PLM-T3SE: Accurate Prediction of Type III Secretion Effectors Using Protein Language Model Embeddings.

The Type III secretion effectors (T3SEs) are bacterial proteins synthesized by Gram-negative pathogens and delivered into host cells via the Type III secretion system (T3SS). These effectors usually play a pivotal role in the interactions between bacteria and hosts. Hence, the precise identification of T3SEs aids researchers in exploring the pathogenic mechanisms of bacterial infections. Since the diversity and complexity of T3SE sequences often make traditional experimental methods time-consuming, it is imperative to explore more efficient and convenient computational approaches for T3SE prediction. Inspired by the promising potential exhibited by pre-trained language models in protein recognition tasks, we proposed a method called PLM-T3SE that utilizes protein language models (PLMs) for effective recognition of T3SEs. First, we utilized PLM embeddings and evolutionary features from the position-specific scoring matrix (PSSM) profiles to transform protein sequences into fixed-length vectors for model training. Second, we employed the extreme gradient boosting (XGBoost) algorithm to rank these features based on their importance. Finally, a MLP neural network model was used to predict T3SEs based on the selected optimal feature set. Experimental results from the cross-validation and independent test demonstrated that our model exhibited superior performance compared to the existing models. Specifically, our model achieved an accuracy of 98.1%, which is 1.8%-42.4% higher than the state-of-the-art predictors based on the same independent data set test. These findings highlight the superiority of the PLM-T3SE and the remarkable characterization ability of PLM embeddings for T3SE prediction.

An Unconventional Immunosensor for Biomolecule Detection via Nonspecific Gold Nanoparticle-Antibody Interactions.

Immunogold, that is, gold nanoparticles (AuNPs) conjugated with biomolecules such as antibodies and peptides, have been widely used to construct sandwiched immunosensors for biodetection. Two main challenges in these immunoassays are difficulties in finding and validating a suitable antibody, and the nonspecific interaction between the substrate and immunogold, which lowers the detection sensitivity and even causes false results. To avoid these issues, we took advantage of the nonspecific interaction between AuNPs and capture antibodies and proposed a new sensing mechanism. That is, after the capture of analyte targets by the capture antibodies on the substrate, AuNPs of certain chemical functionality would preferably bind to the free capture antibodies. Consequently, the amount of deposited AuNPs will inversely depend on the concentration of the analytes. As a proof-of-concept, we designed a mass-based sensor where anti-IgG antibodies were coated on a quartz crystal microbalance substrate. After IgG was introduced, tannic acid-capped AuNPs were applied to bind with the free anti-IgG antibody molecules. A frequency change (Δf) of the quartz substrate was induced by the increased mass loading. To further amplify the loading mass, an Ag enhancer solution was added, and Ag growth was catalyzed by the bound AuNPs. The Δf response showed a concentration-dependent decrease when increasing IgG concentration with a detection limit of 2.6 ng/mL. This method relies on the nonspecific interaction between AuNPs and anti-IgG antibodies to realize sensitive detection of IgG and eliminates the use of detection antibodies. The concept is an alternative to many existing immunoassay technologies.

High-Performance Bifunctional Electrocatalysts for Flexible and Rechargeable Zn-Air Batteries: Recent Advances.

Flexible rechargeable Zn-air batteries (FZABs) exhibit high energy density, ultra-thin, lightweight, green, and safe features, and are considered as one of the ideal power sources for flexible wearable electronics. However, the slow and high overpotential oxygen reaction at the air cathode has become one of the key factors restricting the development of FZABs. The improvement of activity and stability of bifunctional catalysts has become a top priority. At the same time, FZABs should maintain the battery performance under different bending and twisting conditions, and the design of the overall structure of FZABs is also important. Based on the understanding of the three typical configurations and working principles of FZABs, this work highlights two common strategies for applying bifunctional catalysts to FZABs: 1) powder-based flexible air cathode and 2) flexible self-supported air cathode. It summarizes the recent advances in bifunctional oxygen electrocatalysts and explores the various types of catalyst structures as well as the related mechanistic understanding. Based on the latest catalyst research advances, this paper introduces and discusses various structure modulation strategies and expects to guide the synthesis and preparation of efficient bifunctional catalysts. Finally, the current status and challenges of bifunctional catalyst research in FZABs are summarized.

