Importing Diffusion and Re-Designed Backward Process for Image De-Raining.

In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image processing. The effective elimination of unwanted noise plays a crucial role in improving image quality. To meet this challenge, many noise removal methods have been proposed, among which the diffusion model has become one of the focuses of many researchers. In order to make the restored image closer to the real image and retain more features of the image, this paper proposes a DIR-SDE method with reference to the diffusion models of IR-SDE and IDM, which improve the feature retention of the image in the de-raining process, and then improve the realism of the image for the image de-raining task. In this study, IR-SDE was used as the base structure of the diffusion model, IR-SDE was improved, and DINO-ViT was combined to enhance the image features. During the diffusion process, the image features were extracted using DINO-ViT, and these features were fused with the original images to enhance the learning effect of the model. The model was also trained and validated with the Rain100H dataset. Compared with the IR-SDE method, it improved 0.003 in the SSIM, 0.003 in the LPIPS, and 1.23 in the FID. The experimental results show that the diffusion model proposed in this study can effectively improve the image restoration performance.

Explainable machine learning model for identifying key gut microbes and metabolites biomarkers associated with myasthenia gravis.

Diagnostic markers for myasthenia gravis (MG) are limited; thus, innovative approaches are required for supportive diagnosis and personalized care. Gut microbes are associated with MG pathogenesis; however, few studies have adopted machine learning (ML) to identify the associations among MG, gut microbiota, and metabolites. In this study, we developed an explainable ML model to predict biomarkers for MG diagnosis. We enrolled 19 MG patients and 10 non-MG individuals. Stool samples were collected and microbiome assessment was performed using 16S rRNA sequencing. Untargeted metabolic profiling was conducted to identify fecal amplicon significant variants (ASVs) and metabolites. We developed an explainable ML model in which the top ASVs and metabolites are combined to identify the best predictive performance. This model uses the SHapley Additive exPlanations method to generate both global and personalized explanations. Fecal microbe-metabolite composition differed significantly between groups. The key bacterial families were Lachnospiraceae and Ruminococcaceae, and the top three features were Lachnospiraceae, inosine, and methylhistidine. An ML model trained with the top 1 % ASVs and top 15 % metabolites combined outperformed all other models. Personalized explanations revealed different patterns of microbe-metabolite contributions in patients with MG. The integration of the microbiota-metabolite features and the development of an explainable ML framework can accurately identify MG and provide personalized explanations, revealing the associations between gut microbiota, metabolites, and MG. An online calculator employing this algorithm was developed that provides a streamlined interface for MG diagnosis screening and conducting personalized evaluations.

CD3ε of a pan T cell marker involved in mouse Aspergillus fumigatus keratitis.

AIM: To explore whether CD3ε is involved in the adaptive immunity of Aspergillus fumigatus (A. fumigatus) keratitis in mice and the role of innate and adaptive immunity in it. METHODS: Mice models of A. fumigatus keratitis were established by intra-stromal injection and corneal epithelial scratching. Subconjunctival injections of natamycin, wedelolactone, LOX-1 inhibitor (poly I) or Dectin-1 inhibitor (laminarin) were used to treat mice with A. fumigatus keratitis. Mice were pretreated by intraperitoneal injection of anti-mouse CD3ε. We observed the corneal infection of mice under the slit lamp microscope and made a clinical score. The protein expression of CD3ε and interleukin-10 (IL-10) was determined by Western blotting. RESULTS: With the disease progresses, the degree of corneal opacity and edema augmented. In the intra-stromal injection models, CD3ε protein expression began to increase significantly on the 2nd day. However, in the scraping epithelial method models, CD3ε only began to increase on the 3rd day. After natamycin treatment, the degree of corneal inflammation in mice was significantly attenuated on the 3rd day. After wedelolactone treatment, the severity of keratitis worsened. And the amount of CD3ε protein was also reduced, compared with the control group. By inhibiting LOX-1 and Dectin-1, there was no significant difference in CD3ε production compared with the control group. After inhibiting CD3ε, corneal ulcer area and clinical score increased, and IL-10 expression was downregulated. CONCLUSION: As a pan T cell marker, CD3ε participate in the adaptive immunity of A. fumigatus keratitis in mice. In our mice models, the corneas will enter the adaptive immune stage faster. By regulating IL-10, CD3ε exerts anti-inflammatory and repairs effects in the adaptive immune stage.

Development of a Machine Learning Algorithm to Correlate Lumbar Disc Height on X-rays with Disc Bulging or Herniation.

