Attachment material is one of the contributing factors to the degree of wear of the attachment components in mandibular implant-retained overdentures. The purpose of this in vitro study was to compare the wear behavior of 2 different attachment systems of dissimilar materials in mandibular implant-retained overdentures by qualitative and quantitative methods.n = 16); Titach  with a titanium-to-titanium interface and Locator R-Tx  with a titanium-to-nylon interface. One thousand cycles of overdenture insertion and removal simulating 1-year clinical use were performed. All matrices were removed from the overdentures and all patrices were unscrewed from the implants for wear assessment quantitively using a stereomicroscope and qualitatively using a scanning electron microscope. Data were analyzed by using an independent sample t test.Two attachment systems of different materials were utilized (p < 0.001). Moreover, scanning electron microscopy images showed noticeable abrasion in Titach patrix at the area of highest convexity. However, the Locator R-Tx matrix displayed an apparent tear of rubber inserts.After cyclic loading, stereomicroscopic findings showed that the Titach group had statistically significant higher wear value than the Locator R-Tx group (Titach attachment with the titanium-to-titanium interface revealed more wear than Locator R-Tx attachment with the titanium-to-nylon interface. Thus, the type of attachment material influences the degree of wear of the attachment components. Implant-retained overdentures with attachments offer edentulous patients a better alternative to conventional complete dentures, as overdentures increase comfort, patient satisfaction, and retention –3. The MNumerous considerations, including the amount of retention necessary, the rate of wear, maintenance in the long term, and patient satisfaction, influence the choice of a specific attachment design –9. SeverDue to wear over time, loss of attachment retention is the most frequent issue with implant-retained overdentures –13. AccoSeveral factors, including the type of attachment material , 16–18, The purpose of this in vitro study was to compare by qualitative and quantitative methods the wear behavior of a Titach attachment with titanium-to-titanium interface and a Locator R-TX attachment with clear nylon inserts in mandibular implant-retained overdentures. The null hypothesis was that after recurring cycles of overdenture removal and insertion, the wear behavior of the Titach and Locator R-TX attachment systems in mandibular implant-retained overdentures would be the same.In this study, Titach attachment  and Locator R-Tx attachment  were used. Both attachment systems had two parts, male and female part, known as “patrix” and “matrix” respectively, which were placed into one another to facilitate fastening. The matrix was integrated into the overdenture fitting surface and the patrix was attached to the implant. Titach patrix was made of titanium, whereas the locator R-Tx patrix was made of titanium carbon nitride coating with a pink aesthetic color. Female parts were fabricated from dissimilar materials. Titach matrix was composed of a titanium cap surrounded by a silicone sleeve, while locator R-Tx matrix was formed of titanium housing with various nylon inserts confined in it of different retention; zero retention insert (gray), low retention insert (blue), medium retention insert (pink) and high retention insert (clear) , 25.A sample size of 16 mandibular implant-retained overdentures with 32 attachments was calculated with a software program   supposinA set of ready-made maxillary and mandibular completely edentulous silicone molds was used for pouring of one maxillary stone model and 16 mandibular stone models for fabrication of 16 mandibular complete dentures. The same mandibular mold was used for pouring a mandibular model made of epoxy resin  with a 7.5 mm width at the canine region. The epoxy resin was covered with mucosa simulating material made of flexible polyurethane of 1.5 mm thickness . MaxillaSixteen mandibular trial denture bases were constructed on the mounted stone models. The same set size mandibular acrylic teeth (size 22) were arranged on all the trial denture bases utilizing the same mounting while keeping the opposing maxillary trial denture base in place to ensure standardization of all the mandibular implant-retained overdentures. The mandibular trial denture bases were finally processed into heat-polymerized acrylic dentures .Two implants  of length 10 mm and diameter 3.5 mm were placed at the canine area parallel to each other. The 32 attachment caps were directly picked up using autopolymerizing resin. For Titach attachment , the silicone sleeve was placed on the cap during the pick-up, then after setting of the acrylic resin, the protruding part from the cap was cut off by using a sharp scalpel. For locator R-Tx attachment , the black processing insert was used during the pick-up, then substituted with a clear nylon insert  was inspected by measuring the inner circumference of the attachment’s matrix as well as the outer circumference of attachment’s patrix under a stereomicroscope  at × 25 magnification. According to previous studies [n = 32) was observed by scanning electron microscopy  at different magnifications.Quantitative analysis of wear patterns of both attachment systems . The Kolmogorov‒Smirnov test of normality did not show any significance in the distribution of variables; therefore, parametric statistics were used. The test used was an independent sample t test. Furthermore, the interexaminer reliability was calculated by using the intraclass correlation coefficient (ICC) between two operators for each reading. ICC was rated as follows : < 0.40:p < 0.001). In contrast, the patrix of the Titach group showed a statistically significant higher wear value with a mean = 87.37 µm compared with that of the Locator group with a mean = 1.93 µm after cyclic loading (p < 0.001)  was found between the two operators. The matrix of the Locator group displayed a statistically significant higher wear value with a mean = 73.72 µm compared with that of the Titach group with a mean = 24.41 µm after cyclic loading (The attachment surfaces of each group were observed by a scanning electron microscope before and after cyclic loading. The Titach patrix presented more noticeable wear changes than the matrix in the form of scratches, abrasion and deformation, especially in the area of highest convexity. Moreover, the inner surface of Titach showed accumulation of metallic powder produced from titanium-to-titanium friction. Furthermore, the patrix of the locator R-Tx group had an accumulation of nylon remnants along the narrower coronal area. However, the matrix of the Locator R-Tx group displayed more apparent wear changes than the patrix in the form of a tear of rubber inserts, surface irregularities and deformations along the inner circumference Figs. , 7 and 8The current study investigated 2 attachment designs, Titach and Locator R-Tx, as they were the only compatible attachments with the used implant system. Wear investigation was conducted in an in-vitro study to standardize occlusal loadings away from the absence of other intraoral factors. The present study revealed that the repeated cycles of overdenture removal and insertion significantly have led to different wear behavior in each attachment systems; thus, the null hypothesis was rejected.Maxillary and mandibular stone casts were mounted on a mean value articulator and acrylic teeth were arranged. The maxillary record base was kept on the articulator while the 16 mandibular record bases were interchanged on the same mounting to preserve the same maxillo-mandibular relation during arrangement of mandibular acrylic teeth to ensure standardization of all the mandibular implant-retained overdentures.In the epoxy resin edentulous mandibular model, implants were placed parallel to one another based on the suggested implant placement for non-splinted implant-retained overdentures: parallel, in the path of insertion of overdentures, and perpendicular to the occlusal plane to function well –23.Wear frequently occurs within the first 12 months of use, necessitating more frequent maintenance of the attachments , 9, 11. The type of attachment material influences the degree of wear of the attachment components. Thus, stereomicroscopic findings in the current study reported that the Locator R-Tx rubber matrices manifsted more wear and deformity than the Titach titanium ones. In contrast, the Titach titanium patrices demonstrated more wear than the Locator male ones. This may be attributed to the titanium carbon nitride coating of Locator R-Tx which was more wear resistant , 36.Furthermore, locator R-Tx attachment is made of different materials with different hardnesses; nylon matrix and metal patrix, so most of the wear had occurred in the nylon matrix. This was in accordance with a previous clinical study by Hahnel et al. , which sTitach attachment is made of titanium matrix and patrix. Titach patrix has a convex form that may have undergone more wear as a result of friction between metal surfaces of the same hardness and frictional coefficient. The results of studies conducted by Yabul et al.  Sonbaty Moreover, when evaluating the total wear of each attachment system, the Titach group had undergone more wear than the Locator R-Tx group. This was correlated with a previous study by Ramadan and Mohamed  which reHowever, the wear pattern and surface alteration of the Titach attachment in the current study did not affect its final retentive force after cyclic loading, which was reported in a previous study by Ramadan and Mohamed  as higheScanning electron microscopy images of Titach patrix demonstrated an obvious band of wear and abrasion across the height of contour. In addition, surface irregularities were noted in the Titach matrices. This may be attributed to the friction caused by the titanium-to-titanium interface. This finding was the same of previous studies , 18, 20 The limitation encountered in the current study was in its in-vitro nature, as it was challenging to precisely replicate the conditions present in the oral cavity. Attachment wear can be influenced by a number of variables, including parafunctional oral habits, temperature, salivary components, dental plaque, and the usage of denture cleansing solutions , 28. TheTitach attachment with the titanium-to-titanium interface showed more total wear than Locator R-Tx attachment with the titanium-to-nylon interface after mimicking 1 year of clinical use.Titach attachment presented through scanning electron microscopic findings a distinct pattern of wear and deformation in both matrix and patrix, whereas Locator R-Tx attachment with nylon insert showed identifiable tear signs in the matrix after mimicking 1 year of clinical use.Considering the limitations of this in vitro study, the following conclusions were drawn:


