Multi-model predictive analysis of RNA solvent accessibility based on modified residual attention mechanism.

Predicting RNA solvent accessibility using only primary sequence data can be regarded as sequence-based prediction work. Currently, the established studies for sequence-based RNA solvent accessibility prediction are limited due to the available number of datasets and black box prediction. To improve these issues, we first expanded the available RNA structures and then developed a sequence-based model using modified attention layers with different receptive fields to conform to the stem-loop structure of RNA chains. We measured the improvement with an extended dataset and further explored the model's interpretability by analysing the model structures, attention values and hyperparameters. Finally, we found that the developed model regarded the pieces of a sequence as templates during the training process. This work will be helpful for researchers who would like to build RNA attribute prediction models using deep learning in the future.

Sodium-Glucose Cotransporter 2 Inhibitors Reduce the Risk of Hospitalization for Heart Failure and Amputation Rate Compared With Incretin-Based Therapy in Patients With Diabetic Foot Disease: A Nationwide Population-Based Study.

OBJECTIVE: Major adverse cardiovascular event (MACE) outcomes associated with sodium-glucose cotransporter 2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapies remain unclear in patients with type 2 diabetes and newly diagnosed diabetic foot complications (DFCs). This study examined the impact of SGLT2i and GLP-1 RA use on the rates of MACEs and amputations in patients with type 2 diabetes and without cardiovascular disease. METHODS: Data from the Taiwan National Health Insurance Research Database (2004-2017) were analyzed, focusing on patients with type 2 diabetes without previous MACE and newly diagnosed DFCs. The primary outcome was the first MACE occurrence, and the secondary outcomes included MACE components, all-cause mortality, and lower extremity amputation (LEA) rates. RESULTS: SGLT2i users showed a significant decrease in the MACE (hazard ratio [HR], 0.64; 95% confidence interval [CI], 0.46-0.88) and hospitalization for heart failure (HR, 0.54; 95% CI, 0.35-0.83) rates compared with dipeptidyl peptidase-4 inhibitor users. The amputation rates were also lower in SGLT2i users without LEA at the first DFC diagnosis (HR, 0.28; 95% CI, 0.10-0.75) and did not increase in those with a history of peripheral artery disease or LEA. No significant differences were observed between dipeptidyl peptidase-4 inhibitor and GLP-1 RA users in terms of the primary or secondary outcomes. CONCLUSION: In patients with type 2 diabetes initially diagnosed with DFC, SGLT2i are effective in significantly reducing the hospitalization for heart failure and MACE rates. SGLT2i lower the amputation rates, especially in patients who have not previously had a LEA, than the dipeptidyl peptidase-4 inhibitor therapy.

Are current wound classifications valid for predicting prognosis in people treated for limb-threatening diabetic foot ulcers?

This study aims to investigate whether the current wound classifications were valid for the treatment prognosis of subjects treated for limb-threatening diabetic foot ulcers (LTDFU). A total of 1548 patients with LTDFU and infection were studied, with wounds recorded using the Wagner, Texas, PEDIS and WIfI classifications while major lower extremity amputations (LEAs) or in-hospital mortality incidences were defined as poor outcomes. Among them, 153 (9.9%) patients received major LEAs and 38 (2.5%) patients died. After adjustments, the Wagner classification and Texas stage as well as clinical factors such as comorbidity with major adverse cardiac events (MACE), being under dialysis and having serum levels of C-reactive protein (CRP) and albumin were independent factors for prognosis. For patients without dialysis, Wagner and Texas stage stood out independently for prognosis. For patients on dialysis, only levels of CRP (odds ratio [OR] = 2.2 in Wagner, OR = 2.0 in WIfI, OR = 2.2 in Texas, OR = 2.3 in PEDIS) and albumin (OR = 0.4 in four classifications) were valid predictors. The Wagner system and Texas stage were valid for predicting prognosis in treatment for LTDFUs, suggesting a role of vascular perfusion. MACE history, levels of CRP and albumin level should assist in prediction; more significantly, only levels of CRP and albumin appeared valid for those subjects undergoing dialysis.

C-DONN: compact diffractive optical neural network with deep learning regression.

