Opioid overdoses have continued to increase since the start of the COVID-19 pandemic. The pathways through which the COVID-19 pandemic has affected trajectories of opioid use and opioid-related problems are largely unknown. Using the Epidemic-Pandemic Impacts Inventory (EPII), a novel instrument that assess pandemic-related impacts across multiple life domains, we tested the hypothesis that COVID-related impacts  experienced in the early months of the pandemic would predict changes in opioid use and opioid-related problems at follow-up.This analysis was embedded within a cluster randomized type 3 implementation-effectiveness hybrid trial that had enrolled 188 patients across eight opioid treatments prior to the start of the pandemic. Participants had all been recently inducted on medication for opioid use disorder and were actively receiving treatment. Participants reported on their opioid use and opioid-related problems at baseline and 3-, 6-, and 9-month post-baseline assessments. Between May and August 2020, participants were sent an optional invitation to complete the EPII.One hundred thirty-three respondents completed the EPII and 129 had sufficient data to analyze the EPII and at least one subsequent follow-up. In logistic and zero-inflated negative binomial analyses adjusting for covariates, each endorsed pandemic impact in the interpersonal conflict domain was associated with 67% increased odds of endorsement of any opioid use, and each impact in the employment and infection exposure-domains was associated with 25% and 75% increases in number of endorsed opioid-related problems, respectively.Mitigating the effect of the pandemic on patients’ interpersonal relationships and employment, and promoting greater infection control in opioid treatment programs, could be protective against negative opioid-related outcomes.Trial registration The present study describes secondary data analysis on a previously registered clinical trial: clinicaltrials.gov/ct2/show/NCT03931174.The online version contains supplementary material available at 10.1186/s13722-023-00409-7. Prior to the COVID-19 pandemic, the opioid epidemic was deemed a public health emergency in the United States . Since tThe specific pathways through which COVID-19 has exacerbated opioid use and opioid-related problems are not well understood. One potential pathway was through social distancing that, while essential to flattening the curve, placed major constraints on employment, social activities, and childcare –protective factors against opioid misuse , 5. MoreSystematically examining the impact of pandemic-related experiences on patient functioning has been difficult due the lack of assessment tools. The Epidemic-Pandemic Impacts Inventory (EPII) is a new measure developed by Grasso and colleagues  as a comIn the current study, we assessed the longitudinal associations between COVID-19 experiences during the early months of the pandemic, and subsequent opioid use and opioid-related problems among persons receiving medication to treat OUD. We sought to investigate which specific EPII domains were most strongly associated with opioid-related outcomes. Due to the paucity of prior data, our analyses of specific EPII domains were exploratory, though we conjectured that interpersonal conflict-related impacts would have the largest effect on subsequent opioid use and opioid-related problems.Data collection was embedded within a cluster-randomized type 3 implementation-effectiveness hybrid trial called Project MIMIC . Project MIMIC is focused on evaluating two multi-level implementation strategies for helping Opioid Treatment Programs (OTPs) and their staff implement contingency management, an evidence-based behavioral intervention in which patients earn prizes for meeting treatment goals . The conTo qualify for Project MIMIC, participants had to be at least 18 years old and have been inducted on medication for OUD within the last 30 days. Participants also had to be in active treatment at one of the partner OTPs, which typically consisted of daily medication dispensing, weekly group and/or individual counseling sessions, and periodic case management sessions . All parWhen social distancing regulations began in March of 2020, Project MIMIC had enrolled and obtained informed consent from 188 patients receiving treatment at eight OTPs across the New England region. Among this sample, methadone was the most commonly received medication (88%), followed by buprenorphine (11%), and naltrexone (1%). In the early months of the COVID pandemic, all 188 participants in this first cohort of the parent clinical trial were invited to provide informed consent for a supplementary survey on COVID-19-related impacts using the EPII assessment tool.SD = 1.94) after cohort baseline. After providing informed consent for this supplemental data collection, participants completed the EPII and received a $20 gift card.The EPII assessment was administered between May-August 2020, an average of 5.77 months . For the remainder of participants, the first assessment subsequent to the EPII assessment was at the 6-month mark. Among those who completed the EPII, 97% and 87% completed the 6- and 9-month assessments, respectively.Upon enrollment, participants answered questions about their sociodemographic characteristics. Focal covariates included sex assigned at birth , a dichotomous variable coded to represent racial/ethnic identity , and age in years, since these variables have demonstrated associations with opioid use in prior studies . ExpandeAt baseline and each follow-up assessment, participants completed the Timeline Follow-back Interview , which aThe EPII  consistsThe analytical sample was defined as EPII survey completers with at least one follow-up assessment. Preliminary analyses examined whether condition (assignment to implementation strategy condition), or contingency management dosage (number of sessions received) were associated with the focal items but no significant associations were identified. Similarly, exploratory analyses demonstrated that neither assessment origin (outcome assessed at 6- or 9-month mark) nor differences in the lag between the EPII assessment and the next subsequent assessment predicted opioid-related outcomes. Thus, data were pooled across conditions (with condition effects explicitly examined in adjusted models), and none of the other study design features  were controlled for in analyses.We examined associations between EPII domains and opioid use/problems in two steps. First, we examined Pearson correlations between EPII domains and baseline number of days of opioid use, as well as baseline number of opioid-related problems. Second, we estimated multivariate longitudinal models to obtain adjusted associations between EPII domains, featuring associations between other EPII domains, baseline opioid use/problems, condition assignment, age, sex assigned at birth, and racially/ethnically minoritized status as covariates. Follow-up opioid use was modeled as a binomial (yes/no: any opioid use days in past 30 days at follow-up) outcome because 75% of participants did not endorse any past-30-day opioid use, leading to scant data for count analysis. Participants endorsed an average of 2.0 out of 11 problems (SD: 3.5), with 62.4% endorsing zero problems. As such. follow-up opioid-related problems was analyzed with a zero-inflated negative binomial distribution, predicting: (1) the probability of being an excess zero (outside the expected distribution), and (2) the count value, if not zero  and respondents were representative of the full sample in terms of racial and ethnic identity, household income , and age, but non-completers more often identified as male  = 2.790, p = .006). Among the 133 participants that completed the EPII, 83% (n = 110) reported being Non-Hispanic White; 4.5% (n = 6) reported being Black or African American, 4.5% (n = 6) reported Other race, 3.8% (n = 5) reported being of more than one race, 1.5% (n = 2) reported being Hawaiian or Pacific Islander, 1.5% (n = 2) reported being American Indian or Alaska Native, and 0.8% (n = 1) reported being Asian. 10% (n = 13) of participants reported being of Hispanic or Latin descent and 60% (n = 80) of the sample indicated female sex at birth. Patients reported a mean age of 36 years (SD = 9.8), and 82% (n = 109) of the sample reported having attained high school completion. Based on n = 109 patients who completed questions on income, mean household income was $32,980 . Complete data from the EPII and at least one subsequent follow-up assessment were available from 129 respondents , which was defined as the final analysis sample.A 71% response rate was obtained for the EPII (r’s < 0.70) [Table  < 0.70) .Table In the full zero-inflated model , a higher employment domain score  and higher infection exposure domain score  were associated with an increase in past month opioid-related problems.This exploratory study identified significant temporal relationships between pandemic-related impacts and subsequent opioid use and opioid-related problems among patients receiving medication for OUD. Specifically, pandemic-related impacts in the interpersonal conflict domain were associated with an increased odds of past 30-day opioid use, but not with likelihood of opioid-related problems. Similarly, employment and infection exposure were both associated with an increased count of opioid-related problems, but not with increased odds of past 30-day opioid use. Although this study was exploratory, we had expected interpersonal conflicts to be one of the strongest predictors of opioid use and opioid-related problems and these results partially confirmed our expectations. The significant effect of pandemic-related interpersonal conflict on subsequent use of opioids is consistent with a wealth of research indicating that interpersonal conflict predicts opioid use and relapse. Our finding that employment and infection control factors predicted subsequent opioid-related problems, and were more important predictors than the other domains of the EPII, is also congruent with literature on risk and protective factors for patients with opioid use disorder. A body of research has documented the central importance of employment as a protective factor for patients in opioid use treatment. and a number of studies in the early months of the COVID-19 pandemic reported a strong association between infection exposure and opioid use. When attempting to conjecture why some EPII domains were associated with one opioid outcome and not the other, it is important to remember that study participants had recently started medication for opioid use disorder and rates of opioid use and opioid-related problems were relatively low across the sample. We therefore believe it is more important to focus on the overall pattern of results–and specifically which of the EPII domains predicted opioid outcomes in the early months of the pandemic–rather than on differences between the two opioid outcomes.Results of this study highlight potential avenues for policy efforts to mitigate the consequences of pandemic-induced consequences on persons in OUD treatment. Specifically, efforts during future waves of COVID-19 and/or other pandemics could aim to buttress against the harmful co-occurrence of opioid use and interpersonal conflict , promote vocational stability , and encourage virus control in OTPs , as a means of protecting against deleterious opioid use outcomes.Strengths of this analysis included the ability to collect data on early pandemic impacts among a historically marginalized population at high risk of pandemic-related sequalae. Data collection was embedded in an ongoing longitudinal trial , which pStudy strengths must be interpreted in the context of limitations. First, the EPII survey was voluntary, and while there were no major systematic differences observed between completers and non-completers beyond differences in sex at birth, it remains possible that non-completers were those more likely to experience and/or report negative COVID-19 impacts. Second, given the parent study was an implementation-effectiveness hybrid trial that prioritized measurement of organization- and provider-level data over measurement of patient-level data, it is not possible to conclude whether the changes in opioid use and/or opioid-related problems observed in this study were associated with change in subsequent risk of opioid overdose. Third, impacts on patients’ life domains are likely dynamic. The impacts reported in this study were experienced in the early months of the pandemic in the New England region, yet findings may offer insight into experiences among those recovering from opioid use disorders in the current period of relaxing restrictions and in any future pandemic period. Additionally, the current study did not assess the extent of boredom experienced by the sample as a function of isolation, as this boredom might have represented a closer proxy for decisions to engage in opioid use and/or the extent of experiences of craving. Finally, there were generally very few members of distinct non-White racial and categories (and few participants indicating Hispanic or Latin ethnicity) present in the analytical sample. In the context of logistic regression and zero-inflated count regression, sparse cell coverage could have potentially led to misestimation of effects based on distinct categories of ethnic and racial minoritization. For this reason, a combined dichotomous variable representing non-Hispanic White identity, compared to any other identity, was created. This limited our ability to identify and do justice to crucial differences in pandemic experiences that may have been encountered by members of distinct racial and ethnic groups.This timely study provides rare longitudinal data on an evolving issue impacting a high-need patient population. This population was at particular risk during the global COVID-19 pandemic, as the increased market penetration of fentanyl observed during this time means that any instance of opioid use would be more likely to result in overdose . As the Additional file 1: Table S1. Revised domains of the epidemic-pandemic impact inventory (EPII). Table S2. Results from inflation component of zero-inflated binomial prediction of follow-up opioid-related problems (n = 129).