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model.

Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.

Predicting the length of hospital stay for myasthenia gravis (MG) patients is challenging due to the complex pathogenesis, high clinical variability, and non-linear relationships between variables. Considering the management of MG during hospitalization, it is important to conduct a risk assessment to predict the length of hospital stay. The present study aimed to successfully predict the length of hospital stay for MG based on an expandable data mining technique, multivariate adaptive regression splines (MARS). Data from 196 MG patients' hospitalization were analyzed, and the MARS model was compared with classical multiple linear regression (MLR) and three other machine learning (ML) algorithms. The average hospital stay duration was 12.3 days. The MARS model, leveraging its ability to capture non-linearity, identified four significant factors: disease duration, age at admission, MGFA clinical classification, and daily prednisolone dose. Cut-off points and correlation curves were determined for these risk factors. The MARS model outperformed the MLR and the other ML methods (including least absolute shrinkage and selection operator MLR, classification and regression tree, and random forest) in assessing hospital stay length. This is the first study to utilize data mining methods to explore factors influencing hospital stay in patients with MG. The results highlight the effectiveness of the MARS model in identifying the cut-off points and correlation for risk factors associated with MG hospitalization. Furthermore, a MARS-based formula was developed as a practical tool to assist in the measurement of hospital stay, which can be feasibly supported as an extension of clinical risk assessment.

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis.

Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.

Comparisons Between Endoscopic Band Ligation, Radiofrequency Ablation and Endoscopic Thermal Therapy for Gastric Antral Vascular Ectasia: A Meta-Analysis.

BACKGROUND: Endoscopic band ligation (EBL) and radiofrequency ablation (RFA) have emerged as alternative therapies of gastric antral vascular ectasia (GAVE) in addition to endoscopic thermal therapy (ETT), but the optimum choice remains inconclusive. AIM: We conducted a meta-analysis in order to compare these three treatments for GAVE. METHODS: We searched the electronic databases of PubMed, Embase and Cochrane Central Register of Controlled Trials without any language restrictions and also performed a manual literature search of bibliographies located in both retrieved articles and published reviews for eligible publications prior to December 8, 2021. We included comparative trials which had evaluated the efficacy and safety of interventions in adults (aged ≥ 18 years) diagnosed with symptomatic GAVE and was confirmed according to clinical backgrounds and upper gastrointestinal endoscopy. We included reports that compared three interventions, ETT, EBL, and RFA. The study was comprised of adults diagnosed with GAVE and focused on overall mortality, bleeding cessation, endoscopic improvement, complications, hospitalization, hemoglobin improvement, number of sessions and transfusion requirements. RESULTS: Twelve studies were performed involving a total of 571 participants for analysis. When compared with ETT, EBL achieved better bleeding cessation (OR 4.48, 95% CI 1.36-14.77, p = 0.01), higher hemoglobin improvement (MD 0.57, 95% CI 0.31-0.83, p < 0.01) and lower number of sessions (MD - 1.44, 95% CI - 2.54 to - 0.34, p = 0.01). Additionally, EBL was superior to ETT in endoscopic improvement (OR 6.00, 95% CI 2.26-15.97, p < 0.01), hospitalization (MD - 1.32, 95% CI - 1.91 to - 0.74, p < 0.01) and transfusion requirement (MD - 2.66, 95% CI - 4.67 to - 0.65, p = 0.01) with statistical significance, with the exception of mortality (OR 0.58, 95% CI 0.19-1.77, p = 0.34) and complication rate (OR 5.33, 95% CI 0.58-48.84, p = 0.14). CONCLUSION: For GAVE, we suggest that EBL be initially recommended, and APC and RFA be used as alternative treatment choices based upon a very low quality of evidence.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously, this incurs the uncertainty of safety. Recently, in the literature, several studies have been proposed for the above-mentioned issue via Artificial Intelligence (AI). The achievement is exactly the aim that we look forward to, i.e., the autonomous vehicle. In this paper, we realize the autonomous driving control via Deep Reinforcement Learning (DRL) based on the CARLA (Car Learning to Act) simulator. Specifically, we use the ordinary Red-Green-Blue (RGB) camera and semantic segmentation camera to observe the view in front of the vehicle while driving. Then, the captured information is utilized as the input for different DRL models so as to evaluate the performance, where the DRL models include DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient). Moreover, we also design an appropriate reward mechanism for these DRL models to realize efficient autonomous driving control. According to the results, only the RDPG strategies can finish the driving mission with the scenario that does not appear/include in the training scenario, and with the help of the semantic segmentation camera, the RDPG control strategy can further improve its efficiency.