A new method to improve the integration level of an on-chip diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) platform. The metaline, which represents a hidden layer in the integrated on-chip DONN, is composed of subwavelength silica slots, providing a large computation capacity. However, the physical propagation process of light in the subwavelength metalinses generally requires an approximate characterization using slot groups and extra length between adjacent layers, which limits further improvements of the integration of on-chip DONN. In this work, a deep mapping regression model (DMRM) is proposed to characterize the process of light propagation in the metalines. This method improves the integration level of on-chip DONN to over 60,000 and elimnates the need for approximate conditions. Based on this theory, a compact-DONN (C-DONN) is exploited and benchmarked on the Iris plants dataset to verify the performance, yielding a testing accuracy of 93.3%. This method provides a potential solution for future large-scale on-chip integration.

Cyclodextrin-Mediated Cholesterol Depletion Induces Adiponectin Secretion in 3T3-L1 Adipocytes.

Adipocytes store a significant amount of cholesterol and triglycerides. However, whether cholesterol modulates adipocyte function remains largely unknown. We modulated the cholesterol level in adipocytes to examine its effect on the secretion of adiponectin, an important hormone specifically secreted by adipocytes. Treating differentiated 3T3-L1 adipocytes with 4 mM methyl-ß-cyclodextrin (MßCD), a molecule with a high affinity for cholesterol, rapidly depleted cholesterol in adipocytes. Interestingly, MßCD treatment increased adiponectin in the medium without affecting its intracellular level, suggesting a modulation of secretion. By contrast, cholesterol addition did not affect adiponectin secretion, suggesting that cholesterol-depletion-induced intracellular cholesterol trafficking, but not reduced cholesterol level, accounted for MßCD-induced adiponectin secretion. MßCD-induced adiponectin secretion was reduced after 10 µg/mL U18666A treatment that suppressed cholesterol transport out of late endosomes/lysosomes. Depleting Niemann-Pick type C1 (NPC1) or NPC2 proteins, which mediate endosomal/lysosomal cholesterol export, consistently reduced MßCD-induced adiponectin secretion. Furthermore, treatment with 1 µM bafilomycin A1, which neutralized acidic endosomes/lysosomes, also attenuated MßCD-induced adiponectin secretion. Finally, MßCD treatment redistributed cellular adiponectin to lower-density fractions in sucrose gradient fractionation. Our results show that MßCD-mediated cholesterol depletion elevates the secretion of adiponectin, highlighting the involvement of endosomes and lysosomes in adiponectin secretion in adipocytes.

Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis.

Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability. This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes.

A comprehensive investigation of nutritional status and psoas muscle mass in predicting five-year survival in patients with liver transplant.

BACKGROUND/PURPOSE: Although nutrition and sarcopenia have impacts on the surgery outcome of patients who have received living donor liver transplant (LDLT), the use of the prognostic nutritional index (PNI) or psoas muscle mass index (PMI) as an indicator for five-year survival in those patients is still unclear. METHODS: A total of 138 patients receiving LDLT were followed at a medical center in Taiwan. As well as analysis of clinical factors using Cox regression, time-varying PNI and PMI values as before surgery (0) and at 3-, 6-, and 12- months after LDLT were analyzed by time-dependent Cox analysis. For those 124 patients who survived after 3 months of LDLT, the values of PNI-3m, PMI-3m and their combination were further analyzed. RESULTS: PNI and PMI were noted to be highly associated with mortality at three months post-LDLT (PNI-3m hazard ratio [HR] = 0.89, 95% confidence interval [CI]: 0.85-0.94, p < 0.001; PMI-3m HR = 0.58, 95% CI: 0.41-0.82, p = 0.002). Per the Youden index, the cut-off point of PNI-3m was 42.35, and that of PMI-3m was 1.94. Compared to the subjects with higher levels of PNI-3m and PMI-3m (N-high/M-high), the HRs for subjects with N-high/M-low, N-low/M-high, and N-low/M-low were 5.27 (p = 0.004), 4.46 (p = 0.010) and 12.97 (p < 0.001) respectively. CONCLUSION: PNI and PMI at the third month post-LDLT serve as excellent predictors for 5-year survival. For patients with lower levels of PNI-3m or PMI-3m, combination use of these indexes is suggested to provide better prognostic information.