The aim of cross-sectional study was to investigate whether the presence of autistic traits in pregnant women was positively associated with the prevalence and severity of antenatal pain. We analyzed 89,068 pregnant women from a Japanese national birth cohort cross-sectionally. Autistic traits were assessed using the Japanese version of the Autism-Spectrum Quotient short form (AQ-10-J). Antenatal pain was measured using the SF-8 bodily pain item (SF-8-Pain). Antenatal pain in the second to third trimester during pregnancy was categorized into three groups: without pain, mild pain, and moderate-to-severe pain. Participants were divided into eight groups by AQ-10-J score: seven consecutive scoring groups (scores 0–6), and those above the cut-off (≥ 7) for probable autistic spectrum disorders. Odds ratios (OR) for the prevalence of mild and moderate-to-severe pain were calculated for each AQ-10-J scoring group (reference: without pain group) using multinominal logistic regression analysis. Autistic traits were positively associated with mild and moderate-to-severe pain in a dose–response manner, but the association with moderate-to-severe pain was strongest. Fully-adjusted ORs  for moderate-to-severe pain were: 1.01 (0.91–1.13) for 1 point, 1.13 (1.02–1.25) for 2 points, 1.16 (1.04–1.29) for 3 points, 1.20 (1.07–1.34) for 4 points, 1.23 (1.09–1.40) for 5 points, 1.27 (1.10–1.47) for 6 points, and 1.24 (1.05–1.46) for ≥ 7 points (AQ-10-J cut-off). We identified an association between maternal autistic traits and antenatal pain. Maternal autistic traits may need to be considered when addressing antenatal pain during healthcare for expectant mothers. The International Association for the Study of Pain stated that “pain is always personal experience that is influenced to varying degrees by biological, psychological, and social factors”3. Therefore, each pregnant woman may feel pain differently. Although differences in the biological mechanism between antenatal pain and persistent pain other than in the pregnancy period remain unclear, the pregnancy period is an important phase for women that must be considered in public health. Antenatal pain affects household tasks, sick leave, relationships with others, psychological health, and quality of life during pregnancy5. Antenatal pain also increases the risk for depression and maternal bonding disorder after delivery6. Furthermore, antenatal pain has a long-term influence on women’s health. For example, pregnancy-related lumbopelvic pain has been associated with anxiety and depressive symptoms for up to 11 years after delivery7. Although antenatal pain has a major impact on women’s health, it is difficult to control using pharmacological and non-pharmacological treatments8. A recent scoping review reported that risk factors for pregnancy-related pelvic girdle pain were young age, low educational level, overweight/obesity, no pre-pregnancy exercise, physically demanding work, history of childbirth, use of a progestin-intrauterine device, previous back trauma/disease, stress, depression, and anxiety9. However, there are additional risk factors for antenatal pain that remain to be explored.More than two-thirds of pregnant women experience antenatal pain, including lower back pain, pelvic pain, and headache10. Therefore, women with autistic traits may have different experiences and expressions of antenatal pain than women without such traits. A clinical report noted that individuals with ASD had increased tolerance to pain stimulation11. A previous study that used quantitative sensory testing reported that individuals with ASD had higher mechanical pain and light touch detection thresholds and greater ranges of extreme scores compared with those without ASD12. Other research found that people with ASD had different central responses to pleasant and unpleasant stimulation than those without ASD13. In addition, compared with those without ASD, individuals with ASD were reported to show stronger responses to heat pain at high temperature but indifferent responses to low thermal stimulation14. Pain response among individuals with ASD may therefore depend on the context and type of stimulation. Moreover, a recent study that explored pain response in individuals with ASD, suggested that alteration of neural networks among individuals with ASD (detected using neuroimaging) contributed to pain coping15.Alteration of pain responses among individuals with autistic traits such as autistic spectrum disorder (ASD) have been reported, including inconsistent observations of both hyposensitivity and hypersensitivity to pain16, and approximately 14% of children and adolescents who visited pain clinics were diagnosed with comorbid ASD17. However, no previous observational study has examined associations between adult autistic traits and the experience of antenatal pain.However, these observations were related to pain caused by mechanical stimulation , which differs from spontaneous pain. Antenatal pain is considered spontaneous pain rather than evoked pain. In relation to spontaneous pain, children and adolescents with ASD showed a two-fold higher prevalence of repeated or chronic physical pain19. Pain experience may also be amplified by psychological stress and negative emotions, which are regulated by interoceptive pathways in the central nervous system 20. A previous qualitative study reported that pregnant women with ASD tended to experience anxiety, depression, other psychiatric disorders, and sleep difficulties as well as antenatal pain because of their difficulty in social communication with medical staff21. Therefore, we also hypothesized that there was an interaction of comorbid psychological risk factors  in the association between maternal autistic traits and antenatal pain.Individuals with ASD often have comorbid anxiety and depression23. In addition, many ASD cases remain undiagnosed. Previous studies have reported cases of persistent pain explained by undiagnosed ASD25. Therefore, we considered that studies conducted solely in clinical settings were insufficient to identify associations between autistic traits and antenatal pain among pregnant women. Specifically, we focused on the association between autistic traits and antenatal pain among pregnant women from the general population, corresponding to a real-world scenario.Autistic traits are clinically defined in gradations, and the presence of extreme autistic traits is diagnosed as ASD. Individuals with mild autistic traits are broadly distributed across the population 25, we examined the association between maternal autistic traits and severity of antenatal pain.We aimed to investigate whether the presence of autistic traits among pregnant women was positively associated with antenatal pain and clarify the interaction of psychological risk factors in the association between autistic traits and antenatal pain using a large dataset from the Japanese national birth cohort study. In addition, because several clinical cases have reported that individuals with extreme autistic traits expressed extraordinary amplified pain that seriously interfered with social activity27. Two self-reported questionnaires were administered during the first trimester of pregnancy (Survey 1) and again in the second to third trimester (Survey 2). The present study was based on the JECS dataset released in June 2016 and revised in October 2016 (jecs-ag-20160424).We used data from the Japan Environment and Children’s Study (JECS), which was a government-funded birth cohort study. Pregnant women were recruited for the JECS from 15 Regional Centers in Japan from January 2011 to March 2014. The JECS protocol has been published elsewhere28, and 3281 women with incomplete data on age at registration. In addition, we excluded 459 women with history of rheumatoid disease, ulcerous colitis, or Crohn’s disease; these pain-related diseases were considered potential confounders and the number of cases was too small to adjust for these confounders statistically. Moreover, we excluded a further 4646 women with incomplete data for pain  and autistic traits  in the second to third trimester of pregnancy. Finally, we analyzed data for 89,068 pregnant women.The participant enrolment process is shown in Fig. 30. Participants were asked, “How much bodily pain have you had during the past 4 weeks?” Response were on a six-point scale: none (1), very mild (2), mild (3), moderate (4), severe (5), and very severe (6)30. Based on these results, we categorized participants into three groups: “without pain” (responses of 1), “mild pain” (responses of 2 or 3), and “moderate-to-severe pain” .The SF-8-Pain was used to assess the level of pain experienced during pregnancyIn this study, we used pain assessment data that was collected in Survey 2 (second to third trimester). The JECS measured maternal autistic traits once using a questionnaire in Survey 2, which was described as an experimental variable. Although autistic traits are congenital and scores for the questionnaire covering adult autistic traits were theoretically stable, we selected data for measures of maternal autistic traits and pain from the same time to avoid potential bias from temporal change in questionnaire scores.31. The AQ-10-J used in the present study includes 10 items31: Item 1. I prefer to do things with others rather than on my own; Item 2. Other people frequently tell me that what I’ve said is impolite, even though I think it is polite; Item 3. I tend to have very strong interests, which I get upset about if I can’t pursue; Item 4. When I’m reading a story, I find it difficult to work out the character’s intentions; Item 5. I would rather go to the theater than to a museum; Item 6. I am often the last to understand the point of a joke; Item 7. I find it easy to work out what someone is thinking or feeling just by looking at their face; Item 8. I like to collect information about categories of things ; Item 9. I find it difficult to imagine what it would be like to be someone else; and Item 10. I find it difficult to work out people’s intentions. Responses are scored using a binary system, whereby endorsement of the autistic trait (either mildly or strongly) is scored as + 1, whereas the opposite response is scored as a 0. This means that the maximum AQ-10-J score is 10. That is, for Items 1 and 7, responses of “definitely agree or slightly agree” were scored as 0, and responses of “slightly disagree or definitely disagree” as + 1. For Items 2–6 and 8–10, responses of “definitely agree or slightly agree” were scored as + 1, and responses of “slightly disagree or definitely disagree” as 0. A previous study identified an AQ-10-J score of 7 as the cut-off point to indicate probable high-functioning pervasive developmental disorders as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). This was categorized as ASD in the fifth edition of the DSM (DSM-5)32.In Survey 2, autistic traits were assessed using the AQ-10-J, which is a short form of the Autism-Spectrum Quotient (AQ) and widely used in general populationsWe divided pregnant women into eight scoring groups: seven consecutive scoring groups from 0 to 6, with the final group comprising those above the cut-off for ASD (AQ-10-J score ≥ 7). Because the number of women with a high AQ-10-J score was too small to obtain appropriate estimation by multivariate analyses as used in the present study and considering the clinical implications, we categorized participants above the cut-off for probable high-functioning ASD into the same scoring group.34.We also divided pregnant