Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels—cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort. Deep learning algorithms have achieved impressive successes in analyzing and extracting knowledge from images through classification, object detection, semantic and instance segmentation, and other quantitative tasks  in each patch. This information is used to automatically derive the distribution of these class objects in each image. Manually detecting these class objects and determining their distributions in different regions is a tedious and time-consuming process. Images lacking sufficient resolution make it difficult for experts to manually detect certain class objects in such images with certainty. Tools such as BiofilmQ  to sharpen the class objects in each image  grown on mild carbon steel and detect the presence and distribution of three-class objects—cells (and cell clusters), microbial byproducts, and non-occluded surface—across different regions of a biofilm SEM image. We have conducted multiple experiments to evaluate, (1) the applicability and effectiveness of the usage of state-of-the-art self-supervised techniques, (2) compare and contrast the two major self-supervised categories (contrastive and non-contrastive), (3) performance comparison between self-supervised approaches and their fully supervised counterparts on the biofilm dataset, and (4) qualitative analysis of self-supervised model performance through expert feedback.Object classification is one of the fundamental tasks in machine learning. Over the years, image classification has been used extensively in many major domains in their applications, such as medical, engineering, and material science. Previous attempts mostly consist of pipelines with hand-crafted feature extraction to feed into the classification models. Several image processing techniques such as morphological techniques, Fourier spectrum and Wavelet coefficients, and texture and clustering have been used to extract useful feature representations  images. This dataset consists of a set of SEM images of Desulfovibrio alaskensis (DA-G20), a sulfate-reducing bacteria (SRB) and their biofilms on bare mild steel surfaces grown in MIC experiments conducted in batch. Owing to its high ductility, weldability, and low cost, mild steel remains a popular choice of metal in civil infrastructure, transportation, oil and gas, and other industrial applications. However, under aqueous conditions, mild steel is susceptible to MIC caused by microorganisms including the SRB. DA-G20 was cultivated and grown in lactate C media with the following media components including sodium lactate (6.8 g/L), sodium citrate (0.3 g/L), sodium sulfate (4.5 g/L), ammonium chloride (1 g/L), dehydrated calcium chloride (0.06 g/L), potassium phosphate monobasic (0.5 g/L), magnesium sulfate (2 g/L), and yeast extract (1 g/L). The culture medium was deoxygenated by filter-sterilized nitrogen gas purging for 20 min at 15 psi followed by sterilization at 121°C for 20 min at 15 psi. The pure cultures of DA-G20 were inoculated in 150 mL serum bottles containing 80 mL of lactate-C medium and were incubated at 30°C and 125 rpm in an orbital shaker. The exponential phase pure cultures (10%) of DA-G20 were prepared and inoculated (40 mL) into the Paracell Kit (Gamry Instruments) using low carbon steel electrodes with the volume of 360 mL lactate C media. All the electrochemical measurements and their results were discussed in our recent publication  in sodium cacodylate buffer   Cell, and (iii) Surface—were identified and labeled by the experts in each image in the dataset. Cells were marked in blue color, byproducts in pink, and surface in green. Image annotations were performed using the image labeler package in Matlab.Machine learning approaches typically require experts to annotate images by assigning class labels to images and their components. The expert-labeled images serve as ground truth those are used by the algorithms to learn and to test the performance of these learning algorithms. In the current work, three classes—(i) The authors who are biofilm and microscopy experts labeled the cells based on the unique features including cell shape, structure, and size of the bacterium used. However, byproducts include complex substances such as biofilm microstructure, corrosion products, and microbial products that do not have unique shapes and structures. These domain experts categorized these structures all into the same category called byproduct. Future studies are warranted such as surface elemental analysis combined with microscopic techniques to specifically categorize these structures.Note that the problem addressed in this paper is a multi-label classification task, i.e., whether an SEM image patch belongs to more than one class we did not differentiate between cells and cell clusters in this work. Although pixel-level annotations are typically used for semantic segmentation tasks and bounding boxes are used for object detection tasks, our paper uses pixel-level annotations for effective the multi-label classification of SEM images. We also did not differentiate between pristine and contaminated surfaces in this work. Our initial analyses of the data indicated that cells and cell clusters co-occur in all images and hence cells and cell clusters are equivalent for classification. Similarly, all surface patches in the images were found to have byproduct particles and hence pristine and contaminated surface were treated equivalent for classification. However, the proposed approach can be easily extended to consider these additional classes by extending the annotations and incorporating pristine surface data samples. Though all the images in the dataset were annotated, only a small portion (~10%) of the annotated images were used by the self-supervised learning methods for classification.In general, large volumes of data are required to train high performance deep neural networks  augmented versions of the same unlabeled image and generate representations. Here, the input image is called query (q) whereas the encoded representations called keys . The momentum encoder maintains a queue of keys for contrastive loss (InfoNCE) calculation purposes (see Equation 1 below). Here, the temperature parameter(τ) scales the similarity scores. MoCoV2 keeps the size of the mini-batch very small and stores the results with higher memory size. The framework for MoCoV2 is shown in The contrastive learning approach assumes that a dataset has images where some images belong to the C) computed using the output of two identical networks' representations. When the batch-normalized output of the two branches of the framework are zA and zB  are available in the patch or not. These outputs from each classification model merge together to construct the final classification result on a test patch. Therefore, in the final output, one image patch may be assigned more than one class and in some cases, all three classes.We design a learning scheme that converts the classification of Rq1: Feasibility and effectiveness of automatic classification of byproducts, cells, and non-occluded surface by using state-of-the-art contrastive and non-contrastive self-supervised learning approaches. Rq2: Compare and contrast the biofilm image classification performance of MoCo V2 (contrastive self-supervised approach) with Barlow Twins (the non-contrastive self-supervised learning approach). Rq3: Evaluate the savings of the expert annotation effort and performance of self-supervised models compared to their fully supervised counterparts. Rq4: A qualitative analysis of the classification performance of the self-supervised model through expert feedback.We applied the approach described in All the model training and testing tasks were executed on GPU enabled environment using a LAMBDA QUAD Deep Learning Workstation with Intel(R) Core(TM) i9-9920X CPU (3.50 GHz), Nvidia Quadro RTX 6000 GPU with 24 GB memory and 128 GB RAM.We followed the pre-processing pipeline described in Section 2.2 to prepare the dataset containing 7 grayscale SEM images for model training. Using an image processing tool, we manually cropped out the meta-information that appeared on the images. Next, BSRGAN-based image resolution enhancements were applied on SEM images with 10 or higher micrometers scale whose magnifications were less than 1KX. Then we processed 4X magnification on the images to normalize object sizes across images. Then patches of size 64×64 and 128×128 were generated from each processed, non-annotated image as well as each annotated image for experimentation purposes. The sliding windows technique with a stride size of 2 was used as explained in Section 2.3 to generate these patches. With a patch size of 128 × 128, we obtained 24021 image patches from all 7 images in the dataset. We used the pixel colors and masking process to generate, for each patch, at most three ground-truth labels, one per class. We have used 80% of the dataset to learn representations and 10% of the data to fine-tune the model. Here, we used ten-fold cross-validation to fine-tune strongly annotated data (image-level ground truth annotations). The rest of the 10% data was used as test data to generate the final multi-label classification results.Byproduct, Cells, and Surface. The prediction from each binary classification model was collectively used to construct multi -label classification result on each input image patch. For example, for the image patch shown in We implemented MoCoV2 and Barlow twins with ResNet-50  for manually identifying the three classes. A simple user interface was created   was found to be reasonable by experts.The self-supervised learning models were evaluated with respect to the learned representation quality and fine-tuned downstream performance. These notions are explained below. Initially, we conducted empirical experiments to estimate the best-suited