alone, can have many advantages both for the practitioner and the patient. The application can be done on an IV route or an organ directly affected by a suitable carrier. In the light of current literature data, there is no negativity regarding the use of stromal cells obtained from adipose tissue in COVID 19. However, as adipocyte hypertrophy is associated with ACE 2, the application of hypertrophic adipocytes as grafts can be a potential problem. Normal adipocytes, not hypertrophic ones, should be preferred in order not to experience possible problems regarding the use of fat tissue itself in terms of plastic surgery. Using sharp blade systems, without blunt pressure, without killing adipose tissue, hypertrophic adipocytes are cut away and normal adipocytes can be obtained. This is the “new normal” that COVID-19 causes for the most applied fat grafts in aesthetic plastic surgery.


Chronic kidney disease (CKD) is a global health problem, estimated to affect over 500 million people worldwide.The rising burden of progressive CKD in LMICs will be associated with an inevitable rise in CKD-related complications such as secondary hyperparathyroidism and CKD-mineral bone disease (CKD-MBD). CKD-MBD is characterized by abnormalities of calcium and phosphate homeostasis, endocrine feedback, bone turnover and extra-skeletal calcification. The management of CKD-MBD was revolutionized in the 1970s following the introduction of activated vitamin D3  into clinical practice. Extreme phenotypes of the skeletal and extra-skeletal complications of CKD-MBD are now rarely encountered in developed countries. Advanced manifestations of CKD-MBD, however, are likely to be commonplace in LMICs where diagnosis of advanced CKD is likely to be delayed and resources, however simple, limited to manage its complications.Here, we present two historical cases from a tertiary renal unit in Edinburgh, Scotland, which exemplify the skeletal and extra-skeletal manifestations of CKD-MBD and highlight the efficacy of activated vitamin D3 as a treatment to control these sequelae. 3 analogues are inexpensive [c. £60 ($80) per patient year], they are one of the most efficacious drugs at our disposal; a fact that is easy to take for granted in the 21st century.Although activated vitamin DConflict of interest: none.


The COVID-19 pandemic is predicted to significantly affect patients with lung cancer, owing to its rapid progression and high mortality. Studies on lung cancer diagnosis and treatment during an epidemic are lacking. We analyzed the impact of COVID-19 on lung cancer diagnosis in Korea, where lung cancer incidence continues to rise.The number of newly diagnosed lung cancer cases in three university-affiliated hospitals during the pandemic and their clinical features were compared with lung cancer cases diagnosed during the same period in the past 3 years. The effectiveness of measures taken by the study hospitals to prevent nosocomial transmission was reviewed.p = 0.011). The number of lung cancers diagnosed during this period and the previous year remained the same.A total of 612 patients were diagnosed with lung cancer from February through June, 2017–2020. During the pandemic, the number of patients who sought consultation at the division of pulmonology of study hospitals dropped by 16% from the previous year. Responding to the pandemic, the involved hospitals created physically isolated triage areas for patients with acute respiratory infection symptoms. Wide-range screening and preventive measures were implemented, thus minimizing the delay in lung cancer diagnosis. No patient acquired COVID-19 due to hospital exposure. The proportion of patients with stage III–IV non-small-cell lung cancer (NSCLC) significantly increased (2020: 74.7% vs. 2017: 57.9%, 2018: 66.7%, 2019: 62.7%, The proportion of patients with advanced NSCLC increased during the COVID-19 pandemic.The online version contains supplementary material available at 10.1186/s12885-020-07544-3. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first discovered in Wuhan, China, spread throughout neighboring Asian countries and has become a global pandemic . Aside fOur researchers were also able to see a decrease in the number of patients presenting to the hospital. The Korean government policy was to refrain from using the hospital unless a severe symptom developed; the continuous warnings through broadcasts potentially caused hesitations in necessary healthcare visits . Cancer The epidemiology of patients with lung cancer was analyzed using the lung cancer cohorts at three teaching hospitals affiliated with Hallym University Medical Center , 9. EachOn January 20, 2020, the first case of COVID-19 was confirmed in South Korea. Since then, national health authorities have responded to the rapid spread of the virus by raising the infectious disease alert level on January 27, 2020. Preventive measures such as banning entry from Hubei, China, since February, reinforced personal hygiene , and social distancing practices were implemented. Therefore, we defined the period of collateral effect of the COVID-19 pandemic from February to June, when the national prevention policies were lowered. Newly diagnosed patients with lung cancer during this period were compared with patients diagnosed during the same period in the previous years. The inclusion criteria were: 1) patients, aged 18 years or older, who were diagnosed with pathological lung cancer between February and June during 2017–2020 and 2) presence of either small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC). The exclusion criteria were 1) metastatic cancer or uncontrolled cancer of a different organ, 2) recurrent lung cancer, 3) lymphoma, thymic cancer, and malignant pleural mesothelioma, and 4) radiological suspicion without pathologic diagnosis. To analyze the changes in availing healthcare services during the COVID-19 pandemic, we analyzed the weekly number of outpatients in the study hospital.P values < 0.05. Clinical features and relevant variables of patients with lung cancer diagnosed during the COVID-19 pandemic and patients with lung cancer diagnosed in previous years were compared using the chi-square test and t-test. The number of newly diagnosed patients with lung cancer was tallied in weekly intervals by year and month, and the changes in these numbers were comparatively analyzed with those of previous years. The number of patients by lung cancer subtype and stage was compared before and after the pandemic using the chi-square test or linear by linear association. The number of pulmonology outpatients was compared with that of the previous year using the same method.The collected data were analyzed using SPSS Statistics for Windows, version 26.0 . Statistical analyses were performed using two-tailed Data of 169 patients with lung cancer diagnosed during the COVID-19 pandemic between February and June 2020 and 443 patients diagnosed between February and June during 2017–2019 were analyzed (Table p < 0.001). Figure N = 138, 2018: N = 139, 2019: N = 166, 2020: N = 169, p = 0.605). There were no significant differences when the analysis was limited to NSCLC diagnoses only . Even with a decline in the number of outpatient visits, the number of new lung cancer diagnoses remained constant. There were also no differences in the percentage of histological subtypes by year and month  (Table p = 0.239).During the COVID-19 pandemic, the proportion of stage III or IV cancer was 74.7%, which was significantly higher than that of the previous years , while the proportion of stage I or II cancer decreased to 25.3% (1) Table . The decIn South Korea, prompt development and approval of the COVID-19 diagnosis kit enabled quick and wide-ranging screening since the early days of the pandemic. Screening was performed for those in direct contact with COVID-19 patients and those who showed symptoms of acute respiratory infection. Most university affiliated hospitals, including the hospitals in this study, have designated triage outpatient clinics and in-hospital wards as recommended by health authorities . From thWhile bronchoscopy is essential in the process of lung cancer diagnosis and staging, it is a high-risk, aerosol-generating procedure. Recently, bronchology societies published recommendations for the use of bronchoscopy during the pandemic . Most guIn addition to the impact of the infection, collateral effects such as restricted healthcare access and service provision must also be taken into consideration. In the present study, we observed that the percentage of patients with more advanced stage cancers among newly diagnosed NSCLC patients during the pandemic was higher than that of previous years.During the influenza H1N1 epidemic in 2009, a higher incidence of pneumonia