Patient support programs (PSPs) improve medication-taking behavior in the first 12 months of treatment for patients with immune-mediated diseases, but it is unknown if these benefits are sustained. As immune-mediated diseases continue to increase in prevalence and economic burden, understanding the potential value of PSPs in helping patients adhere to their long-term treatment plan and avoid costly hospital visits is crucial. Launched nationally in 2015, HUMIRA Complete  provides an opportunity to study long-term effects of PSP participation, including the impact on medication-taking behavior and hospital visits.To evaluate the sustained relationship between PSP participation, long-term medication-taking behavior, and hospital visits.A longitudinal, retrospective matched-cohort study was conducted of patients initiating adalimumab between January 2015 and February 2016 with or without enrolling in the PSP, using patient-level data from the HUMIRA Complete PSP linked with Symphony Health claims. The sample included adult, commercially insured patients diagnosed with an indicated disease who were biologic-naive and had data available for ≥ 6 months before and ≥ 12 months after initiating adalimumab. Adherence (proportion of days covered) and hospital visits were assessed at 12, 24, and 36 months for patients with sufficient follow-up data. Multivariable generalized models estimated differences between cohorts, controlling for baseline characteristics and hospital visits. Duration of persistence and time to a hospital visit were compared using Kaplan-Meier analyses. Hazard ratios were estimated using multivariable Cox proportional hazards models.P < 0.0001; 29% greater), 24 , and 36  months. PSP participation was associated with a 30% lower hazard of discontinuation (P < 0.0001), and median duration of persistence was 4.8 months longer for the PSP cohort . The PSP cohort had lower rates of hospital visits at 12 , 24 , and 36  months, and PSP participation was associated with a 25% lower hazard of a hospital visit (P < 0.0001). Median time to a hospital visit was 10.8 months longer for the PSP cohort . Findings were consistent across therapeutic areas: hazard of a hospital visit was 28%, 27%, and 37% lower for rheumatology, gastroenterology, and dermatology patients participating in the PSP .The matched cohort included 2,268 patients , and patient attrition was similar across cohorts. The PSP cohort consistently demonstrated higher adalimumab adherence than the non-PSP cohort at 12 (64.8% vs. 50.1%, Patients with immune-mediated diseases receiving adalimumab and utilizing this PSP had improved long-term medication-taking behavior and lower risk of hospital visits, demonstrating the potential of PSPs to improve patient outcomes and lower the burden to the health care system. What is already known about this subjectPatient support programs (PSPs) improve medication-taking behavior in the first 12 months of treatment for patients with immune-mediated diseases.Improved disease management through better medication-taking behavior and PSP support should lower the need for hospital visits, which incur substantial burden for patients and payers.PSPs aim to improve long-term outcomes for patients with immune-mediated diseases by addressing barriers to optimal adherence; however, there is a paucity of evidence due to the novelty of multifaceted PSPs and the rarity of important clinical events such as hospital visits.What this study addsThis study used real-world data to show that participation in the evaluated PSP is associated with sustained improvements in medication-taking behavior and lower risk of hospital visits over 36 months.These findings provide further evidence that PSP participation can improve patient outcomes while also lowering the burden to the health care system over the longer term.3 Better medication-taking behavior (adherence and persistence) with biologic therapy is associated with improved clinical, economic, and humanistic outcomes; however, suboptimal adherence remains common.5 Nonadherence exacerbates disease progression and increases the risk of hospitalization.11 Though PSP use has been consistently associated with improved outcomes for patients initiating treatment, there is a paucity of evidence on (a) whether medication-taking behavior benefits are sustained throughout treatment due to the novelty of multifaceted PSPs and (b) the relationship between PSP participation and hospital visits due to rarity.14 As immune-mediated diseases continue to rapidly increase in prevalence and economic burden, understanding the potential sustained value of PSP participation in helping patients adhere to their long-term treatment plan and avoid hospital visits should be of great interest to patients, providers, and payers.17For patients with immune-mediated diseases, participation in a patient support program (PSP) can augment value-based care by helping patients adhere to complex treatment regimens.25 For patients with immune-mediated diseases, who now comprise 5%-7% of the Western population, studies have shown that better medication-taking behavior is associated with lower risk of hospital visits, including inpatient admissions and emergency department (ED) visits, which account for the majority of disease-related medical costs.11 Common barriers to adherence for immune-mediated disease patients include intentional  and unintentional  factors.28 To address such hurdles, PSPs provide individualized counseling, training, and assistance to motivate patients and improve medication-taking behavior, but the sustainability of these benefits and their impact on hospital visits is unknown.28Nonadherence to treatments for chronic therapies has long been recognized as a major contributor to excess hospitalizations and wasteful health care spending.14 Sufficient data are now available to provide more complete evidence on the benefits of PSPs throughout the duration of treatment by examining the impact of PSP use on sustained medication-taking behavior and risk of hospital visits.Since 2015, AbbVie Inc.  has offered a unique PSP for adalimumab patients, HUMIRA Complete, designed to provide one-on-one, personalized support to increase medication adherence through behavior modification and motivate patients to improve their health literacy. The program offers assistance with accessing treatment , emotional motivation, treatment management, disease education, medication reminders, and a dedicated Nurse Ambassador . Prior studies demonstrated the impact of this PSP in improving access to treatment, medication-taking behavior, and costs, but studies were limited to the first 12 months of adalimumab treatment and did not examine hospital visits.1 Up to 36 months of longitudinal, patient-level data are now available, providing an opportunity to evaluate the sustainability of PSP benefits and the potential to lower the risk of clinically important and costly hospital visits for patients with immune-mediated diseases.The research presented here is an extension study to assess if improvements in medication-taking behavior observed for PSP participants continued beyond the first 12 months of treatment and to evaluate occurrences of hospital visits.1 For this study, claims were available from January 2006 through April 2019. A proprietary deidentification engine  was used by each data provider to generate unique patient tokens based on identifiable information that was removed from the data provided to researchers, allowing for direct patient linking between the PSP records and claims. Any risk associated with linked data content was evaluated by an external HIPAA statistician who certified patient anonymity of the resulting files. Because deidentification was conducted before providing claims to Symphony Health and PSP records to researchers, and no identifiable protected health information was included in the data used, institutional review board approval was not required. As this was an extension study of a preexisting patient cohort, it was not registered publicly prior to conducting the analysis.A retrospective, longitudinal matched-cohort study was conducted using patient-level data from the Symphony Health administrative claims database and PSP enrollment records, details of which have been described previously.1 Patients initiating adalimumab were included if they were at least 18 years of age and had an adalimumab indication , were biologic-naive prior to adalimumab initiation, and did not have government-provided insurance on their initial adalimumab claim, which would make them ineligible for the financial assistance component of the PSP. The index date was defined as the date of PSP enrollment for PSP patients. The length of time between the initial adalimumab claim and PSP enrollment for PSP patients was used to calculate the index date for matched controls as described below.The methodology used to create the study sample has been previously described.International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification (ICD-9/10-CM) diagnosis codes for an adalimumab indication . The first adalimumab claim must have occurred between January 2015 and February 2016 and evidence of continuous medical and drug data coverage was required for at least 6 months before and at least 12 months after the first adalimumab claim and index date (if different). Periods of continuous data coverage were determined based on the observed frequency of medical and drug claims using previously developed algorithms.2 The 6-month period before the index date was considered the baseline period. Follow-up period outcomes were evaluated at 12, 24, and 36 months after the index date for patients with sufficient data availability.Each patient was required to have ≥ 2 claims at least 30 days apart with 1Patients who met the eligibility criteria were categorized into 2 cohorts, the PSP cohort and the non-PSP cohort, based on enrollment in the PSP within 30 days of adalimumab initiation and engagement with a Nurse Ambassador. PSP patients were matched 1:1 to non-PSP patients based on pharmacy type of the initial adalimumab claim, immune-mediated disease indication, and propensity score estimated with covariates for age, sex, year of adalimumab initiation, and baseline comorbidities. Details of the cohort construction and rationale for the matching process used have been previously described.Measures of medication-taking behavior (adherence and persistence) and hospital visits were assessed at 12, 24, and 36 months following the index date. Adherence was measured using the proportion of days covered (PDC): the sum of the number of days covered by the reported days supply of adalimumab claims divided by the number of days in each follow-up period . Persistence was defined as the proportion of patients remaining on treatment throughout each follow-up period, with discontinuation determined by a switch to another biologic medication or a gap in adalimumab treatment exceeding the days supply of the prior adalimumab claim with no further adalimumab claim during the follow-up period. Duration of persistence was calculated as the time from the index date to the earlier of discontinuation or the end of data availability. Hospital visits, including inpatient admissions and ED visits, were identified based on the location of service reported on medical claims, and the proportion of patients with at least 1 hospital visit was summarized at each timepoint. Time to a hospital visit was calculated as the time elapsed from the index date to the first occurrence of a hospital visit.Baseline demographics, clinical characteristics, and hospital visits for the 6 months prior to the index date were summarized using descriptive statistics. Differences in baseline measures and follow-up data availability between cohorts were assessed using Student’s t-tests for continuous variables and chi-square tests for categorical variables. Outcomes were descriptively evaluated using means, medians, proportions, standard deviations (SDs), and 95% confidence intervals (CIs) where appropriate. Relative differences were calculated as the difference between the estimates for the PSP and non-PSP cohorts divided by the estimate for the non-PSP cohort. Separate multivariable generalized linear models were used to compare outcomes across cohorts at 12, 24, and 36 months, and hazard ratios (HRs) of the effect of PSP participation on discontinuation and hospital visits were estimated using Cox proportional-hazards models. All models controlled for baseline demographics, clinical characteristics, and hospital visits. Duration of persistence and time to a hospital visit were compared between cohorts using Kaplan-Meier analyses, censoring for loss of patient-level follow-up data. Outcomes were estimated for the full sample and