women into five groups for the first sensitivity analysis (#1): those that fell in quartiles below the cut-off (AQ-10-J quartiles 1 [low] to 4 [high]) and above the cut-off (AQ-10-J cut-off). This categorization was adapted from a previous study35. In a previous validation study, the Cronbach’s alpha for the AQ-10-J was 0.61 among 25 normally intelligent patients with pervasive developmental disorders and 215 healthy individuals31.The Cronbach’s alpha for the AQ-10-J was 0.51 in the present study, which indicated moderate reliabilityThe demographic factors included as potential confounders were age at study entry , body mass index during non-pregnancy (quintile), smoking status during pregnancy  at Survey 2, drinking status during pregnancy  at Survey 2, and physical activity during pregnancy  at Survey 2.37.Physical activity was measured using the International Physical Activity QuestionnaireThe socioeconomic factors included as confounders were education level  at Survey 2, equivalized income  at Survey 2, marital status  at Survey 1, and current employment status at Survey 2 .38, a low income was defined as an annual equivalized income of less than 1.22 million Japanese yen.Equivalized income was calculated by dividing the median value of the multiple-choice response for annual household income by the square root of the number of people living in the household; number of family members  at Survey 1. Based on the poverty line in Japan in 2015We collected the number of fetuses (singleton or multiple pregnancy) based on participants’ medical records. We also collected data related to participant-reported medical history and psychological risk factors, including: history of anxiety disorder (yes/no), history of depression (yes/no), history of schizophrenia (yes/no), history of other psychological disorders (yes/no), history of delivery (yes/no) at Survey 1; feeling when made aware of the pregnancy , psychological distress during pregnancy (yes/no) at Survey 1; and depth of sleep over the past month  at Survey 2.39. We also included participant-reported history of developmental disorder  (yes/no); the criteria used in the self-reported questionnaire were based on the DSM-IV.Kessler Psychological Distress Scale (K6) scores ≥ 13 at Survey 2 were interpreted as indicating the presence of psychological distressFirst, we compared participant characteristics between women below and above the AQ-10-J cut-off point using t-tests for continuous variables and chi-square analyses for categorical variables. Next, we examined the association between maternal autistic traits and the prevalence of antenatal pain (mild and moderate-to-severe pain). Odds ratios (OR) for the prevalence of mild and moderate-to-severe pain for the AQ-10-J scoring groups were calculated using a multinominal logistic regression analysis adjusted for potential confounders. We used a multivariate multinominal logistic regression analysis with a generalized logit model that had two intercepts and two slopes to assess whether autistic traits were positively associated with antenatal pain, and whether autistic traits were more strongly associated with moderate-to-severe pain compared with mild pain. A general linear model was used to estimate P for linearity for the ORs for mild and moderate-to-severe pain (by AQ-10-J scoring group). Regression coefficients for the ORs for the AQ-10-J scoring groups  between mild pain and moderate-to-severe pain were also tested using a general linear model. The outcome variables (mild pain and moderate-to-severe pain) were ordinal. Although proportional odds ordinal logistic regression analysis using a generalized logit model that had different intercepts, but the same slope is often used to assess ordinal outcomes, this analysis was not appropriate for comparing the strengths of the associations in this study.In addition, we tested whether there were any interactions of psychological risk factors in the association between AQ-10-J score and antenatal pain. The psychological risk factors considered were confusion/upset when made aware of the pregnancy, history of anxiety disorder, history of depression, history of schizophrenia, history of other psychological disorders, sleeping quite lightly, and psychological distress during pregnancy.Based on these tests for the interactions of psychological risk factors, we stratified all pregnant women by the significant factor  to determine the associations between AQ-10-J scoring groups and antenatal pain.Model 1 was adjusted for age. Model 2 added pre-pregnancy body mass index, smoking during pregnancy, drinking during pregnancy, physical activity, education, marital status, equivalized income, employment status, and multiple pregnancy. Model 3 further added history of delivery, history of anxiety disorder, history of depression, history of schizophrenia, history of other psychological disorders, feeling when made aware of the pregnancy, self-reported sleep depth, and psychological distress during pregnancy. Missing values for potential confounders were used as dummy variables. Model 4 was adjusted for all variables in Model 3, except for history of depression.For the second sensitivity analysis (#2), we re-ran a multivariate multinomial logistic regression analysis for 71,206 pregnancies without psychological risk factors other than histories of developmental disorders . All statistical analyses were conducted using SAS, Version 9.4 . P-values < 0.05 (two-tailed tests) were considered statistically significant.All procedures were in accordance with the ethical standards of the Helsinki Declaration of 1975, as revised in 2010. The JECS protocol was approved by the Ministry of the Environment’s Institutional Review Board on Epidemiological Studies, and by the ethics committees at all participating institutions. Written informed consent was obtained from all participants. Authors pre-registered our hypothesis and analytic plan to the JECS Program Office, National Institute for Environmental Studies.Table Compared with women below the AQ-10-J cut-off, women above the AQ-10-J cut-off were more likely to be younger, have smoked during pregnancy, be divorced or widowed, have a low income, be full-time homemaker, have a history of psychological disorders, feel confused or upset when made aware of their pregnancy, sleep quite lightly, and have psychological distress during pregnancy. These women were also less likely to be alcohol drinkers during pregnancy, be physically active, have graduated from high school, and have a history of delivery compared with women below the AQ-10-J cut-off.Table Analysis of potential confounders showed unintended pregnancy and psychological distress were independently associated with a higher prevalence of mild and moderate-to-severe pain compared with felling happy when made aware of the pregnancy and no psychological distress. Sleeping deeply, normally, lightly, and quite lightly in the past month were also independently associated with a higher prevalence of mild and moderate-to-severe pain compared with sleeping quite deeply. Feeling “nothing at all” and “other feelings” when made aware of the pregnancy were associated with a higher prevalence of moderate-to-severe pain, but not with a higher prevalence of mild pain.Among the interactions of psychological risk factors examined, we found that the presence of a history of depression had a significant interaction in the association between AQ-10-J scoring group and antenatal pain.Table The sensitivity analysis #1 using different AQ-10-J scoring groups from the main analyses Table  and sensMaternal autistic traits were positively associated with antenatal mild and moderate-to-severe pain in a dose–response manner. Additionally, maternal autistic traits were more strongly associated with moderate-to-severe antenatal pain compared with mild pain. Moreover, we identified that a history of depression had an interaction in the association between maternal autistic traits and antenatal pain. Specifically, the association between autistic traits and antenatal pain was confined to pregnant women without a history of depression. To our knowledge, this is the first study to show a positive association between maternal autistic traits and antenatal pain along with several psychosocial risk factors in a sample from the general population.41. Autistic traits are clinically defined in graduations. Therefore, we considered the validated AQ-10-J was suitable for assessing autistic traits in the national birth cohort used in the present study. The proportion women with a history of ASD and the prevalence of probable ASD  were 0.2% and 2.7%, respectively. Few large-scale epidemiological studies have examined neurodevelopmental disorders (including autistic traits) in Japan. However, a study that investigated children/students with probable developmental disabilities who needed educational support in regular classes was conducted by Japan’s Ministry of Education, Culture, Sports Science, and Technology. That study reported that 1.8% of male students and 0.4% of female students  presented with probable ASD as measured by the High-Functioning Autism Spectrum Screening Questionnaire (ASSQ)42. The proportion of those with a history of ASD in this study was similar to that of Japanese female students reported in the previous study, but the prevalence of probable ASD in our study was higher than in the previous study. This discrepancy may result from the differences in measurement between the ASSQ and AQ-10-J, or differences between girls and adult women. In addition, people with high-functioning ASD are often only diagnosed when in adulthood43.A previous study used the original AQ was used to measure autistic traits, including broader autism phenotype, in the general population12. Moreover, previous research using functional magnetic resonance imaging (fMRI) reported that people with ASD had different central responses to pleasant and unpleasant stimulation than those without ASD13. Using three different stimulations, including those that were unpleasant , neutral (burlap fabric), and pleasant (soft cosmetic brushes), those authors found that people with ASD tended to show diminished responses to pleasant and neutral stimulations, but exaggerated limbic responses to unpleasant stimulations13. Antenatal pain may be therefore recognized as an exaggerated unpleasant stimulation among women with ASD compared with women without ASD. This may be because of increased responses in the limbic system, which is responsible for the effect of negative affect and cognition of pain. As noted in previous research, it is possible that pregnant women with autistic traits experience more severe antenatal pain compared with pregnant women without autistic traits. However, in terms of short-term reactions to experimental heat stimulation, individuals with ASD showed intact early responses (0–10 s) but diminished sustained responses (18–28 s)15. During sustained responses, decreased signals in the posterior cingulate cortex were detected by fMRI15.Our results may be explained by a higher pain response among women with autistic traits compared with women without these traits. However, the underlying reason for this remains unclear. A previous study showed greater variation in pain sensitivity among individuals with ASD than those without ASD44. Moreover, individuals who experienced more severe pain also displayed significant impairment in attention and cognitive flexibility46. Although these potential overlaps between alterations of neural function that may contribute