Thumb carpometacarpal (CMC) joint arthritis is one of the most prevalent arthritic conditions commonly treated with trapeziectomy alone or trapeziectomy with ligament reconstruction and tendon interposition (LRTI). We evaluate the cost-effectiveness and value of perfect and sample information of trapeziectomy alone, LRTI, and non-operative treatment.A societal perspective decision tree was modeled. To understand the value of future research in comparing quality-of-life after trapeziectomy, LRTI, and non-operative management we characterized uncertainty by fitting distributions to EQ-5D utility data published from the United Kingdom hand surgery registry. We used Monte Carlo simulation for the probabilistic sensitivity analysis and to evaluate the value of perfect and sample information.Both trapeziectomy alone and LRTI were cost-effective compared to non-operative management . Trapeziectomy alone  was dominant compared to LRTI . However, probabilistic sensitivity analysis suggested there is a 12.5% chance LRTI may be preferred at a willingness-to-pay of $50,000/QALY. Sensitivity analysis revealed postoperative utilities are the most influential factors in determining cost-effectiveness. The value of perfect information was approximately $1,503/person. A study evaluating the quality-of-life of 1,000 patients in each arm undergoing trapeziectomy alone or LRTI could provide an expected $1,117 of information value. With approximately 40,000 CMC arthroplasties performed each year in the U.S., the annual value is close to $45 million.Trapeziectomy without LRTI appears to be the most cost-effective procedure in treating late-stage CMC arthritis and should be considered as first-line surgical treatment. There is substantial societal value in conducting additional research to better understand the relative quality-of-life improvements gained from these two common hand surgeries.The online version contains supplementary material available at 10.1186/s12962-023-00438-8. Carpometacarpal (CMC) arthritis of the thumb is the second most common degenerative condition of the hand  with a pPrior studies demonstrated similar outcomes after trapeziectomy and trapeziectomy with LRTI –13. NoneCost-effectiveness analysis may be helpful to policymakers as trapeziectomy with LRTI may be more expensive and have uncertain health benefits. To date, no cost-effectiveness study has compared the economic impact of choosing one procedure over the other and the value of further studies on this topic is unclear. Given that the evidence comparing the two procedures are insufficient, we aimed to contribute to the literature and assist decision-makers by providing an economic analysis perspective. Value of information (VOI) analysis is a systematic strategy to assess whether the cost of acquiring additional information via research to reduce decision uncertainty in cost-effectiveness analyses is worth the potential benefit . All cosWe asked whether trapeziectomy or trapeziectomy with LRTI is more cost-effective in treating late-stage CMC arthritis. We also informed the value of future studies and the information needed to decrease the uncertainty of our cost-effectiveness findings.A comprehensive literature review was conducted to identify complication rates after trapeziectomy and trapeziectomy with LRTI including unresolved pain, tendon rupture, and complex regional pain syndrome (CRPS) , 18–23. Before conducting a value of information (VOI) analysis, a model-based cost-effectiveness analysis was conducted to compare trapeziectomy alone, trapeziectomy with LRTI, and non-operative management. The model was built using a societal perspective in the context of United States healthcare. A decision tree was built with three treatment arms for end-stage CMC arthritis: conservative management, trapeziectomy, and trapeziectomy with LRTI. Respective complications over a lifetime horizon were considered given that CMC arthritis is a life-long condition Fig. . The basHealth utilities after non-operative management, trapeziectomy only, and LRTI were based on a retrospective study of a prospectively collected Euroqol 5 Dimension (EQ-5D) indices at baseline, 3, 6, and 12 months postoperatively in 1,456 patients and quantified the change in EQ-5D index from before to after surgery . UtilitiThe model included the following direct costs: physician fees, anesthesia fees, facility fees, hand therapy costs, and additional costs incurred by complications. Indirect costs included patients’ wages lost from recovery. Physician and facility fees were determined using Medicare reimbursement rates from Centers for Medicare Services . CurrentWe first conducted a cost-effectiveness analysis between trapeziectomy and trapeziectomy with LRTI. The incremental cost-effectiveness ratio (ICER) is calculated by dividing the difference in cost between two interventions by the difference in their effect. The primary analysis was conducted using the base case parameters denoted in Table The primary outcome of this study was expected value of sample information ($/person), which is the amount a decision maker is willing to pay to reduce uncertainty in a decision. Because value of information analysis is based on Bayesian decision theory, probabilistic decision modeling is required –38. TherThe main difference in the direct cost between trapeziectomy and trapeziectomy with LRTI stemmed from the difference in physician reimbursement fee and anesthesia time. For the base case scenario, LRTI had the highest total cost of $8,798 whereas the cost of trapeziectomy was slightly lower at $8,251. Conservative management was modeled to have $0 total expenditure because the frequency of clinic follow-up was assumed to be equivalent to postoperative clinic follow-up visits after either procedure. The average utility increase after trapeziectomy and trapeziectomy with LRTI based on the study by Lane et al. were similar . Corresponding lifetime quality-adjusted life years (QALYs) were higher for the trapeziectomy group (14.14) compared to the LRTI group (13.80), and the conservative management group (11.43). Compared to conservative management, both trapeziectomy and LRTI were cost-effective treatments with ICER of $3,045/QALY and $3,711/QALY respectively. When comparing trapeziectomy to LRTI, LRTI was dominated by trapeziectomy because trapeziectomy cost less compared to LRTI but accrued more QALYs.Literature reported long-term complication rates of either procedure were relatively low at 3 to 6%, not significantly affecting the overall results. Exploration of data with one-way sensitivity analyses revealed that utility was the most important parameter that determined cost-effectiveness  is willing to pay $1,503 per patient to eliminate all uncertainties from the current cost-effectiveness model when choosing between trapeziectomy and trapeziectomy with LRTI. It did not vary significantly when the willingness-to-pay was varied between $50,000 and $200,000 per QALY gained. The expected value of perfect partial information showed that the main uncertainty driving results was the uncertainty in utility gains for trapeziectomy and trapeziectomy with LRTI  can be estimated by the expected value of sample information (EVSI). Previous studies implemented value of information analysis to guide clinical studies –43. RandThe results of this study should be interpreted within the context of several limitations. The utilities used for this study were derived from the U.K. Hand Registry, which may have some differences when compared to the preferences of residents in the U.S. However, given that CMC arthritis generally affects older patients near retirement and both the U.S. and U.K. are developed countries with similar lifestyles, we assumed the utilities are translatable between the two countries. In addition, the utility data comes from a registry, not a randomized trial, so there could be systematic biases. Also, despite our assumption that declines in postoperative utility from long-term complications were captured in the mean utility values from a population registry, the utility decrement from long-term complications is still unclear. Indubitably, the healthcare cost in the U.S. is different when compared to other countries; therefore, our conclusions may not be generalizable to other countries with different healthcare costs. In addition, this is a model that represents the average cohort result within the U.S. healthcare system, which may not be generalizable to patients from other countries. The value of information results are likely an overestimate of the actual societal gain since real-world implementation following publication of study results is likely to be imperfect. Previous studies have demonstrated that imperfect implementation can largely impact the value of information findings . The preBased on current data, trapeziectomy appears to be the more cost-effective surgical intervention for CMC arthritis compared to trapeziectomy with LRTI; however, this result is highly contingent on postoperative utilities which have substantial uncertainty. Future studies should contrast postoperative utilities between patients with long-term complications after CMC arthroplasty to those without to ascertain the effects of complications on utilities. Clinical trials narrowing the uncertainties in postoperative utilities of trapeziectomy and trapeziectomy with LRTI can result in a net value of $200 to $300 million in 10 years outweighing the cost of conducting the study. These results suggest that randomized clinical trials comparing trapeziectomy and trapeziectomy with LRTI will likely result in net societal benefit.Below is the link to the electronic supplementary material.Supplementary Material 1


To identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR).with COVID-19 polymerase chain reaction (PCR) tests for training. We selected a model based on performance on our proxy dataset and applied it to instances without COVID-19 PCR tests. A physician reviewed a sample of these instances to validate the classifier.Statistical classifiers were trained on feature representations derived from unstructured text in patient EHRs. We used a proxy dataset of patients On the test split of the proxy dataset, our best classifier obtained 0.56 F1, 0.6 precision, and 0.52 recall scores for SARS-CoV2 positive cases. In an expert validation, the classifier correctly identified 97.6% (81/84) as COVID-19 positive and 97.8% (91/93) as not SARS-CoV2 positive. The classifier labeled an additional 960 cases as not having SARS-CoV2 lab tests in hospital, and only 177 of those cases had the ICD-10 code for COVID-19.Proxy dataset performance may be worse because these instances sometimes include discussion of pending lab tests. The most predictive features are meaningful and interpretable. The type of external test that was performed is rarely mentioned.COVID-19 cases that had testing done outside of the hospital can be reliably detected from the text in EHRs. Training on a proxy dataset was a suitable method for developing a highly performant classifier without labor-intensive labeling efforts. One approach to identifying a hospital cohort of patients with COVID-19 is to search for positive polymerase chain reaction (PCR) diagnostics within an electronic health record (EHR) laboratory results database. Using this approach, though, a patient who already carries a test-positive COVID-19 diagnosis prior to arrival would be missed, because they may not be retested. This is a problem for downstream use cases, both because more data will make our analyses more robust (and COVID-19 is less frequent in pediatric populations), and because patients with missing tests may be different in important ways . “Computable phenotypes” leveraging structured and/or unstructured EHR data are increasingly used to identify patient cohorts.We sought to augment structured EHR data with natural language processing (NLP)-derived information to identify a cohort of patient visits at a children’s hospital, where the patient had COVID-19 within the last 90 days, but who do not have a PCR test result in the EHR. Our computable case definition uses machine learning classifiers over unstructured clinical text in the EHR. Further, to avoid time-consuming and expensive manual chart review, we designed this classifier under the constraint of minimizing the need for labeled data.This is a retrospective study applying machine learning methods to EHR data from patients of all ages seen at a large academic pediatric teaching hospital. We queried the research data warehouse for patient visits after March 1, 2020 with an emergency department (ED) note or an admission note and requested all notes of either type for each such visit, as well as any associated SARS-CoV2 PCR tests. The study was approved by the Boston Children’s Hospital Institutional Review Board.timestamps , we used a simple procedure to add a 1-day buffer: for each visit, we defined the start date  as the earliest recorded day associated with any of its notes. We derived an end date  for the classification by adding 1 calendar day to the identified start date. Then we identified all ED notes and admission notes with timestamps on either the start date or the end date. This set of notes was concatenated into a single string that served as the input text for a single instance for the classifier.Each classifier instance consisted of all associated ED or admission notes for a patient visit with at least one of either type. Since our goal was to capture patient visits with a positive SARS-CoV2 test prior to presenting to the hospital, we limited the text data we considered to ED and admission notes generated from the first day of a visit. To account for slight variations in note labeled dataset consisted of instances in which a PCR test was performed by the laboratory, and where we had gold standard COVID-19+ or COVID-19− labels. We filtered SARS-CoV2 PCR tests to retain only those with timestamps occurring during a period starting 90 days before the start date and ending on the end date (BCH policy is to not retest if there is a positive PCR test recorded in the last 90 days). The unlabeled dataset consisted of instances in which no PCR test was performed at the hospital.Next, we divided the instances into 2 datasets based on whether they had associated SARS-CoV2 PCR test results from the hospital laboratory. The Test) and those without mentions of positive tests (NoTest). Because there was a low prevalence of Test cases, it would be prohibitively expensive to manually label enough data to learn from. To address this problem, we used a proxy training data approach, using the labeled dataset. We trained the classifier on the labeled dataset  and treated instances labeled COVID-19+ as equivalent to Test, and instances labeled COVID-19− as equivalent to NoTest. This approach essentially hypothesizes that positive tests would be discussed in the notes using similar language in the unlabeled and labeled datasets. We divided the labeled dataset into Proxy-Train, Proxy-Dev, and Proxy-Test for training the model, doing model selection/hyperparameter tuning, and final testing, respectively. This split was stratified so that the training and development data had the same prevalence.We defined 2 categories that we wanted to identify within the unlabeled dataset: those with mentions of positive tests  classifier with bag of words features, To validate the classifier, a clinician expert (AZ) reviewed 100 instances in the unlabeled cohort that our classifier identified as COVID-19 positive and 100 instances that our classifier identified as not COVID-19 positive. The clinician examined the notes for mention of the patient having COVID-19 in the last 90 days and mention of whether the patient had a SARS-CoV2 test completed. If a positive test was mentioned, the clinician identified whether the test type was specified in the note . Based on this information, the clinician determined whether the instance was correctly classified. We then applied the validated classifier to the remaining cases in our cohort without PCR tests performed, to estimate the number of missing cases, and tracked the counts of these cases over time. We also queried for ICD-10 codes associated with these instances, in order to quantify the number of COVID-19 positive cases identified by the classifier that could not have been captured by either PCR tests or ICD-10 codes.Evaluation metrics include true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN), which were used to compute recall , precision , and F1 score in the usual way.We chose F1 as our primary metric, as we were focused on identifying positive cases with a minimum number of FP. Since these data have a highly imbalanced distribution between positive and negative cases, obtaining high scores on negative cases is not difficult, and variations in performance are not particularly meaningful.There were 455 032 ED and admission notes and 104 986 COVID-19 PCR test results from 166 659 patients aged 0–78 and seen from December 31, 2015 to June 3, 2022. The size of the unlabeled and labeled cohorts, as well as the train and test splits, are shown in Of the notes reviewed for the validation, 84 of the 100 randomly selected COVID-19 classifier positive notes and 93 of the 100 randomly selected COVID-19 classifier negative notes were included. The majority of excluded notes were because they were found on manual review to actually have reference to a pending or completed COVID-19 PCR test at the hospital and were thus not appropriately in the unlabeled cohort. 81/84 COVID-19 classifier positive notes were correctly identified as having COVID-19 based on the content in the clinical text. Of the 81 TP, 7 instances specifically mentioned that an antigen test was done and 5 that a PCR test was done. The rest of the instances did not specifically mention a test or did not specify what type of test was performed. 91/93 COVID-19 classifier negative notes were correctly identified as not having COVID-19 based on the content in the clinical text. Based on this validation, the classifier had a sensitivity of 97.6%, specificity of 96.8%, positive predictive value of 96.4% and negative predictive value of 97.8% in detecting cases of COVID-19 without a PCR test performed in hospital, but where one performed outside the hospital was detected in the clinical notes.The highest-scored bag of words features are shown in After applying the classifier to the 53 640 instances of the unlabeled cohort, the COVID-19 classifier labeled 960 of the instances as positive COVID-19 cases, a potential 50.8% increase in cohort size. Of these 960 potential identified cases, only 177 were labeled with the ICD-10 code for COVID-19, U07.1, meaning the added value of the NLP classifier is 783 potential new COVID-19 cases. This represents a recall of 0.184 of ICD-10 relative to the NLP. Overall, ICD-10 codes for COVID-19 were recorded in 262 of the unlabeled instances. A relatively simple text-based classifier can have a large impact on identifying additional COVID-19 cases that did not have a PCR test performed in a tertiary care pediatric hospital context. Overall, the classifier identified an additional 960 likely COVID-19 positive instances without record of COVID-19 PCR tests performed in hospital in the structured data. This is in addition to the 1891 cases found using PCR test results alone. Further, these cases could not be easily obtained by other simple means such as billing codes, as ICD-10 coding only identified 18.4% of these cases. In addition, the ratio of additional COVID-19 cases detected by the classifier seems to show an increasing trend as the pandemic goes on, and therefore an increase in value of our classifier. The trend has a big jump in 2021, which is probably due to increased COVID-19 testing outside of the hospital setting.One interesting result is that our classifier had trouble exceeding an F1 score of 0.6 during development on the proxy corpus, but when validated it had a much higher performance. During model development, we occasionally did informal validations on very small samples of the unlabeled cohort, and found the classifier performed well on these small samples. This was what motivated us to proceed to validate the model that performed best on the held-out proxy data. This does raise the question of how the classifier could perform so well on our target data. One explanation is that in the proxy data, pending COVID-19 PCR tests are mentioned, so even if the patient eventually tests negative, there will be test-related terms in the text. In contrast, perhaps in the unlabeled cohort, tests are only mentioned in the context where the patient is reporting a positive outside test.Another interesting result is the relative simplicity of the best-performing model, compared to the more complex neural models which performed slightly worse on the Proxy-Dev set. This led to us selecting the simpler SVM model with bag of words features for deployment. One potential explanation is that the more complex models were overfitting to some of the noise in the labeled training data.The COVID-19 pandemic presents a rather distinctive scenario, prompting us to contemplate the extent of generalizability of the methodologies we have developed here. Specifically, few other health conditions have mandated hospital-based testing to the universal extent as executed for COVID-19. Additionally, it is uncommon for patients to regularly administer at-home tests for other ailments. Nevertheless, there may exist instances where certain tests are omitted if a patient undergoes testing at external facilities. In such situations, our proposed proxy training approach could emerge as a feasible alternative for training classifiers, especially when manual data labeling proves to be cost-prohibitive.,for COVID-19 with those incidentally with COVID-19 used positive lab tests for their cohort.In most EHR-based COVID-19 work, we found the starting point is typically positive PCR tests, with some studies also using ICD10 codes. For instance, two studies for predicting outcomes of patients admitted for COVID-19 used diagnosis codes and lab tests to define their cohort, Our study demonstrated the utility of a text-based classifier in identifying patients with COVID-19 that did not have a PCR test performed during a hospital visit. Additionally, this classifier was able to identify cases that were not captured by ICD-10 codes. By identifying these cases, the classifier has potential to be used to improve public health surveillance and cohort identification for research related to COVID-19.