separately for each therapeutic area . All analyses were conducted using SAS software version 9.4 .Supplementary Figure 1, available in online article), with similar baseline characteristics and hospital visits . As reported previously, the mean age of the study population was 50.4 years, and approximately 70% of the patient population was female.1 The most common indications for adalimumab treatment in both cohorts were RA (41.4% of patients), CD (19.5%), psoriasis (14.3%), and UC (13.2%). No significant difference was found in the proportion of patients with a hospital visit during the baseline period . Longitudinal, patient-level follow-up data availability was also similar across cohorts . Patients in the PSP cohort remained engaged with the program for 16.8 months on average, while 12% of the non-PSP cohort opted into the PSP during the follow-up period (after not enrolling when initiating treatment).The matched sample included 2,268 patients , 24 , and 36  months , 24 , and 36  months (P < 0.0001). Median duration of persistence with adalimumab was 4.8 months longer for the PSP cohort than for the non-PSP cohort . Improved medication-taking behavior for the PSP cohort, including higher adherence, lower risk of discontinuation, and longer duration of persistence, was found across all therapeutic areas .Medication-taking behavior, as assessed by adherence and persistence, was higher for the PSP cohort at each follow-up timepoint. The PSP cohort consistently demonstrated higher adalimumab adherence than the non-PSP cohort at 12  months . Similar) months . The adjP < 0.001; 19% lower), 24 , and 36  months (P < 0.0001), with the median time to a hospital visit of 11.8 months longer than for the non-PSP cohort  over 36 months.The PSP cohort had lower rates of hospital visits at each follow-up timepoint. The PSP cohort had fewer patients with a hospital visit at 12  months . In survP < 0.05, except 12-month inpatient admissions; P < 0.0001) and a 17% lower risk of inpatient admission  over 36 months (Findings were similar for ED visits and inpatient admissions examined separately. The PSP cohort had fewer patients with an ED visit (13%-24% lower) or inpatient admission (16%-22% lower) at each follow-up period . After adjusting for baseline characteristics, the PSP cohort had a 28%, 27%, and 37% lower risk of a hospital visit for rheumatology (HR = 0.72 [95% CI = 0.60-0.87]), gastroenterology (HR = 0.73 [95% CI = 0.60-0.89]), and dermatology (HR = 0.63 [95% CI = 0.43-0.91]) patients participating in the PSP .Results were also consistent across therapeutic areas, with greater improvements in medication-taking behavior corresponding with lower risk of hospital visits  have been attributed to suboptimal adherence, and hospitalizations account for the majority of estimated costs of nonadherence, a leading contributor to avoidable medical spending.33 Extrapolating the hospital visit results presented here using national average costs of ED visits and inpatient admissions, PSP participation could have reduced hospital care costs by $1.7 million for the non-PSP cohort, or $438 million for all adalimumab patients in the United States over 36 months—22% of total hospital care costs for adalimumab patients.34Hospital care comprises a third of all health care spending in the United States, with the growth rate in the cost of hospital care outpacing all other medical services in recent decades—a trend that is projected to continue for the foreseeable future.10 Nurse Ambassadors, for example, provide education, cognitive support, and motivation for patients to improve health literacy of their disease and treatment. Prior studies have hypothesized that such high-touch and individualized initiatives incorporating multiple support mechanisms to address specific challenges facing patients with chronic diseases are likely to substantially improve a wide range of outcomes, including but not limited to hospital visits.39 Other direct benefits could include lower rates of outpatient health care resource use, prescriptions for comorbid conditions, diagnostic testing, and surgical interventions, while indirect benefits could include reduced physical suffering and better quality of life.40 However, evidence has typically been restricted to short-term outcomes; only 1 of 58 studies included in a recent systematic review of self-management support strategies followed patients beyond 12 months (none in immune-mediated diseases), and systematic reviews of electronic interventions to educate patients and improve adherence overall and in chronic diseases specifically did not find any studies longer than 12 months (or in immune-mediated diseases).42 The sustainability of benefits is often unknown but critical to the effectiveness of support interventions.43 This study provides the first evidence that early improvements for immune-mediated disease patients participating in the PSP continue up to 36 months following treatment initiation.While improved medication-taking behavior is crucial in minimizing disease flares and reducing hospital visits, other components of the PSP may provide additional benefits for patients utilizing the program.44 Providing validation, the impact of PSP participation on the risk of hospital visits for gastroenterology patients found in the current study is consistent with a recent 12-month analysis of the same PSP using a closed-claims database.45 Further research should continue to examine heterogeneity in the benefits of PSPs across therapeutic areas to better understand their potential to lower the risk of hospital visits for the broader chronic disease population.Long-term improvements in medication-taking behavior and lower risk of hospitalization were found across all therapeutic areas. Dermatology patients utilizing the PSP experienced the greatest relative reduction in hazard of both discontinuation and a hospital visit, though significant decreases were also seen in rheumatology and gastroenterology. The reductions found among gastroenterology patients, while relatively smaller in magnitude than in dermatology, may be the most consequential given the higher rate of hospitalizations for gastroenterology patients, which contribute to the high (and increasing) costs of the disease.The intent of this study was to examine real-world outcomes for patients enrolling in the PSP at the time of biologic initiation, and thus findings on the benefits of PSP use are expected to be conservative, as patients in the PSP cohort were not required to remain on adalimumab (which would have increased the medication-taking behavior estimates) or to have remained actively engaged with the PSP (though mean participation was 17 months) during the follow-up period. Conversely, patients in the non-PSP cohort were allowed to enroll in the PSP during the follow-up period (which 12% of the sample did). Both dynamics are expected to make the observed differences in medication-taking behavior and hospital visits conservative. Improvements in adherence and persistence that were previously observed during the first 12 months of treatment with the PSP were sustained up to 24 months, before tapering in magnitude slightly at 36 months (though both adherence and persistence remained higher than for the non-PSP cohort). The decreased magnitude of adherence differences over time (from 29% to 12% higher for the PSP cohort relative to the non-PSP cohort) is expected as patients discontinue adalimumab therapy. Similarly, relative reductions in hospital visits at 36 months were slightly lower than observed at 24 months, likely due to discontinuation of adalimumab and/or PSP participation. Though not presented, hospital visit rates were lowest for patients who remained persistent on adalimumab therapy and actively engaged with the PSP throughout the follow-up period.The importance of treatment adherence to clinical, economic, and societal outcomes has long been recognized, which makes the observed low rates of adherence to biologic therapies for immune-mediated diseases a cause for concern. Initiatives to improve adherence present an opportunity to reduce avoidable health care resource use and curb increases in medical spending. As more attention has been paid to such efforts, it has become clear that effective initiatives provide multiple components of personalized support that are maintained throughout the course of treatment. This study of one such PSP illustrates the long-term medication-taking behavior benefits, along with the lower risk of hospital visits, across several therapeutic areas. The results provide further evidence that PSPs should be considered a potentially valuable service for patients prescribed biologic therapies for immune-mediated diseases.1 Notably, the evaluation of hospital visits required medical claims; however, any visits to providers not captured by Symphony Health would not be observed. While this may downwardly bias hospital visit estimates, there is no reason to expect that the relative difference between cohorts would be affected. The evaluation of medication-taking behavior depended on pharmacy claims, which may not reflect actual administration of the drug, and reasons for nonadherence or discontinuation  could not be determined. As such, comparisons of adherence and discontinuation focused on the relative differences across cohorts, which are less sensitive to potential sources of bias  than the absolute levels.As with all retrospective studies, limitations of the data sources and study design should be noted, many of which have been previously reported.An important limitation of any cohort study is the potential for selection bias . To address this concern, patients were matched on an extensive list of covariates, which were also adjusted for in the estimation of outcomes; however, unobserved heterogeneity could remain.When the cohorts were constructed, available follow-up data beyond 12 months was not known, and thus patient attrition could vary. Though follow-up length did not differ significantly across cohorts and all analyses were censored for loss of follow-up to mitigate this concern, if data availability was correlated with cohort selection or the outcomes assessed, bias could remain. However, given that results were consistent when comparisons were conducted only among patients with sufficient follow-up data, it does not seem patient attrition bias significantly affected any of the findings regarding the benefits of PSP participation.Patients participating in the PSP used several components simultaneously, so the incremental effect of any particular component, such as injection training or education on financial assistance options, could not be evaluated. Missing from this evaluation of PSP participation is an assessment of patient satisfaction, including which services improved medication-taking behavior and what barriers remained, which should be included in future research.Finally, this study included commercially insured patients initiating adalimumab for immune-mediated diseases, and results may not be generalizable to other patient populations, treatments, or PSPs.This study provides novel evidence that participation in a comprehensive, individualized PSP was associated with sustained, long-term improvements in medication-taking behavior and hospital visits for patients initiating adalimumab therapy. Over 36 months, patients who participated in the PSP had higher levels of adherence and persistence and lower risk of hospital visits, including inpatient admissions and ED visits, than patients who did not participate. These findings build on short-term evidence that PSP participation improves patient outcomes and lowers the burden to the health care system. Further research should examine specific aspects of the PSP associated with improved outcomes to inform the design of patient-centric initiatives in other patient populations.