and higher mortality were reported among patients with cancer, compared with that observed with the general population . NeverthThe collateral effects of COVID-19 pandemic on the healthcare system affected both healthcare providers and patients. With a growing number of COVID-19 patients requiring hospitalization, the reallocation of human and other resources is a common phenomenon among healthcare facilities . ConsequHowever, there is a problem that excessive concerns regarding COVID-19 beyond what is necessary could worsen the avoidance of healthcare facilities among patients with cancer and delay the necessary medical diagnosis and treatment. In the present study, the increased percentage of patients with stage III or IV cancer with a decreased percentage of patients with earlier stages of cancer in the NSCLC group suggested a presentational delay in the diagnosis of lung cancer. Patients in symptomatic stage I or II lung cancer may have been diagnosed at an advanced stage after disease progression due to presentational delay. In contrast, asymptomatic early-stage patients are mostly diagnosed through screening; it is postulated that the number of early diagnoses of lung cancer decreased due to a decline in the medical checkup rate. The longer the pandemic period, the more significant the impact can be expected. On the other hand, the percentage of patients with limited stage cancer increased, albeit statistically insignificant, in the SCLC group. Because symptom onset is more common with SCLC than with NSCLC, this result may be attributed to the possibility that these patients consulted at a healthcare facility early on owing to respiratory symptoms during the COVID-19 pandemic .The COVID-19 pandemic also impacted the treatment process. Anticancer therapy or surgery was postponed or canceled. A modeling study that analyzed the impact of delayed cancer surgery due to the COVID-19 pandemic reported that a three-month and six-month delay of surgery decreased the anticipated life-years gain after surgery by 19 and 43%, respectively . MoreoveThe mortality from SARS-COV-2 infection is higher among patients with cancer than in the general population. In a cohort study of 928 cancer patients confirmed with COVID-19 infection in the US, Canada, and Spain, the all-cause mortality rate was high at 13% . FactorsOur research has limitations. First, this study included a limited number of hospitals and patients. Second, long-term follow-up is required to evaluate the prognosis of patients with a delayed diagnosis of lung cancer. Some patients with slowly progressing lung cancer may be safe with late diagnosis. However, most patients progress rapidly, even with early lung cancer , 28. ConWe observed the presentational delay of NSCLC diagnosis during the COVID-19 pandemic. However, the proactive triaging of suspected patients, aggressive COVID-19 screening, and timely lung cancer diagnosis prevented a decrease in the number of diagnoses. Although the mission to control COVID-19 pandemic is essential, national health authorities should prepare accurate assessments and countermeasures of any collateral effects, which may threaten the accessibility of diagnostic management for patients with cancer. Creating a safe healthcare system during this pandemic is essential for effective clinical service delivery to patients with serious diseases such as cancer.Additional file 1 : Figure S1. The weekly mean number of lung cancer diagnoses during the 5 months, 2017–2020 (A). The weekly number of lung cancer diagnoses during the 5 months in the COVID-19 pandemic in Korea (B).


This study aimed to quantify the elongation patterns of the collateral ligaments following TKA during functional activities of daily living. Using mobile video-fluoroscopy to capture radiographic images of the knee in a group of six patients, each with an ultra-congruent knee implant, tibiofemoral kinematics were reconstructed throughout complete cycles of level gait, downhill walking, stair descent, and squat activities. Kinematic data were then used to drive subject-specific multibody knee models to estimate length-change patterns of the LCL as well as three bundles of the MCL. In addition, a sensitivity analysis examined the role of the attachment site in the elongation patterns. Our data indicate a slackening of the LCL but non-uniform length-change patterns across the MCL bundles (ranging from lengthening of the anterior fibers to shortening of the posterior fibers) with increasing knee flexion angle. Near-isometric behavior of the intermediate fibers was observed throughout the entire cycle of the studied activities. These length-change patterns were found to be largely consistent across different activities. Importantly, length-change patterns were critically sensitive to the location of the femoral attachment points relative to the femoral component. Thus, in TKA with ultra-congruent implants, implantation of the femoral component may critically govern post-operative ligament function. The importance of proper tensioning of the collateral ligaments for the clinical success of total knee arthroplasty (TKA) has been comprehensively established.12in vivo and dynamic functionality. The few studies that have assessed in vivo length-change patterns of the MCL and LCL have generally exploited image-based approaches, combining static video-fluoroscopy and 3D modelling of the knee joint in order to track the relative movement of the ligament attachment sites, and thereby estimate their length-change patterns.28To define the optimal ligament tension during TKA and avoid postoperative complications, a thorough understanding of the length-change patterns experienced by the collateral ligaments in natural and replaced knees during different functional activities is crucial. However, such knowledge is extremely limited due to a lack of studies assessing their ex vivo studies have comprehensively described the morphology of the collateral ligament attachment sites,In addition to accurate knowledge of subject-specific kinematics, image-based estimation of ligament elongation patterns requires the ligament attachment sites to be accurately identified. Previous This study aimed to assess the length-change patterns of the collateral ligaments of TKA patients throughout level walking, downhill walking, stair descent, and body-weight squat based on the comprehensive kinematic and anatomical measurements gained within the CAMS-Knee datasets.48For this analytical, observational cohort study (level of evidence III), six subjects  each with an ultra-congruent knee implant  were measured 64 to 87 months post-operatively within the CAMS-Knee project.+ femoral component. A mechanically aligned TKA was targeted for all subjects, however postoperative assessment indicated 3.8° ± 1.8° varus limb alignment. When needed, minimal release of the anterior fibres of the MCL was performed to ensure intraoperative tension balance between the collateral ligaments.All subjects had size M tibial components. Apart from one subject with a size M, all other subjects had a size MThe implanted knee of each subject was scanned pre- and post-operatively using a computed tomography (CT) scanner , and ending at the ligament insertion. The ligament length-change patterns were calculated as a percentage of the reference length and presented against the corresponding normalized time or implant flexion angle. The reference length of each ligament bundle was defined as the length at heel strike averaged across all trials of level walking.