configurations  for learning representations. The representations were learned with all the unlabeled data with different patch sizes (64 × 64 and 128 × 128), different batch sizes (128 and 256), and different epochs . We used all these models with learned representations for further experiment setup.To evaluate the learned representations' quality, we conducted a linear evaluation experiment using a linear classification head on the learned representations as discussed in Chen et al. ; Grill eByproduct, Cell, and Surface) as well as overall averages of the results in According to the results, both MoCoV2 and Barlow Twins models illustrated the highest classification accuracy with the parameter configuration of patch size 128×128, batch size 128 and trained with 200 epochs. For all the evaluation experiments, at the training time, we used random crop and horizontal flip augmentations, and at test time we used center crop augmentations as suggested in Zbontar et al. . We repoIn this experiment setting, we evaluate the data efficiency of each self-supervised model. We fine-tuned each (MoCoV2 and Barlow Twins) model that learned representations for the downstream classification task on the image-level ground truth annotated data for 20 epochs with different portions of the labeled data. However, 10% of the image-level ground truth annotated data showed significantly better classification accuracy. We illustrated the accuracy of the classification tasks from each binary classification model (over each class) as well as averaged overall classification accuracy performance in Sq1: overall feasibility and the utility of the proposed approach in classifying SEM biofilm images in comparison to manual and semi-automated (ImageJ) classification approaches, and aid in the discovery of class objects, Fq1: existence of each class object predicted by the models in the image patches by providing binary (T or F) agreement along with additional remarks and Fq2: justification provided by the model for its prediction for the existence of each class in the image patch by providing (T or F) agreement along with additional remarks.The qualitative evaluation of the applicability and performance of the proposed approach for the classification task involved summative and formative assessments by domain experts. The experts assessed the Rq1) and compared the two self-supervised models along with a supervised model baseline  using linear and fine-tuning evaluations and the amount of annotation savings achieved. In addition to these quantitative analyses results, we also report on the results of our qualitative analyses on the performance of our models by domain experts.We report on quantitative analysis results in terms of the evaluation of the robustness of the two self-supervised approaches with respect to parameter variations (Rq2), a linear classification head was trained while freezing the learned representations. std) values, Barlow Twins' results were more consistent than MoCoV2. Although both self-supervised models did not perform as well as the supervised approach in the linear evaluation, their accuracy was pretty close to the supervised model. In terms of the annotation effort (Rq3) the two self-supervised models achieved performance comparable to the supervised model baseline while using only 10% of the labeled data.In order to evaluate the quality of the representations learned by the self-supervised models in comparison to the supervised baseline  Annotations were incorrect, (2) Annotations were correct but the class predicted by the machine learning model was incorrect, and (3) Annotations were ambiguous where the domain experts are also uncertain in cases involving overlapping cells and byproducts.The domain experts inspected the original image patch, corresponding three image-level annotations (as described in Section 2.3) and the three-class activation maps for evaluating whether (1) The predicted class is present in the original image patch, and (2) The predicted class is present at the location highlighted in the CAM see . For Fq1Fq1, for Fq2, the disagreement between the domain experts' assessment and the machine learning model predictions was larger. One of the main reasons for this disagreement was that the present class activation maps scheme could not highlight the presence of a particular class in the image patch when the objects belonging to this class are separated and are present in multiple regions in the image patch. Also, this evaluation metric of the class activation maps is very subjective, particularly on image patches involving overlapping objects belonging to multiple classes. In summary, approximately 98% of the time domain experts' agreed with the model prediction.In comparison to the Several studies  of image-level ground truth annotations. According to the linear evaluation experiments, it is clear that both MoCoV2 as well as Barlow Twins models are capable of retrieving similar quality representations using the unlabeled dataset. However, with the fine-tuning process toward the final classification downstream task using a limited amount of labeled data, Barlow Twins model gained in performance enhancement not only compared to MoCoV2 but also its supervised counterpart. As both the self-supervised models surpassed supervised model performance and considering the qualitative feedback on the model predictions, we can conclude that the representations learned using the unlabeled data lead to better classification accuracy. Moreover, Barlow Twins model is more preferable due to its superior model classification accuracy performance while incurring a substantially lower computational cost compared to MoCoV2. Note that we use pixel-level annotations in contrast to image-level annotations for our classification task. Our earlier work , and low inter-class variance between classes , binary classification was a favorable choice over direct multi-label approaches such as algorithm adaptation approaches. Even though it is well-known that the adaptation of the binary relevance approach has its potential weakness of ignoring correlations among labels  and Barlow Twins (non-contrastive) on the dataset. Our study clearly showed that self-supervised model (Barlow Twins) built using our pipeline was able to produce 83.18% classification performance on the SEM image patches with the patch size of 128 × 128 while reducing the requirement of labeled data by at least 90%. Further, the qualitative evaluation showed over 98% agreement on summative and formative assessments by domain experts with the model predictions. Although the binary classification models used for multi-label classification might have the weakness of ignoring correlations among labels during the downstream classification, we believe that most of the correlations were captured during the representation learning stage. Given the potential of state-of-the-art self-supervised learning to mitigate the requirement of the large volume of manual label annotations, we plan to extend this work to perform object segmentation tasks and to further extract quantitative measures of the objects such as area and length. We also plan to extend the experiment setup with specific biofilm image augmentations, and additional self-supervising methods.venkataramana.gadhamshetty@sdsmt.edu.The data analyzed in this study is subject to the following licenses/restrictions: to access the dataset, please request. Requests to access these datasets should be directed to VG, DA, MM, MA, and MS contributed to conception and design of the study and wrote the paper. DA and MA implemented the pipeline of models and performed the experiments. PC, JK, and VG reviewed the paper. JK and VG organized the database. PC contributed to funding acquisition. All authors contributed to manuscript revision, read, and approved the submitted version.This project was partially supported by NSF EPSCoR RII Track 2 FEC #1920954.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Chronic neck pain (CNP) is prevalent, and it reduces functional status and quality of life and is associated with deleterious psychological outcomes in affected individuals. Despite the desirability of massage and its demonstrated effectiveness in CNP treatment, multiple accessibility barriers exist. Caregiver-applied massage has demonstrated feasibility in various populations but has not been examined in Veterans with CNP or compared in parallel to therapist-delivered massage.This manuscript described the original study design, lessons learned, and resultant design modifications for the Trial Outcomes for Massage: Care Ally–Assisted Versus Therapist-Treated (TOMCATT) study.TOMCATT began as a 3-arm, randomized controlled trial of 2 massage delivery approaches for Veterans with CNP with measures collected at baseline, 1 and 3 months after intervention, and 6 months (follow-up). Arm I, care ally–assisted massage, consisted of an in-person, 3.5-hour training workshop, an instructional DVD, a printed treatment manual, and three 30-minute at-home care ally–assisted massage sessions weekly for 3 months. Arm II, therapist-treated massage, consisted of two 60-minute sessions tailored to individual pain experiences and treatments per week for 3 months. The treatments followed a standardized Swedish massage approach. Arm III consisted of wait-list control.Retention and engagement challenges in the first 30 months were significant in the care ally–assisted massage study arm (63% attrition between randomization and treatment initiation) and prompted modification to a 2-arm trial, that is, removing arm I.The modified TOMCATT study successfully launched and exceeded recruitment goals 2.5 months before the necessary COVID-19 pause and is expected to be completed by early 2023.ClinicalTrials.gov NCT03100539; https://clinicaltrials.gov/ct2/show/NCT03100539DERR1-10.2196/38950 Neck pain is common in adults aged >50 years and is the fourth leading cause of