Saccharomyces cerevisiae has great potential for application in biological research and biotechnological advancements, and the CRISPR-Cas9 system has been increasingly employed for these purposes. The CRISPR-Cas9 system enables the precise and simultaneous modification of any genomic region of the yeast to a desired sequence by altering only a 20-nucleotide sequence within the guide RNA expression constructs. However, the conventional CRISPR-Cas9 system has several limitations. In this review, we describe the methods that were developed to overcome these limitations using yeast cells. We focus on three types of developments: reducing the frequency of unintended editing to both non-target and target sequences in the genome, inducing desired changes in the epigenetic state of the target region, and challenging the expansion of the CRISPR-Cas9 system to edit genomes within intracellular organelles such as mitochondria. These developments using yeast cells to overcome the limitations of the CRISPR-Cas9 system are a key factor driving the advancement of the field of genome editing.Modification of the genome of the yeast  Saccharomyces cerevisiae traditionally allows for efficient alteration of its genomic DNA through homologous recombination (HR) by introducing donor DNA with short homology arms [The CRISPR-Cas9 system is a highly effective tool for genome editing due to its ability to precisely modify targeted genomic regions by altering only a 20-nucleotide sequence within the guide RNA (gRNA) . Althougogy arms . In partogy arms . The CRIogy arms , the conogy arms , and theogy arms .Streptococcus pyogenes Cas9 (SpCas9) and gRNA [However, the CRISPR-Cas9 system, which was originally developed using the Cas9 protein from and gRNA , has somand gRNA ,9. When and gRNA ,11 and cand gRNA . TherefoThe CRISPR-Cas9 system relies on the use of nucleases, which means that it can only modify genomic DNA sequences and alter gene products. As a result, cellular phenomena beyond genomic sequences are outside the scope of the CRISPR-Cas9 system. A representative example of such a phenomenon is epigenetics, which refers to changes in gene expression caused by a variety of factors independent of the DNA sequence. The well-known examples are chemical modifications of genomic DNA and histone proteins that affect chromatin condensation states and gene expression regulation . BecauseConventional CRISPR-Cas9 systems target DNA in the nucleus of eukaryotic cells. However, potential genome editing targets are also found in some cellular organelles such as mitochondria and chloroplasts. In particular, mitochondria have attracted significant attention as targets for genome editing due to their potential for treating human genetic diseases arising from mutations in mitochondrial DNA (mtDNA) . In receIn this review, we provide a brief overview of developments that aim to overcome the limitations of CRISPR-Cas9 system in yeast. Our focus is on the aforementioned challenges for off-target effects, editing of epigenetic information, and editing of mtDNA. These novel systems were developed by exploiting the ease of yeast genetics and the potency of its platform for genome editing tool development. These examples demonstrate that the development of CRISPR-Cas9 systems utilizing yeast cells to overcome these limitations will continue in the future.The CRISPR-Cas9 system can sometimes cause unintended sequence alterations at non-target sites, which are referred to as off-target effects. Off-target effects result from DNA breaks and the subsequent DNA repair processes. DNA breaks for off-target effects occur in two steps. First, the gRNA recruits the Cas9/gRNA complex to sequences that are similar, but not identical, to the target sequence. Second, Cas9 nuclease breaks DNA despite the mismatch between the gRNA and genomic DNA . The binTherefore, the potential for nuclease activity at non-target sites of the Cas9/gRNA complex can be minimized through the judicious design of the target sequences of the gRNA, using computational methods to rank the target sequences based on mismatched properties within the whole-genomic sequences of the target cells . In addiIn addition, DSBs can cause unintended mutations in target sequences, even when the Cas9/gRNA complex is accurately targeted to the desired sequence and HDR is performed using donor DNA ,34 Figu. GenomicTherefore, to improve the accuracy of the CRISPR-Cas9 system by minimizing off-target effects, systems that do not generate DSBs are desirable. To this end, researchers developed a modified version of SpCas9 known as nickase Cas9 (nCas9). The nCas9 only introduces single-strand breaks (nicks) in the DNA of the target region, owing to a mutation in its nuclease domain  . TherefoAlthough the double-nicking method effectively reduces off-target effects in non-target regions, it still breaks both strands of the DNA in the target region, which can result in unwanted mutations in the target regions when the donor DNA lacks sufficient nucleotide alteration in its target and PAM sequences . To addrRecently, engineering efforts focused not only on the mode of DNA strand breaks but also on considering the cleavage kinetics of Cas9 nuclease activity. To improve the kinetics for reducing off-target effects, a broad range of Cas9 variants was explored, and high-fidelity Cas9 variants were shown in mammalian cells; one of the Cas9 variants was validated in yeast . The strCAN1 to select strains in which genome editing inserted a stop codon into the sequence  in the medium [CAN1 enables their growth in canavanine-containing medium. Sequencing the CAN1 gene in colonies grown on canavanine-containing medium can efficiently evaluate unwanted mutations in target regions [For the development of the yeast CRISPR-Cas9 system, efficient methods to evaluate the precision of genome editing are needed. The conspicuous cellular phenotypes of yeasts are useful for these methods due to readily available selective marker genes ,33,43 F. For exasequence a. CAN1 ee medium . TherefoTRP1 and ADE2 genes. The target sequences were flanked by homology arms; thus, these gene sequences were restored via single-strand annealing (SSA) between these arms only when DSBs were induced at these sites [TRP1 results in tryptophan auxotrophy. On the other hand, disrupting the ADE2 gene leads to the formation of red-colored colonies, as the ADE2 gene encodes an enzyme that converts a red-colored intermediate metabolite to a white one [TRP1 is expressed, only red colonies growing on tryptophan-deficient medium shows successful editing without off-target nuclease activity on the target sequence inserted in ADE2. As the opportunity for nuclease activity in ADE2 increases with repeated strokes of the colonies, a low rate of white colonies with repeated strokes indicates more rigorous specificity of Cas9 variants [Yeast-selective markers have also been employed for high-throughput screening of high-fidelity Cas9 variants . To valise sites   promoter. The fused enzyme was expressed within a uracil-auxotrophic yeast strain containing a complete native URA3 gene expression cassette in the heterochromatin state of the subtelomere region [URA3 region. These findings indicate that the expression of dCas9-Gcn5 along with gRNA targeting the heterochromatic URA3 region improved cell growth in uracil-deficient medium compared to the expression of dCas9-Gcn5 alone without gRNA [In a recent study, yeast cells were used to investigate the potential of the HAT-dCas9 fusion enzyme to shift a heterochromatin state to a euchromatin state  Figure . The epie region , therebyout gRNA . TherefoFurthermore, euchromatic DNA was collected from yeast cells, and chromatic condensation levels were evaluated via optimized FAIRE-qPCR  analysis to determine whether the euchromatin state was induced in the targeted heterochromatin region ,87 Figub. FAIRE-ADA2 results in the loss of gRNA-dependent improvement in chromatin loosening [The study also shows that the deletion of oosening . Ada2 isoosening . This imMitochondria are organelles that are found in numerous eukaryotic cells. They participate in various intracellular processes, including ATP synthesis via respiratory oxidative phosphorylation , the speDespite these demands, the genome editing of mtDNA, particularly using the CRISPR-Cas9 system, remains highly challenging because of the difficulties in transporting gRNA and donor DNA into the mitochondria  Figure . AlthougFor the mitochondrial transportation of gRNA, the systems of using intracellular mechanisms such as the aforementioned improvements in the CRISPR-Cas9 systems have not been well-established. In mammalian cells, there have been controversial reports regarding the existence of sufficient mitochondrial RNA import activity for the CRISPR-Cas9 system . The redFurthermore, even if mtDNA modification occurs, the multi-copy nature of mtDNA in a cell makes it challenging to construct homozygous genotypes and alter cellular phenotypes. Additionally, the degradation of mtDNA resulting from DSBs is a barrier to mtDNA genome editing. Edited mtDNAs are degraded and replaced by wild-type mtDNA through its replication because mtDNAs are essential in mammalian cells; thus, edited mtDNA molecules in small fractions are rapidly depleted in cells . ConventS. cerevisiae can be used as an ideal model organism for investigating mtDNA dysfunction. In addition to its genetic tractability, yeast can grow via anaerobic fermentation without requiring any mitochondrial function. This feature makes it possible to study mtDNA genotypes that are lethal in other organisms [COX4 at the N-terminus (MTS-Cas9) was expressed on a centromeric plasmid with a strong constitutive TDH3 (GPD) promoter. When gRNA targeting ATP8 encoded by mtDNA was expressed on a multi-copy plasmid with the SNR52 promoter, the formation rate of mitochondria-depleted cells that lost viability in non-fermentative glycerol medium was significantly increased in comparison to that of control cells without gRNA expression [LysCUU [Gln [rganisms . Thus, yrganisms . A recenrganisms . To effipression . This repression . The tra [LysCUU  and tRNACUU [Gln  to the mCUU [Gln ,110 and CUU [Gln . BecauseCUU [Gln , the abiConversely, there are no physiological mechanisms for the mitochondrial transportation of DNA molecules that were introduced into the cytosol for genome editing. However, yeast mitochondria possess a system of HR activated by DSBs ; thus, tThe CRISPR-Cas9 system is continuously advancing genome editing by developing technologies with various characteristics due to its ability to accept the fusion of other elements into Cas9 ,117 and In the future, improved CRISPR-Cas9 methods that address the current limitations of yeasts will be integrated into cutting-edge genome editing applications ,6,7 and