to autistic traits and persistent pain could be occasional, causality remains unknown, and it is possible that pregnant women with autistic traits pay sustained attention to pain.There is another possible neuropsychiatric mechanism between autistic traits and antenatal pain that could be assumed, as described below. Individuals with ASD have been found to have decreased gray matter in the posterior superior lobule, which suggests cognitive rigidity against external stimulations such as pain48. Consistent with this mechanism, we found a higher prevalence of antenatal pain in participants with independent psychological risk factors compared with those without psychological risk factors. However, contrary to our hypothesis, the interactions of psychological risk factors other than history of depression in the association between maternal autistic traits and antenatal pain were not significant. Stratification of participants by the presence of a history of depression showed no association between autistic traits and antenatal pain in pregnant women with a history of depression. However, many patients with depression have comorbid chronic pain49, which might have masked an association between maternal autistic traits and antenatal pain.Pain processing is related to a sensitivity (nociceptive and nocifensive) component, and to emotional-affective and cognitive components that are regulated by the interoceptive system25. A previous narrative qualitative study found communication difficulties in pregnant women with ASD led to perceived stress and anxiety about childbirth, which suggested that women with communication challenges need special attention21. For example, recommendations included using written information for explanations or other methods of instruction  and frequent confirmation of understanding21. However, an observational study with a cross-sectional design could not conclude whether these interventions reduced pain intensity during pregnancy through improving symptoms related to maternal autistic traits. Further research such as randomized controlled trials is required to investigate the effect of these interventions on pain symptoms among pregnant women. However, the findings of the present study provided evidence that may inform changes in the treatment of antenatal pain in this population.Interventions for individuals with extreme autistic traits  can be feasible by cognitive-behavioral treatment for anxiety or depressive symptoms, focused social skills training, speech therapy, family education and interventions, and academic assessment to improve their difficulty in social life50, and women may be less likely to be suspected to have and diagnosed with autistic traits than men. Comorbid autistic traits should therefore be considered when healthcare providers encounter pregnant women with severe antenatal pain, and healthcare professionals should carefully consider optimized interventions when providing care for pregnant women with autistic traits.Pregnant women with moderate-to-severe pain may have comorbid extreme autistic traits. The present study highlighted the importance of screening for autistic traits among pregnant women in the treatment and control of severe antenatal pain. If pregnant women with severe pain have comorbid extreme autistic traits, they may benefit from interventions to improve their overall functioning and decrease pain and pain-associated disability. The prevalence of ASD among women is lower than that among men51. The autistic traits also overlap with psychological distress such as depression52. Therefore, the AQ-10-J responded not only to individuals with autistic traits but also to those with ADHD traits and psychological distress such as depression52. The AQ was indeed correlated with the Adult ADHD Self-Report Scale (ASRS) in the general population52. This situation may influence the interpretation of the results. Fourth, this study used a cross-sectional design, and temporal aspects cannot be discussed. However, autistic traits are innate, and it may not be necessary to consider the possibility of reverse causality .Fifth, the associations that we observed could be explained by potential uncontrolled confounders. These confounders may include factors that occurred before or during the antenatal period, such as genetic predisposition and exposure to other environmental factors. Sixth, the validation study of the AQ-10-J31 showed a lower positive predictive value than the previous study53 despite having similar findings of sensitivity and specificity among them. This insufficient psychometric property might cause misclassification; those with high AQ-10-J score might not be that autistic, resulting in an overestimation of the association between maternal autistic traits and antenatal pain.The strength of the present study was that our data were derived from a large nation-wide cohort study of the general population. However, there are several limitations to consider when interpreting the findings of this study. First, we did not examine the site of pain and therefore could not distinguish brief pain such as possible headache, lower back pain, or pelvic pain. In addition, we did not have information about whether antenatal pain started during or before pregnancy. This information may have clinical implications. Second, both the experimental variable (AQ-10-J) and the outcome variable (pain intensity) comprised a small number of items, which might have reduced the variance of responses and generalizability of the results. However, many questionnaires were required for the multidimensional assessment of nearly 10,000 participants in the JCES. As such, it was important to reduce burden on responders by using brief questionnaires where possible. Third, ASD and ADHD traits were highly coexistence in a general adult populationIn conclusions, maternal autistic traits are positively associated with antenatal pain in a dose–response manner, with this association strongest for moderate-to-severe antenatal pain compared with mild pain, after adjusting for psychological confounders. Our findings suggest that maternal autistic traits may have a key role in antenatal pain and should be considered when providing healthcare for expectant mothers.Supplementary Information.Supplementary Tables.


Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and signal-to-noise ratio. Here, we propose a deep learning-based OCT image enhancement framework that exploits raw interference fringes to achieve further enhancement from currently obtainable optimized images. The proposed framework for enhancing spatial resolution and reducing speckle noise in OCT images consists of two separate models: an A-scan-based network (NetA) and a B-scan-based network (NetB). NetA utilizes spectrograms obtained via short-time Fourier transform of raw interference fringes to enhance axial resolution of A-scans. NetB was introduced to enhance lateral resolution and reduce speckle noise in B-scan images. The individually trained networks were applied sequentially. We demonstrate the versatility and capability of the proposed framework by visually and quantitatively validating its robust performance. Comparative studies suggest that deep learning utilizing interference fringes can outperform the existing methods. Furthermore, we demonstrate the advantages of the proposed method by comparing our outcomes with multi-B-scan averaged images and contrast-adjusted images. We expect that the proposed framework will be a versatile technology that can improve functionality of OCT. A deep learning-based optical coherence tomography (OCT) framework enhances spatial resolution and reduces speckle noise in OCT images. OCT operates based on an interferometric technique that uses coherent detection of backscattered light from sample and reference arms using a broad-band light source2. The difference in the optical length of each arm is encoded in the frequency domain of the detected interference signal. Therefore, the depth profile of a sample, commonly referred to as a A-scan, is typically retrieved by applying Fourier transform to the measured interference signal. The laser beam is then scanned laterally to obtain a two-dimensional cross-sectional OCT image with a depth-lateral axis, commonly referred to as a B-scan. Based on these fundamental principles, OCT with microscopic spatial resolution is widely used as a diagnostic tool in various medical fields, such as ophthalmology and cardiology4. However, OCT applications often suffer from systemic limitations arising from the basic operating principle; these limitations include the presence of speckle noise, limited depth-of-focus (DOF), and degradation in spatial resolution. In detail, speckle noise deteriorates detailed morphological information by reducing contrast of OCT images5. The axial resolution is physically determined by the spectral bandwidth of the light source, while the lateral resolution is dominated mainly by the numerical aperture of the imaging optics6. The lateral resolution is also only maintained within a limited DOF, reducing the effective imaging range7. In addition, OCT exploiting a broadband light source requires the development of sophisticated optical systems9. Therefore, to fully utilize the diagnostic potential of OCT in many preclinical and clinical applications, it is of great importance to enhance OCT images by overcoming these drawbacks.Optical coherence tomography (OCT) is an indispensable optical imaging modality that can provide non-invasive three-dimensional imaging in vivo with high-speed and high-sensitivity11, software-based approaches can achieve such enhancement with only minimal modification to the underlying system. Conventional software-based studies have proposed spectral estimation12 and spectrum-shaping13 for enhancing resolution, and B-scan averaging14 and filtering-based methods17 for suppressing noise. However, these methods often require relatively time-consuming iteration algorithms for the enhancement, introducing spurious artifacts and oversmoothing the images. Meanwhile, as an alternative to overcome the limitations of conventional methods, the enhancement of OCT images through deep learning, which has recently shown outstanding performances in numerous fields, is drawing attention. Recent researches have shown that deep learning can outperform handcrafted feature descriptors in a number of imaging processing fields23. Inspired by these studies, deep learning is being applied actively to various optical imaging modalities, including OCT, for super resolution and noise reduction29. Several models have been reported that can restore the resolution of intentionally degraded OCT images32. Elsewhere, deep learning methods have been proposed to reduce speckle by learning frame-averaged OCT images34 or speckle-modulating OCT35 as ground truths. Liang, et al.36 implemented conditional generative adversarial networks (GAN) to enhance spatial resolution while preserving detailed speckle patterns in OCT images.While hardware-based enhancement techniques require expensive high-performance lasers and/or additional optical components to improve OCT imagesHowever, previously-proposed deep learning-based OCT image processing methods have used only grayscale 8-bit OCT images. Considering that OCT A-scans are constructed by applying signal processing steps including a Fourier transform to the interference fringes, valuable information might be lost during Fourier transformation, log compression, and conversion to 8-bit. Therefore, better image enhancement may