α =0.05.A one-way repeated-measures ANOVA, based on statistical parametric mappingr) between the maximum length-change of the ligament bundles and the shift in the position of their attachment points, as well as, the slope of linear best-fit were calculated.A Monte Carlo analysis was performed to assess the influence of the ligament attachment sites on the ligament elongation patterns. The ligament attachment points for a single subject were randomly perturbed from their nominal location based on Gaussian distributions  direction) to generate 500 new models. The baseline tibiofemoral kinematics from the squat activity were fed into the random models and the resultant length-change patterns were analyzed. The Pearson correlation coefficient . However, the other bundles demonstrated bi-phasic length-change patterns including continuous slackening of the LCL  and pMCL  until 70% GC, with steady lengthening of these bundles thereafter. Conversely, the aMCL was stretched to 4.6 ± 1.5% during downhill walking and similarly 4.6 ± 2.0% during stair descent, with recovery to its reference length by the time of the next heel strike.During squatting, the LCL slackened with increasing the knee flexion until it reached its shortest length (− 9.6 ± 2.0%) at deep knee flexion . The maximum elongation of the aMCL was changed by 0.4% when its femoral attachment was moved anteroposteriorly by 1 mm (r = − 0.48). The second largest impact on the maximum length-change of the ligament bundles was due to a variation of the femoral attachments in the proximodistal (P-D) direction . Perturbing the femoral attachment of the ligament bundles in the mediolateral (M-L) direction had no considerable effect on the length-change patterns. Similarly, the tibial attachment locations did not substantially influence the collateral ligament elongations (sensitivities ranging from − 0.1 to 0.2% per mm (− 0.08 < r < 0.10), Fig. The uncertainty analyses on the location of ligament attachment sites revealed that the maximum length-change of the ligaments was most strongly correlated with the anteroposterior (A-P) location of the femoral attachments Fig. . The LCLThis study reported length-change patterns of the collateral ligaments in a group of patients with an ultra-congruent knee implant design.in vitro investigations on natural and replaced knees have consistently reported the LCL to be tightest in full extension, and to relax as the knee flexes,et al.et al.et al.et al.Until now, our current knowledge on collateral ligament function has been almost exclusively obtained from cadaveric studies. Here, in vivo length-change patterns of the collateral ligament bundles during a forward-lunge and the stance phase of the level walking, and reported flexion-dependent patterns both in healthy and TKA subjects.in silico investigation indicating lack of significant task-dependency in the ligament elongation patterns for the knees replaced with three different implant designs.To overcome the invasive nature and technical limitationsWhile image-based investigations of the ligament elongation patterns (including this study) have provided critical insights into the function of the collateral ligaments in living human subjects, a sensitivity study to quantify errors induced into the ligament elongation patterns by the uncertainty in the ligament attachment sites has been clearly lacking. Importantly, a number of parameters including the choice of imaging plane, scan quality and resolution, and the observer’s anatomical knowledge are known to affect the accuracy of the ligament attachment sites obtained from medical images.et al.36Implantation of the TKA components relative to the native anatomy may vary substantially depending on the surgical technique.A number of limitations to this study need to be considered when interpreting the presented results. Our study is based on the measurement of only six patients, each implanted with an ultra-congruent TKA design that is known to limit the kinematics of the knee joint,In conclusion, for the first time, this study has revealed the elongation patterns of the collateral ligament bundles in TKA patients throughout complete functional activities of daily living, while also confirming the previously observed distinct elongation characteristics of the different bundles. Regardless of the activity type, close to isometric behavior of the intermediate bundle, lengthening of the anterior bundle, and shortening of the posterior fibers of the MCL were observed with increasing knee flexion angle. These data emphasize the importance of the anterior fibers of the MCL to support postoperative knee stability and suggest that these fibers are carefully considered during ligament balancing in TKA to prevent them from over-stretching or hyper-laxity during dynamic activities. Moreover, sensitivity of the ligament elongation patterns to the location of their femoral attachment sites relative to the femoral component highlights the critical role of femoral component alignment in governing the ligament elongation patterns after TKA with ultra-congruent implant designs.


Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious “shortcuts.” Because this approach to data collection has also been used to obtain training data for detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, since the undesired “shortcuts” learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of ML healthcare models. While numerous recent publications and preprints report machine learning models with high performance at this task8, the trustworthiness of these models needs to be rigorously evaluated before deployment in a clinical setting9.The prospect of applying artificial neural networks to the detection of COVID-19 in chest radiographs has generated interest from machine learning (ML) researchers and radiologists alike, given its potential to (i) help guide management in resource-limited settings that lack sufficient numbers of the gold-standard reverse-transcription polymerase chain reaction (RT-PCR) assay, and (ii) clarify cases of suspected false negatives from the RT-PCR assayi.e., “shortcuts”10. While such spurious associations may arise in any dataset, we observed that many recent ML models for radiographic detection of COVID-19 were trained using data with the potential for near worst-case confounding: these datasets are composed of an exclusively COVID-19 negative source and a COVID-19 positive source, such that any systematic differences between the sources correlate perfectly with COVID-19 status8. Similar combinations of data sources, where the source label correlates with disease status, have also been used to train AI systems for detection of COVID-19 in computed tomography scans and11 and for other medical imaging tasks13, implying that our findings have broad implications to the field of medical machine learning.Our findings in this study support the troubling possibility that these models fail to learn the true underlying pathology reflecting the presence of COVID-19 and instead leverage spurious associations between presence or absence of COVID-19 and radiographic features that reflect variations in image acquisition, 15 19. These inquiries reveal how seemingly high-performance AI systems may derive the majority of their performance from the exploitation of undesired shortcuts, highlighting the need to verify that AI systems rely on the desired signals.In this study, we evaluate the trustworthiness of recent deep learning models for COVID-19 detection from chest radiographs. After training deep convolutional neural networks15  in the m22.114 that was not only used in these recent publications, but that also has been popular in previous works on radiographic classification. To train and evaluate these models, we created two datasets  to performance on external radiographs from Dataset II. While our models attain high performance on internal test data, taset II , left. T24. These findings argue for routine reporting of metadata on potential patient, hospital system, and preprocessing confounds. By illuminating the construction of radiographic datasets in greater detail, these data will make it easier for domain experts to predict likely sources of confounding. Additionally, these metadata enable the construction of models that explicitly control for confounds, providing a route to AI systems that generalize well even in the context of confounded training data27. In contrast, we note that a popular set of approaches to improve generalization performance, known as “unsupervised domain adaptation,” are precluded by the presence of worst-case confounding because these methods rely on learning models invariant to data-source labels, which will be perfectly correlated with the pathology labels28.While we initially expected that a dataset built from radiographs drawn from a single region would be less likely to contain spurious correlations that enable ML models to take shortcuts, we found that models trained on Dataset II also exhibit high performance on internal test data and low performance on external test data , right. 