Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method.

Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region.

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Titanium catalyzed [2σ + 2π] cycloaddition of bicyclo[1.1.0]-butanes with 1,3-dienes for efficient synthesis of stilbene bioisosteres.

Natural stilbenes have shown significant potential in the prevention and treatment of diseases due to their diverse pharmacological activities. Here we present a mild and effective Ti-catalyzed intermolecular radical-relay [2σ + 2π] cycloaddition of bicyclo[1.1.0]-butanes and 1,3-dienes. This transformation enables the synthesis of bicyclo[2.1.1]hexane (BCH) scaffolds containing aryl vinyl groups with excellent regio- and trans-selectivity and broad functional group tolerance, thus offering rapid access to structurally diverse stilbene bioisosteres.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction.

BACKGROUND: There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking. METHODS: A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed. RESULTS: A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis. CONCLUSION: Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Iodomethane in C1 chemistry: application in palladium-catalyzed [2 + 2 + 1] annulation.

An efficient palladium-catalyzed [2 + 2 + 1] annulation of 3-iodochromones, bridged olefins, and iodomethane is described, affording a range of chromone-containing polycyclic compounds. Additionally, the corresponding deuterated products were smoothly obtained with iodomethane-d3 instead of iodomethane. Moreover, the synthetic utility of this method is further substantiated by gram scale preparation and application to late-stage modification of estrone.

Standardized approach for accurate and reliable model development of ion-exchange chromatography based on parameter-by-parameter method and consideration of extra-column effects.

Developing an accurate and reliable model for chromatographic separation that meets regulatory requirements and ensures consistency in model development remains challenging. In order to address this challenge, a standardized approach was proposed in this study with ion-exchange chromatography (IEC). The approach includes the following steps: liquid flow identification, system and column-specific parameters determination and validation, multi-component system identification, protein amount validation, steric mass action parameters determination and evaluation, and validation of the calibrated model's generalization ability. The parameter-by-parameter (PbP) calibration method and the consideration of extra-column effects were integrated to enhance the accuracy of the developed models. The experiments designed for implementing the PbP method (five gradient experiments for model calibration and one stepwise experiment for model validation) not only streamline the experimental workload but also ensure the extrapolation abilities of the model. The effectiveness of the standardized approach is successfully validated through an application about the IEC separation of industrial antibody variants, and satisfactory results were observed with R2 ≈ 0.9 for the majority of calibration and validation experiments. The standardized approach proposed in this work contributes significantly to improve the accuracy and reliability of the developed IEC models. Models developed using this standardized approach are ready to be applied to a broader range of industrial separation systems, and are likely find further applications in model-assisted decision-making of process development.

Comparation of 5 ml and 10 ml Negative Pressures with Wet-suction Techniques for EUS-FNA of Solid Lesions: A Single-center Randomized Controlled Trial.

BACKGROUND AND OBJECTIVES: The negative pressure selectable for the wet-suction technique remains uncertain. The aim was to investigate the quality of sampling and diagnostic accuracy with solid lesions by 5 mL and 10 mL negative pressure with wet-suction techniques. METHODS: This is a single-center, crossover, randomized controlled trial conducted with a random sampling technique. In all, 160 patients consecutively undergoing EUS-FNA for solid lesions were randomized in a ratio of 1:1 into 2 groups, the 5 mL and 10 mL negative pressure wet-suction group. The main outcome was to compare the sample quality between the 2 groups. The secondary outcome was to compare the histologic and cytologic diagnostic accuracy of solid lesions. RESULTS: Pancreatic (n=129) and nonpancreatic (n=27) lesions from 156 lesions were examined. The sample quality concluding cellularity, adequacy, integrity, and blood contamination were comparable between the 2 groups. However, in subgroup analysis, we found 19G FNA provided more integrity of specimen in 5 mL than in 10 mL group (100% vs. 82.9%, P=0.025). In contrast, this benefit was not noteworthy in the 22G FNA subgroup. And there was no statistically significant in histologic (87.82% vs. 87.18%, P=1.000) and cytologic (78.85% vs. 80.77%, P=0.778) accuracy between 5 mL and 10 mL groups. CONCLUSION: When using the wet-suction technique, 5 mL and 10 mL negative pressure offer equivalent sample quality and diagnostic accuracy. However, the 19G FNA can obtain better sample quality with 5 mL negative pressure than 10 mL negative pressure.