Due to the coadministration of high-dose steroids and the prolonged duration of mechanical ventilation, coinfection with multidrug-resistant organisms (MDROs) among coronavirus disease 2019 (COVID-19) patients is common in critical care patients, with a prolonged length of stay in critical care units. Furthermore, the rush of creating of negative-pressure units during the COVID-19 pandemic may have resulted in some outbreaks associated with molds such as Aspergillus and Zygomyces. In countries where they are prevalent, emerging fungal outbreaks, such as Candida auris outbreaks, have been reported. The control of MDROs among COVID-19 patients is also difficult, given the requirement for airborne plus contact isolation among these patients and the difficulty in wearing and changing personal protective equipment (PPE) in a critical care unit. The control of invasive fungal infections (IFIs) also requires practice of proper infection prevention and control (IPC) during construction and renovation. In this commentary, we provide evidence of how environment contribute to HAIs in Asia. We discuss how to properly disinfect and decontaminate healthcare environment, and we provide suggestions on how to improve air quality and water quality during COVID-19 pandemics.Environmental cleaning and disinfection practices have been shown to reduce microorganism bioburden in the healthcare environment. Also, the constant use of gloves and gowns during a severe acute respiratory syndrome (SARS) outbreak led to an increase in transmission of MDROs, particularly methicillin-resistant Staphylococcus aureus. Outbreaks of MDROs can be more difficult to control in middle- and lower-income countries in Asia where infrastructure may not be adequate . During the COVID-19 pandemic, airborne transmission of some MDR gram-negative pathogens, such as Acinetobacter baumannii, has also been reported. Evidence of airborne transmission of MDR-A. baumannii supports the view that placing patients in cohorts in enclosed cubicles with partitions and a closed door is preferred if single rooms are not available. Such conditions require a practical strategy to control MDROs in resource-limited settings. Additional strategies are also needed in situations that do not allow PPE changing easily between cases in the cohort areas. These strategies must feature multimodal approaches that include risk stratification for index patients that may potentially harbor MDROs with isolation in single rooms, an antimicrobial stewardship program for COVID-19 patients, and policies to discontinue COVID-19 precautions as well as a policy to perform robust terminal environmental disinfection. Such strategies, together with full compliance with infection prevention measures, will help limit the transmission of MDROs in COVID-19 cohort areas in critical care units. Antimicrobial stewardship strategies, including inappropriate antibiotic use featuring the combination of procalcitonin and clinical pulmonary infection score (CPIS), have been associated with successfully reducing inappropriate antibiotic use among severely to critically ill COVID-19 patients, for multidrug-resistant organisms, and for invasive fungal infections during the intervention.It is well recognized that MDROs are selected by use of broad-spectrum antibiotics and often have an environmental reservoir that can facilitate rapid spread in critical care units if appropriate interventions are not introduced. which pose serious threats to immunocompromised hosts, including high-risk patients . Implementing an infection control risk assessment (ICRA) for renovation and construction decreases the risk of healthcare-associated fungal outbreaks. However, ICRAs are not typically performed in developing countries, leading to healthcare-associated fungal outbreaks in settings such as ICUs, hematologic wards, and renal transplant units. Although uncommon, healthcare-associated fungal outbreaks must be suspected when 2 or more cases occur over a short period. Early outbreak investigation and identification of the source are likely to play a key role in limiting the spread of healthcare-associated fungal outbreaks. Such strategies, together with a full compliance with recommendations from a multidisciplinary coordination team, will help limit the construction-related fungal infection especially in a COVID-19 ICU.Likewise, hospital construction and renovations activities are never ending, and they are the main cause of healthcare-associated fungal outbreaks including Aspergillus and Zygomycetes, Contaminated elevator buttons were hypothesized via indirect transmission to lead to outbreaks in a mall and in an apartment building. SARS-CoV-2 has been demonstrated in multiple studies to survive on a variety of surfaces  for minutes to days; survival was better at low temperature and humidity. SARS-CoV-2 RNA has been detected in a mean of ∼15% of hospital room samples. Despite the frequent finding of SARS-CoV-2 contamination, viable virus was not commonly detected. SARS-CoV-2 is susceptible to alcohol hand disinfectant agents , which are rapidly effective  in eliminating >3-log10 SARS-CoV-2 from skin. Povidone-iodine (0.23%-7.5%) is also effective in inactivating SARS-CoV-2 strains in 15 seconds. SARS-CoV-2 is inactivated by sodium hypochlorite and by hydrogen peroxide (0.5%) in 1 minute. SARS-CoV-2 is also inactivated by quaternary ammonium compounds in 30 seconds to several minutes. Disinfectant cloths are also effective in eliminating SARS-CoV-2 in 2 minutes. The environmental survival and germicide susceptibilities of SARS-CoV-2, Middle Eastern respiratory syndrome coronavirus, and Ebola virus have been reviewed. All 3 viruses may survive for hours on environmental surfaces, and because all are enveloped viruses, they are susceptible to standard antiseptics  and disinfectants . Specific germicides that have demonstrated effectiveness against coronavirus are listed by the US Environmental Protection Agency and against Ebola.SARS-CoV-2 can survive on surfaces for minutes to hours, but contaminated surfaces have only rarely been linked to transmission.Candida auris and monkeypox (mpox) may require further understanding on principle of disinfection in healthcare environment as well as more evidence-based on the proper disinfectant. C. auris is most commonly transmitted via direct contact (patient to patient) and indirect contact . Widespread contamination of the surface environment has been reported. In laboratory tests, C auris has been demonstrated to persist for days on environmental surfaces and linens. C. auris is susceptible to sterilants and chemicals used for high-level disinfectants. Effective low-level disinfectants  have included 70% isopropyl alcohol, 1:10 dilution of 5.25% sodium hypochlorite, 0.5% to 1.4% hydrogen, 0.5% quaternary ammonium compound, and 55% isopropyl alcohol and other products. Alcohols are the antiseptic of choice for hand hygiene based on in vitro testing. The US EPA has published a list of germicides active against C. auris. For mpox, environmental contamination in household or hospital rooms occupied by patients with mpox as assessed by PCR was common, but demonstration of viable virus was less common. There are no published studies of the survival of mpox virus, but based on studies of other pox viruses , prolonged survival is expected . Similarly, there are no published studies on the susceptibility of mpox virus to disinfectants, but based on studies of other pox viruses, mpox virus is likely susceptible to sodium hypochlorite (0.636% active chlorine), quaternary ammonium combined with chlorhexidine, and quaternary ammonium combined with glutaraldehyde. Ethanol (50%–70%) and isopropyl (40%–50%) are likely effective with a 1-minute contact time.Selecting appropriate disinfectant is key to effective healthcare environment disinfection. Some emerging infectious diseases such as  highlighted that attention needs to be given to adequate air flow and correct directional flow. The World Health Organization guidance on addressing the inadequacy was a timely help for many during the crisis. There is an urgent call for all healthcare institutions to review the ventilation in all areas, even for daily patient care, because it is fundamental to ensure adequate ventilation to prevent healthcare-associated infections associated with common respiratory viral pathogens such as influenza, respiratory syncytial virus, etc. This review work is best conducted in consultation with the facility engineers and IPC professionals as a team. Inappropriate design of the healthcare facility may often be a root cause for inadequate ventilation. Although there may be specifications for the design in some developed countries, these may be absent in low- and middle-income countries. Nevertheless, it would be expected that a healthcare facility be designed to facilitate safe patient care. The involvement of IPC from the early phase of planning until end of the project is recommended. Provision for more negative-pressure facilities may be made as part of pandemic preparedness. These may either be the installation of portable negative pressure anterooms to existing rooms or the construction of new negative-pressure isolation rooms built according to specifications. Adjunct measures may be considered such as the use of UV-C technology in room decontamination.One of the lessons learned during the COVID-19 pandemic was the important role ventilation plays in the transmission of the virus in healthcare setting. The outbreaks reported among staff, patients, and visitors to the healthcare facility Water stagnation has its associated risks of Legionella growth in water system as reported by some during building closure during the COVID-19 pandemic or renovation. A water management program with the involvement of a multidisciplinary team including the IP professional, is an essential component in the IPC program to address this. The provision of safe water for drinking and keeping biofilms away from the water plumbing system are issues that this program can focus on as a start.Reports on gram-negative bacterial outbreaks in association with biofilms in sinks and drains have alerted us to potential reservoirs in the patient care areas that demand closer attention to use and maintenance of the water system in healthcare facilities.In conclusion, ensuring a clean and hygienic environment in the healthcare facility is one of the core components of an IPC program. This goes beyond environmental cleaning and requires the IPC professional to collaborate with environmental services and facility management colleagues in achieving good indoor air and water quality. The Asia Pacific Society of Infection Control (APSIC) will work to strengthen the evidence on healthcare environmental cleaning and disinfection and will develop strategies to disseminate best practices throughout Asia Pacific in the years to come.


This study aims to examine the impact of the direct and indirect mixing techniques on the dispersion and homogeneity of magnesium oxide (MgO) and silver (Ag) nanoparticles (NPs) mixed with polymethylmethacrylate (PMMA). NPs were mixed with PMMA powder directly (non-ethanol-assisted) and indirectly (ethanol-assisted) with the aid of ethanol as solvent. X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscope (SEM) were used to evaluate the dispersion and homogeneity of MgO and Ag NPs within the PMMA-NPs nanocomposite matrix. Prepared discs of PMMA-MgO and PMMA-Ag nanocomposite were analyzed for dispersion and agglomeration by Stereo microscope. XRD showed that the average crystallite size of NPs within PMMA-NP nanocomposite powder was smaller in the case of ethanol-assisted mixing compared to non-ethanol-assisted mixing. Furthermore, EDX and SEM revealed good dispersion and homogeneity of both NPs on PMMA particles with ethanol-assisted mixing compared to the non-ethanol-assisted one. Again, the PMMA-MgO and PMMA-Ag nanocomposite discs were found to have better dispersion and no agglomeration with ethanol-assisted mixing when compared to the non-ethanol-assisted mixing technique. Ethanol-assisted mixing of MgO and Ag NPs with PMMA powder obtained better dispersion, better homogeneity, and no agglomeration of NPs within the PMMA-NP matrix. Polymers composites play an important role in dentistry, with a wide range of clinical implementations in both restorative and prosthodontics dentistry . PolymerStructural modifications to the composition of PMMA by addition of fillers might enhance the resin’s mechanical  and antiThe effects of size, shape, and volume fraction of adding NPs to nanocomposite polymers on the crystallization were examined in molecular dynamics