be achieved if the raw interference fringe is fully utilized. However, in general, commercial OCT provides only gray-scale images; even with custom-built OCT, raw interference fringes in the wavenumber  and B-scan-based network (NetB). In particular, we fully exploit the information of the raw interference fringe signal, which was partially lost during transformation to OCT images by conventional processing. NetA is mainly responsible for enhancing the axial resolution of A-scans by utilizing spectrograms, which are obtained via short-time Fourier transform (STFT) of raw interference fringes. NetB was designed to enhance lateral resolution and reduce speckle noise in OCT B-scans. The dual models were individually trained and then sequentially applied in the inference phase. The performance was also evaluated using datasets acquired from different OCT systems, thereby demonstrating the versatility and expandability of the proposed technique. The performance of this dual model deep learning-based processing was evaluated through comparative studies to other methods on the same dataset. Advantages were also demonstrated through comparisons with mutli-B-scan averaged images and contrast-adjusted images. By overcoming the aforementioned limitations of conventional OCT image processing, the performance of the proposed deep learning-based OCT signal processing framework suggests that it can be a promising technology to enhance OCT images and expand OCT functionality.37, consisting of generators and discriminators. NetA, which mainly enhances axial resolution, directly receives two adjacent fringes and processes them by transforming through STFT and FFT to generate spectrograms and typical OCT A-scans, respectively 41, which can measure the similarity between two images (see Supplementary Note Using mean square error (MSE), the structural similarity index (SSIM)β), and the edge preservation factor (EPF), which can measure the degree of improvement in image quality. These metrics are commonly used for OCT denoising studies46. PSNR was used to quantify the noise levels in improved OCT images relative to original images. β is a normalized metric that can measure the degree of preservation of morphological features in denoised images and is often used as a performance evaluation indicator in OCT studies for speckle reduction46. EPF shows edge preservation effects with respect to original images, computed using the local correlation45  and six other previous deep learning techniques showing reliable performance in image improvement 49, super-resolution residual neural network (SRResNet)50, Unet51, very-deep super-resolution (VDSR)52, cycle-consistent adversarial network (CycleGAN)53, and paired image-to-image translation (Pix2Pix)54). Deep learning techniques were adopted as-is for each of the proposed implementations, but retrained using the same dataset in this study. The training loss curves of each technique are shown in Supplementary Fig. The potential of the proposed deep learning-based framework to other methods was presented through comparative studies using the same dataset. We compared our method with conventional methods based on statistical filtering (block-matching 3D (BM3D)In this study, we proposed a deep learning-based OCT image processing framework to enhance spatial resolution and SNR; we then verified the performance through comparative study and spectral analysis. The PSNR was improved by 24.96 dB compared to the input, and the resolution enhancement was 1.2 times in both the axial and lateral directions. In particular, spatial features were well preserved without excessive smoothing, which was often observed in previous studies.Importantly, we hypothesized that utilizing the spectral information contained in the OCT interference fringes could enhance the OCT imaging performance. Since raw interference fringes are very complex to be used directly for training deep learning networks, STFT was applied to obtain spectrograms that still contain spectral information. In addition, since the spectrograms are two-dimensional, they can be effectively used in deep learning networks that are known to perform well on images. We believe that this approach of utilizing spectrograms offers unique advantages over previous methods using only OCT images. While the spectral bandwidth directly limits the theoretical axial resolution of the OCT, we postulate that the proposed deep learning network can improve the axial resolution by restoring weak spectral information outside the spectral bandwidth of the light source.β, and EPF that the proposed network is more effective at removing speckle noise than multi-B-scans averaging while preserving spatial feature information better. Additionally, while the B-scan averaging method is still powerful in reducing speckle noise, this method can only be applied to very stable scanning methods in which adjacent frames are very similar, i.e. spatial differences are smaller than the lateral resolution of OCT and free from motion artifacts. Therefore, the B-scan averaging method cannot be applied when the frame interval is large or imaging is not stable due to motion. Of note, our method only uses a single B-scan image. Therefore, the proposed method can be applied to more diverse scanning situations, including intravascular OCT, in which helical scanning is applied and adjacent frames exhibit different features due to the relatively large frame interval. Furthermore, we demonstrate that the proposed method can enhance the resolution even for weak signals by comparing with contrast-adjusted images  having axial and lateral resolution of 10 µm (air) and 13 µm, respectively. The OCT system has a central wavelength of 1290 nm, a bandwidth of 110 nm, an average output power of 40 mW, and a frame rate of 117 frames/s. The acquired training data consists of a total of 12 samples . For each sample, a total of 5 sets were obtained in different regions. Each set consists of 1000 B-scans consisting of 1024 A-scans with a depth of 2048 pixels; thus, the total number of B-scans in the dataset is 60,000. The training set and the test set were constructed by dividing the pullback sets for each sample into a ratio of 8 to 2, resulting in 48,000 and 12,000 B-scans, respectively. The thyroid tissue specimen imaging was reviewed and exempted from deliberation by the Institutional Review Board of Gil medical center (GBIRB2021-241).Data were collected using a customized benchtop-based SS-OCT2 microspheres  were acquired. In addition, arterial data were collected using a previously reported catheter-based SS-OCT39, with an axial resolution of 11 µm (air) and a lateral resolution of 21 µm. The OCT system has a central wavelength of 1294 nm, a bandwidth of 110 nm, an average output power of 25 mW, and a frame rate of 114 frames/s. More details on the OCT system can be found in the previous works39. The acquired data consists of a pullback sets, some from in vivo swine coronary arteries implanted with bioresorbable scaffolds ; others from rabbit abdominal aortas with atherosclerotic plaque . Note that the OCT images helically scanned in polar coordinates were provided as inputs of the models, and the output images after the deep learning process were subjected to Cartesian transformation for visualization. All animal experiments were approved by the Institutional Animal Care and Use Committee of Korea University  and were performed in accordance with national and institutional guidelines.To investigate the expandable performance of the proposed deep learning approach, datasets not referenced during training were additionally acquired. With the same system as before, OCT images of droplets with 3 µm TiO7. On the other hand, the input was processed by applying imperfect numerical dispersion compensation and bandwidth truncation. Imperfect compensation in terms of dispersion was applied by randomly adjusting the polynomial fitting coefficients of dispersion, resulting in degraded axial resolution. The degree of degradation has been determined empirically to deteriorate the axial resolution by up to a factor of 2; detailed procedures can be found in the Supplementary Note 7. Accordingly, by truncating the bandwidth of the laser source, A-scans with degraded axial resolution can be obtained. The degree of bandwidth truncation was randomly selected within 0.5 times by applying Gaussian windows to the raw fringe before applying FFT. Example results of optimal and degraded OCT images, A-scans, and spectrograms for one of those A-scans are shown in Supplementary Fig. Training datasets for each model were separately generated by directly processing the raw interference fringes. The input and ground truth of the dataset for NetA were constructed by pairing OCT A-scans and interferograms with degraded axial resolution and the currently optimized OCT A-scans, respectively. Here, the currently optimized OCT A-scans are the A-scans with the best axial resolution achievable using our OCT system, which applies the best post-processing methods including background removal, k-linearization, and numerical dispersion compensationEach piece of data was normalized to improve the training efficiency of a gradient descent algorithm. Specifically, the transform results of STFT and FFT are a log compressed amplitude spectrum with a single precision floating point. Therefore, the range of the valid intensity range of the STFT and FFT results was normalized to between -1 and 1, respectively. After preprocessing, to augment the training dataset, two adjacent A-scans were randomly selected in each B-scan and randomly flipped in the horizontal direction.N, x, and y are the total number of outputs, the discriminator’s result, and the actual label. For example, in the case of the ground truth, the actual label is 1, and the closer the discriminator’s result is to 1, the smaller the loss. Using these functions, the losses of the generator and the discriminator are defined as follows:D(·) and G(·) refer to the outputs of the discriminator and the generator, respectively. In λs are empirically defined as 1, 0.6, 0.9, and 10−4, respectively. The reason why The schematic of NetA architecture is shown in Fig. The dataset for training NetB was generated from the same raw data utilized for NetA, but with different preprocessing. The ground truth data were generated by B-scan averaging of 7 adjacent OCT images with the best compensation applied. By defining the frame interval of the averaged OCT images to be lower than the lateral resolution of the OCT system, proper noise reduction was achieved while preventing excessive spatial blurring. The interval between each frame is ~2 µm. Gaussian weights were also taken and averaged over the B-scans to retain as much spatial information as possible while suppressing speckle noise. B-scan images with degraded lateral resolution and prominent noise were generated and used as input data, after three processes of bandwidth truncation, lateral Gaussian filtering, and SNR deterioration. Note that the input B-scan is a single image in the middle of the OCT B-scans used to create the averaged image. Bandwidth truncation larger than 0.8 times the normal bandwidth was applied to introduce variation in the noise pattern with a slight degradation of axial resolution. After reconstructing the B-scan OCT images, lateral Gaussian filtering and SNR deterioration were performed. The filter size and sigma of Gaussian filtering were randomly selected within 5–15 and 1–5, respectively, to blur the B-scan without oversmoothing. Finally, SNR deterioration was applied either by lowering the intensity level to a maximum of 3 dB or by amplifying the noise level to a maximum of 3 dB. These processes were applied randomly. Each pair of input and ground truth data was normalized to a scale between −1 and 1.55. Therefore, MS-SSIM loss was additionally adopted to ensure that structural features are well preserved; this process is defined as:The schematic of NetB architecture is shown in Fig. λs are empirically determined to be 0.8, 0.6, 1, 0.9, and 10−4, respectively. Details of the system implementation for training both networks are summarized in Supplementary Note The gradient loss is also utilized to compare the difference in gradient and variation in the height and width directions. By adding the gradient loss, the morphological edge features and texture information can be well preserved. The gradient loss is defined as follows:The number of data and the statistical analysis are described in figure legends. All statistical analyses were performed using GraphPad Prism .Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