The analysis for time of referral to a medical center among patients with diabetic foot infection.

BACKGROUND: Diabetic foot infection (DFI) is a limb- and life-threatening complication for diabetic patients needing immediate and comprehensive treatment. Early referral of DFI patients to a diabetic foot center is recommended but there appears limited validated evidence, with the association between referral time and clinical outcomes of limb- preservation or in-hospital mortality still lacking. METHODS: This retrospective research studied consecutive type 2 diabetic patients with DFI treated at the major diabetic foot center in Taiwan from 2014 to 2017. Six hundred and sixty-eight patients presented with limb-threatening DFI. After stratifying their referral days into quartiles, the demographic information and clinical outcomes were analyzed. RESULTS: One hundred and seventy-two patients were placed in the first quartile (Q1) with less than 9 days of referral time; 164 in the second quartile (Q2) with 9-21 days; 167 in the third quartile (Q3) with 21-59 days; and 165 in the fourth quartile (Q4) with >59 days. End-stage renal disease (ESRD), major adverse cardiac events (MACE) and peripheral arterial disease (PAD) were noted as being higher in the Q4 group compared with the Q1 group (25.45% vs 20.35% in ESRD, 47.27% vs 26.16% in MACE and 78.79% vs 52.33% in PAD respectively). The Q1 group had more patients presenting with systemic inflammatory responsive syndrome (SIRS) (29.07% in Q1 vs 25.45% in Q4 respectively, P=0.019). Regarding poor outcome (major lower-extremity amputation (LEA) or in-hospital mortality), the Q4 group had 21.21% of patients in this category and the Q1 group had 10.47%. The odds ratio of each increased referral day on poor prognosis was 1.006 with 95% confidence interval 1.003-1.010 (P=<0.001). In subgroups, the impact on poor prognosis by day was most obvious in patients with SIRS (OR 1.011, 95% CI 1.004-1.018, P=0.003) and those with PAD (OR 1.004, 95% CI 1.001-1.008, P=0.028). CONCLUSIONS: The deferred referral of DFI patients to the diabetic foot center might be associated with poor treatment outcome either in major LEA or mortality, particularly in patients with SIRS or PAD. Both physician and patient awareness of disease severity and overcoming the referral barrier is suggested. TRIAL REGISTRATION: Not applicable.

Joint Unsupervised Learning of Depth, Pose, Ground Normal Vector and Ground Segmentation by a Monocular Camera Sensor.

We propose a completely unsupervised approach to simultaneously estimate scene depth, ego-pose, ground segmentation and ground normal vector from only monocular RGB video sequences. In our approach, estimation for different scene structures can mutually benefit each other by the joint optimization. Specifically, we use the mutual information loss to pre-train the ground segmentation network and before adding the corresponding self-learning label obtained by a geometric method. By using the static nature of the ground and its normal vector, the scene depth and ego-motion can be efficiently learned by the self-supervised learning procedure. Extensive experimental results on both Cityscapes and KITTI benchmark demonstrate the significant improvement on the estimation accuracy for both scene depth and ego-pose by our approach. We also achieve an average error of about 3° for estimated ground normal vectors. By deploying our proposed geometric constraints, the IOUaccuracy of unsupervised ground segmentation is increased by 35% on the Cityscapes dataset.

Traffic Intersection Re-Identification Using Monocular Camera Sensors.

Perception of road structures especially the traffic intersections by visual sensors is an essential task for automated driving. However, compared with intersection detection or visual place recognition, intersection re-identification (intersection re-ID) strongly affects driving behavior decisions with given routes, yet has long been neglected by researchers. This paper strives to explore intersection re-ID by a monocular camera sensor. We propose a Hybrid Double-Level re-identification approach which exploits two branches of Deep Convolutional Neural Network to accomplish multi-task including classification of intersection and its fine attributes, and global localization in topological maps. Furthermore, we propose a mixed loss training for the network to learn the similarity of two intersection images. As no public datasets are available for the intersection re-ID task, based on the work of RobotCar, we propose a new dataset with carefully-labeled intersection attributes, which is called "RobotCar Intersection" and covers more than 30,000 images of eight intersections in different seasons and day time. Additionally, we provide another dataset, called "Campus Intersection" consisting of panoramic images of eight intersections in a university campus to verify our updating strategy of topology map. Experimental results demonstrate that our proposed approach can achieve promising results in re-ID of both coarse road intersections and its global pose, and is well suited for updating and completion of topological maps.