Therapeutic effects of Huayu Jiedu formula on endometriosis via downregulating GATA 6 expression.

Background: Endometriosis (EMs) is a common chronic inflammatory disease which is characterized by multiple clinical symptoms and high recurrence rate due to the absence of effective therapies. Huayu Jiedu Formula (HYJDF), is a traditional Chinese medicine prescription with five major herbs. It has been used as traditional medicine to treat EMs for more than twenty years and exerted a good therapeutic effect. However, the underlying mechanism is unclear. Here we aim to observe the effects of HYJDF on EMs and investigate the therapeutic mechanism. Methods: The extract components of HYJDF were identified and quantified by an UHPLC-QE-MS method. Network pharmacology was used to obtain the core targets of HYJDF for the treatment of EMs and the specific biologic processes involved. A total of 68 EMs cases were randomly divided into control (gestrinone) and observation (HYJDF) groups. The overall effectiveness, pain scores, cyst-size changes, serum CA125 levels, quality-of-life scores, safety, and adverse events were evaluated before and after treatment. For the mechanism research, DNA methylation-chip analysis was performed to determine the differential genes. EMs mice models and human ectopic stromal cells (ESCs) were treated with HYJDF and its pharmaceutical serum, respectively. The ectopic foci was measured via H&E staining while the expressions of the target genes were verified by real-time PCR and Western blot analysis. The inflammatory cytokine levels in the peritoneal fluid of mice were detected by ELISA. The proliferative potential of cells was analyzed by MTS whereas the apoptosis and cell cycle were determined through flow analysis. Results: The total number of components detected in positive and negative ion modes was 839 and 597, respectively. Network pharmacology suggested that HYJDF treated EMs through DNA methylation. We found that HYJDF and gestrinone exerted good therapeutic effect with no obvious difference, but the HYJDF treatment group had fewer side effects. GATA 6, which was hypomethylated and abundant in endometriotic cells, potently induced inflammatory response. This finding indicated the important role of GATA 6 in EMs development. Moreover, HYJDF ameliorated inflammatory response (i.e., reduced the levels of IL-1ß and PGE2 in peritoneal fluid), suppressed ESCs proliferation, and increased cell apoptosis by down-regulating GATA 6 expression. Conclusion: We demonstrated that HYJDF has anti-inflammation activity and increased cell apoptosis through the reduction of GATA 6 expression in ectopic tissues, which showed good therapeutic effect without any obvious side effects. These findings suggest that HYJDF may be a new and efficient traditional Chinese medicine for the treatment of EMs.

Risk Factors of the Totally Implantable Venous Access Device-Related Infection in Patients With Brain Tumors Undergoing Chemotherapy After Surgery.

Background: The complication of totally implantable venous access device (TIVAD) is an infection, which causes the death of patients. Therefore, it is critical to identify risk factors for TIVAD infection to prevent death. Patients and Methods: The enrolled patients were divided into two groups and subsequently divided into subgroups according to various factors in which the correlation between infection and risk factors was analyzed. Multivariable logistic analysis of odds ratios (ORs) for seven risk factors was performed, meanwhile, the receiver operating characteristic (ROC) curve analysis of neutrophil and serum albumin was conducted for the prediction of TIVAD infection occurrence. Results: Catheter-related blood stream infection was the common infection type, which was dominantly caused by Staphylococcus aureus. Removal of TIVAD and antibiotic therapy were the preferred approaches for the treatment of infection. Seven risk factors were closely associated with the TIVAD infection, however, two risk factors, including age and outpatient, were excluded according to the multivariate logistic analysis of ORs. Receiver operating characteristic curve analysis revealed neutrophil count and serum albumin could predict the occurrence of TIVAD infection. Conclusions: Five risk factors were positively related to TIVAD infection; neutrophil and serum albumin could be used to predict the occurrence of TIVAD infection.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography.