Calibrating the phase in integrated optical phased arrays (OPAs) is a crucial procedure for addressing phase errors and achieving the desired beamforming results. In this paper, we introduce a novel phase calibration methodology based on a deep neural network (DNN) architecture to enhance beamforming in integrated OPAs. Our methodology focuses on precise phase control, individually tailored to each of the 64 OPA channels, incorporating electro-optic phase shifters. To effectively handle the inherent complexity arising from the numerous voltage set combinations required for phase control across the 64 channels, we employ a tandem network architecture, further optimizing it through selective data sorting and hyperparameter tuning. To validate the effectiveness of the trained DNN model, we compared its performance with 20 reference beams obtained through the hill climbing algorithm. Despite an average intensity reduction of 0.84 dB in the peak values of the beams compared to the reference beams, our experimental results demonstrate substantial agreements between the DNN-predicted beams and the reference beams, accompanied by a slight decrease of 0.06 dB in the side-mode-suppression-ratio. These results underscore the practical effectiveness of the DNN model in OPA beamforming, highlighting its potential in scenarios that necessitate the intelligent and time-efficient calibration of multiple beams. In particular, silicon-on-insulator (SOI) platform-based OPAs have been extensively studied due to their high index contrast, providing high integration, compactness, and compatibility with complementary metal-oxide-semiconductor (CMOS) technology15. These inherent advantages have led to the development of Si-based integrated OPAs with over 1000 channels16, specifically designed to achieve a small beam divergence angle, making them highly advantageous for LiDAR applications. However, as the number of channels in Si-based OPAs scales up, ensuring precise phase control for each channel becomes increasingly challenging. The presence of crosstalk  between arrayed channels and fabrication errors can no longer be negligible in densely integrated areas with a large number of channels17. These factors contribute significantly to phase errors, requiring the phase calibration for each channel in order to achieve precise beamforming during beam steering.Integrated optical phased arrays (OPAs) have emerged as a crucial technology for enabling free-space beam forming and steering in various applications, including light detection and ranging (LiDAR), optical wireless communication, virtual reality (VR), augmented reality (AR), and bio-sensing18, genetic algorithm (GA)19, and particle swarm optimization (PSO)20. These iterative and optimization-based calibration processes can effectively align the wavefront and generate desired beam-forming outcomes. However, it is crucial to acknowledge that reliance on these optimization methods requires significant computational time, especially for the OPAs with increased channels. For example, the HA is difficult to implement for a large-scale OPA as it exhaustively searches the optimum electrical signals in an iterative manner. GA or PSO can be deployed to make phase calibration of large-scale OPA. However, such methods have the main drawback of long convergence time and they may get stuck in the local optimum. With the advancing complexity and functionality of OPAs, there is a strong demand for an enhanced beamforming method, which can provide an accurate and efficient phase calibration process.Conventional approaches for phase calibration in OPAs have relied on optimization-based methods such as the hill-climbing algorithm (HA)23. Specifically, successful implementations with high performances were achieved in two-photon microscopy24, optical metrology25, inverse designing nanophotonic devices31, resolving complex ellipsometry problems32, and photonic design correction33. With their capability to handle complex nonlinear mappings, ANNs present an ideal foundation for developing a framework to address OPA calibration and there have been some notable advancements in utilizing ANN techniques for OPA beamforming. Researchers have explored the application of an ambiguity-resolved fully-connected layer to achieve OPA phase calibration for a single beam point with a high side-mode-suppression ratio (SMSR)34. Additionally, a convolutional neural network (CNN) has been employed to predict the required current set for phase control, enabling successful 2-D beam steering with multiple beam points35. Although these studies highlight the potential of ANN-based approaches in enhancing the calibration and beamforming capabilities of OPAs, they were primarily devoted to OPAs with smaller channels, typically 16 or fewer. There has been research on DNN-based phase control for 128-channel OPA beamforming as well36. However, it necessitated a substantial dataset of 350,000 samples for training with training epochs of 2500. Therefore, further research is required to evaluate the performance and scalability of these ANN-based methods when applied to OPAs with a larger number of channels, with an emphasis on efficient approaches for training the network.Recently, the emergence of artificial neural networks (ANNs) has introduced innovative solutions across various fields in nanophotonics31, and further enhance its performance through selective data sorting and hyperparameter tuning. For evaluation, we utilize 20 beams generated by the HA algorithm as evaluation datasets and fed them into the trained network. The experimental results indicate a slight intensity degradation of 0.84 dB and a minor decrease of 0.06 dB in the side-mode-suppression-ratio (SMSR) compared to reference beams. Nonetheless, they clearly demonstrate a substantial alignment between the DNN-predicted and reference beams, underscoring the practicality of the DNN model for intelligent and efficient calibration in a variety of scenarios.In this paper, we introduce a DNN-based phase calibration method to enhance beamforming in a 64-channel optical phased array (OPA). To effectively handle the complexity of voltage control, we employ a tandem network architecture3, which features a 300 37. In addition, thermo-optic (TO)-based tunable grating radiators were applied for longitudinal beam-steering, employing the n-i-n heaters38. Applying currents to the heaters, the effective refractive index of the grating region is changed by the TO effect and the beam can be longitudinally steered within a range of 1038. However, we should note that this study primarily concentrates on the phase control aspects related to transversal beam steering, and the details of longitudinal beam steering using thermo-optic tuning are not discussed in-depth.The schematic of the 64-channel integrated silicon-based OPA chip is illustrated in Fig. N-channel OPA operating at an input wavelength, the theoretical far-field beam intensity distribution I using a CMOS-compatible fabrication process on an 8-inch silicon-on-insulator (SOI) wafer with a top silicon layer thickness of 220 nm and a buried oxide layer thickness of 2 39, allowing precise control over the voltage applied to each electrode. These extracted data from the setup are used to train our DNN model. After the training process, the trained model is then implemented to reproduce the beam intensity distribution in the desired direction by inputting the generated voltage set to the OPA, as depicted in Fig. Figure In order to train our model for OPA phase control using a data-driven approach, a substantial number of samples were required from this experimental setup. These samples consisted of a voltage dataset for 64-channel phase tuning  for the loss function to minimize the overall deviation between the predicted values and the actual values during the training process. In addition, we employed the Optuna library31. The loss function comprises 2 components: the voltage set loss , where the phase calibration process is expected to be even more complicated41.In essence, our approach provides a substantial advantage over HA, as it involves a one-time implementation for dataset collection. Once collected, this dataset can be seamlessly applied to all beam points without additional data collection. This indicates that it is a matter of choosing the most suitable approach, whether iterative algorithms like HA or DNN-based beamforming, depending on the number of beam points that the user intends to search for. For example, in scenarios like point-to-point optical wireless communication, which involve a relatively small number of OPA beam points35. Moreover, in the future work, we expect the application of streamlined optimization algorithms, such as gradient descent-based optimizations and reinforcement learning43, which can potentially replace the data preparation method proposed in this study. These approaches are expected to provide solutions to the issue of non-uniqueness in voltage sets and the potential for encountering local optima. These aspects of the problems will be investigated in another study.Although not within the scope of the present work, it was observed that our structure supports longitudinal beam steering with up to 3W tuning applied to the n-i-n heater, while maintaining the beam intensity distribution in the transversal direction with minimal impact on the SMSR and beam direction angle. This allows for a 2In this study, we have presented the phase calibration methodology for scaled integrated 64-channel OPA based on deep-learning technology. Through meticulous data collection and optimization of the neural network model, we demonstrate its effectiveness in determining the optimal voltage sets for achieving desired beam distributions and experimentally retrieving far-field patterns at several beam direction angles within the steering range of 35