Programmable matrix operation with reconfigurable time-wavelength plane manipulation and dispersed time delay.

We propose a novel optical computing architecture for massive parallel matrix manipulation based on reconfigurable time-wavelength plane manipulation and and dispersed time delay. Two linear weighting methods in either wavelength or time domain are proposed and validated. We perform the autocorrelation function of a 7-bit m-sequence with the speed at 1.18×1011 multiplications and accumulations per second (MACs/s) and a multiplication of a 4 × 4 matrix and a 4 × 1 vector at 2.69×109 MACs/s. The edge extraction of 32 × 32 binary images is also realized in simulation by optical 2D convolution at 5×108 MACs/s. The proposed optical computing unit can be a key building block to process complex computing tasks with advanced deep learning algorithms and it is promising for the future photonic neural network circuits.

Risk factors for in-hospital mortality in patients with type 2 diabetes complicated by community-acquired Klebsiella pneumoniae bacteremia.

BACKGROUND/PURPOSE: Patients with diabetes are at a high risk of infection-related morbidity and mortality. Klebsiella pneumoniae bacilli are prevalent among diabetic patients, especially in Asian populations. The present study aimed to identify risk factors for in-hospital mortality among diabetic patients complicated by community-acquired K. pneumoniae bacteremia. METHODS: We evaluated the clinical characteristics of 341 Taiwanese type 2 diabetic patients who were treated for community-acquired K. pneumoniae bacteremia. We then analyzed outcome predictors, and in particular comorbidities and the site of infection. RESULTS: The overall in-hospital mortality rate was 14.1%. Comorbid cancer was the leading factor, accounting for 32.1% of all cases of mortality. Pulmonary infection, primary bacteremia, afebrile or shock presentation and low serum albumin level were risk factors for in-hospital mortality. Regardless of comorbidities, pulmonary infection [odds ratio (OR) 10.74, 95% confidence interval (CI) 2.02-57.09] and albumin level (OR 0.15, 95% CI 0.03-0.76) were the main risk predictors. The receiver operating characteristic curve indicated that a serum albumin level lower than 2.4 g/dL (71.1% sensitivity and 77.4% specificity) suggested a poor prognosis in the diabetic patients with K. pneumoniae bacteremia. In patients with pulmonary infection, the capsular serotypes of K. pneumoniae were not related to poor outcomes, and an initial presentation of blunted fever or shock were independent factors for mortality. CONCLUSION: Cancer, pulmonary infection, and low serum albumin levels were independent indicators of in-hospital mortality in the diabetic patients complicated by K. pneumoniae bacteremia. The sites of infection and host characteristics should always elicit medical attention when treating these patients.

Effect of glycemic control on microalbuminuria development among type 2 diabetes with high-normal albuminuria.

This study was aimed at revealing the factors and the interrelationships between factors on microalbuminuria development among type 2 diabetes (T2D) patients. Between 2004 and 2011, 461 T2D patients with a baseline urine albumin-to-creatinine ratio (UACR) of <30 mg/g, and an estimated glomerular filtration rate (eGFR) of >60 mL/min were evaluated retrospectively. Sixty-eight (14.8%) subjects had developed microalbuminuria in a mean follow-up of 6.82 years. Statistical analysis had revealed that the higher baseline UACR (10 mg/g; sensitivity, 80.9%, specificity, 63.6%; AUC = 0.774) and glycohemoglobin level (HbA1c) (8%; sensitivity, 72.1%, specificity, 61.6%; AUC = 0.698) were the two independent microalbuminuria risk factors. When considering the risk of microalbuminuria, the data were normalized with respect to subjects with low-normal UACR (<10 mg/g) and HbA1c < 8%. The adjusted hazard ratio for subjects with low-normal UACR/HbA1c > 8%, high-normal UACR/HbA1c < 8%, and high-normal UACR/HbA1c >8% were 2.59 (p = 0.107), 6.15 (p = 0.001), and 16.96 (p < 0.001), respectively. It was determined that an increase of HbA1c levels (<8, 8-9, 9-10, >10%) showed a progressively increase of the hazard risk in baseline high-normal UACR group. But the same correlation was not shown in the low-normal UACR group. This study identified the relationships of high-normal albuminuria and glycemic control on microalbuminuria development among T2D patients. Glycemic control is especially beneficial for T2D patients with baseline high-normal UACR in preventing microalbuminuria development.