Ba3.5Cu7.55In1.15Se9: A Wide-Bandgap Copper Indium Selenide Reveals Strong Luminescence and Third-Harmonic Generation.

A new copper indium selenide, Ba3.5Cu7.55In1.15Se9, was synthesized by the KBr flux reaction at 800 °C. The compound crystallizes with orthorhombic Pnma, a = 46.1700(12) Å, b = 4.26710(10) Å, c = 19.8125(5) Å, and Z = 8. The structural framework mainly consists of four sites of cubane-type defective M4Se3 (M = Cu, Cu/In) units with disordered Cu+/In3+ ions present at the part corner of each unit. The single crystal emits intense photoluminescence at 657 nm with a relative quantum yield (RQY) 0.2 times that of rhodamine 6G powder. The compound belongs to a direct band gap at 1.91 eV, analyzed by Tauc's plot, and the energy is close to the PL position. The Hall effect measurement on a pressed pellet reveals an n-type conductivity with a carrier concentration of 3.358 × 1017 cm-3 and a mobility of 24.331 cm2 V-1 s-1. Furthermore, the compound produces a strong nonlinear third-harmonic generation (THG), with an χS(3) value of 1.3 × 105 pm2/V2 comparable to 1.6 × 105 pm2/V2 for AgGaSe2 measured at 800 nm.

Exploring the Antiovarian Cancer Mechanisms of Salvia Miltiorrhiza Bunge by Network Pharmacological Analysis and Molecular Docking.

Background: Ovarian cancer was one of the gynecological malignant tumors. Salvia miltiorrhiza Bunge (SMB) was a kind of herbal medicine with an antitumor effect. However, the inhibitory effect of SMB on ovarian cancer and its potential mechanism were still unclear. Objective: The antitumor effect of SMB on ovarian cancer was studied by network pharmacology and molecular docking techniques, and its possible molecular mechanisms were analyzed. Method: The active ingredients of SMB and the target data of ovarian cancer were obtained from the Traditional Chinese Medicines for Systems Pharmacology Database (TCMSP) and the GeneCards database. The relationship between active ingredients of SMB and ovarian cancer targets was analyzed by String database, David 6.8 online database, and Cytoscape 3.7.2 software, and then potential pathways were screened out. In addition, molecular docking technology was used to verify further the binding effect of antiovarian cancer pathway targets with active ingredients of SMB. Finally, survival analysis was performed for all potential targets. Results: We analyzed 71 SMB-ovarian cancer common targets, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the PI3K-Akt signaling pathway might be an essential pathway for SMB to inhibit ovarian cancer. Luteolin, Tanshinone IIA, and Cryptotanshinone in SMB might play an important role. HSP90AA1, CDK2, and PIK3CG might be potential targets of SMB in inhibiting ovarian cancer. Conclusion: Through network pharmacology and molecular docking analysis, we found that SMB might partially inhibit ovarian cancer by the PI3K-Akt signaling pathway. We believe that SMB might be a potential therapeutic agent for ovarian cancer patients.

Prevalence and Risk Factors of Zoonotic Dermatophyte Infection in Pet Rabbits in Northern Taiwan.

Dermatophytes are the group of keratinophilic fungi that cause superficial cutaneous infection, which traditionally belong to the genera Trichophyton, Microsporum, and Epidermophyton. Dermatophyte infection is not only a threat to the health of small animals, but also an important zoonotic and public health issue because of the potential transmission from animals to humans. Rabbit dermatophytosis is often clinically identified; however, limited information was found in Asia. The aims of this study are to investigate the prevalence and to evaluate the risk factors of dermatophytosis in pet rabbits in Northern Taiwan. Between March 2016 and October 2018, dander samples of pet rabbits were collected for fungal infection examination by Wood's lamp, microscopic examination (KOH preparation), fungal culture, and PCR assay (molecular identification). Z test and Fisher's exact test were performed to evaluate the potential risk factors, and logistic regression analysis was then performed to build the model of risk factors related to dermatophyte infection. Of the collected 250 dander samples of pet rabbits, 29 (11.6%) samples were positive for dermatophytes by molecular identification. In those samples, 28 samples were identified as the T. mentagrophytes complex and 1 sample was identified as M. canis. Based on the results of the Firth's bias reduction logistic analyses, animal source (rabbits purchased from pet shops) and number of rearing rabbits (three rabbits or more) were shown as the main risks for dermatophyte infection in the pet rabbits in Taiwan. The results of the present study elucidate the prevalence of rabbit dermatophyte infection, pathogens, and risk factors in Taiwan, and provide an important reference for the prevention and control of rabbit dermatophytosis.