disability in the United States . ChronicMedications are most commonly used to treat CNP in clinical practice; however, systematic reviews find limited evidence for effective treatments relative to low back pain, and current therapies show only modest effect sizes . BecauseIn all, 58% of the older adults surveyed used some type of alternative treatment , and paiDespite its safety and potential benefits, the expense associated with massage therapy limits its accessibility. Teaching informal care allies to provide massages has the potential to improve accessibility. Kozak et al  demonstrThe Trial Outcomes for Massage: Care Ally–Assisted Versus Therapist-Treated (TOMCATT) study began as a 3-arm, randomized controlled trial of 2 massage therapy delivery approaches for CNP. The primary aim of this study was to compare care ally–assisted massage (CA-M) and therapist-treated massage (TT-M) with a wait-list control group (WL-C) for pain-related disability. The secondary outcomes included pain severity, health-related quality of life, depression, anxiety, and stress. Before pausing the TOMCATT activities due to the COVID-19 pandemic, the study design was modified to a 2-arm study. The CA-M arm was discontinued owing to recruitment and adherence challenges.The primary purpose of this manuscript is to describe the original TOMCATT study design, the challenges faced during the study’s first 2 years of recruitment, and the resultant design modification to a 2-arm study. Specifically, we describe the original TOMCATT methodology, materials, and procedures in the Methods section and report the initial enrollment and intervention initiation outcomes for the first 30 recruitment months, rationale for the modified study design, and subsequent modified TOMCATT methodology in the Results section. TOMCATT is an active study that has restarted from the COVID-19–related pause and is projected to be concluded by early 2023.TOMCATT’s study population consisted of Veterans with CNP randomized to one of 3 study arms: CA-M, TT-M, or WL-C. All eligible patients had an identified care ally, but only those randomized to the CA-M arm ultimately participated in TOMCATT with their care ally. Those randomized to the CA-M arm attended a group training workshop that taught them a massage routine. After the training, CA-M dyads were asked to complete the learned 30-minute massage routine 3 times per week for 12 weeks and document the massages that were delivered in a log. The TT-M intervention consists of twice weekly, hour-long massage sessions provided by massage therapists trained to deliver semistandardized, individualized treatments.TOMCATT outcome assessments were conducted via interviews at baseline and 1, 3, and 6 months in all study arms. The primary outcomes were neck pain and related disabilities. The secondary outcomes were other pain measures, health-related quality of life, depression, anxiety, and pain cognition.TOMCATT was reviewed and approved by the Indiana University Institutional Review Board (#1604689005_9034) and Veterans Administration Research Review Committee (VA project ID:IIR 15-333) and registered with ClinicalTrials.gov (NCT03100539). All participants engaged in a one-on-one informed consent process with the study personnel and provided written informed consent.Veterans are eligible if they meet criteria outlined in Inclusion criteriaAged at least 18 yearsChronic neck pain for ≥6 monthsAt least moderate disability per the Neck Disability Index (score of ≥10)Access to a working telephoneAbility and willingness to attend 2 treatments per week for 12 weeksHave a care ally  willing to learn and provide message therapy during the study periodExclusion criteriaNeck pain secondary to vertebral fracture or metastatic cancerComplex neck pain Any professional massage therapy within the last 6 months excluding as part of physical therapy, or visit to the chiropractor or hairdresserPotential contraindication to massage Hospitalized for psychiatric reasons in the last 3 monthsOccurrence of stroke, transient ischemic attack, heart attack, cervical injury such as whiplash, or hospitalization for chronic obstructive pulmonary disease, emphysema, or congestive heart failure within 6 months of enrollmentActive suicidal ideationModerate to severe cognitive impairmentPending neck surgeryInvolvement in ongoing pain trial or massage studypartnered Veteran was randomized to the care ally arm, they attended the CA-M training workshop with their Veteran partner and did not have medical concerns that might interfere with giving a massage.Eligible Veterans who provided written informed consent were enrolled and underwent a baseline assessment. Randomized care allies also provided written consent to participate at the beginning of the intervention workshop training. Care allies were eligible if their Primary care providers at the Roudebush Veterans Affairs (VA) Medical Center in Indianapolis, Indiana, and surrounding community-based outpatient clinics were informed of the TOMCATT study details and asked to provide signed approval for the research team to contact their patients for possible study participation. Potential participants were identified by querying the VA’s electronic medical record to create a master list of Veterans meeting the following criteria: (1) neck pain diagnoses per International Classification of Diseases, Ninth Revision codes 721.0 to 723.9 and (2) primary care clinic visits in the past year (a proxy measure of VA care engagement).A recruitment letter signed by their provider is mailed to qualifying Veterans to describe the study. Letters contain an initial screening for neck pain–related disability that interested Veterans may return if they would like to be contacted to assess their eligibility and possible participation. An appointment is scheduled for eligible Veterans to sign an informed consent statement and the Health Insurance Portability and Accountability Act authorization for those indicating a desire to participate. Baseline interviews and assessments are conducted by a research assistant  before randomization to minimize ascertainment bias. Veterans also self-identified as being interested in study participation after learning of TOMCATT through acquaintances, word of mouth, or study pamphlets.Patients were initially assigned to one of the three study arms  using randomization lists created by the study statistician. Stratified block randomization, with random block sizes of 3 and 6, was used for the original plan to enroll 468 participants . Sex  was the only randomization strata used.Veteran participants were reimbursed US $25 per completed outcome assessment, with 4 scheduled assessments . Participants in the TT-M and WL-C arms were invited to receive all CA-M training and materials after their 6-month interview. Veterans randomized to WL-C are invited to receive a massage from one of the TOMCATT massage therapists. Care allies received a US $50 gift card at the completion of the care ally training and a complimentary massage session.The CA-M intervention consisted of three components: (1) an in-person training workshop led by NM (coinvestigator and licensed massage therapist), (2) an instructional DVD to reinforce the taught concepts, and (3) a printed treatment manual with illustrations and images from workshop materials. Participants were asked to engage in at least three 30-minute CA-M sessions every week at home for the 3-month intervention period. To standardize the delivery and facilitate reproducibility, the content and general structure of the CA-M routine were taught during the workshop. The DVD included a real-time demonstration of the routine for participants to play during applications if desired. Participants were asked to document their massage activities in a study log and return the log sheets monthly.The CA-M routine consisted of 13 progressive components ordered to reflect logical seated treatment progression and partially followed the therapist-applied treatment progression from the TT-M arm of the study. Participants were asked to follow the general routine flow and time per area allotments outlined in the protocol and to individualize their massages using the learned techniques. Participant dyads  randomized to CA-M were scheduled to attend a single 3- to 4-hour (including breaks) in-person training workshop held at the Roudebush VA Medical Center. In all, 1-7 dyads  attended training workshops. Each training workshop consisted of six parts: (1) introductions and objectives; (2) general instruction (via lecture) on massage, communication approach, safety, CNP, and trigger points that may exacerbate neck pain; (3) massage technique demonstration and supervised practice; (4) specific self-care aspects of the routine and additional and individualized trigger point treatment strategies; (5) demonstration and practice of standardized care ally–assisted massage routine; and (6) questions, closure, and wrap-up.NM conducted training that demonstrated and encouraged the safe performance of massage tailored to the participants’ needs and abilities. Basic Swedish massage techniques  were taught during the training, as well as how to use the training DVD and accompanying workbook given to participants. The levels for depth of touch were quantified from very light to so deep that they were not used and described based on tissue-level engagement intention and visual cues from applicant fingertips and recipient skin.The TOMCATT DVD comprised 5 sections that highlight NM discussing key aspects of the massage routine and workshop-taught techniques. In addition, the DVD included a real-time, full demonstration moving from start to finish through the 30-minute care ally–assisted massage routine. Dyads were encouraged to use the DVD to review technique and positioning instructions and to guide each of the 3 weekly applications of the routine to help with timing and treatment fidelity and to reinforce