Microbiome of limb-threatening diabetic foot ulcers indicates the association of fastidious Stenotrophomonas and major amputation.

BACKGROUND: Proper identification of the polymicrobial microorganisms in patients with limb-threatening diabetic foot ulcers (LTDFUs) using conventional culture is insufficient. This prospective study evaluates the potential value of adjuvant molecular testing assisting in identify fastidious micro-organisms in LTDFUs compared to standard treatment alone. METHODS: Ninety patients with LTDFUs received interdisciplinary and standard antibiotic treatment in a referral diabetic foot center. A simultaneous 16S amplicon sequencing (16S AS) specimen along with conventional culture collected at admission was used to retrospectively evaluate the microbiological findings and its association with amputation outcomes. RESULTS: The microorganism count revealed by 16S AS overwhelmed that of conventional culturing (17 vs. 3 bacteria/ulcer respectively). The Stenotrophomonas spp. revealed in 29 patients were highly correlated with major (above ankle) amputation (OR: 4.76, 95% CI 1.01-22.56), while only one had been concomitantly identified by conventional culturing. Thus, there were 27 cases without proper antibiotics coverage during treatment. CONCLUSIONS: Adjuvant molecular testing assisted identification of fastidious pathogens such as Stenotrophomonas infection and might be associated with major amputation in patients with LTDFUs.

Effects of ON101 for Hard-to-Heal Diabetic Foot Ulcers in A Randomized Phase III Trial: A Post hoc Analysis.

OBJECTIVE: Hard-to-heal diabetic foot ulcers (DFUs) are associated with higher mortality rates and an increased medical burden for patients. ON101, a new topical cream, exhibited better healing efficacy than the control dressing in a Phase III trial. In this post-hoc analysis, we further identify whether ON101 can improve the healing of ulcers with hard-to-heal risk factors in this cohort of DFU patients. APPROACH: To compare the efficacy of ON101 with absorbent dressing among various hard-to-heal wounds in patients with DFU, a post hoc analysis of a randomized phase III trial included 276 DFU patients was performed by subgrouping those patients based on ulcer depth, location, size, duration, and patients' glycated hemoglobin (HbA1c) levels and body mass index (BMI). RESULTS: In the full analysis set, the proportion of patients achieving healing was 61.7% in the ON101 group and 37.0% in the comparator (P =0.0001). In sub-group analysis according to risk factors, ON101 demonstrated superior healing capacity on Wagner grade 2 ulcers (P < 0.0001); plantar ulcers (P = 0.0016), ulcers size ≥5 cm² (P = 0.0122), ulcers duration ≥3 months (P = 0.0043); for patients with HbA1c ≥9% (P = 0.0285); and patients with BMI ≥25 (P = 0.0005). INNOVATION: ON101, a novel therapeutic drug, can modulate the functions of macrophages and demonstrate superior healing rates to conventional absorbent dressing in patients with hard-to-heal DFUs. CONCLUSIONS: The results of this post hoc study suggest that ON101 is a better therapeutic option than conventional dressing used in treatment for DFU patients with higher HbA1c, BMI, or ulcers with complex conditions such as longer duration, deeper wounds, larger size, and plantar location.

A comparative analysis of complementary therapies use among patients attending diabetic clinics in Taiwan: 2007 vs. 2023.