2.3i.e., those with higher capacity, may be particularly prone to learning confounds29, so we evaluated the generalization performance of simpler models, including a logistic regression and a simple convolutional neural network architecture, but found that the generalization gap did not improve  vs. posterior-anterior (PA) view27. Reliance on such confounds, which do not consistently correlate with COVID-19 status in outside datasets, helps explain the previously observed poor generalization performance.We further interrogated the trained AI models using saliency mapsediction , to deteediction , which sediction , which dtion  to transform COVID-19 negative radiographs to resemble COVID-19 positive radiographs and vice versa. This technique should capture a broader range of features than saliency maps, as the GANs are optimized to identify all possible features that differentiate the datasets. Consistent with our knowledge of how radiologists detect evidence of COVID-19 in chest radiographs, the GAN increases the radiopacity or radiolucency of the lung fields bilaterally to respectively add or remove evidence of COVID-19, indicating that neural network models are capable of learning genuine markers of COVID-19  and 8. H2.5We next aimed to experimentally validate the importance of spurious confounds to our models by manually modifying key features –d. We fistill be due to spurious correlations rather than genuine radiographic evidence of COVID-19. For example, if there is a consistent difference in the proportion of men and women who are COVID-19 positive, and a model can predict patient sex with high accuracy in both internal and external test data, this indicates that the model’s external test performance could be due to this clinical, but non-radiographic information. We find that the ability of deep convolutional neural networks to detect patient sex and radiographic projection generalize well  improved choice of the prediction task to involve more clinically relevant labels, such as a numeric quantification of the radiographic evidence for COVID-1937. However, we demonstrate that shortcut learning may occur even in a more ideal data collection scenario, highlighting the importance of explainable AI and principled external validation. While AI promises eventual benefits to radiologists and their patients, our findings demonstrate the need for continued caution in the development and adoption of these algorithms9.Our findings support common-sense solutions to alleviate shortcut learning in AI systems for radiographic COVID-19 detection, including (i) improved collection of training data, 44.114. This architecture has not only been used in a variety of recent models for COVID-19 classification5, but has also been used for the diagnosis of non-COVID pneumonia32, as well as for more general radiographic classification38.For our primary neural network, we used a convolutional neural network with the DenseNet-121 architecture to predict the presence versus absence of COVID-195, we first pre-trained the model on ImageNet, a large database of natural images39. Forcing models to first learn general image features should also serve as an inductive bias to prevent overfitting on domain-specific features27. After ImageNet pre-training, the final 1000-node classification layer of the trained ImageNet model was removed and replaced by a 15-node layer, corresponding to the 14 pathologies recorded in the ChestX-ray14 dataset plus an additional node corresponding to COVID-19 pathology; while only the prediction for COVID-19 was used for evaluating the model, we followed previous works that showed simultaneous learning of multiple tasks was useful for achieving highest predictive performance32. To obtain a consistent label scheme, labels in the GitHub-COVID, PadChest, and BIMCV-COVID-19+ repositories were mapped to the 14 ChestX-ray14 categories.Following the approach in recent COVID-19 models−4, and learning rate of 0.01, which was decreased by a factor of 10 every 5 epochs. We chose a binary cross entropy loss as the optimization criterion. To prevent overfitting, we monitored the area under the ROC curve (AUROC) for COVID-19 classification on a held-out validation set, and chose the epoch with the highest validation AUROC as the final model. All models were trained for 30 epochs, which was long enough for all models to reach a maximum in the validation AUROC. All models were trained using the PyTorch software library40, version 1.4, on NVIDIA RTX 2080 graphics processing units and required approximately 5 hours of training time per replicate.The model was optimized end-to-end using mini-batch stochastic gradient descent with a batch size of 16, momentum parameter of 0.9, weight decay of 1029, we also trained two lower-capacity models. The first, an AlexNet model15, was trained in the same manner as the DenseNet-121, with the weights randomly initialized rather than pretrained on ImageNet. The second was a logistic regression with “deep features”: since individual pixels do not have stable semantic meaning over different samples in the dataset, we first extract a set of 1024 higher-level features using the feature embedding  of a DenseNet-121 trained on ImageNet and then fit a logistic regression to these fixed features. This procedure is accomplished by training the DenseNet-121 architecture with the weights of its feature embedding subnetwork frozen. The AlexNet and logistic regression were optimized using the same training parameters as the full DenseNet-121 model specified above. The fact that lower-capacity models did not generalize better in our setting may be due to the fact that Sagawa et al. focus on a reweighted training scheme29, while our models were trained to minimize empirical risk in order to replicate the training schemes used by recent COVID-19 detection models (see above).To test the hypothesis that lower-capacity models may not learn spurious correlations4.24. In this dataset, we collected COVID-19 negative images from the NIH ChestX-ray14 repository, representing 112,120 radiographs from 30,805 patients from the NIH Clinical Center21. We collected COVID-19 positive images from the GitHub-COVID repository20, representing 408 radiographs from 262 patients, where this data was originally collected from figures in scientific publications and assorted web sources of COVID-19 positive cases.To train and evaluate our models, we combined images from four large open-access repositories of chest radiographs into two datasets . The fir22. The COVID-19 positive images in our dataset were taken from the BIMCV-COVID-19+ dataset, which represents 1,596 images from 1,015 patients, from the same regional hospital system in Valencia, Spain23. We note that while PadChest and BIMCV-COVID-19+ originate from the same region, potential for confounding remains since (i) PadChest was collected from a single hospital whereas BIMCV-COVID-19+ was collected from multiple hospitals, and (ii) the repositories were collected over different time periods, over which image acquisition techniques may have changed.The second dataset, which we refer to as Dataset II, was designed to represent a more ideal case in terms of domain confounding – both COVID-19 positive and COVID-19 negative images were acquired from hospitals from a common region and were published by a shared research team. We collected COVID-19 negative images from the PadChest repository, representing 96,270 radiographs from 63,939 patients from a hospital in Valencia, Spain41, we filtered radiographs from the online repositories to include only PA and upright AP radiographs. Lateral radiographs, AP supine radiographs, radiographs with unknown projections, and computed tomography scans were excluded from the datasets. Images with absent radiographic windowing information, which was necessary to display radiographs from the BIMCV-COVID-19+ repository, were also excluded.Following the recommendations by Cohen et al.We partitioned each repository into training, validation, and test folds, ensuring that all radiographs of any given patient belong to a single fold. Since the ChestX-ray14 dataset specifies a “test” partition, we used these radiographs as part of our dataset I test fold. Of the remaining portion, 5% were reserved as a validation fold, while the rest were used directly for training. In the PadChest and BIMCV-COVID-19+ repositories, we reserved 5% of the radiographs for testing, and 5% of the remaining radiographs for validation. Due to the smaller size of the GitHub-COVID repository, we reserved 10% of the radiographs for testing, and 10% of the remaining radiographs for validation. With the exception of the ChestX-ray14 test fold, which was held fixed as explained above, the folds were drawn at random for each model replicate.4.3Expected Gradients19. Broadly, this approach captures the notion of “importance” by tracking how each pixel of an image impacts the output of the model when contrasted with a set of noninformative baseline examples, where the impact is measured by accumulating the model’s gradients  as the image is interpolated from the baseline example to the image of interest. Formally, the Expected Gradients attribution ∅ for an input sample x and input feature i is defined:D represents a background distribution from which reference samples x′ are drawn. This method is an extension of the popular saliency map approach Integrated Gradients, which is the special case of Expected Gradients in which there is only a single reference sample.To generate saliency maps, which enable interpretation of machine learning models by assigning importance values to each pixel of an input image, we apply a state-of-the-art