simulations. It was observed that regardless of the shape, decreasing the size of particles at the same volume fraction results in decreased final crystallinity. On the other hand, using the same particle size increases the volume fraction, resulting in a decrease in the crystal growth rate and final crystallinity. The shape of NPs has shown to have an impact on the growth rate and crystallinity, for example, using cubic particles causes a higher growth rate and crystallinity compared to spherical particles, solely due to flat surfaces of cubic NPs . This isControlling the NPs’ dispersion in the polymer matrix is the most significant challenge in nanocomposite production due to the high probability of aggregation of the nanoparticles . AdditioSeveral studies were conducted in which NPs were mixed with PMMA without examining the dispersion and agglomeration of NPs ,19,20. OClear orthocryl (cold cure acrylic) PMMA  was used in this study (DENTAURUM). In addition, Silver NP , magnesium oxide NP , and absolute ethanol  were used.w/w) in order to obtain 5% PMMA-MgO and PMMA-Ag nanocomposite Using sensitive balance, 0.736 gm of each of the NPs was dissolved in 10 mL absolute ethanol and then sonicated for 5 min in order to deagglomerate the NPs aggregation that might happen during transport and storage ,24 by UPB.Ethanol-assisted mixing of NP with PMMA powder (with ethanol)As mentioned above, the sonicated NPs were dissolved in absolute ethanol and added to the PMMA powder, resulting in the formation of a viscous solution. This was followed by magnetic stirring at 400 rpm for 2 h at 50 °C until the ethanol had evaporated, and the mixed powder was then ground with a mortar and pestle. Afterwards, to ensure the evaporation of the solvent, the powder was placed in the oven at 50 °C for 4 h .Orthocryl (cold cure acrylic) PMMA powder was used as control after mixing in both non-ethanol-assisted and ethanol-assisted techniques with absolute ethanol, which was followed by magnetic stirring at 400 rpm for 2 h at 50 °C until the ethanol evaporated.Five disc samples  for each study group were prepared using a stainless steel mold sandwiched between two glass pads . PMMA poAn X-ray diffraction machine was used to test the coating of NPs on the surface of PMMA particles and to determine the crystallite size of PMMA-NP. The required quantities of PMMA-NP nanocomposite powder for each study group  were placed in 10 mL test tubes and desiccated in the oven at 50 °C, and then the samples were examined by XRD machine.XRD measurements were carried out by using a PAN analytical X’ Pert PRO (Cu Kα = 1.5406 Å). The scanning rate was 1°/min in the 2θ range from 5° to 80°.EDX was used to scan PMMA-NP nanocomposite powder to identify the elemental composition of NPs over the surface of PMMA particles. In addition, it was used to detect the homogeneity and agglomeration of NP over PMMA particles.To assess the dispersion and homogeneity, PMMA-NP nanocomposite powder and discs of each study group were tested using a scanning electron microscope .Discs with 0.2 mm thickness were evaluated for each study group under stereo microscope at 4× magnification power to assess dispersion of NPs through PMMA matrix.The results of the XRD test for each study group are shown in D is the average crystallite size (diameter), By studying the full width at half maximum (FWHM) value of the XRD pattern, the crystallite size of NPs in PMMA-NP nanocomposite powder was calculated by using the following Debye–Scherrer equation:termined . It is cEDX spectrum photos illustrated that PMMA-MgO and PMMA-Ag nanocomposite powders mixed in ethanol-assisted method have better attachment and good dispersion over the surface of PMMA particles, with less NPs agglomeration compared with the non-ethanol-assisted mixing method, i.e., in the ethanol-assisted method, NPs followed the shape of PMMA particles, and the color shade of NPs was evenly dispersed without the dark- and light-colored areas on PMMA particles as present in non-ethanol-assisted method, which indicates the state of NPs agglomeration .SEM analysis revealed that the average particle size of the PMMA powder was approximately 50 µm, within a range of 10 µm to 100 µm. Adding ethanol resulted in no difference in control group . AdditioSEM of 5% PMMA-MgO nanocomposite  and 5% PRegarding the discs of PMMA-MgO and PMMA-Ag nanocomposite, the good dispersion and homogeneity of NPs in the matrix of PMMA was again noticed, with the least NPs agglomeration from ethanol-assisted mixing compared to the non-ethanol-assisted mixing ; this caAgain, the stereo microscope analysis of MgO-PMMA and Ag-PMMA nanocomposite discs (0.2 mm thickness) showed that the ethanol-assisted mixing of NPs with PMMA resulted in better dispersion and no agglomeration of NPs within the PMMA matrix when compared to non-ethanol-assisted mixing .Many types of polymers are reinforced with NPs  to enhanPMMA is among the most commonly used dental materials. It is mainly used for the fabrication of acrylic denture bases including removable orthodontic appliances, with successful results attributed to its optimal esthetics and biocompatibility. However, its poor mechanical properties, particularly its low tensile and impact strength, are among its major drawbacks, potentially leading to easy fracture or cracking . This isEthanol has been commonly used as stabilizer because it is a polar solvent with the OH group that reacts with ions; thus, dispersion in ethanol lasts longer. Furthermore, dispersing PMMA in ethanol provides more surface area for NPs to be absorbed by the PMMA particles. It is worth mentioning that in this technique, the dispersion of inorganic NPs on PMMA is based on the electrostatic interactions that occur between the negatively charged PMMA particles and the positively charged NPs (MgO and Ag). This technique, called noncovalent attachment, can be carried out via the dispersion of NPs and the polymer in a media (ethanol in the current study) ,32. HoweIn the present research, the two common methods of mixing of NPs with PMMA powder were used: non-ethanol-assisted mixing and stirring  and ethaShahabi et al. evaluated the effects of the non-ethanol-assisted mixing of chitosan NPs with PMMA on mechanical properties, and it was found that the flexural strength, compressive strength, microhardness, and impact strength of the PMMA–chitosan nanocomposite decreased in comparison to the control group . This coThe introduction of nanoparticles in ethanol at low temperatures in order to obtain a nanocomposite plays an important role in deagglomeration and dispersion of the NPs . The betThe examination of other NPs, such as calcium β-pyrophosphate and zinc oxide, by non-ethanol-assisted mixing with PMMA and SEM revealed that the non-ethanol-assisted mixing of NPs with PMMA is associated with non-homogeneity and poorly dispersed NPs over PMMA ,42. ThesThis study only examined the effect of ethanol on the mixing of MgO and Ag with PMMA, and further studies are required to check the role of ethanol on mixing other NPs with PMMA and other polymers. Further analysis such as transmission electron microscopy is recommended to compare the size and morphological changes of NPs before and after mixing with PMMA. This study has the limitations that only two NPs were examined and that the mechanical properties have not yet been examined in relation to each corresponding technique. Nonetheless, the results of this study provide valuable pointers for consideration in the future.The results of this study suggest that using ethanol as a solvent for mixing MgO and Ag NPs with PMMA microparticles obtains better dispersion and homogeneity, which ultimately might result in better mechanical properties of modified acrylic resin (PMMA-NP nanocomposite). Further studies are recommended to test the mixing efficacy of these NPs with PMMA in a melting state and nanoscale.


This paper presents a benchmark analysis of NVIDIA Jetson platforms when operating deep learning-based 3D object detection frameworks. Three-dimensional (3D) object detection could be highly beneficial for the autonomous navigation of robotic platforms, such as autonomous vehicles, robots, and drones. Since the function provides one-shot inference that extracts 3D positions with depth information and the heading direction of neighboring objects, robots can generate a reliable path to navigate without collision. To enable the smooth functioning of 3D object detection, several approaches have been developed to build detectors using deep learning for fast and accurate inference. In this paper, we investigate 3D object detectors and analyze their performance on the NVIDIA Jetson series that contain an onboard graphical processing unit (GPU) for deep learning computation. Since robotic platforms often require real-time control to avoid dynamic obstacles, onboard processing with a built-in computer is an emerging trend. The Jetson series satisfies such requirements with a compact board size and suitable computational performance for autonomous navigation. However, a proper benchmark that analyzes the Jetson for a computationally expensive task, such as point cloud processing, has not yet been extensively studied. In order to examine the Jetson series for such expensive tasks, we tested the performance of all commercially available boards  with state-of-the-art 3D object detectors. We also evaluated the effect of the TensorRT library to optimize a deep learning model for faster inference and lower resource utilization on the Jetson platforms. We present benchmark results in terms of three metrics, including detection accuracy, frame per second (FPS), and resource usage with power consumption. From the experiments, we observe that all Jetson boards, on average, consume over 80% of GPU resources. Moreover, TensorRT could remarkably increase inference speed  and reduce the central processing unit (CPU) and memory consumption in half. By analyzing such metrics in detail, we establish research foundations on edge device-based 3D object detection for the efficient operation of various robotic applications. Deep learning-based 3D object detection is essential for mobile platforms, such as autonomous vehicles, robots, and drones to measure the position and heading direction of neighboring objects quickly and accurately . To enabRecent open-source frameworks for deep learning-based 3D object detection have shown promising performance in enabling autonomous driving but require expensive computations. Two-stage detectors ,5 achievThere have been benchmark studies that measure the performance of Jetson platforms. Some studies ,12,13 teIn this paper, we present a benchmark analysis of 3D object detection frameworks on NVIDIA Jetson platforms. This study extensively examines the performance of the Jetson when operating open-source 3D object detectors. The benchmarking concept of this paper is shown in This paper presents extensive benchmarks of the NVIDIA Jetson platforms for a computationally expensive task, which is deep learning-based 3D object detection. Unlike image-based 2D object detection, which mostly depends on GPU resources, the 3D detection task presents a challenge for resource utilization since point cloud processing occupies considerable CPU and memory usage, and neural network processing highly relies on GPU-based computation. Thus, we conducted a thorough analysis of the performance and utilization to provide a guideline for users in choosing an appropriate platform and 3D object detector.We evaluated various 3D object detection frameworks with all commercially available Jetson platforms, including the lightest platform (Nano) and the most recent one (NX). We provide various metrics-based results, such as