BACKGROUND: We conducted a comparative study to examine the differences in the use of complementary therapies (CT) among patients who attended diabetic clinics for follow-up treatment between 2007 and 2023 in Taiwan. METHODS: This study employed a cross-sectional survey design to recruit individuals with diabetes from two regions (northern and southern) of Taiwan. A total of 183 and 307 participants were included in the surveys of 2007 and 2023, respectively. The data were analyzed using IBM SPSS Statistics version 28.0 to compare the survey results between the two time periods. RESULTS: Among the various CTs, nutritional supplements remained the most prevalent, with a significant increase in usage from 68.3% in 2007 to 89.9% in 2023. Conversely, other therapies, such as Chinese herbal medicines, manipulative-based therapies, supernatural healings, and bioelectromagnetic-based therapies, demonstrated a significant decrease in usage between the two time periods. Furthermore, the disclosure rate of CT use to healthcare professionals remained persistently low, with only 24.6% in 2007 and a slight increase to 30.3% in 2023. CONCLUSION: The significant rise in the use of nutritional supplements in conjunction with conventional medicine, without adequate monitoring and guidance from healthcare professionals, poses a substantial risk of unregulated blood sugar control, compromised diabetes management, and potential harm to health outcomes.

Photonic machine learning with on-chip diffractive optics.

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.

Diabetes-related kidney, eye, and foot disease in Taiwan: an analysis of the nationwide data for 2000-2009.

BACKGROUND/PURPOSE: Diabetes is one of the leading causes of dialysis, blindness, and amputation worldwide. However, the prevalence of diabetes-related kidney, eye, and foot diseases has not been investigated in national surveys. METHODS: In this study, we reviewed data sets of the National Health Insurance claims for the years 2000-2009. In 2009, the total population of Taiwan was 23 million. We de-identified the data and then analyzed them on inpatients and outpatients with diabetes mellitus, kidney diseases, eye diseases, peripheral vascular diseases (PVDs), and diabetic foot according to the International Classification of Diseases, 9(th) Revision with Clinical Modification diagnosis codes. RESULTS: The prevalence of diabetic nephropathy increased from 13.32% in 2000 to 15.42% in 2009. The corresponding diabetes dialysis rate increased from 1.5% to 2.46% during the same period (p < 0.001). The prevalence rates of retinopathy and PVD also increased (from 6.17% to 8.91%; p = 0.002 and from 1.87 to 2.47; p < 0.001, respectively). More than 94% of the patients treated for diabetic foot in the hospital had an associated foot infection. The prevalence of in-hospital diabetic foot decreased from 1.68% to 1.02% during the years 2000-2009 (p < 0.001), while the rates of lower extremity amputation as the treatment outcome did not show improvement (mean amputation rate: 28.35%). CONCLUSION: During the years 2000-2009, patients with diabetes in Taiwan had an increased risk for kidney, eye, and PVDs. Multidisciplinary teams need to be set up for the treatment of complications related to diabetic foot, and preventions programs that are specifically designed to target these complications should now be made mandatory.

Novel Giomers Incorporated with Antibacterial Quaternary Ammonium Monomers to Inhibit Secondary Caries.

The objective of this study was to develop novel modified giomers by incorporating the antibacterial quaternary ammonium monomers (QAMs), dimethylaminododecyl methacrylate (DMADDM) or dimethylaminohexadecyl methacrylate (DMAHDM) into a commercial giomer. The material performances including mechanical properties, surface characteristics, color data, cytotoxicity and fluoride release of the novel giomers were evaluated. Antibacterial activity against severe early childhood caries (S-ECC) saliva-derived biofilms was assessed by lactic acid production measurement, MTT assay, biofilm staining and 16S rRNA sequencing. A rat model was developed and the anti-caries effect was investigated by micro-CT scanning and modified Keyes' scoring. The results showed that the material properties of the QAMs groups were comparable to those of the control group. The novel giomers significantly inhibited lactic acid production and biofilm viability of S-ECC saliva-derived biofilms. Furthermore, caries-related genera such as Streptococcus and Lactobacillus reduced in QAMs groups, which showed their potential to change the microbial compositions. In the rat model, lesion depth, mineral loss and scoring of the QAMs groups were significantly reduced, without side effects on oral tissues. In conclusion, the novel giomers incorporated with antibacterial QAMs could inhibit the cariogenic biofilms and help prevent secondary caries, with great potential for future application in restorative treatment.