approach known as 19 and the inclusion of multiple reference samples, which avoids the choice of a single reference that may be arbitrary but nonetheless impactful upon the resultant saliency maps42. Finally, path-based approaches like Expected Gradients and Integrated Gradients are preferable to other methods for generating saliency maps because they are theoretically principled: these methods are provably guaranteed to attribute importance to important pixels and guaranteed not to attribute importance to unimportant pixels  and pdata(Y) represent the data distributions for each domain. In addition to the adversarial loss, the networks are also trained to enforce cycle consistency, meaning that F (G(X)) = X. This is desirable, since it enforces a similarity between the original and transformed images. The loss here is:Since generative adversarial networks have previously been shown to be effective for the interpretation of neural networks, we learn these two transformations using the CycleGAN approachG or F to map the image to the opposite domain. We then compare which image features are changed in the transformation.The full loss that is optimized then is simply the sum of these three losses:43. To attain comparable training time, the networks for trained for 3000 epochs (Dataset I) or 1000 epochs (Dataset II). Each network required approximately one week of training time on an NVIDIA RTX 2080 graphics processing unit.Our CycleGAN networks were implemented in Python 3.7 using the PyTorch software library and an open-source implementation of the CycleGAN approach4.544. For example, saliency maps highlight dataset-specific laterality markers and text within the images. If these text markers are indeed important, then moving a marker from a COVID-19 positive image to a COVID-19 negative image should increase the predicted log odds of COVID-19. For a pair of COVID-19 positive and COVID-19 negative images, we swap the text markers and measure the change in the output for each image. To assess the significance of the change in the model’s output, we generate empirical p-values by comparing to a null distribution generated by swapping 1,000 random patches of each image of the same dimensions as the text markers  for a set of random, non-specific modifications as described in each caption. The p-value was then calculated as (r + 1)/(n + 1) where r is the number of non-specific modifications that produced a greater increase in model output (greater magnitude decrease in n is the total number of non-specific modifications45.In our experiments involving manual modification of radiographs , we comp5https://github.com/suinleelab/cxr_covid.All radiographs are compiled from publicly-available data repositories and links for download are provided at 6https://github.com/suinleelab/cxr_covid.All of the code necessary to reproduce our experimental findings can be found at 1


The management of device implantation during the COVID-19 infection has not well defined yet. This is the first case of complete atrioventricular block in a symptomatic patient affected by the COVID-19 infection treated with early pacemaker implantation to minimize the risk of virus contagion. Heart disease patients with the concomitant COVID-19 infection could have higher risk of mortality, and their management could be particularly challenging [Recently, the European Society of Cardiology (ESC) Guidance  and Italth 2020, a 75-year-old female, with no previous cardiovascular history or previous drugs taking, was admitted to our Emergency Department because of a traumatic syncope during fever. Five days prior to her admission, she had myalgia, cough, dyspnea, and fevers up to 39.5°C. At the electrocardiogram (ECG), the patient showed complete AV block with ventricular escape rhythm and atrial fibrillation. The laboratory tests revealed normal high-sensitivity troponin and ionemia, but high C-reactive protein . The echocardiogram showed normal biventricular function, while the chest X-ray revealed bilateral consolidations. The moderate hypoxia (PO2 = 64 mmHg) at the blood gas confirmed the hypothesis of a pneumonia; thus, a nasopharyngeal swab was performed, with a positive result for SARS-CoV-2 on the real-time reverse transcriptase–polymerase chain reaction assay.On March 12Although the consensus to prevent cardiac implantable electronic device (CIED) infections recommends to delay the procedure until a patient has afebrile for at least 24 hours . At lastIn the era of COVID-19, the management of heart disease patients with the concomitant virus infection has not been completely defined yet. Moreover, as other known cardiotropic virus in case of myocardial involvement , the hypSeveral guidelines have already proposed different approaches about the management of AV conduction disorders patients; however, no real-world data are available until now in this specific setting. Hence, the ESC recommendation  suggesteInterestingly, it is known that tPM patients are up to 2.5 times more prone to develop an infection before the pPM implantation , with hiTo our knowledge, this is the first report of a COVID-19 infection patient symptomatic for AV block treated with early pPM implantation. This approach allowed us to preserve all the medical equipment and the other patients from the potential viral contagion in a no COVID-19 hospital and to obtain the best outcome for the patient, transferring her to a dedicated COVID-19 area to continue the appropriate medical treatment.


COMT heterozygous-HEA-haplotype showed higher PLS1 FFA-MRI scores, which were positively associated with the FRA in an old/new face recognition task, than those with the COMT homozygous HEA haplotype and HEA non-carriers, suggesting that individuals with an appropriate (intermediate) level of dopamine activity in the FFA might have better FRA. In summary, our study provides empirical evidence for the genetic and neural basis for the heritability of face recognition and informs the formation of neural module functional specificity.Previous studies have shown that face-specific recognition ability (FRA) is heritable; however, the neural basis of this heritability is unclear. Candidate gene studies have suggested that the catechol-O-methyltransferase (COMT) rs4680 polymorphism is related to face perception. Here, using a partial least squares (PLS) method, we examined the multivariate association between 12 genotypes of 4 COMT polymorphisms (rs6269-rs4633-rs4818-rs4680) and multimodal MRI phenotypes in the human fusiform face area (FFA), which selectively responds to face stimuli, in 338 Han Chinese adults . The MRI phenotypes included gray matter volume (GMV), resting-state fractional amplitude of low-frequency fluctuations (fALFF), and face-selective blood-oxygen-level-dependent (BOLD) responses (FS). We found that the first COMT-variant component (PLS1) was positively associated with the FS but negatively associated with the fALFF in the FFA. Moreover, participants with the  Previous behavioral genetic studies have shown that face recognition ability is heritable6; however, the neural basis for this heritability is unclear. One candidate cortical region underlying inter-individual variability in face recognition is the human fusiform face area (FFA) especially in the right hemisphere, which shows more significant responses to visual face stimuli than to other non-face stimuli7 and represents a critical hub in the face network10. More importantly, features of the FFA are associated with face-specific recognition ability (FRA)3. However, information about the effects of genetic variability on FFA structure and function and their relationship with face recognition ability remain limited.Face recognition is a highly developed skill in humans that shows considerable variability between individuals11 and functional magnetic resonance imaging (fMRI) responses12 in the occipitotemporal cortex encompassing the FFA in monozygotic twins than dizygotic twins. Additionally, neural responses to facial expressions are modulated by common genetic variance in a set of regions constituting the face network that include the FFA13. These studies suggest that the FFA may be modulated by genetic variations. However, previous studies have not specifically localized the FFA, and it is unclear whether the observed genetic effects are derived from changes in the FFA or other regions. Notably, a previous genome-wide association neuroimaging study explored specific genes contributing to the FFA but failed to identify any significant genome-level correlations between single nucleotide polymorphisms (SNPs) and FFA-responses to facial expressions14. One possible reason for this negative result is insufficient statistic power due to a relatively small number of participants (N < 300).Previous twin studies have demonstrated the higher intra-pair similarity of cortical structure19. The COMT gene codes for the COMT enzyme, a protein that degrades catecholamine neurotransmitters and modulates dopamine metabolisms20. Among the COMT polymorphisms, Val158Met (rs4680) has been most reported to play a role in face processing24. For example, individuals with the Met/Met (rs4680) genotype are more sensitive to configural changes in faces than those with other genotypes15. Healthy homozygous subjects with the Met allele of rs4680 are better at recognizing facial emotions16 and show a stronger bias to perceive neutral faces as expressions of anger21 than those with other genotypes. Previous studies also investigated the impact of the COMT gene on the brain activity during face emotional processing25 and demonstrated that the COMT Met allele seems to contribute to neural substrates of negativity biases24, and the COMT Val allele is associated with increased amygdala activity in response to fearful/angry facial expressions25. It should be noted that several meta-analyses suggest that the effects of COMT Val158Met on cognitive abilities are either very small or nil due to insufficient statistical power, unknown moderators including genetic variations at different loci, publication bias, or effect heterogeneity28. Therefore, individual studies should pay more attention to the methodological rigor and scientific nature. Accumulating studies have revealed that there are multi-to-multi relationships between genotypes and brain phenotypes linked to learning and cognitive ability31. Previous studies have indicated that four COMT polymorphisms  have a strong linkage disequilibrium and always function as haplotype blocks to affect dopamine (DA) degrading enzyme activity33. However, little is known about whether certain patterns of the 4 COMT polymorphisms exert significant effects on the FFA and therefore affect the face recognition ability.Candidate gene studies have demonstrated the association of the catechol-O-methyltransferase (COMT) gene, located on chromosome 22q11.2, with face recognition37; accordingly, this study used multimodal MRI measures for the FFA , including GMV, fractional amplitude of low-frequency fluctuations (fALFF) from resting-state fMRI, and BOLD responses specific to faces  from task-state fMRI. Second, the partial least squares (PLS) approach was applied to examine relationships between sets of multivariate measures. This approach has demonstrated utility in studies with relative small samples and problems with multicollinearity40. Third, bilateral FFAs are involved in face processing simultaneously8, notwithstanding some hemispheric lateralization in their size and functional recruitments42. We conducted analyses for the right FFA (rFFA) and the left FFA (lFFA) separately to validate the genetic variants that modulate the FFA. Using these three strategies, we identified profiles of 4 COMT polymorphisms that would be robustly associated with profiles of structural or functional FFA, in a population of Han Chinese individuals.In this study, we employed a combined genetic and neuroimaging approach and incorporated three strategies to improve statistical power without dramatically increasing the number of participants. First, previous studies focused on a single measure of the FFA  or blood oxygen level-dependent [BOLD] responses); notably, multiple modals and joint modeling can improve sensitivity for identifying disease-associated biological pathways in common complex disordersCOMT genotypes contributed to the 3-modal right FFA-MRI phenotype in 338 participants. The PLSCA yielded 3 sets of paired latent variables (LVs) capturing the rFFA-SNP association, ordered by the size of the explained variance . Each component (LV pair) was a linear combination of the weighted COMT SNP scores that most strongly correlated with weighted MRI scores and was orthogonal to each other. The omnibus p value of the model was 0.004 and only the top PLS component (PLS1) was significant . Thus, the PLS1 represented a significant association  between a specifically patterned COMT genotypic profile and a specifically patterned rFFA phenotype , rs4818 AA genotype , rs4818 AG genotype , rs4633 TT genotype , and rs6269 AG genotype  reliably contributed to the PLS1 COMT-SNP component  and fALFF  reliably contributed to the PLS1 rFFA-MRI component  was conducted to examine whether the 12 p = 0.002, 95%CI = ), negatively correlated with fALFF , and not correlated with GMV of the rFFA   of the 12 COMT genotypes  modulates the 3 raw MRI measures of the rFFA, we correlated the PLS1 COMT scores with FS, GMV, and fALFF of the rFFA. The results showed that the PLS1 COMT profile was positively correlated with FS  Fig. . Thus, tp = 0.821). Moreover, the intra-class correlations (ICC) for the two half GMV, fALFF, FS, PLS1-SNP scores, and PLS1-rFFA scores were 0.985 , 0.995 , 0.989 , 0.999 , and 0.994 , respectively. Thus, both the rFFA measures and the association pattern between the rFFA phenotypes and the COMT polymorphisms exhibit high reliability. Second, to test whether the threshold for identifying the rFFA affects the main results, we conducted control analyses with 4 different thresholds  and found that the association between the 12 COMT genotypes and the rFFA-MRI phenotypes was significant in all of the 4 threshold conditions . Thus, the lFFA exhibits a similar association pattern with the COMT polymorphisms as the rFFA does.To valid the results and improve reliability, we conducted the following control analyses. First, we randomly split the voxels within the rFFA into two halves and entered each half of the rFFA measures along with COMT polymorphisms into a PLSCA. We found that both of the rFFA-COMT associations were significant  a high enzymatic activity (HEA) haplotype (rs6269/G-rs4633/C-rs4818/G-rs4680/G) with a frequency of 0.328, (2) an intermediate enzymatic activity (MEA) haplotype (rs6269/A-rs4633/T-rs4818/C-rs4680/A) with a frequency of 0.239, and (3) a low enzymatic activity (LEA) haplotype (rs6269/A-rs4633/C-rs4818/C-rs4680/G) with the highest frequency of 0.389  = 0.003, p = 0.958]. Tukey’s HSD post hoc tests showed that COMT heterozygous-HEA carriers had significant larger PLS1 rFFA-MRI scores than COMT homozygous HEA carriers  and non-HEA carriers . Thus, participants with the COMT heterozygous-HEA-haplotype (intermediate DA degrading enzymatic activity therefore an intermediate level of DA) showed increased FS and decreased rFFA-fALFF (positive FFA scores of PLS1), whereas participants with the COMT homozygous HEA haplotype (high DA degrading enzymatic activity therefore a low level of DA) and HEA non-carriers (low DA degrading enzymatic activity therefore a high level of DA) showed decreased FS and increased rFFA-fALFF (negative FFA scores of PLS1).Because the four COMT SNPs has been previously found to be in strong linkage disequilibrium and define three common haplotypes with functional consequences on dopamine (DA) degrading enzyme functionlotypes:  a high elotypes:  a high ep = 0.029; left Panel of Fig. p = 0.003, Cohen’d = 0.4, 95%CI = ), suggesting that the COMT-modulated rFFA-MRI profile was more relevant to the face recognition ability than to the flower recognition ability. Further, the association between the FRA  and the PLS1 rFFA-MRI scores was significant  when sex, age, and Raven scores were treated as nuisance covariates  , suggesting that the COMT-modulated bilateral FFA-MRI profile might have similar association patterns with the FRA. To make our results comparable to those in previous studies using the signal detection theory, we calculated the sensitivity index (d’) for face recognition and flower recognition. No significant correlations were found between face sensitivity (d’) and the PLS1 FFA-MRI score  and flower recognition accuracy score with the PLS1 rFFA-MRI scores and the PLS1 COMT scores, respectively. The results showed that face recognition accuracy was positively correlated with the PLS1 rFFA-MRI scores  face recognition and new (relatively unfamiliar) face recognition. To control the effect of response bias on the accuracy48, we calculated the response bias [c = −Z(hit rate) + Z]49 and regressed the bias out of the hit rate and correct rejection rate to obtain the old-face recognition score and new-face recognition score. A significant association was found between the new-face recognition score and the bilateral PLS1 FFA-MRI scores , suggesting that the COMT-modulated FFA-MRI profile was more sensitive to the new-face recognition ability than to the old-face recognition ability.Finally, because the FFA has been demonstrated to be involved in the process of recognizing familiar and unfamiliar facesCOMT