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

The characteristics of H6 against Microcystis aeruginosa.

Algal bloom caused by Microcystis aeruginosa has always been the focus of attention; microbial algal control has the advantages of significant effect, low investment cost, and environmental friendliness; the use of microbial technology to inhibit the bloom has a broad prospect for development. In this study, a strain of Enterobacterium algicidal bacteria screened from a river was used to study the algicidal characteristics against Microcystis aeruginosa using SEM, 3-D EEM and zeta potential. The results showed that the optimal dosage (v/v) of the strain was 5% and the removal rate of algal cells was 70% after 7 days. When the algal density was OD680nm = 0.3, the removal rate of algal cells reached 83% after 7 days. In the pH range of 5 ~ 11, the removal rate of algal cells was 70 ~ 80% after 7 days. Algicidal bacteria H6 is mainly indirect algae lysis and is supplemented by direct algae lysis. Algicidal bacteria H6 removes algicidal substances by secreting high temperature resistant algicidal substances and algicidal products are humic acids. Algicidal bacterium H6 was a strain of Enterobacterium with good algicidal effect in a wide pH range, which enriched the bacterial resources in the control of cyanobacteria bloom in water. The high temperature resistance of the algae-soluble substance secreted by the algae-soluble substance provided convenience for the subsequent preparation and application of bacterial powder.

Research on triamcinolone-loaded thermosensitive chitosan hydrogels for preventing esophageal stricture induced by endoscopic submucosal dissection.

Early-stage esophageal cancer is primarily treated by endoscopic submucosal dissection (ESD). However, extensive mucosal dissection creates a significant risk of postoperative esophageal stricture. Clinically, postoperative stricture can be prevented by glucocorticoids; however, there are drawbacks to both systemic and local administration of glucocorticoids, and improving drug administration methods is crucial. In this study, we developed a chitosan-based thermosensitive hydrogel for triamcinolone (TA) delivery. Our results indicated that the hydrogel remains liquid at low temperatures and can be injected into the esophageal wound site through an endoscopic biopsy channel. Upon reaching body temperature, the hydrogel undergoes spontaneous gelation and firmly adheres to the wound surface. The liquid phase enables convenient and precise delivery, while the gel phase achieves remarkable adhesion, tensile strength, and resistance to degradation. Moreover, the hydrogel exhibited an extended release duration of >10 days when loaded with a 10 mg dose. In vitro studies revealed that the hydrogel suppresses the proliferation and fibrogenesis of human scar fibroblasts (HKF). In a rat skin dermal defect model, the hydrogel attenuated keloid formation during the healing process. Consequently, the chitosan-based thermosensitive hydrogel developed in this study for triamcinolone delivery may be an effective tool for preventing post-ESD esophageal stricture.

Deep Learning Classification of Angle Closure based on Anterior Segment OCT.