learning objectives. The DVD main menu  providedTraining workshop attendees were also taught how to use the study developed and provided training DVD and an accompanying workbook. The training DVD was a professionally produced, multisectioned DVD designed to complement and reinforce the techniques learned during the training workshop. One section of the DVD was a real-time, full demonstration moving from start to finish through the 30-minute, care ally–assisted massage routine.The CA-M treatment manual is a 32-page, full-color, spiral-bound reference for material taught in the training workshop. The manual content included slides from the training and content arranged to mirror the progression of the training. The manual contained images of associated referral pain patterns, trigger point locations, and treatment of particular trigger points. The treatment manual was given to the participant dyads at the beginning of the CA-M training workshop and had space within it for participants to take notes during the training and use regularly during the study activity as a reference.Attempts were made to boost adherence and provide posttraining support to participants. Study personnel contacted Veterans and care allies at 2 weeks and in months 2 and 4 to inquire about any barriers to home massage . For adherence monitoring, participants were asked to track their use of the DVD and record the time spent on massage on a log form. Monthly log returns were made using postage-paid and addressed envelopes.The TT-M intervention delivery protocol was based on prior research and standardized by treatment intention ordering reflective of typical treatments within therapeutic massage practice for specific pain -31. A SwThe massage sessions involved a short intake interview followed by 60 minutes of hands-on table time and occurred twice a week  for 3 months. During the first session, the massage therapist provided an introduction and overview of massage. Thereafter, the TT-M intervention involved a standardized 9-component sequence , each wiA total of 9 Indiana state–licensed massage therapists with a range of 3-25 years of experience (median 6 years) were recruited to deliver the massage protocol to participants randomized to the TT-M arm. Therapists were solicited to participate through general and word-of-mouth advertisement approaches and querying professional message organizations.Study therapists required two types of training: (1) specific study protocols and procedures and (2) VA-required training for privacy and information security.The study-specific training for protocol and procedures was conducted in person and lasted approximately 3.5 hours. The training was divided into general environment orientation, research processes including duties and responsibilities, and learning specifics related to delivering the massage protocol.The massage therapist protocol adherence and fidelity were addressed in 2 specific ways. First, the therapists launched a timed and silent PowerPoint slide show on a treatment room computer positioned for easy viewing by the therapist during the session. The slide show was timed to advance every 30 seconds through the duration of the 60-minute session and displayed how much time had passed in the protocol, how much time was left in the protocol, the treatment component or components that could occur at that time, and the potential most (in red) and least (in purple) time available to complete any of the possible components . For appSecond, massage therapists completed an electronic fidelity checklist after every session to further support intervention adherence and fidelity. The checklist consisted of 7 sections to ensure that each aspect of the treatment protocol was consistently completed: pretreatment protocol, treatment application protocol, and supported additional notes and documentation. The pretreatment protocol section asked therapists to verify the treatment date, if the participant attended, and that the hands-on, prespecific work aspects of the treatment were completed. The treatment application fidelity section asked therapists to indicate that each treatment protocol component was completed and any specific deviations that occurred. Finally, a note and checklist of allowable general techniques were available at the end of each fidelity checklist to indicate any additional notations.Participants in the WL-C arm received check-in calls at months 2 and 4 from the study staff and were administered outcome assessments on the same schedule  as the treatment groups. Participants in the WL-C were instructed to continue their medical care as normal and to not begin any massage treatment during the 6 months of the study.t test. With 396 patients (132 per treatment group), TOMCATT would have 80% power to detect a medium effect size (0.4 SD) among treatment groups in the NDI at the 3-month time point with type I error set at 0.017 (0.05/3) to maintain familywise error at 0.05. An approximate 15% dropout rate at 3 months was expected, and it was initially planned to enroll 468 patients (156 per treatment group).The primary contrasts of interest were between the treatment arms (TT-M and CA-M) and WL-C, although analysis of all pairwise comparisons was planned. On the basis of the results from Sherman dosing study ,31, the Balanced baseline characteristics of the study participants is expected across the 3 treatment groups due to randomization. Baseline demographic and clinical characteristics will be compared among the treatment groups using the appropriate tests. Variables found to be significantly different will be included in the subsequent regression models.All outcomes are collected at baseline and 1, 3, and 6 months. For the primary outcome of the NDI total score, a linear mixed effect model will be used with an appropriate covariance structure to compare each treatment arm to the WL-C arm on change scores at the 3-month time point. Fixed effects will include treatment, time , and treatment by time interaction. Differences in change scores among groups at other time points (1 and 6 months) will also be reported. Associations among repeated measures within participants will be examined, and a data-driven approach will be used to determine the appropriate variance-covariance structure . The ŠídTOMCATT is not specifically powered for secondary outcomes, and a cautious interpretation of the secondary analysis results is planned. For pain intensity, those with a clinically relevant >30% reduction from baseline will be reported as “responders.” For the NDI, a “responder” will be defined as a decrease of >5 points from baseline to 1, 3, and 6 months for each participant. A generalized linear mixed model with predictors of group, time, and their interaction will be used. The model will also include a random subject effect to accommodate the potential correlation among observations from the same participant . The priBaseline anxiety  and depression  will be tested as potential moderators of the primary outcome (NDI), as well as secondary outcomes, as secondary analysis. Testing these measures as moderators will provide insight into the generalizability of the interventions. For example, if the intervention loses effect in patients with high anxiety or depression, they may not be suited to the intervention.Missing data in 2 different forms are expected: missing data by attrition and intermittent missing of observations. Attrition of <15% was expected at the 3-month follow-up. Differential attrition among study arms was expected during study planning or initiation, yet the potential that care ally burden could lead to more attrition in the CA-M arm and transportation barriers could lead to more attrition in the TT-M arm was acknowledged by the study team. The effects of missing observations due to attrition will be examined by analyzing the patient characteristics associated with dropout. Multiple imputation techniques may be used to alleviate the impact of missing data. However, if the pattern of missing data is nonignorable, more complex modeling approaches to model the missing data may be used. Finally, sensitivity analyses will be conducted to ensure the validity of study findings.The TOMCATT pretrial activities were launched in July 2016 and included massage therapist recruitment, training, and onboarding; the finalization of training materials; data collection documents and databases created; and research personnel hired and trained.Participant recruitment began in May 2017. Randomization resulted in 102, 102, and 101 participants in the CA-M, TT-M, and WL-C arms, respectively, in the first 30 months. All WL-C participants progressed to the 12-week nonintervention phase of the study, with 93.1% (94/101) completing an outcome assessment (data not shown). In all, 98% (100/102) of participants in the TT-M initiated the intervention. Of the 102 participants, only 2 (2%) failed to schedule and attend their first TT-M treatment and withdrew from the study owing to schedule conflicts.For CA-M, only 54.9% (56/102) of the randomized patients attended training and initiated treatment. Disinterest was the primary reason  indicated for noninitiation, followed by scheduling issues . Among those who attended training, an average of 42 days passed between baseline data collection and training or treatment initiation. Participant engagement was further challenged by inconsistent treatment log-return compliance, despite most participants  completing one or more follow-up outcome assessments (data not shown). Nearly 45% (25/56) of participant dyads returned no compliance logs, whereas nearly one-third of the participants (17/56) returned logs for each of the 12 intervention weeks. A majority of CA-M dyads  returned at least one week of compliance logs.Several mitigatory steps were taken to improve treatment initiation in the CA-M arm over approximately 14 months. To accommodate individual schedules, training workshops