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Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu’s threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Objectives: Staying physically active is a cost-efficient strategy for disease prevention during a pandemic. The purposes of this study were to explore precautionary behaviors, psychological factors associated with physical activity and sedentary behavior, and impacts of active and sedentary lifestyles on the quality of life in the early stage of the coronavirus disease 2019 (COVID-19) outbreak.Methods: Participants were community-dwelling adults aged over 20 years who had not been infected with COVID-19 and who lived in the United States. A study with a cross-sectional design was conducted between July and October 2020. Quantitative data were collected by a self-reported questionnaire.Results: In total, 467 valid responses were obtained. Participants who engaged in an active lifestyle had significantly higher scores on all domains of quality of life compared to those who engaged in an inactive lifestyle. Participants with a non-sedentary lifestyle had significantly higher scores of psychological and social domains of quality of life than those with a sedentary lifestyle.Conclusion: Engaging in an active lifestyle and avoiding a sedentary lifestyle are recommended when facing future, unpredictable pandemics similar to COVID-19.

Recessive variants of SLC26A4 are a common cause of hereditary hearing impairment and are responsible for non-syndromic enlarged vestibular aqueducts and Pendred syndrome. Patients with bi-allelic SLC26A4 variants often suffer from fluctuating hearing loss and recurrent vertigo, ultimately leading to severe to profound hearing impairment. However, there are currently no satisfactory prevention or treatment options for this condition. The CRISPR/Cas9 genome-editing technique is a well-known tool for correcting point mutations or manipulating genes and shows potential therapeutic applications for hereditary disorders. In this study, we used the homology-independent targeted integration (HITI) strategy to correct the SLC26A4 c.919-2A>G variant, the most common SLC26A4 variant in the Han Chinese population. Next-generation sequencing was performed to evaluate the editing efficiency of the HITI strategy. The results showed that only 0.15% of the reads successfully exhibited HITI integration, indicating that the c.919-2 region may not be a suitable region for HITI selection. This suggests that other site selection or insertion strategies may be needed to improve the efficiency of correcting the SLC26A4 c.919-2A>G variant. This experience may serve as a valuable reference for other researchers considering CRISPR target design in this region.

BackgroundLaparoscopic greater curvature plication (LGCP) is a new bariatric procedure that is similar to laparoscopic sleeve gastrectomy (LSG) in that it uses a restrictive mechanism. Comparative studies between LGCP and LSG were still limited. The aim of this study was to compare the clinical outcomes of the two procedures based on the same clinical conditions.MethodsFrom January 2012 to December 2015, 260 patients with morbid obesity underwent LGCP and LSG in a single center. Data on patient demography, operation time, complications, hospital stay, body mass index loss, percentage of excess weight loss (%EWL), and improvement in comorbidities were collected. A propensity-matched analysis, incorporating pre-operative variables, was used to compare the short-term outcomes between LGCP and LSG.ResultsPropensity matching produced 48 patients in each group. Patients who underwent LGCP were predominately female (75.5%, 41.1% of the LSG patients were female, p = 0.028). Baseline BMI and excess weight were significantly lower in the LGCP group (p < 0.001). The LSG group showed a greater decrease in excess body weight than the LGCP group (LSG, 47.36 ± 12.95% in 3 months, 57.97 ± 19.28% in 6 months, 66.28 ± 25.42% in 12 months; LGCP, 39.67 ± 12.58% in 3 months, 47.40 ± 19.30% in 6 months, 48.02 ± 20.17% in 12 months, p = 0.008, 0.032, 0.010). Perioperative complications and resolution of obesity-related comorbidities were not significantly different between the two groups.ConclusionLGCP and LSG are both feasible and safe procedures for surgical weight reduction. In short-term follow-ups, LSG demonstrates a better excess body weight reduction while having perioperative complications similar to LGCP.

Objective. Patients with anti-Jo-1 antibodies (Abs) and anti–melanoma differentiation-associated protein 5 (MDA5) Abs are at a higher risk of interstitial lung disease (ILD) and have a mortality rate higher than that of patients with anti-Jo-1 Abs. This study investigated differences in the clinical characteristics and prognosis of patients with anti-Jo-1 Abs and anti-MDA5 Abs with dermatomyositis (DM). Methods. We retrospectively reviewed the medical records of 38 patients with DM from January 2000 to December 2021. The patients were divided into anti-Jo-1 Abs and anti-MDA5 Abs groups. The basic demographic data, clinical manifestations, and 1-year mortality rates of the groups were compared. Results. Among the 38 patients, 30 were anti-Jo-1-Abs positive and 8 patients were anti-MDA5 Aba positive. The patients with anti-MDA5 Abs presented with more apparent cutaneous symptoms and aggressive pulmonary manifestations than did those with anti-Jo-1 Abs. The mortality rate in the anti-MDA5 Abs group (1.95/person-year (PY)) was much higher than that in anti-Jo-1 Abs group (0.094/PY), and most of the mortalities occurred within the first 1–3 months of follow-up. Conclusion. Distinct cutaneous and pulmonary manifestations were observed in the anti-Jo-1 Abs and anti-MDA5 Abs groups. The mortality rate in the anti-MDA5 Abs group was significantly higher than that in the anti-Jo-1 Abs group. Early recognition is crucial to ensuring higher chances of survival for patients with anti-MDA5 Abs.