CCPG1 involved in corneal Aspergillus fumigatus infection.

AIM: To investigate whether non-canonical autophagy transport receptor cell cycle progression 1 (CCPG1) is involved in the corneal antifungal immune response. METHODS: Human corneal epithelial cells (HCECs) and human myeloid leukemia mononuclear cells (THP-1) macrophages stimulated by Aspergillus fumigatus (A. fumigatus) were used as cell models. The expression of CCPG1 mRNA was detected by qRT-PCR. Western blot was used to determine the protein expression of CCPG1 and interleukin-1ß (IL-1ß). The dectin-1 neutralizing antibody was used to detect the association between dectin-1 and CCPG1. Immunofluorescence was used to observe the colocalization of CCPG1 and C-type lectin-like receptor-1 (CLEC-1) in THP-1 macrophages. RESULTS: The expression of CCPG1 started to increase at 4h after infection and increased in a time-dependent manner in HCECs and THP-1 macrophages. With dectin-1 neutralizing antibody pretreatment, the expression of IL-1ß was down-regulated. CCPG1 up-regulation in response to A. fumigatus infection was independent of dectin-1. Immunofluorescence showed the colocalization of CCPG1 and CLEC-1 in THP-1 macrophages. CONCLUSION: As a specific autophagy protein of non-canonical autophagy pathway, CCPG1 is involved in corneal infection with A. fumigatus.

Thymol Protects against Aspergillus Fumigatus Keratitis by Inhibiting the LOX-1/IL-1ß Signaling Pathway.

OBJECTIVE: To explore the anti-inflammatory effects and mechanisms of action of thymol in Aspergillus fumigatus (A. fumigatus) keratitis. METHODS: The minimum inhibitory concentration of thymol against A. fumigatus was detected. To characterize the anti-inflammatory effects of thymol, mouse corneas and human corneal epithelial cells were pretreated with thymol or dimethyl sulfoxide (DMSO) before infection with A. fumigatus spores. Slit-lamp microscopy, immunohistochemistry, myeloperoxidase detection, quantitative real-time polymerase chain reaction, and Western blotting were used to assess infection. Neutrophil and macrophage recruitment, in addition to the secretion of LOX-1 and IL-1ß, were quantified to evaluate the relative contribution of thymol to the inflammatory response. RESULTS: We confirmed that the growth of A. fumigatus was directly inhibited by thymol. In contrast with the DMSO group, there was a lower degree of inflammation in the mouse corneas of the thymol-pretreated group. This was characterized by significantly lower clinical scores, less inflammatory cell infiltration, and lower expression of LOX-1 and IL-1ß. Similarly, in vitro experiments indicated that the production of LOX-1 and IL-1ß was significantly inhibited after thymol treatment, in contrast with the DMSO-pretreated group. CONCLUSION: Our findings demonstrate that thymol exerted a direct fungistatic activity on A. fumigatus. Furthermore, thymol played a protective role in fungal keratitis by inhibiting LOX-1/IL-1ß signaling pathway and reducing the recruitment of neutrophils and macrophages.

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach.

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.

Assessment of Sarcopenia and Obesity in Patients with Myasthenia Gravis Using Dual-Energy X-ray Absorptiometry: A Cross-Sectional Study.

Sarcopenia and obesity can negatively impact quality of life and cause chronic fragility, and are associated with neuromuscular diseases, including myasthenia gravis (MG). The long-term consequences of body composition changes in chronic MG remain unknown; we therefore evaluated changes in body composition, including sarcopenia, obesity, lean body mass, and the prevalence of sarcopenic obesity in patients. In this cross-sectional study, 35 patients with MG (mean age: 56.1 years) and 175 matched controls were enrolled. Body fat mass and skeletal muscle mass were measured using whole body dual-energy X-ray absorptiometry. Patients with MG exhibited a higher prevalence of obesity and higher android adiposity and total body fat percentage than those of controls. Although the prevalence of sarcopenia and sarcopenic obesity did not increase with age, there was a decrease in arm and android muscle mass in patients with MG compared with controls. Lower muscle mass percentages were correlated with increased age and MG severity, but not with corticosteroid use. Thus, MG is associated with increased risk for obesity and decreased muscle mass with aging, regardless of corticosteroid use. Therefore, accurate diagnosis of body composition changes in MG could facilitate the application of appropriate therapies to promote health, improve quality of life, and prevent fragility.