were scheduled 3 times per month, which included at least one Saturday, a morning training session during the week, and an afternoon training session during the week per month; any of which was held for as long as at least one dyad attended. Several additional training workshops were scheduled per individual dyad scheduling needs.In an effort to retain participants in the CA-M arm and facilitate treatment initiation for those with care ally barriers, a matching approach was launched whereby former Veteran participants who expressed willingness were matched with enrollees who ultimately did not have a care ally willing or able to attend training and provide care ally–assisted intervention for 12 weeks. An enrollee with care ally participation barriers agreed to be matched but did not attend the scheduled training, despite the matched, stand-in care ally attending.Although not initiated, a modified training approach was developed for CA-M arm participants as an alternative to the 3.5-hour training seminar. The modified approach was composed of a combination of at-home learning and supportive applied laboratory experiences. The developed approach included participants accessing digitally recorded didactic information and materials from a supportive DVD on their own time in a structured and prompted format. Once complete, the dyad would schedule a 1-hour hands-on application session with NM to reinforce content and ensure safe and appropriate application of the intervention techniques.The decision to modify the TOMCATT design was informed by disproportionate attrition before treatment initiation and poor adherence to CA-M treatment logs. These challenges persisted, despite several procedural modifications to support participation and adherence. The modified study design to remove the CA-M arm was developed and approved by the study funder and institutional review board in November 2019 and December 2019, respectively. Participants enrolled and randomized to the study before the postmodification date progressed through study completion based on their original group assignment.The inclusion criterion for participants to have an identified care ally was removed from the modified study design. The recruitment and enrollment procedures remained the same. After modification to a 2-arm study, a new set of randomization lists was created. Within each strata defined by sex, patients were randomized 1:1 to the TT-M or WL-C group using block sizes of 4.Because of high attrition in CA-M , TOMCATT was modified into a 2-arm study (TT-M and WL-C). The modified study will focus on comparison of the TT-M and WL-C. At modification, it was assessed that 100 patients per group (N=200) would provide 80% power to detect a 0.4 SD in NDI change from baseline between TT-M and WL-C with type I error of 0.05. The premodification attrition rate at 3 months in the TT-M and WL-C groups was 23.9% (47/197). Thus, the enrollment of 264 (200/0.76) or 132 per group was planned during the postmodification period. The comparison of CA-M to WL-C obtained before the study modification will be considered a secondary analysis and reported in a subsequent manuscript.The analysis plan for the modified study will be conducted in a manner similar to that of the initial 3-arm randomized controlled trial. Randomization is expected to achieve balanced baseline characteristics of study participants among the study groups. Primary (NDI) and continuous secondary outcomes  will be assessed using a linear mixed model approach. Type I error will be set at 0.05. The Šídák method will be used to adjust for multiple comparisons at a given time point for the secondary outcomes. Noncontinuous outcomes will be assessed using a generalized linear mixed model. In the revised design, missing data from attrition and intermittent missing observations are expected. Attrition of approximately 24% is expected by the 3-month follow-up and has been accounted for in the modified sample size calculation. The effects of missing observations due to sample attrition will be examined based on the patient characteristics associated with early dropout.The TOMCATT trial was initially designed to test the effectiveness of 2 different massage interventions versus WL-C. Treatment engagement and retention challenges have emerged and raised questions about the feasibility of CA-M. Consequently, the study design was modified, resulting in a simpler, 2-arm study. This decision, although difficult, allowed the TOMCATT study team to focus and redirect recruitment efforts on the 2 remaining study arms. Promising recruitment and retention success for the postmodification efforts point to promising expectations for TOMCATT’s resumption efforts from the COVID-19 pause.Caregiver-delivered massage has been shown to be feasible and effective in 2 previous Veteran-focused studies ,11, but The qualitative interview data collected in a subset of TOMCATT participants are anticipated to elucidate some challenges that emerged within the original design and may provide potential insights into ways to improve treatment engagement, retention, and interpret eventual trial results. In these interviews, the participants are asked about their prerandomization arm preferences, treatment perceptions, and outcome expectations. Qualitative data collection and analysis may highlight a desire to incorporate treatment preferences into the design of future massage trials. Furthermore, assessing the sociodemographic and clinical correlates of treatment engagement and retention, especially among CA-M participants, are planned.This manuscript reports only preliminary findings related to enrollment and intervention uptake to explain the modification rationale from a 3-arm to a 2-arm study design and to describe the modification methodology. Furthermore, the COVID-19 pandemic has caused significant disruptions to TOMCATT, especially the delivery of TT-M. All in-person noncritical research activities were suspended in March 2020. Thus, all in-person TOMCATT activities, including delivery of massages, were forced to halt owing to safety concerns and institutional mandates. Because of intermittent surges in COVID-19 infection rates throughout 2020 and early 2021, TOMCATT continued to suspend in-person research activities owing to safety concerns for participants, massage therapists, and study staff. As a result, recruitment and massage treatment delivery for TOMCATT did not restart until June 2021, after infection rates had declined and safety mitigation factors were in place and believed to be effective. Study recruitment and enrollment began gradually with study capacity reached and a smooth process implemented by December 2021.Although the use of informal caregivers to provide massage is an innovative care delivery model, it was not feasible in our study of Veterans with CNP, which was hampered by low engagement and high attrition. TOMCATT’s continued efforts with the modified study design will provide a better understanding of the feasibility and effectiveness of an on-site therapist-applied massage treatment model for Veterans with CNP, an approach that demonstrates feasibility and acceptability. The incorporation of patient preferences into the study design is planned for future trials to improve engagement and retention in all care approaches.


Mass customization is the development of items tailored to specific customers, but produced at low unit cost in high-volume. In this context, hybrid manufacturing (HM) combines fused deposition modeling (FDM) and injection molding (IM) to fabricate a single personalized part with minimum manufacturing cost. In this technique, inserts with different physical features are first FDM-fabricated and then IM-overmolded. This study investigated the effect of hybrid FDM-IM production technology, FDM insert geometry on mechanical properties, and micro-structural evolution of Polylactic Acid (PLA) samples. The findings indicated a comparable tensile properties of FDM-IM samples (68.38 MPa) to IM batch (68.95 MPa), emphasizing the potential of HM in the manufacturing industry. Maximum tensile stress of FDM-IM specimens shows an upward trend due to the increased infill density of preforms. In addition, overmolding interface direction results in a big gap for the maximum tensile strengths between half-length series specimens (12.99 MPa to 19.09 MPa) and half-thickness series specimens (53.83 MPa to 59.92 MPa). Furthermore, four joint configurations resulted in different mechanical performances of finished specimens, in which the female cube sample exhibits the highest tensile stress (68.38 MPa), while the batch with male T joint shows a lower value in maximum tensile strength (59.51 MPa), exhibiting a similar tensile performance with the half-thickness 75% batch without joint configuration. This study lays the groundwork for using HM to produce bespoke and mechanically improved parts over FDM alone. The global market for manufactured goods has become even more competitive in recent years . To expeIM is a conventional manufacturing technique which has been applied in mass production for decades. It normally starts with the mold closing, the injection of molten material into the mold cavity, and products ejected from the mold following by the material solidification . Up to nAdditive manufacturing (AM) in its modern form made its debut in the 1980′s, when the first fully operational production system based on the use of photo-curable polymer resins for the creation of object by applying concentrated energy to a specific region of the liquid, forcing it to solidify . This acSpecifically to FDM technique, fabrication of specimens occurs by controlling the position of extruded molten filament and thereafter collecting onto the build platform in which layers are fused together . Its lowThe unique characteristics of polylactic acid (PLA), such as renewability, biodegradability, 4D printability and few