Human pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer type with a very high mortality rate. Inflammatory cytokine such as tumor necrosis factor- alpha (TNF-α) plays a pivotal role in the progression of PDAC. Recently, suppression of cell invasion by preventive agents has received considerable attention in the prevention of metastatic tumors. Several clinical studies suggested that natural forms or analogues of fat-soluble vitamins such as vitamin A and vitamin D can work as anti-cancer agents to inhibit the development of cancer. In this study, our results demonstrated that co-treatment of 13-cis retinoic acid (13-cis RA) and 1,25-dihydroxyvitamin D3 (1,25-VD3) significantly inhibited TNF-α mediated cell invasion in PDAC in vitro . Cotreatment of 13-cis RA and 1,25-VD3 also inhibited TNF-α mediated expression of matrix metalloproteinase-9 (MMP-9) protein through blocking c-Jun N-terminal kinase (JNK) and nuclear factor kappa B (NF-κB) signaling pathways. Our results demonstrated that treatment of TNF-α lead to a decreased expression of tissue inhibitor of metalloproteinase- 3 (TIMP-3) protein and an induction of MMP-9 protein and cell invasion through an upregulation of microRNA-221 (miR-221) in human PDAC cells. Moreover, treatment of SP600125 (a specific inhibitor of JNK pathway) or cotreatment of 13-cis RA and 1,25-VD3 significantly induced a decreased expression of miR-221 and an increased expression of TIMP-3 protein. These results suggest that 13-cis RA and 1,25-VD3 significantly suppress TNF-α mediated cell invasion and therefore potentially act as preventive agents against PDAC.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Background The c.G6055A (p.G2019S) mutation in leucine-rich repeat kinase 2 ( LRRK2 ) is the most prevalent genetic cause of Parkinson’s disease (PD). CRISPR/Cas9-mediated genome editing by homology-directed repair (HDR) has been applied to correct the mutation but may create small insertions and deletions (indels) due to double-strand DNA breaks. Adenine base editors (ABEs) could convert targeted A·T to G·C in genomic DNA without double-strand breaks. However, the correction efficiency of ABE in LRRK2 c.G6055A (p.G2019S) mutation remains unknown yet. This study aimed to compare the mutation correction efficiencies and off-target effects between HDR and ABEs in induced pluripotent stem cells (iPSCs) carrying LRRK2 c.G6055A (p.G2019S) mutation. Methods A set of mutation-corrected isogenic lines by editing the LRRK2 c.G6055A (p.G2019S) mutation in a PD patient-derived iPSC line using HDR or ABE were established. The mutation correction efficacies, off-target effects, and indels between HDR and ABE were compared. Comparative transcriptomic and proteomic analyses between the LRRK2 p.G2019S iPSCs and isogenic control cells were performed to identify novel molecular targets involved in LRRK2-parkinsonism pathways. Results ABE had a higher correction rate (13/53 clones, 24.5%) than HDR (3/47 clones, 6.4%). Twenty-seven HDR clones (57.4%), but no ABE clones, had deletions, though 14 ABE clones (26.4%) had off-target mutations. The corrected isogenic iPSC-derived dopaminergic neurons exhibited reduced LRRK2 kinase activity, decreased phospho-α-synuclein expression, and mitigated neurite shrinkage and apoptosis. Comparative transcriptomic and proteomic analysis identified different gene expression patterns in energy metabolism, protein degradation, and peroxisome proliferator-activated receptor pathways between the mutant and isogenic control cells. Conclusions The results of this study envision that ABE could directly correct the pathogenic mutation in iPSCs for reversing disease-related phenotypes in neuropathology and exploring novel pathophysiological targets in PD.

Therapeutic antibodies play an important role in the public healthcare system to treat patients with a variety of diseases. Protein characterization using an array of analytical tools provides in-depth information for drug quality, safety, efficacy, and the further understanding of the molecule. A therapeutic antibody candidate MAB1 exhibits unique binding properties to both cation and anion exchange columns at neutral pH. This uniqueness disrupts standard purification processes and necessitates adjustments in manufacturing. This study identifies that the charge heterogeneity of MAB1 is primarily due to the N-terminal cyclization of glutamine to pyroglutamine and, to a lesser extent, succinimide intermediate, deamidation, and C-terminal lysine. Using three approaches, i.e., deferential chemical labeling, H/D exchange, and molecular modeling, the binding to anion exchange resins is attributed to negatively charged patches on the antibody’s surface, involving specific carboxylic acid residues. The methodologies shown here can be extended to study protein binding orientation in column chromatography.