polymorphisms. Participants with the COMT heterozygous-HEA-haplotype showed higher PLS1 FFA-MRI scores, which represented an increased FS and a decreased fALFF and were associated with a better face-specific recognition ability, than those with the COMT homozygous HEA haplotype and HEA non-carrier, suggesting that participants with an appropriate (intermediate) level of dopamine in the bilateral FFA may have better face-specific recognition ability. Our study provides empirical evidence of genetic modulation of a functionally-specialized cortical region in humans.In the present study, we explored the neural basis of the heritability of face recognition by investigating associations between 12 COMT genotypes and 3-modal FFA-MRI phenotypes. The results revealed that a profile of face-selective responses and resting-state neural fluctuations of the bilateral FFA were modulated by a profile of 3, and this association is domain-specific3. In this study, we revealed that a profile (linear combination) of higher face selectivity BOLD responses, as well as lower resting-state activities in the bilateral FFA, was associated with a higher face recognition  ability, and the association could not be accounted for by FFA responses to objects such as flowers, suggesting that the face-specific recognition ability might be modulated by a certain covariation pattern of resting-state and task-state activity in the FFA, which was modulated by a certain profile of the COMT polymorphisms.Previous studies have found that participants with higher face selectivity in the FFA have higher face recognition abilityCOMT Val/Met (rs4680) polymorphism has been suggested to play a role in face perception22. Our study extends previous findings by identifying a neural site where COMT affects face recognition. Specifically, we investigated a broader modulation pattern of COMT genetic effects on the FFA by including multiple SNPs. We found that individuals with the heterozygous HEA haplotype exhibited higher face-selective responses but smaller fALFF. In contrast, individuals with the homozygous HEA haplotype or HEA non-carriers had lower face-selective responses and larger fALFF. Because COMT encodes catechol-O-methyltransferase, a protein that degrades catecholamine neurotransmitters and modulates dopamine metabolism20, neural activity (including the face-recognition-task-state and resting-state activities) in the FFA is likely modulated by changes in dopamine. Moreover, we found that an intermediate level of dopamine in the FFA was associated with higher face-selective responses (in the task state) and smaller fALFF (in the resting state), and the profile of higher face-selective responses and lower fALFF in the FFA was associated with better face-specific recognition ability. Thus, an intermediate level of dopamine in the FFA is likely beneficial for behavioral performance in face recognition. That is to say, participants with an appropriate level of dopamine  in the FFA possess a functional characteristic profile of higher face-selective responses and lower intrinsic FFA activities, therefore, they might have better face-specific recognition ability.The COMT rs4680 and other dopamine-function related generic variants imply that dopamine-related neurons may affect the baseline brain resting state activity by synchronizing the low-frequency oscillation50. Moreover, dopamine could stabilize  neural signaling in brain networks responsible for behavior-stimuli oriented attention52. Thus, an appropriate level of dopamine may improve the functional segregation-integration of a functional-specialized region by maintaining excitation-inhibition stability of neural signal transduction to ensure that individuals with the HEA heterozygous haplotype have a relatively higher rFFA activation in face recognition tasks (which has been found to be related to a better face-specific recognition ability3), as well as a relatively lower rFFA activity during resting state . This is consistent with previous studies demonstrating associations between dopamine hyper- or hypo-function in cortical or subcortical regions and cognitive dysfunctions56. Therefore, it would be interesting to examine the level of dopamine in the FFA in individuals with developmental prosopagnosia59, who show specific deficits in face recognition with unclear genetic basis60 and those with mental disorders who might have disturbances in COMT modulation of face processing23.Previous studies have rarely reported the direct impact of COMT polymorphisms on resting-state FFA activity in healthy people. Some studies focusing on the interaction effect between the 47, there seems to be no difference in the increase of neural response activity of the FFA when recognizing familiar and unfamiliar faces47. Compared with recognizing unfamiliar faces, recognizing familiar faces involves more activity in the anterior and mid-fusiform gyrus and extended face areas45. In our study, the stimuli were not familiar to the subjects, and the social and emotional information involved in the face stimuli was relatively sparse; therefore, the recognition difference between the old face and the new face might be mainly reflected in the familiarity and unfamiliarity of the facial features. We found that the COMT-polymorphisms modulated PLS1 rFFA-MRI profile was sensitive to the recognition of new faces, but not to that of the old faces. The findings may be related to the fact that the novel signals can trigger the dopaminergic metabolic system55 in the task-related brain areas and enhance perception62.Our study found that the 4 COMT-polymorphisms-modulated PLS1 rFFA-MRI profile mainly regulated the recognition ability of new faces. Although previous studies have revealed that it takes more time and is more likely to make mistakes to recognize faces of different identities than faces of same identities63. The posterior superior temporal sulcus (pSTS) is responsible for dynamic features64 and sensitive to emotional information67 from faces, and the prefrontal-amygdala circuit is involved in social and emotional regulation in face processing23. In fact, face perception as a whole is a network involving multiple brain regions9. Studies have found that the COMT Met allele is related to the modulation of neural substrates of negative face emotional bias23. Therefore, in future studies, it would be interesting to evaluate the role of multiple COMT polymorphisms in the core face network and its relation with face-specific recognition ability and face-emotion recognition.The FFA is sensitive to the static or invariant properties of facesThis study has some limitations. First, although we have adopted multiple strategies to improve the statistical power, the findings still need to be validated using data from other cohorts of participants. Second, measurements for the face recognition ability and the general mental ability are limited to the face memory test and the Raven test, respectively. More multivariate measurements could be introduced in the future to improve the reliability of the individual face recognition ability and general mental ability, and further elaborate the specificity of the effect of COMT on the face recognition ability. Third, all participants had the same ethnicity ; therefore, how the ethnicity of the individual participants affected the results needs to be examined using data from multiple ethnicities in the future. Fourth, the Caucasian children’ faces were used as stimuli to define the FFA in Han Chinese participants. As demonstrated in previous studies, there is an other-race effect in face recognition; therefore, how our findings are affected by stimulus attributes still needs to be investigated in further studies.COMT gene affected both resting- and task-state neural activity in the FFA through rs6269-rs4633-rs4818-rs4680 haplotypes, and the COMT-polymorphisms-modulated FFA was associated with face-specific recognition ability. The present findings provide a basis for the behavioral observation of face-specific recognition heritability and the approach used in this study might be s a feasible method for exploring and validating the neural basis of heritable behavioral performance. In addition, this study may inform the genetic etiology of developmental prosopagnosia and other mental disorders showing abnormities in face processing.In conclusion, our study demonstrated that the Three hundred and sixty-two Han Chinese college students  were recruited from Beijing Normal University (BNU) in Beijing, China through campus advertisement. Ethnicity was determined by the ethnic information displayed on the ID card of the participants. All participants had normal or corrected-to-normal vision, and none of them reported a history of neurological or psychiatric