P < 0.001) in predicting bRFS. Based on this newly-developed deep learning network, NAFNet, our DL-nomogram could accurately predict adverse pathology and poor prognosis, providing a potential AI tools in medical imaging risk stratification.We aimed to apply a potent deep learning network, NAFNet, to predict adverse pathology events and biochemical recurrence-free survival (bRFS) based on pre-treatment MRI imaging. 514 prostate cancer patients from six tertiary hospitals throughout China from 2017 and 2021 were included. A total of 367 patients from Fudan University Shanghai Cancer Center with whole-mount histopathology of radical prostatectomy specimens were assigned to the internal set, and cancer lesions were delineated with whole-mount pathology as the reference. The external test set included 147 patients with BCR data from five other institutes. The prediction model (NAFNet-classifier) and integrated nomogram (DL-nomogram) were constructed based on NAFNet. We then compared DL-nomogram with radiology score (PI-RADS), and clinical score (Cancer of the Prostate Risk Assessment score (CAPRA)). After training and validation in the internal set, ROC curves in the external test set showed that NAFNet-classifier alone outperformed ResNet50 in predicting adverse pathology. The DL-nomogram, including the NAFNet-classifier, clinical T stage and biopsy results, showed the highest AUC  and accuracy (0.850) compared with the PI-RADS and CAPRA scores. Additionally, the DL-nomogram outperformed the CAPRA score with a higher C-index (0.732,  Notably, approximately 29% of patients encounter Gleason grade upgrading, and 25% experience adverse pathology following radical prostatectomy, potentially leading to biochemical recurrence or the development of metastasis3. Addressing the challenges associated with adverse pathology (AP) and biochemical recurrence survival (BCR) prediction is vital for enhancing the precision and efficacy of prostate cancer management strategies.Prostate cancer was the second most frequently diagnosed cancer and the fifth leading cause of cancer-specific death among men in 2020. With an estimated 1.4 million new cases diagnosed worldwide and over 375,000 deaths attributed to this disease annually5. Integrating mpMRI with clinicopathologic parameters may allow for better risk classification of prostate cancer before radical surgery or radiation6.Multiparametric magnetic resonance imaging (mpMRI) plays important roles in diagnosing high-grade or clinically significant prostate cancer (csPCa), planning for definitive treatment and predicting the presence of AP, such as seminal invasion7. Recently, several studies have indicated that radiomic features derived from biparametric MRI, which evaluates subvisual texture patterns for the quantitative characterization of tumour phenotypes, might aid in prostate cancer risk stratification11. However, these systems require considerable effort in carefully ruling out the features before training a machine learning classifier, and suffering limitation of relatively low efficiency. Therefore, there is a high medical need for faster procedures.AP is known as an important predictor of BCR and metastasis13. One of the most widely used algorithms in computer vision, ResNet50, has been shown its potent potential in medical imaging. However, with 50 convolutional layers, ResNet50 needs substantial resource for further analyses14. The interpretation and classification of MRI rely on the understanding of each pixel in the MRI image. Since the lesion area occupies a small proportion in the image, these pixels must be fully utilized by the model to make accurate diagnoses. We here introduce NAFNet, a nonlinear activation-free network that simplifies the architecture and replacing the complex nonlinear activation functions. Although simplified, NAFNet still achieved state-of-the-art results with lower computational costs16. Moreover, the pixelwise feature segmentation capability of NAFNet enables it to extract and identify subtle differences in medical imaging, thus making it promising in medical imaging classification, especially in MRI.One possible solution could be the use of pretreatment parameters including clinical characteristics and MRI images to predict AP after radical prostatectomy (RP). Convolutional neural networks have successfully been used to analyse urological medical images in the fields of cystoscopy, ultrasound, pathology, and radiologyIn summary, within the present study, we aimed to explore the use of a deep learning algorithm (NAFNet) to identify AP on MRI image, and compare its performance with ResNet50. We next developed a customed nomogram by integrating this classifier and pretreatment clinical variables to predict AP and BCR events. We then clinically validated its performance in external cohorts by comparing it to current benchmark models such as preoperative Cancer of the Prostate Risk Assessment (CAPRA) nomograms and PI-RADS score.We retrospectively enrolled 618 patients with prostate cancer from six institutes throughout China. As depicted in Fig. P = 0.013). Further DCA analyses also indicated that NAFNet-classifier could provide greater benefits than ResNet50-classifier , DL-nomogram had a significantly higher AUC value than the clinical score (CAPRA) (P < 0.001). Besides, DL-nomogram had the highest AUC value  among these five models, and also had the highest accuracy value of 0.850. DCA further showed an improved net benefit brought by DL-nomogram compared with other models , radiology score (PI-RADS) and ResNet50-classifier on the external test set. As shown in Fig. els Fig. .Fig. 4PeAccording to the calculated threshold values listed in Supplementary Table To fully explore the predictive potential of the NAFNet-classifier-based model, we assessed NAFNet-classifier in BCR-free survival prediction. In the external test set, we performed Kaplan‒Meier analyses of bRFS according to the NAFNet-classifier, radiology score (PI-RADS), DL-nomogram and clinical score (CAPRA) score. The cut-off value of each model was determined by its optimal threshold in ROC curves. As illustrated in Fig. P = 0.003). Additionally, NAFNet-classifier alone also outperformed the PBCG score in both ROC analysis and DCA  based on a more powerful algorithm, NAFNet, integrating biopsy ISUP group, and clinical T stage to predict AP and bRFS in prostate cancer patients. With additional independent multi-site validation, the remarkable performance of DL-nomogram surpasses traditional models. This advancement holds the potential to revolutionize the pretreatment risk stratification of localized prostate cancer patients.All the enrolled patients met the following criteria: patients who had (1) undergone RP after mpMRI; (2) no history of neoadjuvant, adjuvant or other additional therapy; (3) preoperative 3 Tesla (3 T) prostate MRI images of high quality; and (4) visible lesions in images.The internal set consisted of 367 patients diagnosed between 2019 and 2021 at the Fudan University Shanghai Cancer Center (H1). All the patients in this set had whole-mount histopathology slide images (WSIs) of RP specimens, and the median follow-up time was 10 months. A second cohort of 147 patients was used as an independent external test set. These patients were diagnosed between 2017 and 2019 and were from five other tertiary hospitals: Shanghai Proton and Heavy Ion Center , Ningbo No.2 Hospital , Yunnan Cancer Hospital , Jiangsu Cancer Hospital  and Henan Cancer Hospital . Patients in the external test set did not have WSIs, but they had comparatively longer follow-up period, and the median follow-up time was 32 months.24; biochemical recurrence-free survival (bRFS) was defined as the interval between the date of RP and the date of BCR. Patients who were still alive without BCR at the last reported follow-up were labelled as censored9. AP was defined as the presence of high ISUP group (ISUP ≥ 4) tumours, extensive positive surgical margin (large tumour area contacting the specimen ink)25, extra-prostatic extension (EPE), seminal vesicle invasion (SVI), or pelvic lymph node metastasis on the radical prostatectomy (RP) specimen as previously described26.BCR was defined as at least two consecutive serum PSA levels >0.2 ng/mL after surgeryThis study protocol was approved by the ethical review board of Fudan University Shanghai Cancer Center (No. 2108241-3) and was conducted in accordance with the Declaration of Helsinki. Written consent was obtained from each patient. This study was also registered in the Chinese Clinical Trial Registry (ChiCTR2000036123). The TRIPOD and STARD guidelines were also followed  identified tumour index lesion regions on T2WI, ADC and DWI MRI imaging using the marked slides as a reference in the training set. In the external test set, the suspicious lesion was determined by study radiologists who were blinded to the pathology report.The MRI images were reviewed by experienced radiologists (XH.L. with 15 years of experience and ZZ.C. with 7 years of experience). PI-RADS version 2.1 (PI-RADS v2.1) scores were reassessed , and the regions of interest (ROIs) were delineated on T2WI, DWI and ADC maps using ITK-SNAP software. In order to ensure the accuracy of delineating the lesions from MRI imaging, we used whole-mount histopathology of RP specimens’ images to delineate the extent of lesions on MRI images, and used them as ROIs. Besides, the pathology results from whole-mount histopathology of RP specimens were treated as the gold standard of AP. To construct the deep learning model more precisely, a senior urological pathologist (YY.K.) was invited to label the slides with AP and mark the index lesion  on the whole-mount histopathology RP specimens for each patientAfter MRI image input, we then normalized MRI images, which involved scaling the image values to the range of 0 to 1 by dividing them by the maximum value of the image. Of all three channels, 2D MRI images were used. Next, we resized the images to the same dimensions for ease of model processing, and applied data augmentation techniques including flipping and rotating to increase the diversity of samples. All of these steps were taken to enhance the performance and robustness of the model.15. NAFNet achieved state-of-the-art results in the field of image restoration, e.g., denoising, deblurring, and stereo super-resolution29. The original intention behind the design of NAFNet is to address pixel-level issues. Firstly, NAFNet avoids the pixel accuracy