Three-dimensional printing has revolutionized drug manufacturing and has provided a solution to the limitations associated with the conventional manufacturing method by designing complex drug delivery systems with customized drug release profiles for personalized therapies. The present investigation aims to design a gastric floating tablet with prolonged gastric floating time and sustained drug release profile. In the present study, a gastro retentive floating device (GRFD) was designed and fabricated using a fused deposition modelling (FDM)-based 3D printing technique. This device acts as a multifunctional dosage form exhibiting prolonged gastric retention time and sustained drug release profile with improved oral bioavailability in the upper gastrointestinal tract. Commercial polyvinyl alcohol (PVA) and polylactic acid (PLA) filaments were used to design GRFD, which was comprised of dual compartments. The outer sealed compartment acts as an air-filled chamber that imparts buoyancy to the device and the inner compartment is filled with a commercial propranolol hydrochloride immediate-release tablet. The device is designed as a round-shaped shell with a central opening of varying size , which acts as a drug release window. Scanning electron microscope (SEM) images were used to determine morphological characterization. The in vitro buoyancy and drug release were evaluated using the USP type II dissolution apparatus. All the designed GRFDs exhibit good floating ability and sustained drug release profiles. GRFDs fabricated using PLA filament show maximum buoyancy (>24 h) and sustained drug release for up to 10 h. The floating ability and drug release from the developed devices were governed by the drug release window opening size and the filament material affinity towards the gastric fluid. The designed GRFDs show great prospects in modifying the drug release characteristics and could be applied to any conventional immediate-release product. The oral route is the most preferred route of drug administration due to high patient compliance, non-invasiveness, low cost, fewer side effects, and better drug absorption profiles achieved by most drug products . HoweverCurrently, 3D printing technology has been extensively employed for the development of innovative novel drug delivery approaches. Three-dimensional printing techniques can develop complex structures with pre-determined release characteristics with customized dosing for personalized drug delivery ,14. ThreThe present investigation aims to fabricate a hollow dual-compartment gastro-retentive floating device (GRFD). The outer compartment is an air-filled chamber and the inner compartment is enclosed with an immediate release conventional tablet. In this study, propranolol hydrochloride immediate-release tablets were used to impart floating and modify its release characteristics. Propranolol hydrochloride is a non-selective beta blocker used in the treatment of hypertension and cardiovascular disorders ,41. Prop®, AstraZeneca, Cambridge, UK) were supplied by Najran University Hospital Pharmacy Najran, Saudi Arabia. Ethanol and hydrochloric acid were purchased from Sigma-Aldrich, Gillingham, UK. All other reagents used were of analytical grade.The PVA filament (1.75 mm) was purchased from Fused materials 3D filaments New York, and the PLA filament (1.75 mm) was purchased from FILO3DPRO, made in Holland. Propranolol hydrochloride tablets  were selected as a model drug and incorporated in the designed gastro-retentive floating device. Commercial filaments of PVA and PLA were selected for fabricating gastro-retentive floating devices (GRFD). The selection of these filaments was based on the anti-acidic ability of these materials for 3D printing. Both the filaments of PVA and PLA were immersed IN 0.1 mol/L Hcl solutions and their physical ability to resist the acidic corrosion was observed at different time periods. Propranolol Hcl commercial tablets  was designed as a dual-compartment round tablet of 12 mm diameter and 5 mm thickness with an opening of varying diameters  at the center of the device for drug release. The propranolol Hcl tablets were placed in the inner compartment of the device and the outer compartment was used as an air-filled compartment to facilitate the gastric floating of the device. The design of the GRFD is represented in The 3D printing of the GRFD was carried out by using an FDM 3D printer . The pre-dried PVA and PLA filaments were loaded in the extruder of the 3D printer. The 3D printing parameters/conditions are listed in Digital images of the GRFD were taken and scanning electron microscopy (SEM) was performed to observe the surface morphology of the GRFD. The height of the layers were measured by using SEM and the presence of gap or flaws between the layers (intactness of the layers) were observed visually and via SEM images.The size of the GRFD (diameter and thickness) was measured by using a micrometer. The diameter of the center opening in the GRFD was measured by using ImageJ 1.54d software. For weight variation test, twenty GRFDs were weighed individually by using a digital weighing balance  and the weight variation was represented as mean ± SD.The in vitro floating ability of PVA-based and PLA-based GRFDs was determined by placing them in simulated gastric fluid  and the floating lag time (FLT) and total floating time (TFT) were visually determined.The in vitro drug release studies were performed for all the floated GRFDs by using USP type II apparatus . The dissolution was performed using simulated gastric fluid  as media at 37 ± 0.5 °C. A total of 3 mL of aliquots was withdrawn and replaced with the new buffer media to maintain sink conditions. The samples were withdrawn at 5, 15, 30, 45, 60, 120, 180, 240, 300, 360, 420, 480, 540, and 600 min. The withdrawn samples were filtered through a 0.45 µm syringe filtered and analyzed by using a UV spectrophotometer at a wavelength of 290 nm.The in vitro drug release data of GRFDs were fitted to best fit model  to determine the mechanism of drug release from the GRFDs.p < 0.05. All the results were expressed as mean ± SD.The graphical presentation and statistical analysis were performed using Windows 10 graph pad prism and one-way analysis of variance (ANOVA) followed by Tukey’s test and considered statistically significant when The GRFDs were printed successfully by using PVA and PLA commercial filaments. The aim of fabricating a GRFD is to achieve an extended or prolonged gastric retention time for the device. Based on the observation from the anti-acidic ability of these two filaments between 0 to 3 h, PLA filament tends to resist acidic corrosion and remain rigid even after 3 h. In contrast, the PVA filament tends to become soft and dissolves after 3 h and makes the solution turbid, thus, indicating that PLA-filament-based GRFDs could achieve long-term floating compared to PVA-based GRFDs.The structural parameter that is altered in the design of GRFD is the diameter or size of the opening. The GRFDs were designed with 1 mm, 2 mm, 3 mm, and 4 mm openings to study the influence of opening size on drug release characteristics. The infill percentage in FDM-based 3D printing varies between 0 to 100%, facilitating the building of hollow structures to filled (densely compact) structures . In the x, y, and z-axis [The printing parameters significantly influence the quality of 3D-printed products . The prid z-axis . The priThe 3D printing of the GRFD was carried out in three steps, as shown in The morphological characterization of all the PLA- and PVA-based 3D-printed GRFDs indicate that the surface of the GRFDs is smooth with no deformities. The PLA filament is white in color and the PVA filament is slightly yellow in color. The fabricated GRFDs are of the same color as the filament. This is in accordance with the previously reported articles, which state that the 3D printing process fabricates objects of the same color as the feeding material ,44,50. TThe dimensions of the 3D-printed GRFDs are accurate as they are set in the design represented in The in vitro floating ability of commercial propranolol Hcl tablets and 3D-printed GRFDs was determined and is represented in The in vitro drug release of propranolol Hcl immediate-release tablets and PVA-based GRFDs are shown in The PLA-based GRFDs exhibit sustained release of the enclosed propranolol tablet, as shown in 2 values, it is evident that PVA-based GRFD1 and GRFD2, with narrow opening size, exhibit zero-order release kinetics describing a drug release that is independent of the concentration gradient. This is in accordance with the reported literature that reveals that PVA-based gastro-retentive drug delivery systems (GRDDS) exhibit zero-order release kinetics [n values, the drug release mechanism is diffusion and erosion [n indicates Fickian and non-Fickian-based drug diffusion. All the PLA-based GRFDs exhibit an n value greater than 0.5, which indicates non-Fickian-diffusion-based drug release. During the entire drug release time course, the device remains buoyant and does not exhibit any swelling, erosion, or eruption due to the hydrophobic nature of the PLA filament that keeps the device intact, and the drug release is governed by the opening size in the device. Thus, the FDM-based 3D printing technology, due to its low cost, easy processing, and high flexibility, could be utilized to design a product with customized characteristics. This research investigation further extends the progress in the design and development of gastro-retentive drug products.The in vitro drug release data were fitted to the best-fit release kinetic model. The drug release kinetic data of PVA- and PLA-based GRFDs are represented in kinetics ,60. The kinetics ,51. In c erosion . SimilarThe FDM 3D printing technique could produce gastro-retentive floating systems with structural modifications in the design by varying in fill percentage and by producing hollow compartment systems. In conventional methods of producing gastro-retentive floating systems, the polymers and materials play a vital role in achieving buoyancy. These materials or polymers impart buoyancy to the designed floating system by producing effervescence by gas generation or by non-effervescent methods by using low-density materials, by raft-forming, or by producing gel layers. The erosion or dissolution of these polymeric material affects these systems’ floating ability and may lead to buoyancy loss. From the literature, it has been proven that the FDM-based 3D printing has produced a floating device that has floated in vivo for more than three days. This makes this technique the most suitable and efficient method to design gastro-retentive floating systems. In this study, 3D-printed GRFDs were successfully designed by FDM 3D printing using PVA and PLA filaments. These hollow GRFDs enclosed with an immediate release propranolol hydrochloride tablet exhibit efficient buoyancy and sustained drug release profiles. Among the two filament-based devices, the PLA-based GRFDs show maximum buoyancy (>24 h) and sustained drug release (>95% of drug release in 10 h) in comparison to PVA-based GRFDs. The opening in the device  was the critical parameter influencing the drug release rate from the device. The drug release rate increases with an increase in the opening size. Thus, this approach provides an easy, efficient, and cost-effective alternative to conventional methods for designing GRFDs to encapsulate conventional immediate-release tablets to modify their release characteristics. Moreover, the current results show that the structural modification in the GRFDs such as a change in the opening size, number of openings in the device, and their position, affects the buoyancy and drug release rate from the devices. Further in vitro/in vivo correlation studies should be performed to evaluate in vivo floating behavior and drug release profiles. Thus, the FDM-based 3D printing technique enables the researchers to develop patient-tailored drug delivery systems with tunable drug-release characteristics.