detection accuracy, FPS, and resource usage.In addition, we evaluated the effect of the TRT library  on optimThe following are the contributions of this work:The goal of our study is to benchmark NVIDIA Jetson platforms for the deep learning-based 3D object detection task. We introduce the following two research trends: (i) studies on 3D object detection; (ii) benchmark tests on the Jetson platforms.Recently, there have been studies focusing on improving the quality of images for object detection ,17. HoweThe single-stage approach, which operates region proposals and classification simultaneously, could be a suitable solution for robotic platforms with resource-constrained devices since it usually runs faster owing to its simpler procedure. In , the autTo enhance the detection accuracy of the single-stage method, some algorithms add post-processing not only to the feature extraction phase. In , the autJetson platforms have been deployed to robotic tasks owing to their deep learning compatibility . The algIn addition, there have been benchmark studies on Jetsons for robotic applications. For instance, a benchmark result evaluates visual–inertial odometry algorithms on drones equipped with Jetson boards . This stIn this work, we benchmarked various deep learning-based 3D object detectors on Jetson boards. Point cloud data processing for object detection usually demands time-consuming resource consumption due to the massive data sizes. In addition, consecutive post-processing for constructing 3D bounding boxes of detected objects poses a significant computational challenge. We aim to analyze the performance of such complex point cloud processing on Jetson boards. Additionally, we evaluate the effect of the TRT library on enabling faster inference with fewer resource usage by optimizing the neural network models of the 3D detection frameworks. We provide experimental results in terms of detection accuracy, FPS, and computational resource usage with power consumption.This section presents the preliminaries of our study. First, we describe the specifications of the four Jetson platforms. Additionally, we compare the inference performances, measured in terms of average precision (AP), of the 3D object detection frameworks that are the targets of our benchmark.Nano: This single board offers the opportunity to run simple deep-learning models by using a built-in GPU. However, it may suffer from the lack of memory resources when used for large-scale deep learning models.TX2: Jetson TX2 has been widely used for deep learning-based robotic applications owing to its better resources than those of Nano.Xavier NX: NX provides sufficient resources for deploying large-scale deep learning models. Moreover, NX is beneficial for various robotic systems with small payloads due to its light size and weight.AGX Xavier: This includes the most powerful hardware among the Jetson series. It could be suitable for industrial robots and drones that require many software functions for a mission.There are four commercially available Jetson series, including Nano, TX2, NX, and AGX. Among those boards, Nano and AGX contain the lowest computing resources and the highest resources, respectively. All the frameworks compared in this study employ point cloud data collected by a LiDAR sensor to detect the 3D positions of objects using a deep learning-based detection scheme. In this section, we present extensive benchmark results of the Jetson platforms that run deep learning-based 3D object detectors. During the benchmark, we measure the following three metrics: (i) FPS for computational complexity measurement; (ii) resource usage, including CPU, memory, and GPU; (iii) power consumption.To thoroughly measure the performance of the detectors, we set the Jetson platforms to maximum CPU usage mode, which uses all the CPU cores with the maximum frequency and consumes maximum power . In order to check the effect of an optimization scheme, we select an open-source framework (CenterPoint [As described in terPoint ) that moterPoint  using TRterPoint . We alsoterPoint  that is First, we check the feasibility of the compared frameworks to determine whether each Jetson device can handle such complex algorithms. As shown in As identified in The end-to-end measurement results show that the AGX platform obtains the fastest inference  tend to consume fewer CPU and more memory resources compared to the tiny versions. Such frameworks demand more memory and GPU resources due to the larger size of neural network models. We notice that Complex-YOLOv4-tiny is loosely bounded and shows higher CPU consumption than GPU usage compared to Complex-YOLOv3-tiny, which continuously requires high GPU resources. The v4-tiny sometimes exploits CPU resources with the full load  fully utilize GPU, as shown in the rightmost figures of We validate the effect of the TRT library by comparing CenterPoint  and CentThe corresponding computational time measurements are shown in From end-to-end FPS measurements as shown in  library  can succIn this work, we examined deep learning-based 3D object detection frameworks on all commercially available Jetson platforms . Our objective was to analyze Jetson platforms in terms of detection accuracy, FPS, and resource usage when operating complex point cloud processing that demands high computational resources. We selected the state-of-the-art 3D detectors within two categories (one-stage and two-stage). From the experiments, it was observed that Nano and TX2 are unable to handle complex computations of the two-stage detectors and most of the one-stage detectors due to the shortage of memory resources. Otherwise, it was found that all Jetson platforms successfully run the Complex-YOLO series. For NX and AGX platforms, PointPillar would be suitable since it provides high AP and fast inference with stable resource usage. We observed that all of the platforms, on average, consume 50% and 80% of the resources of CPU and GPU, respectively. Remarkably, Jetson AGX only requires less than 32% of RAM resources for the entire compared algorithms. In addition, we confirm that the TRT library  not only


Background: The administration of the third (or booster) dose of COVID-19 vaccine is important in maintaining protection against SARS-CoV-2 infection or the severity of the disease. In Indonesia, health care workers (HCWs) are among the first to receive a booster dose of the COVID-19 vaccine. In this study, we evaluated the antibody response and adverse events following heterologous booster vaccine using mRNA-1273 among HCWs that were fully vaccinated with inactivated viral vaccine as the priming doses. Methods: 75 HCWs at Dr. Soetomo General Hospital in Surabaya, Indonesia, participated in this study. The level of antibody against the SARS-CoV-2 receptor binding domain was analyzed at 1, 3, and 5 months following the second priming dose and at 1, 3, and 5 months after the booster dose. Results: We found a significantly higher level of antibody response in subjects receiving a booster dose of the mRNA-1273 vaccine compared to those receiving an inactivated viral vaccine as a booster. Interestingly, participants with hypertension and a history of diabetes mellitus showed a lower antibody response following the booster dose. There was a higher frequency of adverse events following injection with the mRNA-1273 vaccine compared to the inactivated viral vaccine, although the overall adverse events were considered minor. Conclusions: A heterologous booster dose using mRNA vaccine resulted in a high antibody response; however, participants with hypertension and diabetes mellitus displayed a lower antibody response. The national vaccination program for Coronavirus Disease 2019 (COVID-19) in Indonesia started on 13 January 2021 with health care workers (HCWs) being one of the first groups of people receiving the vaccine. The success of the vaccination program among HCWs is crucial to protecting them from the risk of contracting COVID-19 from their work. Recent reports have shown a considerable high acceptance of the COVID-19 vaccine in people from low- and middle-income countries (LMIC) . Our ownThe inactivated viral vaccine was the main type of vaccine used by the Indonesian authorities at the beginning of vaccination. One of the inactivated SARS-CoV-2 vaccines, CoronaVac, which was used in the national vaccination program, showed a very good level of protection against severe COVID-19, hospitalization, and mortality ,7. HowevThe importance of the COVID-19 vaccine booster is evident. It has been demonstrated that a third (booster) dose enhanced both humoral and cellular immunity regardless of the type of the priming doses and the type of the booster dose itself . MoreoveWe have evaluated the serum antibody levels against SARS-CoV-2 among HCWs in East Java, Indonesia, following vaccination using inactivated virus. We observed a significant increase in the serum antibody level . HoweverIn this present study we performed a follow up analysis of the same cohort of HCWs as described above, who received a booster dose of SARS-CoV-2 vaccine at 5 months following the priming dose. Most of the participants received the mRNA-1273 vaccine (Moderna) as a booster dose following recommendations from the Indonesian Health Authority, while a few of them opted to receive a booster dose using an inactivated viral vaccine (CoronaVac). Here we assessed the antibody response, the adverse effects of the vaccine, and the association of the antibody response with hypertension following the third (booster) dose of vaccines in our cohort of HCWs.This is a follow-up observation on our previously reported study . We recrAll participants received two doses of the inactivated SARS-CoV-2 vaccine (CoronaVac) as the priming doses. An analysis of serum samples was conducted at 1, 3, and 5 months after the second dose of vaccination. All participants were offered a booster dose with either the mRNA-1273 vaccine (Moderna) or the inactivated viral vaccine (CoronaVac) at 5–6 months following the second dose. Serum samples were again analyzed at 1, 3, and 5 months after the booster dose.We used a commercially available kit  to examine the level of IgG against the SARS-CoV-2 receptor-binding domain (RBD) in the serum samples. We followed the protocol as recommended by the manufacturer.At the beginning of the study, we interviewed participants regarding demographic data and the presence or history of comorbidities . Blood pressure was also measured at the beginning of the study and during follow-up visits. During the follow-up visits, participants were also asked about the presence of vaccine adverse reactions.p value less than 0.05 was considered statistically significant. We used GraphPad Prism ver. 9  to analyze the data. To control for the possible confounding effects of each comorbidity, a multivariate logistic regression analysis was performed with inclusion of histories of hypertension, diabetes mellitus, and cardiovascular diseases.The serum IgG level is presented as geometric mean titres and 95% confidence intervals (CI). To analyze the difference in IgG level between booster vaccination with mRNA vs. inactivated viral vaccine, we used a non-parametric multiple comparisons (Mann–Whitney U) test. The same test was also used to analyze the effects of comorbidities on the antibody response. A This is a follow-up observation on our previously reported study . HoweverAll of the participants received inactivated viral vaccine (CoronaVac) as the priming doses (first and second doses). Of the 75 HCWs in our cohort, 69 of them received the mRNA-1273 vaccine (Moderna) as a booster (third dose), whereas 6 subjects opted to have an inactivated viral vaccine (CoronaVac) for the booster dose. We analyzed serum IgG levels