problems caused by batch normalization and instead uses pixel-level layer normalization. Additionally, the U-Net structure and skip connections in NAFNet allow for better information transfer from input to output, thus avoiding accuracy degradation due to information loss. Thus, the dense prediction (pixelwise prediction) of NAFNet allows it to cut the features in the ROI more precisely, offering promising potential in MRI classification. The original Python codes of NAFNet are available on https://github.com/megvii-research/NAFNet. Compared to NAFNet, ResNet50, a widely-used residual networks developed by He at in 201514, has a deep architecture with 50 convolutional layers, divided into several modules, each containing residual blocks of different depths. This design allows ResNet50 to better propagate gradients, improve network convergence, and accuracy, and suffered from more complicated network design.Nonlinear Activation Free Network, NAFNet, is a newly developed deep learning network originally proposed by the co-author of our study, LY.C. in 2022NAFNet comprised three distinct input channels: T2WI, ADC and DWI maps. The image augmentation strategies and optimized configuration were as follows: we adopted random flipping/rotation and resized the maps to a spatial size of 384×384 by bilinear interpolation. The network was optimized using an AdamW optimizer, with a weight decay of 0.05 and a learning rate of 0.00001.We labelled AP events as 1 and no AP events as 0. Training and validation on the internal set were performed patient-wise using threefold repeated validation with an 80% (training) to 20%  stratified, random split for each fold. The results derived from the highest value of the cross-validation area under the curve (AUC) of the receiver operating characteristic (ROC) curve from the validation set were chosen to evaluate performance on the external test set. A binary entropy loss function was used in the deep learning network.Firstly, we classified each pixel within the ROI region of the input image. Then, we aggregated the scores of all pixels in the ROI region to obtain the overall classification result of the entire image. Specifically, the input image and ROI region are fed into the network, which outputs features with the same size as the image. Then, these features are passed through a fully connected layer to obtain a single-channel value, which is further transformed into a binary probability using the Sigmoid function. In this way, we obtain pixel-level probability. The final probability is determined by the pixel-level probabilities in the ROI region. After averaging the probability values of each patient’s three channels’ images, the final probability for that patient is obtained.All deep learning experiments were performed in the Python framework . All models were trained with the same hardware configuration , supported by the Medical Science Data Center of Fudan University.P-values were selected for further nomogram construction.Multivariable logistic regression models were fitted with a deep learning-based image classifier (NAFNet-classifier) and various clinicopathologic parameters, including preoperative PSA, biopsy ISUP, Prostate Imaging Reporting and Data System (PI-RADS) score, and clinical stage (cT), to evaluate the prediction of AP after RP. We treated NAFNet-classifier as a continuous variable and included it in the multivariable logistic regression model along with other clinicopathological factors, such as PI-RADS score, biopsy ISUP, cT stage and PSA value. We use the enter method for regression model. After calculation, factors with statistically significant 30. The Prostate Biopsy Collaborative Group (PBCG) risk score  is used to predict the risk of high-grade prostate cancer before prostate biopsy31. We used CAPRA score and PBCG score as the clinical score and the PI-RADS score as the radiology score when performing comparisons. In summary, we performed three further analyses to clinically validate the performance of NAFNet-classifier and DL-nomogram: (1) comparison of DL-nomogram with the radiology score (PI-RADS) and the clinical score (CAPRA) in the prediction of AP; (2) a head-to-head comparison between NAFNet-classifier, DL-nomogram, the radiology score (PI-RADS) and the clinical score (CAPRA) for bRFS prediction in terms of the concordance index (C-index) and Kaplan‒Meier curves; and (3) comparison of NAFNet-classifier and DL-nomogram with the clinical score (PBCG) in the prediction of clinically significant prostate cancer prebiopsy.The CAPRA score  is one of the most widely used model to predict AP, risk of BCR and metastasis, and prostate cancer-specific survival32. We report the AUCs with 95% CIs and other performance metrics  in the external test set. The 95% CI was calculated based on the R package “pROC” with “delong” methods32 to obtain the 95%CI of AUC following the documentation of “pROC” package33. Multivariable analyses were used to develop the nomograms. Decision curve analyses (DCAs) were used to illustrate the overall net benefit of using one model versus another. Kaplan‒Meier survival curves were used for bRFS analysis. In order to achieve the best performance of the binary classification, we obtained the optimal cut-off points by maximizing the accuracy in the ROC curves of each model as previously described17. The log-rank test was used in bRFS to determine statistically significant differences. All tests were two-sided, and statistical significance was defined as P < 0.05. Statistical analyses were performed using R software version 4.2.1.Significant differences in areas under the curve (AUCs) between the models were tested using DeLong’s testFurther information on research design is available in the Supplementary InformationReporting Summary


Conclusions: Early treatment of SARS-CoV-2 infection with TIX/CIL showed favourable outcomes in a small group of immunocompromised patients, reporting no significant difference compared to similar patients treated with other mAbs.Objectives: Monoclonal antibodies (mAbs) have proven to be a valuable tool against COVID-19, mostly among subjects with risk factors for progression to severe illness. Tixagevimab/cilgavimab (TIX/CIL), a combination of two Fc-modified human monoclonal antibodies, has been recently approved to be employed as early treatment. Methods: Two groups of immunocompromised patients exposed to different early treatments  were compared in terms of clinical outcomes  and time to the negativity of nasal swabs. We used either Pearson’s chi-square or Fisher’s exact test for categorical variables, whereas the Wilcoxon rank–sum test was employed for continuous ones. Kaplan–Meier curves were produced to compare the time to nasopharyngeal swab negativity. Results: Early treatment with TIX/CIL was administered to 19 immunocompromised patients, while 89 patients received other mAbs. Most of them were solid organ transplant recipients or suffering from hematologic or solid malignancies. Overall, no significant difference was observed between the two groups regarding clinical outcomes. In the TIX/CIL group, one patient , who was admitted to the emergency room within the first 14 days from treatment and was hospitalised due to COVID-19 progression, died. Regarding the time to nasal swab negativity, no significant difference ( Since the coronavirus disease 2019 (COVID-19) pandemic began, vaccination has represented the primary strategy to contain the diffusion of the virus. However, some subjects do not retain a competent immune system and, therefore, might not respond with a complete adaptive immune response. Thus, the concept of passive immunisation through the use of monoclonal antibodies has been deeply explored. These molecules have become part of the therapeutic strategy against SARS-CoV-2 with their innate capacity to protect against disease irrespective of one’s immune system status. Early treatment with monoclonal antibodies has proven to be a valuable tool against COVID-19, mostly among subjects with risk factors for progression to severe illness , with COVID-19 diagnosis, evaluated at the outpatient clinic or hospitalised for a non-COVID-19-related reason in the ward of the Infectious Diseases Unit, IRCCS Ospedale Maggiore Policlinico, Milano, Italy, from 28 August to 15 October 2022, who received early treatment with TIX/CIL. This group was compared to subjects who had received other mAbs  between 25 November 2021 and 25 January 2022, as previously published .We compared clinical outcomes  and time to the negativity of nasal swabs. Categorical variables were compared by using either Pearson’s chi-square or Fisher’s exact test, whereas the Wilcoxon rank–sum test was employed for continuous variables. Kaplan–Meier curves were produced to compare the time to nasopharyngeal swab negativity.The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of IRCCS Fondazione Ca’ Granda Ospedale Maggiore Policlinico .Early treatment with TIX/CIL was administered to 19 immunocompromised patients, whereas 89 individuals were treated with other mAbs. The majority of patients included in the TIX/CIL cohort were SOT or individuals suffering from hematologic or solid malignancies. TIX/CIL treatment was administered on average 5(±5) days after symptom occurrence. p = 0.088) emerged between the two groups, with 36/89 (40.4%) and 5/19 (26.3%) of patients being negative at 14 days since treatment administration in the mAbs and TIX/CIL group, respectively , who was admitted to the emergency room within the first 14 days from treatment and was hospitalised due to COVID-19 progression, died. Regarding the time to nasal swab negativity, no significant difference , but have been described as inactive against the new ones . Recently published in vitro data has suggested how emerging Omicron sub-lineages are resistant to most  or all (BQ.1.1) mAbs used in routine practices, including TIX/CIL . Along wOur study has some inherent limitations related to its retrospective design. Particularly, TIX/CIL treatment has been compared with a historical group of patients treated with other mAbs, with different SARS-CoV-2 VOCs representing the dominant strain at the time and with a population who received fewer vaccine doses and experienced fewer past SARS-CoV-2 infections. Nonetheless, considering the similarity of the patients included in the two study groups, and the impossibility for ethical reasons to compare TIX/CIL with mAbs with known inefficacy against Omicron VOC, we believe our results are still interesting. Another theoretical limit is the follow-up time for clinical outcomes being restricted to 14 days since treatment administration, which may have reduced the detection of long-term outcomes due to COVID-19.Overall, TIX/CIL early treatment has demonstrated favourable outcomes among immunocompromised patients, supporting its employment in this population, which usually does not have access to other therapies because of drug–drug interactions  or comorbidities . TIX/CIL should be offered as early treatment until the evolution of circulating VOCs leads to its ineffectiveness.Evidence on the effectiveness of TIX/CIL treatment in clinical practice is limited, specifically among fragile subjects, who have been poorly represented in major randomised controlled trials but may benefit the most from these approaches. There is, therefore, an urgent need to shed light on the safety, efficacy, and long-term outcomes of early treatment with TIX/CIL among this peculiar population, especially in the context of SARS-CoV-2 VOCs BQ.1/1.1 diffusion.