In this article, we reviewed the important findings of this study. Together with our findings, we synthetically discussed the role of the fungal mycobiome in orchestrating the immune response and thereby modulating tumor progression.Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in humans due to late diagnosis and poor response to treatments. The tumor microenvironment (TME) of PDAC is characterized by a distinctive, suppressive immune profile, which inhibits the protective functions of anti-tumor immunity and thereby contributes to PDAC progression. Recently, the study of Alam  Traditionally, members of the human mycobiome, such as the well-known Candida spp., are known as opportunistic pathogens which reside in most healthy individuals and cause local or systemic infectious diseases only under certain circumstances. However, a growing body of evidence has suggested that the mycobiome plays a critical role in the onset and progression of cancers. A noteworthy example is the implication of the mycobiome in the carcinogenesis of pancreatic ductal adenocarcinoma (PDAC).The recent development of various –omics approaches has greatly expanded our knowledge of the diverse fungal species colonizing different body sites, which constitute an important component of the human microbiome, termed the “mycobiome”. The dismal prognosis mainly results from a lack of specific symptoms for early diagnosis, the early metastatic spread and the poor response to available treatments. Previously, a seminal study discovered that PDAC harbors a distinct mycobiome profile as compared to that of the normal pancreas, while fungal ablation with antifungal treatments showed protective effects against oncogenic progression. These results suggested the potential of the mycobiome in PDAC as a new target for the development of novel biomarkers and therapeutic strategies.PDAC is a highly aggressive malignancy with a 5-year overall survival (OS) rate of around 10%, ranking the fourth leading cause of cancer-related deaths in the Western worldH2 cells and innate lymphoid cells 2 (ILC2), which can inhibit the functions of anti-tumor T cell immunity and thereby contribute to PDAC progression. In addition, these infiltrated TH2 cells have also been found to fuel PDAC progression in the early stage of tumorigenesis via the secreted type 2 pro-tumorigenic cytokines, such as interleukin (IL-) 4 and IL-13.The tumor microenvironment (TME) of PDAC is characterized by a distinctive immune profile dominated by immune-suppressive cells including T revealed for the first time that the intratumoral mycobiome could enhance the secretion of the chemoattracting cytokine IL-33 from cancer cells, which subsequently recruited and activated TH2 and ILC2 cells in the TME of PDAC, thus promoting pancreatic oncogenesis. This study was the first to show that TME of PDAC has an increased infiltration of TH2 and ILC2 cells as compared to the normal pancreas both in a PDAC mouse model and in human PDAC samples. In order to determine the chemotactic factors secreted by cancer cells that may recruit and activate these immune cells, the authors also conducted a transcriptomic analysis of multiple PDAC cell lines and identified a 30-fold upregulation of IL-33, which was mediated by oncogenic KrasG12D signaling. Immunohistochemistry staining showed that IL-33 expression was indeed relatively high in human PDAC tissues, while it was undetectable or below 25% nuclear staining in exocrine cells in normal pancreas specimens in the Human Protein Atlas database  sequencing and fluorescence in situ hybridization, a higher load of fungi was found in the PDAC specimens as compared to the normal pancreas, with Malassezia being the most abundant genus, in accordance with previous findings. In addition, in both studies, the fungal depletion or repopulation was shown to retard or accelerate PDAC tumor growth, respectively. However, various mechanisms may be responsible. M. globosa was shown to promote tumor progress via mannose-binding lectin that can recognize fungal pathogens and the subsequent activation of C3 complement cascade which belongs to innate immunity. Interestingly, M. globosa was also shown to be involved in regulating the adaptive immune response to promote tumor growth by facilitating the extracellular expression of IL-33 and consequently enhancing the infiltration of TH2 and ILC2 cells.Other experiments focused on the role of the mycobiome in PDAC on IL-33 secretion as well as in PDAC tumorigenesis and it is well known for its dichotomous functions, acting both as a traditional extracellular cytokine and as a nuclear transcription factor. Unlike the traditional inducible cytokines, IL-33 is constitutively expressed by several cells including human endothelial and epithelial cells. The full-length IL-33 can translocate to the nucleus upon synthesis and be stored there,16. This nuclear IL-33 might function as a transcriptional repressor to decrease inflammation. Once released or secreted, the extracellular IL-33 can be cleaved to its more active form and induce the type 2 immune response,18. Interestingly, there are different opinions on the function of type 2 immune response in intestinal immunity and in the development of pancreatitis and PDAC. Some studies showed that IL-33 deficient mice were highly susceptible to colitis, colorectal cancer and pancreatitis,20, which suggest a protective function of IL-33. However, other studies showed increased IL-33 levels in biopsies obtained from patients with active inflammatory bowel disease, while elevated serum IL-33 was found in patients with severe acute pancreatitis. Opposite findings described that the IL-33-induced ILC2 infiltration in PDAC cells correlated positively with long-time survival in patients. In The Cancer Genome Atlas (TCGA) database, the mRNA expression of IL-33 is significantly higher in PDAC tissues compared to normal pancreatic tissues but was not associated with a significantly different OS [However, IL-33 is a member of the IL-1 cytokine familyerent OS  and 1C.. Of note, FMRP mRNA and protein expression levels were not associated with clinical outcomes in several cohorts of cancer patients, but a gene signature reflecting FMRP’s cancer regulatory activity  was prognostic for reduced OS across multiple human cancers. These discrepancies underline the importance of understanding the role of IL-33, its induced type 2 immune response and the network of genes and cells in the TME that contribute to the capability of PDAC to evade immune destruction and resist chemotherapy. Elucidation of the processes is essential before designing novel treatment strategies.However, in many human cancers including PDAC, it was found that the fragile X mental retardation protein (FMRP) repressed immune attack by up-regulating IL-33 together with tumor-secreted protein S and extracellular vesicles (EVs), which promote M2-like tumor-associated macrophages, while down-regulating the chemoattractant C-C motif chemokine ligand 7-29. Bacterial taxa, Proteobacteria (Pseudoxanthomonas) and Actinobacteria (Saccharopolyspora and Streptomyces), are positively correlated to the short-term survival of PDAC patients. The 18S ITS sequencing can only detect the presence of fungal mycobiome but not the presence of microbiome. It is therefore unclear whether microbiome could function similarly to the mycobiome, inducing the secretion of extracellular IL-33 and thus activating the type 2 immune response. The factors which can trigger the release or secretion of extracellular IL-33 are not fully identified. Besides fungi or fungal components, it has been shown that cellular injury or death is one of the mechanisms by which IL-33 reaches the extracellular environment. Hence IL-33 will also act as an alarm when there is a breach in the primary defenses of intestinal epithelium against pathogens and other threats. Other factors, such as extracellular ATP concentrations, mechanical stress or oxidative stress, can also enhance the secretion of IL-33. Extracellular ATP concentrations are regulated by ectonucleotidases CD39 and CD73, which are known to play a role in immune function as well,32. Multiple bacterial species, such as Klebsiella pneumoniae (for autoimmune pancreatitis), Helicobacter pylori (for gastric ulcers), Staphylococcus aureus (for lung infection), have been shown to enhance the secretion of IL-33, indicating the involvement of tumoral microbiome, possibly together with mycobiome, in orchestrating the innate and adaptive immunity and modulate tumor progression.Since the mycobiome is living in symbiosis with bacteria as commensals in the human body, it is very likely that the mycobiome exerts an important influence on the microbiome. The involvement of microbiome in the development of PDAC and its chemoresistance has been convincingly demonstrated by several studieset al. have shown that fungal ablation via antifungal treatment enhanced the efficacy of gemcitabine-based chemotherapy in PDAC-bearing mice. In humans, it was suggested that the gut mycobiome might modulate the response to preoperative chemotherapy (gemcitabine-cisplatin) in patients with bladder cancer. Compared to the non-responders, the responders had a distinct mycobiome featured by a higher diversity and lower abundance of Agaricomycetes and Sacchaaromycetes. However, the mechanisms underlying mycobiome-induced chemoresistance are still unclear. There are several hypotheses: (1) mycobiome may confer chemoresistance through metabolism and enzymatic degradation of chemotherapeutic drugs, similar to the previously reported intratumoral bacteria-mediated chemoresistance; (2) studies on breast cancer have identified IL-33 as a key driver of chemoresistance of tumor cells. IL-33 overexpression transformed tumor cells into polyploidal giant cancer cells that are highly resistant to chemotherapy due to their dormancy or abnormal cell cycle,38. Thus, the fungal mycobiome may also elicit chemoresistance of cancer cells via the enhanced secretion of IL-33, as shown in the current evaluated study of Alam et al..Furthermore, mycobiome may also play a role in chemoresistance. Aykut H2 and ILC2 cells in TME of PDAC and promoting tumor progression [In summary, recent data revealed that the intratumoral fungal mycobiome can contribute to PDAC pathogenesis by stimulating the extracellular secretion of IL-33 from cancer cells, thus driving the recruitment and activation of Tgression . Recent gression . These f