PURPOSE: To assess the performance and generalizability of a convolutional neural network (CNN) model for objective and high-throughput identification of primary angle-closure disease (PACD) as well as PACD stage differentiation on anterior segment swept-source OCT (AS-OCT). DESIGN: Cross-sectional. PARTICIPANTS: Patients from 3 different eye centers across China and Singapore were recruited for this study. Eight hundred forty-one eyes from the 2 Chinese centers were divided into 170 control eyes, 488 PACS, and 183 PAC + PACG eyes. An additional 300 eyes were recruited from Singapore National Eye Center as a testing data set, divided into 100 control eyes, 100 PACS, and 100 PAC + PACG eyes. METHODS: Each participant underwent standardized ophthalmic examination and was classified by the presiding physician as either control, primary angle-closure suspect (PACS), primary angle closure (PAC), or primary angle-closure glaucoma (PACG). Deep Learning model was used to train 3 different CNN classifiers: classifier 1 aimed to separate control versus PACS versus PAC + PACG; classifier 2 aimed to separate control versus PACD; and classifier 3 aimed to separate PACS versus PAC + PACG. All classifiers were evaluated on independent validation sets from the same region, China and further tested using data from a different country, Singapore. MAIN OUTCOME MEASURES: Area under receiver operator characteristic curve (AUC), precision, and recall. RESULTS: Classifier 1 achieved an AUC of 0.96 on validation set from the same region, but dropped to an AUC of 0.84 on test set from a different country. Classifier 2 achieved the most generalizable performance with an AUC of 0.96 on validation set and AUC of 0.95 on test set. Classifier 3 showed the poorest performance, with an AUC of 0.83 and 0.64 on test and validation data sets, respectively. CONCLUSIONS: Convolutional neural network classifiers can effectively distinguish PACD from controls on AS-OCT with good generalizability across different patient cohorts. However, their performance is moderate when trying to distinguish PACS versus PAC + PACG. FINANCIAL DISCLOSURES: The authors have no proprietary or commercial interest in any materials discussed in this article.

Computational Study of Ground-State Destabilization Effects and Dipole-Dipole Interaction Energies in Amphidynamic Crystals.

Ground-state destabilization is a promising strategy to modulate rotational barriers in amphidynamic crystals. Density functional theory studies of polar phenylenes installed as rotators in pillared paddle-wheel metal organic frameworks were performed to investigate the effects of ground-state destabilization on their rotational dynamics. We found that as the steric size of phenylene substituents increases, the ground-state destabilization effect is also increased. Specifically, a significant destabilization of the ground-state energy occurred as the size of the substituents increased, with values ranging from 2 to 11.7 kcal/mol. An evaluation of the effects of substituents on dipole-dipole interaction energies and rotational barriers suggests that it should be possible to engineer amphidynamic crystals where the dipole-dipole interaction energy becomes comparable to the rotational barriers. Notably, while pure dipole-dipole interaction energies reached values ranging from 0.6 to 2.4 kcal/mol, the inclusion of electronic and steric effects can alter dipolar orientations to significantly greater values. We propose that careful selection of polar substituents with different sizes may help create temperature-responsive materials with switchable collective polarization.

Evaluation of dynamic control of continuous capture with periodic counter-current chromatography under feedstock variations.

Multi-column periodic counter-current chromatography is a promising technology for continuous antibody capture. However, dynamic changes due to disturbances and drifts pose some potential risks for continuous processes during long-term operation. In this study, a model-based approach was used to describe the changes in breakthrough curves with feedstock variations in target proteins and impurities. The performances of continuous capture of three-column periodic counter-current chromatography under ΔUV dynamic control were systematically evaluated with modeling to assess the risks under different feedstock variations. As the concentration of target protein decreased rapidly, the protein might not breakthrough from the first column, resulting in the failure of ΔUV control. Small reductions in the concentrations of target proteins or impurities would cause protein losses, which could be predicted by the modeling. The combination of target protein and impurity variations showed complicated effects on the process performance of continuous capture. A contour map was proposed to describe the comprehensive impacts under different situations, and nonoperation areas could be identified due to control failure or protein loss. With the model-based approach, after the model parameters are estimated from the breakthrough curves, it can rapidly predict the process stability under dynamic control and assess the risks under feedstock variations or UV signal drifts. In conclusion, the model-based approach is a powerful tool for continuous process evaluation under dynamic changes and would be useful for establishing a new real-time dynamic control strategy.

3 L split-dose polyethylene glycol is superior to 2 L polyethylene glycol in colonoscopic bowel preparation in relatively high-BMI (≥ 24 kg/m2) individuals: a multicenter randomized controlled trial.