requirements to fabricate perfect-quality samples have prompted its use in medical and tissue engineering applications ,51,52. HIn this study, the microstructure and mechanical properties of HM samples combining FDM and IM with varying production parameters were investigated in the pursuit of mass customization (MC). MC is a manufacturing approach in various sectors that focuses on creating uniquely customized items for the user while maintaining the economies of scale afforded by mass production. The HM used here combined FDM for the production of a substrate base and followed by IM , in a prThe PLA filament  assembled in the filament reel was purchased from the Real Filament Company in Wateringen, The Netherlands. The virgin PLA 4043D biopolymer utilized in the IM process were supplied from NatureWorks Corporation in 15305 Minnetonka Blvd., MN, USA.All manufactured samples were separated into three series in this study, the FDM, IM and HYM specimens, whose dimensions comply with the ASTM-D638-3 Standard. The fabrication of HM specimens can be divided into two stages, in which the FDM inserts were created firstly and pushing these FDM preforms into the mold cavity to proceed the overmolding process.The AM technique utilized in the fabrication process was FDM. The PLA filament used here was firstly dried in the oven using 60 °C for 4 h due to PLA’s moisture absorption which can adversely affect the mechanical output of fabricated samples , and theThe optimal parameters utilized in the process of FDM were derived in previous publications ,57,58,59Injection Molding was performed on a Babyplast 6/12 injection molding machine from Rambaldi Corporation from Molteno, Italy. The temperature of plasticizing zone, chamber and nozzle were controlled and set at 185 °C, 180 °C and 170 °C, respectively. To obtain great-precision samples, the shot size was set at the same volume that is needed to fill the entire cavity . SimilarThe mechanical properties of the samples based on ASTM-D638-3 were determined through the Lloyd LRX Universal tester from Lloyd Instruments Ltd., Bognor Regis in UK at a movement speed of 5 cm/min. All samples were placed in a dryer using 40 °C for a night prior to the tensile test.In this study, a vernier caliper measured the length and thickness of gauge prior to the tensile test.The macrostructures of all fabricated samples/cross sections of gauges for inserts were observed by a Nikon ShuttlePix P-MFSC Firmware Digital Microscope with a range of *20 magnitudes prior to the tensile tests.p-value was set as 0.05.The data analysis was carried out through GraphPad Prism 9 . The data was imported into the software, in which the mean and standard deviation values were obtained from the replicated data. In addition, a one-way analysis of variance (ANOVA) with a Tuckey’s Multiple Comparison Test focusing on the joint configurations was applied here to determine the significance of the differences between each batch, where the The scanning electron microscopy (SEM) was carried out with a Mira SEM  with a magnification of *50 to observe the surface morphology of samples after the tensile test using secondary electron. Samples tested via SEM were placed on aluminum stubs and were then gold coated with Baltec SCD 005 sputter coater  to mollify charging’s impact on the observation .The MC philosophy separates sophisticated manufacturing processes into several distinct stages to offer customers bespoken products at reasonable costs ,61,62. TManufacturing cost has always been a hotspot in modern industry . In thisTo date, several prior studies concluded that FDM-fabricated samples would exhibit worse mechanical performance compared to injection-molded samples, particularly in the tensile test ,65,66. IThe test results, including their average values and standard deviations, of the maximum tensile stress (σ) and Young’s Modulus (E) for FDM and IM samples are tabulated in The results of all batches of samples ranged as tensile strength went from lowest to highest, σ =  MPa respectively., Young’s Modulus (E) on the other hand range went from 612.32 MPa to 1130.07 MPa  due to their varied parameter settings, emphasizing the possibility of mechanical performance and customization possibilities for applications where these performances would be required for parts performance. In addition, larger standard deviations for the maximum tensile stress and Young’s Moduli can be found in HL-NJ series than HT-NJ series due to the certain manufacturing condition, which agrees with the findings of the effects on mechanical performance based on the variations of building orientations and infill angles for FDM manufactured samples in previous studies ,43,44,67To determine the correlation between the parameters applied in the study on the tensile performance, below there are two graphical representations based on the tensile results displayed in The effects of parameters applied in the HM technique on the tensile performance of PLA samples are determined in the following sections.The tensile performances of one IM batch and three batches of FDM samples  were compared in To figure out the effect of HM on the mechanical performance, two types of HM samples were taken into account in this study: the half-length samples which were overmolded at a single mid-point along the tensile bar  and half-thickness samples  which were overmolded along the full length of the FDM inserts. The tensile results for HM samples displayed in Infill density refers to the quantity of material utilized to fill the interior region of a layer , which iA first glance at the effect of infill density in the FDM samples , where wObserved in In short, infill density shows its crucial impact in tensile performance . Thus, the high infill density (75%) exhibits the greatest mechanical performance in the tensile stress for FDM-75, HL-NJ 75 and HT-NJ 75, respectively. In addition, we can figure out a ductile behavior in HL-NJ 75 and HT-NJ 75 if compared with the ones with 25% and 50% infill density in A large tensile result gap can be found between the half-length samples with the half-thickness samples, indicating two series of FDM inserts played anisotropic roles in the mechanical performances (maximum tensile stress) of HM samples. Three batches of HT-NJ showed their greater mechanical performances in comparison with the HL-NJ 25 to 75, approximately from 76% (HT-NJ 25 with HL-NJ 25) down to 66.7% (HT-NJ 50 with HL-NJ 25) for maximum tensile stress, which can be found in This kind of phenomenon can be explained through the failure modes in the tensile test: inter-layer failure and trans-layer failure, which agreed with some previous pieces of literature related to the building orientations for FDM samples ,41,44,63These results underscored that the interface direction had a significant role in affecting tensile performance for HYM samples and this triggered to a superior tensile performance in HT series samples. However, a half-length series sample can be more popular in market due to the direct availability of customer-tailored features, even if its mechanical performance is worse to that of half-thickness series.According to Moreover, an interesting phenomenon can be found from p-value for tensile stress and found the varied joint configurations were statistically significant with the p-values < 0.05 for tensile stress. Moreover, the results of Tukey Test displayed in The results of ANOVA shown in In this study, 14 batches of samples  were manufactured and tested. Half-length series samples that were overmolded at a single mid-point along the tensile sample, and half-thickness series samples that occurred along the entire length of the substrate were evaluated. Mechanical performance for all samples was determined using dimensional accuracy, tensile testing, and fractographic analysis to determine the effect of FDM-IM hybrid manufacturing on the final samples.The tensile test revealed that the IM batch had the best mechanical properties of all batches. In the study, the half-thickness series samples, particularly FC 75, demonstrated comparable tensile strength and stiffness to the the IM samples. FDM samples had better mechanical properties than half-length series samples but worse than half-thickness series batches. The outstanding mechanical performance of half-thickness series samples, as well as their highly customized character, have demonstrated their advantages here, and these will do more in some everyday products with greater choice for the consumer.The SEM revealed that half-length series samples were fractured in their joining surfaces, whereas half-thickness series samples were first broken in the joining areas and then cracks diffused to the brittle areas, resulting in the fracture. The tensile performance of the HM samples could be explained by the following statements: (1) the direction of the interface and tensile loading would significantly affect the tensile behavior ; (2) voids located in the joining of HM samples reduce mechanical strength; (3) crack inhibition during tensile loading was brought by the over-molded polymer in HM samples with joint configurations, and this provided FC 75 with the best tensile behavior; (4) the bonding condition varies the final mechanical performance (cube shape is recommended).However, the current study does not include any analysis of some HM parameters, such as injection molding pressure, nozzle temperature, and print speed in the FDM technique. Furthermore, more mechanical tests, such as impact and flexural tests, should be performed on these HM specimens in order to investigate mechanical performance metrics other than tensile. This catalog of PLA HM samples and their mechanical properties will aid in identifying industries and products that currently lack customization, and where this technique could be used to achieve a more complete integration of MC philosophies offered by this unique manufacturing strategy.