The rapid advancements in artificial intelligence (AI) technology in recent years have led to its integration into biomedical publishing. However, the extent to which AI has contributed to developing biomedical literature is unclear. This study aimed to identify trends in AI-generated content within peer-reviewed biomedical literature. We first tested the sensitivity and specificity of commercially available AI-detection software . Next, we conducted a MEDLINE  search to identify randomized controlled trials with available abstracts indexed between January 2020 and March 2023. We randomly selected 30 abstracts per quarter during this period and pasted the abstracts into the AI detection software to determine the probability of AI-generated content. The software yielded 100% sensitivity, 95% specificity, and excellent overall discriminatory ability with an area under the receiving operating curve of 97.6%. Among the 390 MEDLINE-indexed abstracts included in the analysis, the prevalence with a high probability (≥ 90%) of AI-generated text increased during the study period from 21.7% to 36.7% (p=0.01) based on a chi-square test for trend. The increasing prevalence of AI-generated text during the study period was also observed in various sensitivity analyses using AI probability thresholds ranging from 50% to 99% . The results of this study suggest that the prevalence of AI-assisted publishing in peer-reviewed journals has been increasing in recent years, even before the widespread adoption of ChatGPT  and similar tools. The extent to which natural writing characteristics of the authors, utilization of common AI-powered applications, and introduction of AI elements during the post-acceptance publication phase influence AI detection scores warrants further study. The rapid advancements in artificial intelligence (AI) technology in recent years have led to its integration into many areas of medicine such as drug discovery , biomediWhile public utilization of AI has been limited historically, the availability of the large language model Generative Pre-trained Transformer-3  in July 2020 and Chat Generative Pre-trained Transformer   in NovemIn parallel with the increasing utilization of generative AI, software purporting high accuracy in detecting AI-generated content is now widely available. Although the specific algorithms used by these tools are proprietary, they typically evaluate writing characteristics such as text perplexity and burstiness. Notably, humans tend to write with high degrees of these characteristics, whereas AI-generated writing reflects lower levels of perplexity and burstiness . These dThe extent to which this widespread availability of ChatGPT and other AI-assisted writing tools has contributed to developing biomedical literature is unclear. Thus the purpose of this study was to identify recent trends in AI-generated content within peer-reviewed biomedical literature.We conducted a quantitative bibliographic analysis to identify recent trends in AI-developed content within peer-reviewed biomedical literature. AI-detection software sensitivity and specificity testingThe AI-detection software used in this study, Originality.AI , was selected based on its claimed superior accuracy in detecting AI-generated content compared to other AI detectors . First, Please write a 250 to 300-word abstract for a peer-reviewed journal specializing in  that reports the results of a randomized controlled trial where outcomes in the investigational group were  those in the control group. Write the abstract using  format.Overall, we issued 60 unique commands covering the 10 therapeutic areas, three clinical scenarios, and two abstract formats listed above, where each command was entered in a new chat window to minimize redundant answers that may be associated with previous conversations. An example of an abstract generated by ChatGPT (Model GPT-4) during the sensitivity testing is provided in Figure We next tested software specificity, which is the ability of the software to correctly identify human-generated text. We randomly selected 60 randomized controlled trials with available abstracts indexed in MEDLINE  between January 1980 and December 1989, a period with negligible AI utilization. We pasted the abstract from each paper into the AI detection software, which reported the probability of AI-generated content on a scale ranging from 0% to 100%.Literature search and AI-detection testingWe subsequently conducted a MEDLINE search to identify randomized controlled trials with available abstracts indexed between January 2020 and March 2023. The MEDLINE search used the following string: “2020/01/01:2023/03/31[Date - Entry] AND randomizedcontrolledtrial[Filter] AND fha[Filter]”. Among all eligible abstracts in the study period, we used a random number generator to randomly selected 30 abstracts per quarter, each containing at least 50 words based on the minimum requirements of the AI-detection software. As before, we pasted the abstracts into the AI-detection software to determine the probability of AI-generated content.Data analysisAssuming that 15% of abstracts in 2020 were AI-generated and that the prevalence would increase by 7.5% annually, the study had 89% statistical power using a two-sided Z test and a significance level of 0.05. We used the chi-square test for trend to test the hypothesis that AI-generated abstracts were becoming more prevalent over time. Abstracts that received a score ≥90% were deemed to be AI-generated based on the recommendations of the software manufacturer. We tested the robustness of this threshold by performing sensitivity analysis using thresholds of 50%, 75%, 95%, and 99%.ResultsAmong the 60 abstracts generated by ChatGPT, all were scored with a 100% probability of being AI-generated. Among the 60 abstracts that were MEDLINE-indexed in the 1980s, the median probability of AI generation was 8% (interquartile range = 3-19%), with 57 of 60 receiving a score less than 90%. Ultimately, these results yielded 100 sensitivity, 95% specificity, and excellent overall accuracy with an area under the receiving operating curve of 97.6% (standard error = 1.6%).Among the 72,941 results identified in the MEDLINE search spanning 2020 to 2023, 390 abstracts were included in the analysis. The prevalence of abstracts with a high probability (≥ 90%) of AI-generated text increased during the study period, from 21.7% to 36.7% (p=0.01). The increasing prevalence of AI-generated text during the study period was also observed in each sensitivity analysis  . Typical AI-powered writing assistance applications such as the Editor feature in Word  , Smart CThe current state of biomedical publishing is characterized by a significant transition due to rapid advancement in AI technology. Although some journals and societies are starting to acknowledge and define the ethical considerations involved in AI-assisted publishing , there rDespite the novelty of this study’s research question, this work has several limitations. We only analyzed the abstracts from randomized controlled trials indexed in MEDLINE. While this provided some standardization in abstract content and access to text without cost, we did not examine the full text of papers or abstracts with different study designs. Therefore, the generalizability of the trends observed in this study may be limited. Future studies could address this limitation by analyzing the same trends associated with different study designs or by analyzing the full text of manuscripts as opposed to only abstracts. Specifically, examining the individual components of a manuscript such as the abstract, introduction, methods, and other sections could identify the most common locations of AI-generated content within papers. A second limitation of the study was that we used a single AI detector in this study. While the sensitivity and specificity of the detector were excellent, other AI-detection software programs are available Table  that migThis bibliographic analysis of biomedical literature demonstrated that the prevalence of AI-assisted writing in peer-reviewed journals has been increasing in recent years, even before the widespread adoption of ChatGPT. These findings highlight the growing integration of AI technology into the writing process, a trend that is anticipated to continue in the future. Given the recent heightened scrutiny surrounding ChatGPT and its related applications, we must recognize that AI is already deeply embedded within many commonly used writing tools, perhaps unknowingly to many users. The development of guidelines to promote the responsible and ethical use of AI technology when developing biomedical literature is warranted.


Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties of the target RUL function, neural networks can result in unreasonable RUL estimates such as going upwards and wrong endings. In the paper, we apply the fundamental principle of Physics-Informed Neural Network (PINN) to enhance Recurrent Neural Network (RNN) based RUL estimation methods. Through formulating proper constraints into the loss function of neural networks, we demonstrate in our experiments with the NASA IGBT dataset that PINN can make the neural networks trained more realistically and thus achieve performance improvements in estimation error and coefficient of determination. Compared to the baseline vanilla RNN, our physics-informed RNN can improve Mean Squared Error (MSE) of out-of-sample estimation on average by 24.7% in training and by 51.3% in testing; Compared to the baseline Long Short Term Memory , our physics-informed LSTM can improve MSE of out-of-sample estimation on average by 15.3% in training and 13.9% in testing. Discrete power electronic devices like Insulated-Gate Bipolar Transistors (IGBTs) are widely used in power management circuits  of safety-critical application domains such as automotive, locomotive, aerospace, and power grids, etc. To enable prognostic health management, one key element is to be able to estimate their Remaining Useful Lifetime (RUL) in actual operations.1, Neural Network (NN) has been a popular data-driven approach to studying the IGBT RUL prediction problem. Various NNs such as Multi-Layer Perceptron (MLP)2, Long Short Term Memory (LSTM)3, Attention Neural Network4, etc., have been tried to address this problem. Despite showing promises, NN-based methods can sometimes produce non-realistic estimates, for example, values going up or even becoming negative in extreme cases. This is inconsistent with the target RUL function, which is monotonically decreasing till zero at the end. This phenomenon occurs in RUL estimation of IGBT5 and other components such as turbofan engine7 and bearing9, etc.Due to the rising interest in deep learning11. The fundamental principle of PINN is to formulate physical rules based constraints into the loss function of NNs, such that the NNs can be trained to respect physical conditions rather than arbitrarily optimizing parameters in the training process. This is appealing because the trained NN models can generate more realistic results. PINN was initially proposed to solve mathematical problems11, such as ordinary and partial differential equations. Very recently it was also applied in the domain of power electronics for parameter estimation15. However, it has not been employed to address the RUL estimation problem of power electronic devices.Physics-Informed Neural Network (PINN) has been proposed to enhance pure data-driven neural networks with physical rulesFor the first time, we apply the principle of PINN to estimate the RUL of discrete power electronic devices. In particular, we identify and formulate physical rules as mathematical conditions, and then embed them as regularization terms into the loss function of neural networks for RUL estimation.We demonstrate that our PINN-based RUL estimation method can improve the regression performance in both Mean Squared Error (MSE) and coefficient of determination, In the paper, we address the RUL estimation problem for IGBTs based on PINN. Our baseline is the RUL estimation method using RNN, being vanilla RNN  or LSTM (a more powerful variant of RNN). We identify physical rules and formulate them as regularization terms into the loss function of the RNN to realize PINN-based RUL estimation. Our approach can overcome the potential mis-estimation behavior of NN-based estimation methods. The major contributions of the paper can be summarized as follows.model based approach19, data-driven approach21, and hybrid approach22.Various methods have been developed for IGBT RUL/lifetime modeling and estimation. These methods can be broadly classified into 23 are related to physics-of-failure models since their modeling processing is based on known failure modes under certain conditions25. These models estimate the number of cycles to failure  in the model equation18; The Bayerer’s model contains a lot of parameters including maximum junction temperature (I), diameter of the bond wire (D), and blocking voltage (V)19. Cumulative damage models with rainflow counting algorithm are commonly accompanied by the analytical models to conduct the estimation29. Apart from the mainstream analytical models, physical lifetime models based on energy assumptions also exist30.The model-based approach uses an analytical formula to express the relationship between the device’s lifetime and