disorders. All the experimental protocol and procedures were approved by the Committee for Protecting Human and Animal Subjects of the Faculty of Psychology at Beijing Normal University. All experiments of the study were conducted in accordance with the relevant guidelines and regulations of Beijing Normal University’s Institutional Review Board (Human Subjects Division). All participants gave written informed consent prior to their participation in this study.3). Resting-state scanning lasted for 8 min and consisted of 240 contiguous volumes. Participants were instructed to relax and remain still with their eyes closed during resting state scanning9. A GRE-EPI sequence was also used to acquire task-state fMRI . A high-resolution structural T1-weighted image was acquired with a 3D magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence . Earplugs were used to attenuate scanner noise, and a foam pillow and extendable padded head clamps were used to restrain head motion3. None of the participants were excluded due to excessive head motion (>2 mm in translation or 2° in rotation) during MRI scanning.Functional and structural MRI was performed at the BNU Imaging Center for Brain Research on a Siemens 3 T scanner  with a 12-channel phased-array head coil. Resting-state fMRI was acquired using a T2*-weighted gradient-echo echo-planar-imaging (GRE-EPI) sequence  isotropic Gaussian kernel. Finally, the modulated images were masked using absolute masking with a threshold of 0.2 to exclude noisy voxels. Masked-modulated GM images were used for further statistical analyses69.Voxel-based morphometry (VBM) was performed using the SPM8 . First, unified segmentation in SPM8 was used to segment T1-weighted anatomical images into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF). Second, the Diffeomorphic Anatomical Registration through Exponential Lie algebra (DARTEL) registration method63. Movie clips of close-up faces of 7 Caucasian children (filmed when children were dancing or playing), objects (moving toys), scenes , and scrambled objects (constructed by scrambling each frame of the object movie clips) were included to examine category selectivity in the ventral visual cortex63. Participants were instructed to passively view movie clips during scanning. Because Adults’ FFA have been effectively identified by children’ faces in previous studies67 and the FFA is sensitive to the invariant properties of faces, but not to the dynamic or emotional information of faces70, we expected that the dynamic children’ faces would accurately and unbiasedly localize the FFA in adults.A dynamic face localizer was used to define the face-selective area and derive FSIn total, participants completed three 198-s runs. Each run consisted of 2 block sets intermixed with 3 18-s rest blocks at the beginning, middle, and end of the run. Each block set contained four 18-s (including six 3-s movie clips) blocks corresponding to 4 stimulus categories. The order of category blocks in each run was palindromic and randomized across runs Fig. .Figure 4www.fmrib.ox.ac.uk/fsl). Preprocessing included head-motion correction, brain extraction, spatial smoothing , grand-mean intensity normalization, and high-pass temporal filtering (120-s cutoff). A first-level analysis was performed separately on each run for each participant using FMRIB’s Improved Linear Model with a local autocorrelation correction. A boxcar kernel was convolved with a gamma hemodynamic response function and its temporal derivative was used to model BOLD signal. Six parameters from motion-correction were also included in the model as regressors of no interest to account for the effect of head movement. A second-level fixed-effect analysis was conducted to combine all runs. Specifically, the parameter  image from the first-level analysis was first aligned to the individual’s structural images using FMRIB’s linear image registration tool with 6 degrees of freedom and then warped to the MNI152 template using FMRIB’s nonlinear image registration tool with default parameters. The spatially normalized parameter images (resampled to 2-mm isotropic voxels) were then summarized across all runs using a fixed-effect model69. In this study, statistical images from the contrast between faces and objects were used to define the right fusiform face area and derive its face selectivity (FS), which was calculated as the average z-score (faces vs. objects) across all voxels in the defined face-selective region.Functional images were analyzed with the FMRI Expert Analysis Tool version 5.98, part of FMRIB’s Software Library , which is defined as the fractional sum of the amplitudes within the low frequency range divided by the sum of amplitude across the entire frequency range (0–0.25 Hz)3. Specifically, all contiguous voxels that surpassed the threshold within the posterior fusiform area  were defined as the FFA  was used to define the bilateral FFA based on the random-effect group-level Z-statistic image for faces vs. objects72. All the 4 COMT SNPs met the following criteria: genotyping call rate >0.95, minor allele frequency (MAF) > 0.05, and Hardy–Weinberg equilibrium (HWE) P > 0.05. Participants were excluded from analyses if they had a missing genotype rate of >25% . Twenty-four participants were excluded, and the remaining 338 participants  were included in the data analyses. Allele frequencies in this sample were very similar to those in the Han Chinese sample from the HapMap dataset (HapMap Data Release 27 Phase II + III).Genomic DNA was extracted from peripheral blood samples of each subject using QuickGene-Mini80 equipment and the QuickGene DNA whole blood kit S (Fujifilm). Sixty-four cognition-related candidate SNPs 48 was also calculated for face recognition and flower recognition.We used an old/new cognitive memory task to measure the face-specific recognition ability (FRA). There was a face block  and a flower block  in this task . PLS is particularly suitable for identifying associations between 2 sets of variables, especially when variables of each set are highly interdependent or multi-collinear74. The PLS approach maximizes the covariance between a set of variables (a linear combination of X measures) and another set of variables (a linear combination of Y measures). More specifically, the cross-correlation matrix between measures of SNPs and measures of brain-MRI was first computed. Then, a set of paired latent variables (LVs), which were uncorrelated  with each other and ordered by the explained covariance of brain and SNP measures, were generated via singular value decomposition. LVs are linearly weighted by the raw variables, and these weights or saliences measure the contribution of each raw variable to the LV73. Finally, the significance of the variance of singular values was tested by comparison with the distribution of variance arising from random permutation tests (1000 repetitions), and a bootstrap procedure (500 times) was used to test the reliability  of each variable in the significant PLS component73.The PLS correspondence analysis (PLSCA) was conducted using the R Package ExPosition  as categorical variables, and each variable was weighted according to the information it provided. The weight of a variable was defined as the inverse of its relative frequency because a rare variable provides more information than does a frequent variable75. This fully categorical coding scheme allows us to detects linear and nonlinear relationships within  and between datasets37. Thus, treating each genotype as a level of a categorical variable is suitable for genetic association studies, especially when the inheritance pattern, direction, and/or size of the effect are unknown76. Accordingly, a matrix of measures of 12 COMT genotypes estimated in the 338 participants and a matrix of measures of 3-modal FFA-MRI phenotype estimated in the 338 participants were entered into the PLS analysis.The SNPs-FFA multivariate association analysis was used to examine whether specific patterns of COMT polymorphisms contributed to the multimodal FFA-MRI phenotypes. First, GM, fALFF, and FS within the right FFA and the left FFA ROI were extracted for each participant. Then, each SNP was coded in accordance with genotypic (codominant) models77 was conducted to investigate how SNPs in strong linkage disequilibrium work as blocks to modulate FFA phenotypes. Haplotype blocks of SNPs affecting the FFA phenotype were assessed and illustrated using Haploview software version 4.2 (https://sourceforge.net/projects/haploview/) with the solid spine of the linkage disequilibrium method and parameter 0.80. Associations of haplotypes with the FFA were examined using R software version 3.5.2 with generalized linear models.A haplotype block analysisSupplementary Information