against the RBD domain at 1, 3, and 5 months following the 2nd dose of priming vaccine and at 1, 3, and 5 months after receiving the booster dose. As shown in Our previous observation has suggested that subjects with hypertension display a lower antibody response against the priming doses of inactivated viral vaccine . To undeThe antibody response in participants with histories of diabetes mellitus (DM) and allergic diseases was also analyzed. Similar to participants with hypertension, we observed significantly lower antibody levels in subjects with a history of diabetes mellitus at 1–5 months following booster vaccination B. HoweveTo analyze the possible confounding effect between these three comorbidities, we conducted a multivariate linear regression analysis. Data presented in The pattern of the adverse effects is depicted in The main finding of this study is that a heterologous booster dose of the mRNA-1273 vaccine induces a strong antibody response in individuals who have been vaccinated with an inactivated viral vaccine. This data is in line with previous reports and adds to the growing body of evidence showing the effectiveness of heterologous booster vaccination in enhancing antibody levels against SARS-CoV-2 ,15,16. AThe effectiveness of mRNA vaccines as booster doses in enhancing antibody titers is evident. When compared to other platforms of COVID-19 vaccine, for example, adenovirus, recombinant protein, and inactivated viral vaccine, the mRNA vaccine showed superiority in terms of the level of antibody response . Based oOne novel finding of this study is that the antibody response against the booster dose is significantly lower in participants with hypertension (BP ≥ 140/90) and those with a history of DM compared to subjects without these comorbidities. Previous observations have indicated a reduction in antibody response following the COVID-19 vaccine in subjects with hypertension ,21,22 anIt is important to note that the median age of the participants was relatively high (57 years). Interestingly, the proportion of participants with hypertension in our cohort was lower compared to the prevalence of hypertension in the Indonesian population within the age group of 55–64 years old . HoweverThe other important finding of this study was that the antibody response to the booster dose declined over time. Our analysis suggested that the antibody titer at 5 months after the booster dose declined by more than 70% compared to the antibody titer at 1 month post-booster. Evidence showing the waning immunity following priming doses of COVID vaccine is accumulating ,10, and In addition to antibody response, another important aspect that needs to be assessed is the effectiveness of controlling the incidence and severity of COVID-19. In our previous observation, we analyzed the incidence and severity of COVID-19 among a cohort of HCWs who received vaccination with an inactivated viral vaccine . We asseThe mRNA vaccine has been widely associated with more side effects compared to other types of vaccines ,32. Our Our present study has several limitations. First, we only examined humoral responses by assessing serum IgG levels. As mentioned previously, cellular immunity is important in protecting against disease severity. Further studies are needed to understand whether cellular immunity is enhanced following a heterologous primary vaccine-booster combination of inactivated viral vaccine with mRNA vaccine and whether cellular immunity is waning over time following booster. Second, we only have a small number of participants in this study. It is important to note that a number of study subjects were excluded due to infection with the SARS-CoV-2 virus during the study period. We recruited infection-naïve subjects, and so some of the participants who had detectable antibodies at the beginning of the study were excluded. Another limitation was the possibility of asymptomatic SARS-CoV-2 infection among the participants following the priming doses, which might affect the level of serum IgG antibody. We did not perform an analysis of anti-nucleocapsid antibodies, which could detect asymptomatic infection, due to limited resources. However, we closely monitored for the presence of COVID-19 symptoms among all the participants and followed this up with a PCR test during the study, so the possibility of asymptomatic SAS-CoV-2 infection was probably minimal. The detection of anti-nucleocapsid antibody may also be useful in comparing antibody responses against inactivated virus vs. mRNA vaccines, since inactivated viral vaccines may contain N-proteins in addition to S-proteins, whereas mRNA vaccines only contain S-antigens. Studies to focus on this aspect are needed in the future.It is clear that HCWs need to be protected against COVID-19 since they may have higher exposure to the virus due to possible contacts with patients. In our recent observation, we found a higher incidence of COVID-19 in medical staff  compared to non-medical hospital workers, such as administrative staff, during the first year of the COVID-19 pandemic in 2020 . This isIn summary, our findings show that heterologous booster doses using mRNA vaccine in a cohort of HCWs who received priming vaccine with inactivated virus induce a higher antibody response than homologous booster vaccine with inactivated SARS-CoV2. However, the level of serum IgG waned over time and participants with hypertension displayed a lower antibody response. Our data adds to the growing body of evidence showing the importance of the COVID-19 booster vaccination to improve protection against the disease.


Approximately 3% of US adults are immunocompromised and less capable of fighting infections such as SARS-CoV-2 (the causative agent of COVID-19). Individuals may be immunocompromised for reasons related to an underlying medical condition or to immunomodulatory therapies that alter the immune response. In general, vaccination with mRNA–based vaccines is effective at reducing COVID-19–associated hospitalization and death among immunocompromised populations, particularly after 3 or more doses. However, the immunocompromised population is heterogeneous, with COVID-19 vaccine-elicited immune responses and risk for severe COVID-19 existing on a continuum. Therefore, understanding the impact of vaccination and the complexity of immune responses across heterogeneous immunocompromised individuals is essential for guiding effective vaccination regimens including additional (booster) doses. In this article, we provide an overview of the immunocompromised population and the burden of disease attributable to COVID-19, while discussing key opportunities and challenges of vaccinating immunocompromised individuals. Approximately 3% of the adult population in the United States is immunocompromised  and lessDue to weakened immune responses, immunocompromised individuals are more susceptible to severe outcomes and complications of infectious disease . AccordiThe emergence of the SARS-CoV-2 that caused the global COVID-19 pandemic further underscored the vulnerability to infection and the complexity of vaccination in this population. Early in the pandemic, some countries developed criteria to assist with identifying those individuals at high risk of poor outcomes from COVID-19 who should be appropriately shielded from exposure  is a marker that can be used to reliably predict the efficacy of a vaccine . So far,Defining the clinical vulnerability for COVID-19 across the complex spectrum of immunocompromised individuals is challenging, and there remains detailed nuance to the burden of disease and response to vaccination among this population. As the COVID-19 pandemic continues and eventually moves into a more endemic state, there is an urgent need to further estimate the risks associated with COVID-19 disease and the complexities of vaccination in each disparate group of immunocompromised individuals. As such, this review aims to provide a high-level overview of the immunocompromised population and the burden of disease attributable to COVID-19, while discussing key opportunities and challenges of vaccinating immunocompromised individuals; additional articles within this supplement will go into more detail on specific immunocompromised populations.In contrast to immunocompetent individuals, who can generally mount a sufficient immune response against SARS-CoV-2, those with immune deficiencies may not and, thus, have an increased likelihood of COVID-19–associated morbidity and mortality , 22, 23.In general, vaccination with mRNA–based vaccines  is effective at reducing COVID-19–associated hospitalization among immunocompromised populations, particularly after 3 or more doses , 36. HowNot only does COVID-19 severity vary by immunocompromising condition, but the effectiveness of COVID-19 vaccination can also vary based on the exact immunocompromised population under investigation. In a multicenter, observational study in the United States, 2 doses of an mRNA-based vaccine had 29% effectiveness against COVID-19 hospitalization among solid organ transplant recipients (SOTRs), a severely immunosuppressed population, while effectiveness was 72% for patients with other immunocompromising conditions . HoweverImmune responses against SARS-CoV-2 involve complex synergy between both humoral- and cellular-mediated immunity , 40. As A growing body of evidence has highlighted that the level of immunosuppression of an immunocompromised individual can play a role in vaccine-elicited immune responses. For example, a recent systematic review evaluated response to COVID-19 vaccination among individuals with immune-mediated inflammatory diseases (IMIDs); significantly lower seroconversion rates after a 2-dose regimen of an mRNA vaccine were observed in patients with IMIDs compared with healthy controls  . Disease+ T-cell count ) as opposed to a heterologous vector-based vaccine (4 of 22 [18%]);  = .006) . However = .038) .Although individuals with weakened immune systems can be broadly classified as immunocompromised, the disparate immune responses to COVID-19 vaccination and outcomes among this population highlight the overall lack of homogeneity among this group. The underlying medical condition and the use of certain immunosuppressants can substantially affect immune responses to vaccination among immunocompromised individuals. As such, regulatory agencies have recommended extending the primary vaccination series to include 3 doses as well as the administration of additional (booster) doses of COVID-19 vaccines, which have been shown to increase humoral and/or cellular response in certain immunocompromised individuals with initially poor immune responses . AlthougKnowledge of COVID-19 vaccine–elicited immunogenicity among this population is critical for guiding effective healthcare for these patients. Accordingly, understanding the current challenges and potential limitations of COVID-19 vaccination in this group is essential and can inform not only decisions on additional vaccine doses, but also recommendations for nonpharmaceutical preventive strategies as well as prophylaxis approaches. This also becomes particularly important as variants evolve, making monoclonals and prophylaxis minimally effective. Investing in ways to optimize protection for these individuals can significantly impact the course of this pandemic as prolonged infection can give rise to SARS-CoV-2 variants of concern with increased potential to escape vaccine- and natural immunity–mediated protection. Furthermore, as COVID-19 potentially transitions from a pandemic to an endemic disease, the establishment of effective healthcare measures remains a public health priority to protect immunocompromised individuals from a persistent infectious threat.