To investigate the possibility of silver (Ag)-induced adverse events and the degree of bone fusion in posterior lumbar interbody fusion surgery using an Ag-containing hydroxyapatite (HA) lumbar interbody cage.An Ag-HA cage consisting of highly osteoconductive HA interfused with Ag was developed, and we applied it clinically at three university-affiliated hospitals from April 2020 to December 2020. During the 12-month postoperative observation period, Ag-related adverse events, neuropathy, and postoperative complications were investigated as indicators of safety, while clinical improvement and the fusion status were investigated as indicators of efficacy. Clinical improvement was defined as improvement beyond the minimum clinically important difference (MCID) in the numerical rating scale  for low back and lower limb pain and the Oswestry Disability Index .We performed lumbar interbody fusion using an Ag-HA cage for 48 patients . The mean preoperative NRS was 6.4 , while the mean preoperative ODI was 44 [12]. No adverse effects  were identified during the 12-month observation period. Surgical site infection occurred in one case, although the implant was preserved via immediate debridement. In total, 39 (81%) participants showed clinical improvement beyond MCID for both NRS and ODI. Bone fusion was achieved at 45 levels (88%) at 6 months and 48 levels (91%) at 12 months postoperatively.The results of this study suggest that Ag-HA cages can be safely used in spinal fusion procedures and have the potential to prevent postoperative infections, prevent deterioration of the quality of life, and result in favorable outcomes. Larger-scale and longer-term follow-up studies will be required to corroborate these conclusions.Trial registration UMIN 000039964 . Posterior lumbar interbody fusion (PLIF) and transforaminal lumbar interbody fusion (TLIF) are established treatments for various pathologies of the lumbar spine  . With thSurface coating technologies represent a strategy to address these complications because they improve osteoconductivity and provide antibacterial properties. Several antibacterial coatings have been developed for orthopedic implants . NotableThus, to simultaneously achieve antimicrobial and osteogenic properties, we developed Ag-containing HA (Ag-HA) by interfacing osteoconductive HA with antibacterial Ag , 13. AftNo postoperative infections or adverse reactions associated with the use of Ag have been observed , 17. BasTo evaluate the appropriateness of further large-scale trials comparing Ag-HA cages with conventional cages and to obtain information on the basis of the study protocol, we first performed a multicenter pilot study wherein we investigated the possibility of Ag-induced adverse events and the degree of bone fusion in PLIF surgery using an Ag-containing HA lumbar interbody cage.This multicenter, single-arm, prospective study evaluated the safety and osteoconductivity of the Ag-HA-coated intervertebral cage used in PLIF or TLIF. The study was approved by the institutional review board of our institution (#2019-07-R-11) and has been registered in the University Hospital Medical Information Network clinical trials registry (UMIN 000039964). Moreover, it adhered to the principles of the Declaration of Helsinki. After receiving full approval from the local ethical committee, participants were recruited from three university-affiliated hospitals between April 2020 and December 2020. Written informed consent was obtained from all participants.The inclusion criteria were as follows: (1) age ≥ 20 years; (2) ≥ grade II lumbar degenerative spondylolisthesis or lumbar spinal canal stenosis with local coronal imbalance  necessitating single or dual-level TLIF or PLIF as determined by the physician in charge at each facility; (3) no history of metabolic bone disease, bone tumors, or cancer metastasis; and (4) ≤ 2 fused vertebrae. The exclusion criteria were as follows: (1) known allergy or hypersensitivity to Ag based on the patient’s medical history, (2) presence of general inflammatory disease or osteoporosis , and (3) a history of lumbar surgery.2O powder to HA powder . Ag-HA was thermal sprayed as a coating material for the creation of an Ag-HA-coated titanium cage (Ag-HA coating thickness: 2 mm)  Fig. .Fig. 1AgPatients underwent a PLIF or TLIF procedure with open or percutaneous pedicle screws, bone graft , and an Ag-HA-coated cage. Local bone grafts were obtained from the spinous processes, lamina, and facets. One or two interbody cages filled with bone graft were inserted into the interbody space. In standard cases, two cages were used; however, in cases of degenerative scoliosis and revision surgery, a single cage was considered, with the final decision left to the surgeon. After the screws were inserted, rods were placed on both sides, and moderate pressure was applied posteriorly to prevent cage deviation and achieve proper local lumbar kyphosis. Postoperatively, all patients wore a soft lumbar corset for 3 months and underwent standardized physical therapy, including exercises for strengthening the trunk and extremity muscles and walking.Data on participants’ demographic and clinical characteristics, such as age at surgery, sex, comorbidities, indications for surgery, and the PLIF or TLIF segment level, were obtained from medical and surgical records.The primary outcome of interest was Ag-induced adverse events. During the postoperative follow-up, each patient was monitored for adverse reactions to Ag  by certified spine surgeons at each facility. Systemic/local argyria was evaluated based on the presence of ash-colored skin, which can occur in argyria (systemic Ag intoxication). Local argyria was assessed for the skin around the surgical wound, and systemic argyria was assessed for the skin all over the body. Delayed wound healing was defined as the lack of wound healing at more than 10 days after surgery. Neurological symptoms were evaluated via medical examinations. Neurological symptoms that appeared for the first time after surgery and could not be explained organically after various examinations, including imaging studies, were defined as neurological symptoms due to Ag toxicity. In addition, postoperative complications were examined.Effectiveness indicators included clinical improvement and fusion status. The numerical rating scale (NRS) for low back pain (LBP)/lower limb pain and Oswestry Disability Index (ODI) scores  were evaBaseline characteristics and outcomes were reported for the overall population. Normally and non-normally distributed continuous variables are reported as mean (standard deviation [SD]) and median (interquartile range [IQR]), respectively. Categorical variables are presented as the frequency (percentage). For the effectiveness measures, NRS and ODI, the percentage of participants who achieved an improvement beyond the minimum clinically important difference  was also reported . Inter- Figure During the postoperative follow-up period, no participant showed any signs of systemic and/or local argyria or neurological symptoms due to Ag toxicity. However, a 58-year-old man with no underlying disease developed SSI (deep infection). The participant was reoperated for debridement as soon as possible and treated with antibiotics postoperatively. After the reoperation, the infection was quiescent and did not require implant removal. Another patient developed pain in the left leg and a disc herniation at the L3/4 level 3 months after L4/5/S1 PLIF. Fusion extension surgery was performed at 5 months after the first surgery.The results of clinical improvement and fusion status are summarized in Table An 81-year-old woman with lumbar spinal canal stenosis that caused LBP and leg pain underwent PLIF using an Ag-HA cage. Six months after surgery, there was no LBP or metal artifact around the cage, and intervertebral fusion was achieved. Moreover, the VECF that existed preoperatively was completely resolved Fig. .Fig. 3ReIn this study, we observed 48 participants who underwent TLIF or PLIF using Ag-HA-coated interbody cages and collected information regarding the appropriateness of cage use. Of the 48 participants , only one experienced SSI, and no Ag-induced complications occurred. In addition, > 80% of participants showed clinically meaningful symptomatic improvement at 12 months postoperatively, and the fusion rate was 91%, which is clinically acceptable. The results of this study could provide valuable information for conducting subsequent clinical trials comparing Ag-HA cages with conventional cages.To the best of our knowledge, this is the first trial to evaluate the safety and efficacy of the Ag-HA cage for lumbar interbody fusion in patients with lumbar spine disease. Although Ag has antibacterial activity, it is also associated with adverse effects such as cytotoxicity or poor cytocompatibility . The antAg-related adverse reactions, such as argyria and mental or neurological disorders , hepaticIn this study, a 58-year-old man with no underlying disease developed a deep infection. In the case of deep SSI in instrumented spine surgery, biofilm formation on the instrument is a major factor in the severity and refractoriness of the infection. In such cases, the removal of the instrument is frequently required. Although infection of the cage was considered in this case, the cage was not removed at the first revision surgery because of the expected effect of Ag-HA in inhibiting biofilm formation . It may 2: 64.4, degree of freedom (df): 25, p < 0.001; I2: 61.2%; τ2: 0.03) and 94%  [The efficacy of the Ag-HA cage was evaluated based on clinical and radiological assessments. With regard to clinical findings that are effectiveness indicators, all patients showed an improvement in the NRS score for LBP and lower limb pain and the ODI score for LBP-related quality of life. In a systematic review of the quantitative evaluation of bony fusion in lumbar interbody fusion, Formica et al.  analyzed2: 0.06) , 17, 30,2: 0.06) .Recently, it has been reported that VECF after lumbar interbody fusion using a cage in the early postoperative period may be a predictor of pseudoarthrosis or non-fusion , and theThe present study was associated with some limitations. First, the lack of a control group made it impossible to evaluate the superiority of AG-HA cages over conventional cages in terms of antimicrobial resistance and fusion rate. This research question should be addressed in subsequent clinical trials based on the results of this study. In addition, the safety of the Ag-HA cage may not have been adequately confirmed because of the small sample size and short follow-up period. This study population needs to be closely monitored, and further cases need to be accumulated. Therefore, a prospective multicenter clinical trial (UMIN 000039964) is currently underway.We developed an Ag-HA-coated cage, which has both the antibacterial activity of Ag and the osteoconductive activity of HA, for spinal fusion. The clinical trial was successful, with no cases of Ag-induced adverse effects and acceptable clinical results. The Ag-HA cage has the potential to reduce postoperative infections, prevent deterioration of the quality of life, and result in favorable outcomes in patients undergoing PLIF and TLIF. Larger-scale and longer-term follow-up studies will be required to corroborate these conclusions.