The purpose of this study was to look at the long-term effects of retinal phototoxicity after macular hole repair surgery using xenon endolight illumination and Brilliant blue G (BBG) dye.An elderly man in his late seventies underwent para plana vitrectomy with BBG dye to repair an idiopathic full-thickness macular hole (MH) in his right eye. Prior to macular hole surgery, his visual acuity in the right eye was 6/60, N24 at the time of presentation. The MH closed with type 1 closure immediately after surgery, but there was extensive damage to the outer retinal layers and retinal pigment epithelium (RPE) at the macula, resulting in a reduction in visual acuity to 2/60. We presumed that the combination of BBG and xenon light, is the probable reason of retinotoxicity in the current patient. There was a progressive increase in the area of retinal and RPE layer damage and choroidal thinning over a 4-year period.Due to combined BBG-induced dye and endoilluminator toxicity, a rare case of continuously progressing RPE layer damage with choroidal thinning over a long follow-up interval was described. Such long-term effects of BBG and endolight induced retinotoxicity have not been reported in the literature, to the best of our knowledge. A number of pre-, intra- and post-operative factors help to determine the final visual acuity following pars plana vitrectomy and brilliant blue G (BBG)-assisted internal limiting membrane (ILM) peeling for macular hole repair surgery , 2. The The current case report demonstrates the progressive changes in the outer retina, RPE and choroidal layers following the use of BBG over a 4-year follow-up duration after macular hole repair surgery.A 77-year-old male with a 15-year duration of type 2 diabetes mellitus was referred from the cataract department to the retina clinic for further management of a full-thickness macular hole in the right eye. In the past six months, he complained of decreased vision in the right eye. There was no history of ocular trauma in the past. On ocular examination, his right and left eye visual acuity were 6/60, N24 and 6/9, N6 respectively. In the right and left eyes, nuclear cataracts of grade 3 and grade 2 severity (Lens Opacities Classification System III) were observed, respectively. The examination of the right eye’s dilated retina revealed a circular, full-thickness retinal defect at the fovea and a taut posterior hyaloid membrane. In the right eye, a mild stage of non-proliferative diabetic retinopathy was observed. The fundus of the left eye was normal. A full-thickness idiopathic macular hole was diagnosed, and an optical coherence tomography (OCT) scan was advised. The OCT scan of the right eye obtained using the Spectralis  and passing through the center of the fovea revealed a full-thickness macular hole with the posterior hyaloid attached to its margins. Inner retinal fluid-filled cystoid spaces were observed. On the vertical radial line scan passing through the center of the fovea, the inner diameter, outer basal diameter, and height of the macular hole were 615 m, 1885 m, and 534 m, respectively. Normal reflectivity was observed in the RPE layer (Fig. 2 and the other of which was adjacent to the fovea and measured 4.51 mm2. On the fundus autofluorescence image, patches of RPE changes were observed (Fig. The patient visited the retina clinic after four years for undergoing cataract surgery on the opposite eye. At this visit, his right eye’s visual acuity remained unchanged at 2/60, N36. Fundus autofluorescence and OCT were performed. The OCT scan revealed a type 1 closure of the macular hole and damage to the outer retinal layers. The RPE layer was absent at the fovea and inferiorly, and the visibility of the underlying choroidal layers was enhanced (Fig. Over a 4-year follow-up period, this case report suggests progressive subfoveal choroidal thinning and damage to the retinal and RPE layers in an eye with combined BBG and endolight-induced retinal toxicity after macular hole repair surgery. In this case, the RPE changes at the macula progressed over time to develop complete RPE atrophy. Such long-term effects of BBG-induced retinal damage have not been described in the literature, to the best of our knowledge.The macular damage following macular hole repair surgery have been attributed to the combined mechanisms of phototoxicity by the endo illuminator light and BBG dye-induced changes in the outer retina and RPE layers , 5. HoweTo summarise, a rare case of continuously progressing RPE layer damage with choroidal thinning over a long follow-up interval was described due to combined BBG-induced dye and endoilluminator toxicity. To avoid any immediate phototoxic damage to the RPE cells and photoreceptors, intra-operative measures such as avoiding repeated ILM with BBG for long periods of time and avoiding use of high focal illumination close to the macula for longer periods of time should be considered. In addition, in patients undergoing vitreous surgery in conjunction with BBG dye use, yellow-tinted intraocular lenses or spectacles may be recommended post-surgery to block the maculotoxic blue light and thus prevent progressive phototoxic damage to the RPE cells.


The most dire consequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may not manifest during the initial infection but appear later and are disproportionately associated with compromised neural function. Central nervous system (CNS) complications often appear after the acute phase but persist and become prominent later and last for weeks to months . Indeed, while severe neurological diseases accompanying COVID-19 are rare during the acute phase , 4, withForemost to understanding SARS-CoV-2’s impact on the CNS is distinguishing which cells are impacted by this virus and whether these effects are direct or indirect. Given the complexity and diversity of the CNS symptoms, discerning whether health impacts arise from direct infection of the nervous system or indirect hypoxia, coagulopathy, and systemic inflammation that are able to trigger downstream neuroinflammatory responses is essential. Central to addressing these issues is determining which CNS populations are directly infected by the virus. To date, inferences of SARS-CoV-2 infection have largely come from examination of postmortem tissue. Neurotropism, referring to the ability of a virus to infect and replicate in neural tissue, of SARS-CoV-2 remains unclear. Recently, groups have utilized human pluripotent stem cell (hPSC)-based models as a convenient tool to investigate neurotropism of SARS-CoV-2 on various brain cell types and perform large-scale screening of therapeutic targets . InteresMany studies have suggested that SARS-CoV-2 relies on its obligate receptor to enter cells . The canWhat are the functional consequences of SARS-CoV-2 infection of cortical astrocytes? First of all, Andrews et al.  found thTo summarize, Andrews et al.  found thIf SARS-CoV-2 infection of brain astrocytes in COVID-19 patients is the proximal cause of the observed neurological dysfunctions, the question arises as to how to prevent viral entry preinfection, as well as the means to relieve the ensuing CNS symptoms postinfection. Possible strategies may include, but are not limited to, the blockage of viral infection or replication in pericytes or astrocytes and interventions to reduce neuroinflammation. To relieve the symptoms, approaches to augment metabolic energy to the brain as a means to restore brain homeostasis may also have promise. Although brain organoids do not yet fully replicate the complete cellular and regional diversity in the brain, they are a powerful tool to understand the mechanism of viral tropism and screen for treatment targets. Together, the combination of clinical observations, the in vitro use of human tissues, and organoid cultures will help us better understand COVID-19 pathology in humans and facilitate the development of treatments against SARS-CoV-2 infection.