BACKGROUND: Whether body mass index (BMI) is a risk factor for poor bowel preparation is controversial, and the optimal bowel preparation regimen for people with a high BMI is unclear. METHODS: We prospectively included 710 individuals with high BMIs (≥ 24 kg/m2) who were scheduled to undergo colonoscopy from January to November 2021 at 7 hospitals. Participants were randomly allocated into 3 L split-dose polyethylene glycol (PEG) group (n=353) and 2 L PEG group (n=357). The primary outcome was the rate of adequate bowel preparation, and the secondary outcomes included Boston Bowel Preparation Scale (BBPS) score, polyp detection rate, cecal intubation rate, and adverse reactions during bowel preparation. Furthermore, we did exploratory subgroup analyses for adequate bowel preparation. RESULTS: After enrollment, 15 individuals didn't undergo colonoscopy, finally 345 participants took 3 L split-dose PEG regimen, and 350 participants took 2 L PEG regimen for colonoscopic bowel preparation. 3 L split-dose PEG regimen was superior to 2 L PEG regimen in the rate of adequate bowel preparation (81.2% vs. 74.9%, P = 0.045), BBPS score (6.71±1.15 vs. 6.37±1.31, P < 0.001), and the rate of polyp detection (62.0% vs. 52.9%, P = 0.015). The cecal intubation rate was similar in both groups (99.7%). Regarding adverse reactions, individuals were more likely to feel nausea in the 3 L PEG group (30.9% vs. 19.3%; P = 0.001); however, the degree was mild. In the subgroup analysis for adequate bowel preparation, 3 L split-dose PEG regimen performed better than 2 L PEG regimen in the overweight (BMI 25-29.9 kg/m2 ) (P = 0.006) and individuals with constipation (P = 0.044), while no significant differences were observed in relatively normal (BMI 24-24.9 kg/m2) (P = 0.593) and obese individuals (BMI ≥ 30 kg/m2) (P = 0.715). CONCLUSIONS: 3 L split-dose PEG regimen is superior to 2 L PEG regimen for colonoscopic Bowel Preparation in relatively high-BMI individuals, especially overweight individuals (BMI 25-29.9 kg/m2 ). TRIAL REGISTRATION: This trial was registered in the Chinese Clinical Trials Registry (ChiCTR2000039068). The date of first registration, 15/10/2020, http://www.chictr.org.cn.

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs.

Glaucomatous optic neuropathy (GON) can be diagnosed and monitored using fundus photography, a widely available and low-cost approach already adopted for automated screening of ophthalmic diseases such as diabetic retinopathy. Despite this, the lack of validated early screening approaches remains a major obstacle in the prevention of glaucoma-related blindness. Deep learning models have gained significant interest as potential solutions, as these models offer objective and high-throughput methods for processing image-based medical data. While convolutional neural networks (CNN) have been widely utilized for these purposes, more recent advances in the application of Transformer architectures have led to new models, including Vision Transformer (ViT,) that have shown promise in many domains of image analysis. However, previous comparisons of these two architectures have not sufficiently compared models side-by-side with more than a single dataset, making it unclear which model is more generalizable or performs better in different clinical contexts. Our purpose is to investigate comparable ViT and CNN models tasked with GON detection from fundus photos and highlight their respective strengths and weaknesses. We train CNN and ViT models on six unrelated, publicly available databases and compare their performance using well-established statistics including AUC, sensitivity, and specificity. Our results indicate that ViT models often show superior performance when compared with a similarly trained CNN model, particularly when non-glaucomatous images are over-represented in a given dataset. We discuss the clinical implications of these findings and suggest that ViT can further the development of accurate and scalable GON detection for this leading cause of irreversible blindness worldwide.

Radical Crotylation of Aldehydes with 1,3-Butadiene by Photoredox Cobalt and Titanium Dual Catalysis.

Metal-hydride hydrogen atom transfer (MHAT) has been recognized as a powerful method for alkene functionalization; however, photochemical MAT-mediated chemoselective functionalization of dienes remains undeveloped. In this study, we report a radical strategy (1e-) through MHAT using photoredox cobalt and titanium dual catalysis for aldehyde crotylation with butadiene, achieving excellent regio- and diastereoselectivity.

Physics-informed neural networks to solve lumped kinetic model for chromatography process.

Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.