GelStereo sensing technology is capable of performing three-dimensional (3D) contact shape measurement under various contact structures such as bionic curved surfaces, which has promising advantages in the field of visuotactile sensing. However, due to multi-medium ray refraction in the imaging system, robust and high-precision tactile 3D reconstruction remains a challenging problem for GelStereo-type sensors with different structures. In this paper, we first propose a universal Refractive Stereo Ray Tracing (RSRT) model for GelStereo-type sensing systems to realize 3D reconstruction of the contact surface. Moreover, a relative geometry-based optimization method is presented to calibrate multiple parameters of the proposed RSRT model, such as the refractive indices and structural dimensions. Furthermore, extensive quantitative calibration experiments are performed on four different GelStereo sensing platforms; the experimental results show that the proposed calibration pipeline can achieve less than 0.35 mm in Euclidean distance error, based on which we believe that the proposed refractive calibration method can be further applied in more complex GelStereo-type and other similar visuotactile sensing systems. Such high-precision visuotactile sensors can facilitate the study of robotic dexterous manipulation. Tactile perception is one of the main ways for humans to interact with real-world environments ,2, and hCurrently, various types of tactile sensors have been developed, with different structures used to adapt to different sensing requirements and integration scenarios. A detailed survey of visuotactile sensing technologies can be found in . NeverthRecently, our previous studies proposed the GelStereo route based on a binocular stereo vision system ,17,21. TIn this paper, we carry out an in-depth study of the universal 3D reconstruction pipeline of GelStereo-type sensors, with an emphasis on refractive calibration. To begin with, a Universal Refractive Stereo Ray Tracing model, which we call GU-RSRT, is presented for GelStereo-type sensors with various structures. The GU-RSRT performs ray tracing modeling on the GelStereo imaging systems, in which the parameters include intrinsic and extrinsic parameters of the binocular camera, refractive indices, and structural geometry. To obtain these parameters in GU-RTST model, we propose a Universal Multi-Medium Refractive (UMMR) calibration method using the embedded relative geometric features of checkerboards. Furthermore, a Marker-Based Self-Calibration (MBSC) method is proposed for specific GelStereo-type sensors with known structured markers embedded on the sensor surface, which omits the step of checkerboard calibration and updates the sensor parameters during daily use, significantly improving the service life of the sensor. Extensive calibration and evaluation experiments are performed on four GelStereo-type sensors with different refracting and contact surfaces. The experimental results show that the proposed refractive calibration method can obtain reasonable parameters for the GU-RSRT model, and the constructed 3D reconstruction system achieves 3D points measurement with less than 0.35 mm Euclidean distance error on different sensor platforms. Furthermore, the proposed 3D reconstruction pipeline with refractive calibration can be practically applied to high-precision 3D deformation measurement in various GelStereo-type sensors and similar visuotactile sensors, including those with binocular cameras and undergoing multi-medium light refraction, as shown in A universal refractive stereo ray tracing model that can handle sensors with arbitrary refracting and contact surfaces is presented for GelStereo-type sensors.A universal multi-medium refractive calibration method using the embedded geometric features of checkerboards is proposed to obtain refractive parameters in GelStereo imaging systems. The results show that the proposed calibration method can realize high precision (less than 0.35 mm Euclidean distance error) in 3D contact geometry measurements on different sensor platforms.A marker-based self-calibration method that can automatically perform refractive calibration every time the sensor starts up is proposed for specific GelStereo-type sensors with known structured markers embedded on the sensor surface, allowing for prolonged sensor life.In summary, the contributions of this paper can be summarized as follows:The rest of this paper is organized as follows. We first provide a tactile 3D reconstruction pipeline for GelStereo-type sensors using a GU-RSRT model . The refm times refraction and use the general equation GelStereo-type sensors utilize a binocular vision system for 3D geometry sensing. With sparse or dense stereo matching points pairs on left and right tactile images ,21, a 2DTaking the left ray as an example, we backpropagate the rays from the camera optical center to the 3D points on the sensor surface in the left camera coordinate system.Given a point on the left tactile image  and lefquations  and 3)  fi , the, the7), m on the sensor surface. This intersection point is considered as the reconstructed 3D point. In order to compute this point, we transform the right ray m from the right to the left camera coordinate system:The left and right rays intersect in medium P can be computed byThis equation set is overdetermined; thus, that the least square method is employed. The reconstructed 3D point Equation .To achieve high-precision 3D geometry sensing, a refractive calibration method is desirable in order to obtain a fine set of parameters for GU-RSRT model. The model parameters are divided into three parts, including camera parameters, refractive indices, and structural parameters. Zhang’s method  is emploIn GelStereo-type sensor imaging systems, the shapes of refracting surfaces are already known, as they are determined by the sensors’ structure design. However, the pose of the refracting surface in the camera coordinate system is uncertain due to deviations during sensor assembly. The pose is determined by several translation and orientation parameters  calibration method for GelStereo-type sensors. In practice, we fully pressed checkerboards onto the surface of the sensor’s transparent elastomer. The 3D point of each corner was then reconstructed by the GU-RSRT model. The objective functions were designed to ensure the spatial invariance of the checkerboard corners.l, and As shown in The objective function for the Euclidean distance can be designed as follows:The horizontal edges of the checkerboard are perpendicular to the vertical edges. Then, the objective function for perpendicularity can be designed asThis objective function expresses that the red vector is perpendicular to the blue vector in The final objective function is a linear combination of Equation ).Instead of the checkerboard mentioned in the UMMR method, the structured markers embedded on the sensor surface can provide the ground truth of relative geometric features. A Marker-Based Self-Calibration (MBSC) method is proposed for specific GelStereo-type sensors. These sensors should have curved refracting surfaces, curved sensor surfaces, and markers with known structures.S is the number of markers after downsampling and Unlike the checkerboard with a planar structure, markers are distributed in 3D space. The Euclidean distance between markers is mainly used in self-calibration. In order to improve the computational efficiency without loss of geometric constraints, the voxel downsampling method is employed to downsample all markers into a few key markers. The objective function is designed as follows:In order to verify the effectiveness of the proposed tactile 3D reconstruction pipeline, including the GU-RSRT model and the refractive calibration methods, we carried out the following experiments on several sensor platforms.Quantitative experiments on 3D reconstruction. First of all, we quantitatively evaluate the accuracy of the proposed tactile 3D reconstruction pipeline using ground truth 3D points obtained from high-precision measuring instruments. Specifically, the 3D reconstruction errors on four different GelStereo-type sensors with various refracting surfaces and sensor surfaces were evaluated using the Mean Absolute Error (MAE) in the X, Y, Z directions and the Mean Euclidean Distance Error (MEDE) between the reconstructed 3D points and the ground truth. Moreover, on these sensor platforms we analyzed the reconstruction errors of 3D points with different contact depths and regions.Method comparison experiments. In addition to the methods proposed in this paper, two other commonly used 3D reconstruction methods were used to ensure a comprehensive evaluation.Traditional Triangulation Method (TTM): Without considering multi-medium refraction, the traditional triangulation method was applied using binocular camera parameters calibrated in the air.Camera Parameters Absorption Method (CPAM): The 3D reconstruction errors caused by multi-medium refraction can be absorbed by the camera parameters to a certain extent ,41. In pGU-RSRT+UMMR: The GU-RSRT model with parameters calibrated through the universal multi-medium refractive calibration method was applied to GelStereo-type sensors for tactile 3D reconstruction.GU-RSRT+MBSC: The GU-RSRT model with parameters calibrated through marker-based self-calibration method was applied to GelStereo-type sensors for tactile 3D reconstruction.Ablation studies. In-depth ablation studies on the UMMR calibration method were carried out to study the importance of each relative geometric feature.GU-RSRT+UMMR . Considering the pixel position of the black dot on the images, the estimated 3D position was computed using the proposed pipeline. The 3D readings from the linear guide were converted to the left camera coordinate system using a transformation matrix, which was obtained by ArUco-based pose estimation [A platform for collecting binocular tactile image pairs and corresponding ground truth 3D points was needed for evaluation. As shown in timation  and the timation . The conUMMR calibration was conducted during sensor fabrication, specifically, before painting markers and the coating layer. First, checkerboard images on the sensor surface were captured for refractive calibration using the UMMR method. Specifically, we pasted a checkerboard pattern on the flat surface of the 3D-printed calibration board. We manually pressed this calibration board onto the transparent gel surface, as shown in Then, the optimization problem was formulated based on Equation  for refrEquation  were setFinally, this optimization problem was solved using a differential evolution algorithm. The bounds of parameters are listed in Using the platform in To evaluate the 3D reconstruction errors of different regions on the sensor surface, these sampling points were divided into several groups, which are depicted by yellow lines and numbers in 3D reconstruction accuracy. The refractive calibration results and 3D reconstruction errors of GelStereo Tip sensor, GelStereo Palm2.0 sensor, GelStereo Palm1.0 sensor, and GelStereo BioTip sensor are shown in Methods comparison. In Self-calibration. The results of marker-based self-calibration and the 3D reconstruction errors of the GelStereo Palm1.0 and GelStereo BioTip sensors are shown in In addition, we applied this marker-based self-calibration method to the GelStereo Tip and GelStereo Palm2.0 sensor; however, the performance was not satisfactory, as shown in 3D reconstruction errors with different contact depths and regions. We further studied the reconstruction accuracy of the proposed method on different sensor platforms for different contact depths and contact regions in order to evaluate its robustness. The error distributions at different contact depths are illustrated using a violin plot in Ablation studies. The results of ablation studies on the UMMR calibration method are demonstrated in In GelStereo-type sensor imaging systems, the shapes of refracting surfaces play an important role in ray tracing. Although the refracting surface function depends on sensor design, the manufacturing process  of the transparent supporting plate might bring errors into this function, especially on the curved supporting plate in the GelStereo Palm1.0 and GelStereo BioTip sensors. To solve this problem, a method for correcting the function of the refracting surface should be integrated into the refractive calibration process. In this way, the precision of tactile 3D reconstruction can be further improved in GelStereo-type sensors.As mentioned in In this paper, we present a universal Refractive Stereo Ray Tracing model for GelStereo-type sensors to model the tactile 3D reconstruction under multi-medium light refraction. In addition, a Universal Multi-Medium Refractive (UMMR) calibration method is proposed to obtain the refractive and structural parameters in the GU-RSRT model, in which relative geometric features on checkerboards are employed to build an optimization problem for calibration. In addition, a self-calibration method based on structured markers on the sensor surface is provided for specific GelStereo-type sensors.Extensive calibration and evaluation experiments are conducted on four different GelStereo-type sensors with various structure designs. The experimental results show that the proposed refractive calibration method can obtain reasonable parameters of the GU-RSRT model and the 3D reconstruction error of the mean Euclidean distance error is less than 0.35 mm, which outperforms the other 3D reconstruction methods. In addition, the accuracy of the marker-based self-calibration method is slightly better than the UMMR calibration method on GelStereo-type sensors with curved refracting surfaces. The self-calibration method has great potential in improving calibration efficiency and sensor service life. Moreover, our experimental results show the robustness of the proposed 3D reconstruction pipeline with different contact depths and regions.The feasibility of the proposed tactile 3D reconstruction pipeline is fully demonstrated in this paper. Its practical application scenario is visuotactile sensing  based on binocular cameras and undergoing multi-medium light refraction. With high-precision sensing capability, GelStereo-type sensors and other similar visuotactile sensors could provide more possibilities for robots to achieve rich-contact and dexterous manipulation. In the future, we intend to further improve the 3D reconstruction performance of GelStereo-type sensors and apply GelStereo-type sensors to robotic perception and manipulation tasks.