The COVID-19 pandemic has required restrictive measures to mitigate transmission of the virus. Evidence has demonstrated increased generalized anxiety and depression among young adults due to the COVID-19 pandemic. However, minimal research has examined the longitudinal effect of COVID-19 over the course of time and its impact on anxiety and depression. Additionally, age and gender have been found to play a significant role on individuals’ mental health, with young adults and women particularly at risk.The aim of this study was to examine the impact of the COVID-19 pandemic on anxiety and depression upon admissions to treatment.This was an observational study that was completed longitudinally in which the grouping variable split the time interval into five equal groups for assessments over each period of time. A total of 112 young adults (aged 18-25 years) were recruited for the study. Participants completed assessments online through a Qualtrics link.Psychometric properties of the admission assessments were uniformly highly statistically significant. There was a significant difference in generalized anxiety between the group-1 and group-3 time intervals. No significant difference was found across the time intervals for depression. Differences in predicting the impact of the psychometrics scores were found with respect to gender. Only the ability to participate and the quality-of-life subfactor of the Functional Assessment of Chronic Illness Therapy (FACIT) assessment were significant.This study sought to understand the impact that COVID-19 has had on young adults seeking mental health services during the pandemic. Gender emerged as a clear significant factor contributing to increased anxiety in young adults seeking mental health services during the pandemic. These findings have critical importance to ensuring the potential treatment success rate of clients, while providing an overarching understanding of the impact of the pandemic and establishing clinical recommendations for the treatment of individuals who are seeking out treatment. On March 11, 2020, the World Health Organization declared the coronavirus SARS-CoV-2 COVID-19) outbreak as a pandemic. To mitigate transmission, restrictive measures were imposed across the United States, including the closure and modification of businesses and mandates for the use of face masks and vaccinations, which have had an immediate and unprecedented impact on psychological health  outbreak,11. Age Several studies have demonstrated an increased incidence of depression and anxiety during the COVID-19 pandemic among adults ,12-17. OPredictive factors related to the impact of COVID-19 stressors have also begun to emerge in the current literature. Age has been found to play a significant predictive role in the impact of COVID-19 on mental health. Evidence from several recent studies suggests that young adults are particularly at risk for experiencing mental health problems during the pandemic ,14,20-24Gender has also been found to play a significant predictive role in the relative impact of COVID-19 on mental health. Turna et al  examinedEmerging adulthood is a time of uncertainty and exploration, and the COVID-19 pandemic has had a compounding effect on this process, resulting in a significant decline in life satisfaction and a significant increase in mental health problems . The aimWe attempted to recruit 133 individuals for this study, 112 of whom ultimately volunteered. The 21 individuals who did not complete the study cited lack of interest for not participating. Among the 112 young adults that agreed to participate , 53 (47.3%) self-identified as female. Inclusion criteria were: (1) admitted to The Dorm, (2) provided informed consent, and (3) at least 18 years of age. Exclusion criteria were: (1) unfit to complete the survey due to medical or psychological constraint and (2) not fluent in the English language.The Dorm is an intensive outpatient program for young adults aged 18-35 years operating in New York City and Washington, DC. Empirically supported behavioral psychosocial methodologies are implemented to serve a variety of mental health illnesses and co-occurring disorders over the course of a phased treatment, which is typically a 1-year admittance (on average). In addition, holistic approaches such as exercise, yoga, Reiki, horticulture, community service, meditation, and mindfulness are an integral part of the treatment model. All clients participate in family programming, including weekly parent coaching, parent groups, and family groups. Clients work with both a therapist and a clinical coach, and participate in 3-30 hours a week of group therapy depending on the treatment phase. The GAD-7 is a 7-item, self-rated scale developed in correspondence with the Diagnostic and Statistical Manual and updated for the 5th Edition, which is used as a screening tool and severity indicator for generalized anxiety disorder. Reliability of this assessment demonstrated a Cronbach α of .92 .The PHQ-9 is a 9-item self-report scale that is used for screening, diagnosing, monitoring, and measuring the severity of depression  . ReliabiThe Barratt Impulsivity Scale (BIS-11) is a 4-point Likert scale from “rarely/never” (score=1) to “almost always/always” (score=4), where 4 indicates the most impulsive response ,35. HoweThe Functional Assessment of Chronic Illness Therapy (FACIT) Measurement System is a 27-item questionnaire that focuses on the domains of physical well-being, social/family well-being, emotional well-being, and functional well-being. Items are scored on a 5-point Likert scale, where participants endorse “not at all” to “very much” as the statement applies over the past 7 days .The Patient-Reported Outcomes Measurement Information System (PROMIS) Ability to Participate in Social Roles and Activities is an 8-item self-report questionnaire that assesses the perceived ability to perform one’s usual social roles and activities. Items are worded negatively in terms of perceived limitations and responses are reverse-coded, where 5 indicates “never” and 1 indicates “always”; thus, higher scores represent fewer limitations (better abilities). The item bank does not use a time frame  when assessing the ability to participate in social roles and activities.PROMIS General Self-Efficacy is a 10-item measure used to assess a person’s belief in their capacity to manage daily stressors and have control over meaningful events . This mePROMIS Friendship (Ages 18+) Fixed Form is an 8-item scale evaluating the perceptions of the respondent on their availability of friends or companions with whom to interact or affiliate with in the past month on a 5-point Likert scale .PROMIS Perceived Rejection (Ages 18+) is an 8-item scale evaluating how often people perceive others to be arguing/yelling at them and the perceived insensitivity over the past month on a 5-point Likert scale .The PROMIS Sleep-Related Impairment Questionnaire is an 8-item questionnaire that measures self-reported alertness, sleepiness, tiredness, and functional impairments associated with sleep problems during waking hours within the past 7 days . This meAll potential participants were consented either through a private meeting using Zoom virtual conference software or in person under standardized COVID-19 protocols. Consent forms were read orally, and any questions were answered by the investigator or research assistant before asking the participant to electronically sign their name on the tablet. By electronically signing their name, the participant was assigned a random 6-digit number generated by Qualtrics survey management tools. All consented individuals were tracked and followed through a password-protected Microsoft Access file. Each participant was asked to complete the assessments online through the Qualtrics link. The research assistant stayed with the participants and provided assistance as needed until the completion of the survey. The total length of time required to complete the survey did not exceed 40 minutes.The study was approved by Yale School of Medicine’s institutional review board prior to participant recruitment (IRES number 2000026514). This research was performed in alignment with the World Medical Association Declaration of Helsinki (2018).Data were analyzed using SPSS for Windows, version 26.0. The grouping variable split the time interval into five equal phases .χ2 tests. Strengths of association between variables were examined by the Pearson correlation coefficient and by linear regression analyses with generation of odds ratios and accompanying 95% CIs. All statistical tests were two-tailed with an α level of .05. Additionally, linear regression was used with gender  as the dependent variable to evaluate its impact on psychometric assessments.Mean group and overall differences were analyzed using one-way analysis of variance, whereas dichotomous variables  were analyzed using Fisher-exact or The demographic characteristics of the participants are summarized in F101=2.025, P=.04) to predict the impact of the psychometrics scores on gender. However, as noted in the coefficients subtable, only ability to participate (P=.002) and the quality-of-life subfactor of the FACIT (P=.02) assessments were significant with other values being not significant. In contrast to initial indications, depression and anxiety were not significant factors across gender at admission to this program.Additionally, the findings from the linear regression demonstrated differences across gender and the admission psychometrics. Significant findings were noted (Restrictive measures put in place to mitigate transmission of the coronavirus SARS-CoV-2 (COVID-19) have had an immediate and unprecedented impact on people’s psychological health and well-being, especially as they relate to the incidences and severity of depression and anxiety. This study sought to understand the impact that COVID-19 has had on young adults seeking mental health services at an outpatient program during the pandemic. The results indicated a significant increase in anxiety in young adults across the measured time periods of the pandemic, in particular between March and November of 2020, or phases 1-3 as defined in this study. This increase in anxiety coincides with a rising number of cases and COVID-19 becoming the third leading cause of death in the world, as death tolls were hitting their highest peak since 2015 (15.9% increase) . The AmeThe results also demonstrated that self-identified gender was a significant predictor of mental health. Female clients scored lower than their male counterparts on measures of “quality of life” and “ability to participate in daily activities,” both of which are subscores that are reliably indicative of broader mental well-being. This is consistent with the literature, which suggests that female-identified individuals are more significantly impacted by psychological distress than their male-identified counterparts -42. In pMental health conditions account for 16% of the global burden of disease and injury in people aged 10-19 years. Young adulthood is a critical time of interpersonal, intrapersonal, social, educational, and vocational development . ResearcThe findings emphasize the importance of secondary mental health concerns among young adults, especially female clients in this case. While this study demonstrated that young adults experienced a significant increase in anxiety during the pandemic and that gender was a significant factor in the compromised mental well-being of young adults, it will be necessary to continue to study the lasting and secondary impact of anxiety to prepare for and provide successful treatment to young adults. Anxiety is correlated with and can exacerbate secondary mental health concerns and symptoms such as substance use, PTSD, and suicidality. For instance, the prevalence of PTSD in 2485 home-quarantined college students was reported to be 2.7% . In a stThere are several limitations of this study. The longitudinal research design limits the generalizability of the findings due to not having a repeated measurement. Future research should evaluate the long-term impact of similar findings in a more systematic manner that includes a more rigorous research design methodology. Additionally, the sample size is relatively small, thus providing an initial understanding of the acuity of anxiety and depression in young adults seeking intensive outpatient programming during the pandemic. Another limitation is the lack of racial and socioeconomic diversity within the sample. A majority of the sample were white individuals. Additionally, research has demonstrated that marginalized populations such as non-white Hispanic and Asian populations experienced higher levels of the subjective perception of distress, worry, and fear . FurtherThis study attempted to understand the impact of COVID-19 on the admissions of an outpatient program for young adults. It was apparent that COVID-19 had a larger impact on the quality of life for women than for men. Additionally, it was apparent that there was a significant difference in generalized anxiety between the first and third intervals during the COVID-19 pandemic. These findings highlight the need to better understand the mental health impact of the COVID-19 pandemic.