its dependent factors. Analytical lifetime models24. Common IGBT failure precursor signals are collector current . Efforts were also made on preprocessing the precursor signal data. Principal component analysis (PCA) was applied to the time domain features of the precursor signal before it was fed into a feed-forward NN2. More complex deep learning algorithms were investigated recently. LSTM was introduced to predict the RUL of IGBTs3. Compared with two model-based methods, the LSTM method achieves a higher accuracy, while a larger dataset is required to train the model. Attention mechanism was first applied to the IGBT RUL prognostics4.Besides statistical learning methods, NNs have also been applied to IGBT RUL prediction. It was shown in34 and deep neural network35 were proposed to predict useful lifetime of solder joints in electronic devices. In, Samavatian et al.36 enhanced the CDNN method by establishing a novel iterative machine learning-aid framework to improve the useful lifetime prediction results.To deal with package failure in solder joints, correlation-driven neural network (CDNN)22. A hybrid framework adopting the PINN idea was proposed37. The framework achieves better results on the turbofan engine RUL prediction than the pure data-driven approach with less training data required.A hybrid approach intends to fuse physical information into the model-building process of the data-driven approach. It is considered a promising approach as it can combine the advantages of both model-based and data-driven approaches. A particle filter based method incorporating the crack propagation physics law was developed to predict the RUL of IGBT modules12, PINN was used to estimate the parameters of the DC-DC buck converter. In13, PINN was adopted to evaluate the impedance of voltage source converter by means of its physics knowledge. In, Wu et al.14 proposed AutoPINN, a combination of AutoML and PINN, that can automatically design PINN models. It shows that the PINN model designed by AutoPINN reaches better performance in the parameter estimation of power electronic converters. Chen et al.15 proposed PI-LSTM, which is a combination of PINN and LSTM, to estimate the parameters of DC-DC buck converter.Very recently, the principle of PINN has also been applied to the domain of power electronics. InOur work may belong to the hybrid approach for IGBT RUL estimation. It advances the current data-driven NN-based approach by incorporating physical rules into the network training. The physical rules are directly derived from the target RUL function. It can overcome possible drawbacks  of the pure data-driven NN-based approach and further enhance its performance.38. With this dataset, we can consider the collector-emitter voltage 39, and use it to estimate the device RUL, as prior studies33 did. Informally, the RUL estimation problem aims to find a mapping relationship from a collector-emitter voltage To overcome the practical difficulty in collecting data from realtime operations, RUL estimation has largely resorted to utilizing experimental data from aggregated aging tests. In our study, we use the IGBT aggregated aging test dataset from NASA Prognostics Center of Excellence (PCoE)t is in (test) cycle, and Given is a collector-emitter voltage series RUL(t). As of now, the commonly used target RUL function RUL(t) is a simple linear function, starting from a normalized value of 1 down to 0. It can be mathematically expressed as follows.t and RUL(t), even though the degradation rate Monotonic decreasing condition: There is a monotonously decreasing linear relationship between Boundary condition: Apparently, Conventional neural network training will ignore such conditions since they are not embedded into the loss function of the neural network. In our work, we will utilize the two conditions to formulate two PINN constraints into the loss function.The RUL estimation problem can be treated as a supervised learning problem, meaning that there is a target RUL series s time steps. In our model, s is set to 10. This means that the network takes in 10 continuous values of one input feature and produces one output.To better handle sequence data, the neural network designed for the RUL estimation is a many-to-one type RNN, as shown in Fig. t; t, and tanh.tanh.The mathematical equations of the constructed RNN structure are given for the first layer in Eq. , for theY (ground truth) and predicted value n is the number of training samples.Since RUL estimation is a regression problem, we use Mean Squared Error (MSE) to measure the loss during network training. Let Y and predicted value Ordinary least squares (OLS). OLS is the common least squares measure for minimizing RNN, Eq. .Monotonic decreasing constraint (MDC). In RUL estimation, one physical rule is that the RUL should only decrease over time. The previous predicted RUL Boundary condition constraint (BCC). The boundary condition for normalized RUL is Now we have three components constituting the customized loss function for PINN. OLS is responsible for minimizing the distance between the predicted and labeled RUL, MDC is to enhance the decreasing trend of the predicted RUL curve, and BCC punishes the predicted value exceeding the boundaries. Instead of simply combining them, we introduce weights  Physics-Informed Neural Network (PINN) is not an independent NN but a technique that utilizes physical rules to strengthen the underlying NN. It is built on top of an underlying NN. PINN is a general term applicable to all kinds of underlying NNs. Depending on the underlying NN, it may be precisely termed PI-RNN (Physics-Informed RNN), if the underlying NN is RNN; it may be precisely termed PI-LSTM (Physics-Informed LSTM), if the underlying NN is LSTM. The loss function, 38. When applying our PINN formulation, we consider two types of baseline underlying RNNs: vanilla RNN and LSTM. We will detail both in-sample and out-of-sample estimation performance for vanilla RNN, and report out-of-sample estimation performance for LSTM.In-sample estimation: The training data and testing data use samples from the same device or the whole set of devices. The purpose is to evaluate the model’s learning performance.Out-of-sample estimation: The training data and testing data use samples from different devices. The purpose is to evaluate the model’s generalization performance. In the meanwhile, we look into how the two PINN physical constraints influence the model’s learning and performance.For network training, we employed the well-known Adaptive moment estimation (Adam) algorithm40, which is an improved version of stochastic gradient descent optimization algorithm. As evaluation metrics, we use both Mean Squared Error (MSE) and coefficient of determination called n is the number of samples. It can measure the proportion of the variation as a percentage which makes it easier to compare different models. The best score is 1.0 indicating the predicted values and labels are perfectly matched. The score is 0 if In our experiments, we evaluate the performance of our PINN-based RUL estimation against RNN-based RUL estimation. We use the full IGBT degradation aging dataset from NASASince we use 38. The type of device is International Rectifier IRG4BC30KD IGBT with 600V/15A current rating in TO220 package . The data were collected from an IGBT thermal overstress experiment, where a square signal was applied at the IGBT gate and parameters like gate-emitter voltage (39.The IGBT dataset is an open-source dataset from NASA Prognostics Center of Excellence (PCoE) Data Set Repository39, a temperature threshold controller was used to prevent this damage from happening by turning off the load power supply to terminate the test once the thermal runaway (temperature exceeding threshold) occurred. In this way, the device can still be functional after the latch-up failure point but the failure mechanism was simulated.The failure mode is transistor latch-up (not package failure). The latch-up failure leads to a high current between the collector and the emitter, which can be captured by the drastic drop in the collector-emitter voltage  algorithm41 was applied to smooth the standardized data, facilitating the neural network to learn and fit. In contrast to Simple Moving Average (SMA) algorithm, EMA puts more weight on the most recent data points. The EMA function is given as follows:span is set to 15 as the width of the sliding window applied to the original series.As an example, Fig. In the in-sample experiment, we trained RNN models for each individual device and four devices as a whole, with 80% of data for training and 20% of data for testing. Since our monotonic decreasing condition needs the information about previously predicted RUL to calculate the loss, we should keep the sequence order of data samples. To this end, for every 5 samples, the last one was extracted and they were concatenated as test data as illustrated in Fig. Table To evaluate the out-of-sample performance, we employed 4-fold cross validation whereas 4 cases were set up as listed in Table Four groups of experiments were conducted by tweaking the parameter For all four cases, the slope of the predicted RUL becomes flatter as The boundary condition intends to limit the predictions within the value range from 0 to 1. PI-RNN with only boundary condition but without monotonic decreasing condition  differentiate and determine multiple device health stages, e.g., healthy, sub-healthy, pre-failure, and failure; (2) When the pre-failure health stage is reached, a polynomial model may be fitted instead to estimate RUL for the rest of lifetime. Since this work focuses on PINN for RUL estimation, this can be a direction for future investigation.7. To show that our PINN formulation can also work well with LSTM, we evaluated the out-of-sample estimation performance of pure LSTM and physics-informed LSTM (PI-LSTM) with the same 4-fold cross-validation described in the previous subsection. The structure of LSTM is similar to that of RNN in the previous experiments: 1 neuron in the input layer, 80 LSTM cells in the first hidden layer, 10 neurons in the second hidden layer, and 1 neuron in the output layer. Due to the additional three gating mechanisms  in an LSTM cell, it has four times the number of weight/bias parameters of a corresponding vanilla RNN unit. As a result, the total number of parameters in the LSTM is approximately four times as many as that of the RNN .As a more powerful variant of RNN, LSTM has also been proposed for RUL estimationBoth physical conditions were applied in the physics-informed LSTM with the parameters After introducing two physical conditions, the performance of the LSTM is increased in both MSE and We have proposed an RUL estimation method for IGBTs using PINN. The physical rules are identified from the target RUL function and formulated as two regularization terms (monotonic decreasing and boundary conditions) in the loss function of the underlying NN. By adjusting their weighted importance, the regularization makes the trained neural network conform to a monotonic decreasing trend and removes negative values. We have applied our method to RNNs for RUL estimation using the NASA IGBT data set. In the in-sample estimation experiments with vanilla RNN, our physics-informed RNN can improve the MSE of the baseline underlying RNN on average by 38.86% in training and by 35.69% in testing. In the out-of-sample estimation experiments with vanilla RNN, our physics-informed RNN can improve the MSE of the baseline underlying RNN on average by 24.7% in training and by 51.3% in testing. In the out-of-sample estimation experiments with LSTM, our physics-informed LSTM can improve the MSE of the baseline underlying LSTM on average by 15.3% training and 13.9% in testing. This implies a large expansion of the NN models’ generalization capability. In both in-sample and out-of-sample estimation, PINN does not compromise