Ionic Signal Enhancement by the Space Charge Effect through the DNA Rolling Circle Amplification on the Outer Surface of Nanochannels.

DNA species are recognized as a powerful probe for nanochannel analyses to address the issues of specific target recognition and highly efficient signal conversion due to their programmable and predictable Watson-Crick bases. However, in the conventional view, abundant sophisticated DNA structures synthesized by DNA amplification strategies are unsuitable for use in nanochannel analyses owing to their low probability to enter a nanochannel restricted by the smaller opening of the nanochannel, as well as the faint ion signal produced by the steric effect. Here, we present an integrated strategy of nanochannel analyses that combines the target recognitions by encoded rolling circle amplification (RCA) in solution and the ionic signal enhancement by the space charge effect through the immobilization of highly negative-charged RCA amplicons on the outer surface of the nanochannels. Owing to the highly negative-charged RCA amplicons with 100 nm sizes, a sharp increase of ionic current up to 7454% has been achieved. The RCA amplicon triggered by mRNA-21 on the outer surface of the poly(ethylene terephthalate) membrane with a single nanochannel realized the single-base mismatch detection of mRNA-21 with a sensitivity of 6 fM. The DNA amplicon endows the nanochannel with high sensitivity and selectivity that could extend to other applications, such as DNA sequencing, desalination, sieving, and water-energy nexus.

Clinical Predictors of Prolonged Hospital Stay in Patients with Myasthenia Gravis: A Study Using Machine Learning Algorithms.

Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients.

Changes in Physical Fitness and Body Composition Associated with Physical Exercise in Patients with Myasthenia Gravis: A Longitudinal Prospective Study.

There is a lack of guidelines for physical exercise in patients with myasthenia gravis (MG). A few pilot studies have shown that exercise can be safely applied to patients with MG. However, how physical exercise affects body composition, disease function, and disease severity remains unknown. In this prospective study, we enrolled 34 patients with MG with stable condition and evaluated the disease severity, physical fitness parameters, and body composition (measured using whole-body dual-energy X-ray absorptiometry (DXA)), before and after conducting a 24-week physical exercise regimen of aerobic and resistance strength training. The outcomes were measured by DXA, quantitative MG (QMG) score, quality of life score, handgrip strength and walking speed. During the training regimen, participants were free to decide how many exercise sessions per week and regularly reported their weekly exercise time. The physical exercise program was well tolerated by the participants, the parameters of the QMG score and handgrip strength improved, and participants' body composition did not change significantly. The high exercise group experienced greater deterioration in muscle mass in the arms, but exhibited a greater improvement in forced vital capacity, walking speed, and symptom severity. The group with low QMG scores improved more in terms of physical fitness, including walking speed. These findings indicate that physical exercise is well tolerated by patients with MG, and is accompanied by improved muscular and physical functions. We propose that physical exercise is safe, effective, and appropriate for patients with well-regulated MG.

Regional and functional division of functional elements of solid-state nanochannels for enhanced sensitivity and specificity of biosensing in complex matrices.

Solid-state nanochannels (SSNs) provide a promising approach for biosensing due to the confinement of molecules inside, their great mechanical strength and diversified surface chemical properties; however, until now, their sensitivity and specificity have not satisfied the practical requirements of sensing applications, especially in complex matrices, i.e., media of diverse constitutions. Here, we report a protocol to achieve explicit regional and functional division of functional elements at the outer surface (FEOS) and inner wall (FEIW) of SSNs, which offers a nanochannel-based sensing platform with enhanced specificity and sensitivity. The protocol starts with the fabrication and characterization of the distribution of FEOS and FEIW. Then, the evaluation of the contributions of FEOS and FEIW to ionic gating is described; the FEIW mainly regulate ionic gating, and the FEOS can produce a synergistic effect. Finally, hydrophobic or highly charged FEOS are applied to ward off interference molecules, non-target molecules that may affect the ionic signal of nanochannels, which decreases false signals and helps to achieve the highly specific ionic output in complex matrices. Compared with other methods currently available, this method will contribute to the fundamental understanding of substance transport in SSNs and provide high specificity and sensitivity in SSN-based analyses. The procedure takes 3-6 d to complete.