Constrution of Quinazoline-Linked Covalent Organic Frameworks via a Multicomponent Reaction for Photocatalysis.

Quinazoline (Qz)-linked covalent organic frameworks (COFs) have been constructed via a three-component reaction of ortho-acylanilines, benzaldehydes and NH4OAc. The structure of Qz-COFs has been confirmed by solid-state nuclear magnetic resonance spectroscopy, Fourier transform infrared and powder X-ray diffraction patterns. The Qz-COFs possess high chemical stability, showing good endurance to strong acid, strong base, oxidant, reductant and other conditions. Particularly, Qz-COF-3 can catalyze the aerobic photooxidation of toluene and other compounds containing C(sp3)-H bonds.

PreDBP-PLMs: Prediction of DNA-binding proteins based on pre-trained protein language models and convolutional neural networks.

The recognition of DNA-binding proteins (DBPs) is the crucial step to understanding their roles in various biological processes such as genetic regulation, gene expression, cell cycle control, DNA repair, and replication within cells. However, conventional experimental methods for identifying DBPs are usually time-consuming and expensive. Therefore, there is an urgent need to develop rapid and efficient computational methods for the prediction of DBPs. In this study, we proposed a novel predictor named PreDBP-PLMs to further improve the identification accuracy of DBPs by fusing the pre-trained protein language model (PLM) ProtT5 embedding with evolutionary features as input to the classic convolutional neural network (CNN) model. Firstly, the ProtT5 embedding was combined with different evolutionary features derived from the position-specific scoring matrix (PSSM) to represent protein sequences. Then, the optimal feature combination was selected and input to the CNN classifier for the prediction of DBPs. Finally, the 5-fold cross-validation (CV), the leave-one-out CV (LOOCV), and the independent set test were adopted to examine the performance of PreDBP-PLMs on the benchmark datasets. Compared to the existing state-of-the-art predictors, PreDBP-PLMs exhibits an accuracy improvement of 0.5 % and 5.2 % on the PDB186 and PDB2272 datasets, respectively. It demonstrated that the proposed method could serve as a useful tool for the recognition of DBPs.

Association Between Sarcopenia, Clinical Outcomes, and Survival in Patients with Extensive-Stage Small Cell Lung Cancer Treated with First-Line Immunochemotherapy: A Prospective Cohort Study.

OBJECTIVE: To investigate the association between sarcopenia, short-term efficacy, and long-term survival in patients with extensive small-cell lung cancer (SCLC) treated with standard first-line immunochemotherapy. METHODS: A total of 63 patients initially diagnosed with extensive-stage small cell lung cancer were enrolled in the prospective study from December 1, 2020 to December 31, 2022. The clinical characteristics, body composition, blood test results, and image data were obtained before treatment. Patients were divided into sarcopenia and non-sarcopenia groups according to the diagnostic criteria of the Asian Sarcopenia Working Group 2019. The primary outcome was overall survival (OS) and comprehensive survival analyses were performed. Secondary outcomes included short-term efficacy and adverse events associated with first-line immunochemotherapy. RESULTS: The median age of the 63 patients enrolled in our study was 63.0 years (40-80 years). The incidence of sarcopenia was 19.0% (12/63) in patients with extensive SCLC. Compared with non-sarcopenia patients, extensive-stage SCLC patients with sarcopenia were significantly older (69.0 vs. 62.0, P = 0.017), and had lower body mass index (BMI) (20.29 vs. 24.27, P < 0.001), hand grip strength (HGS) (20.42 vs. 30.75, P < 0.001), and albumin (35.9 vs. 41.40, P < 0.001). The objective response rate after two cycles of standard first-line immunochemotherapy in the sarcopenia group was lower than in the non-sarcopenia group (30.0 vs. 78.9%, P = 0.012). There was no significant difference in chemotherapy-related hematological toxicity between the two groups. During a median follow-up of 15 months (3-33 months), patients with extensive SCLC had a median OS of 24 months, with 1-year survival of 75% and 2-year survival of 52%, respectively. Compared to non-sarcopenia patients, the median OS in the sarcopenia group was significantly shorter (9 vs. 24 months, P = 0.0014). Multivariate Cox analysis showed that sarcopenia was an independent risk factor for OS in patients with extensive SCLC (HR = 4.993, 95%CI = 1.106-22.538, P = 0.037). CONCLUSIONS: Patients with Extensive SCLC and sarcopenia had worse clinical outcomes and shorter OS. Sarcopenia is a prognostic factor affecting first-line treatment efficacy and long-term survival of patients with SCLC in the era of immunotherapy.

Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction.

The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.

Numerical investigation on the convergence of self-consistent Schrödinger-Poisson equations in semiconductor device transport simulation.

Semiconductor devices at the nanoscale with low-dimensional materials as channels exhibit quantum transport characteristics, thereby their electrical simulation relies on the self-consistent solution of the Schrödinger-Poisson equations. While the non-equilibrium Green's function (NEGF) method is widely used for solving this quantum many-body problem, its high computational cost and convergence challenges with the Poisson equation significantly limit its applicability. In this study, we investigate the stability of the NEGF method coupled with various forms of the Poisson equation, encompassing linear, analytical nonlinear, and numerical nonlinear forms Our focus lies on simulating carbon nanotube field-effect transistors (CNTFETs) under two distinct doping scenarios: electrostatic doping and ion implantation doping. The numerical experiments reveal that nonlinear formulas outperform linear counterpart. The numerical one demonstrates superior stability, particularly evident under high bias and ion implantation doping conditions. Additionally, we investigate different approaches for presolving potential, leveraging solutions from the Laplace equation and a piecewise guessing method tailored to each doping mode. These methods effectively reduce the number of iterations required for convergence.

Association between dietary magnesium intake and gallstones: the mediating role of atherogenic index of plasma.

BACKGROUND: Dyslipidemia and abnormalities in cholesterol metabolism are commonly observed in individuals with gallstone disease. Previous research has demonstrated that dietary magnesium can influence lipid metabolism. The atherogenic index of plasma (AIP) has emerged as a novel lipid marker. This study aimed to examine the possible correlation between dietary magnesium intake and gallstones and the potential mediating role of AIP in US adults. METHODS: A total of 4,841 adults were included in this study from the National Health and Nutrition Examination Survey (NHANES) conducted from 2017 to 2020. A variety of statistical techniques such as logistic regression, subgroup analysis, smoothed curve fitting, and causal mediation analysis were utilized to analyze the information collected from the participants. RESULTS: In the fully adjusted model, a statistically noteworthy inverse relationship was observed between dietary magnesium intake and the presence of gallstones, as indicated by an odds ratio (OR) of 0.58 and a 95% confidence interval (CI) of (0.42, 0.81). Causal intermediary analysis revealed that the association between magnesium intake and gallstones was partially mediated by AIP, with a mediation ratio of 3.2%. CONCLUSION: According to this study, dietary magnesium intake had a significant linear negative association with the prevalence of gallstones, in which AIP played a mediating role. This discovery offers novel perspectives on the prevention and management of gallstones.

Coronal deformity in ankylosing spondylitis with concomitant thoracolumbar kyphosis: patterns, manifestations and surgical strategies.

PURPOSE: To evaluate different patterns of coronal deformity secondary to ankylosing spondylitis (AS), to propose relevant treatment strategies, and to assess efficacy of asymmetrical pedicle subtraction osteotomy (APSO). METHODS: Coronal deformity was defined as coronal Cobb angle over 20º or coronal balance distance (CBD) more than 3 cm. 65 consecutive AS patients with concomitant coronal and sagittal deformity who underwent PSO were included. The average follow-up time was 40.4 months. Radiographic evaluation included coronal Cobb angle and CBD. Furthermore, sagittal parameters were used to assess magnitude and maintenance of kyphosis correction. RESULTS: Based on curve characteristics, coronal deformity caused by AS included four different radiologic patterns: Pattern I: lumbar scoliosis; Pattern II: C-shaped thoracolumbar curve; Pattern III: trunk shift without major curve; Pattern IV: proximal thoracic scoliosis. APSO was performed for patients in Pattern I to III while conventional PSO was applied for patients in Pattern IV. Significant improvement in all the sagittal parameters were noted in 65 patients without obvious correction loss at the last follow-up. Besides, significant and sustained correction of coronal mal-alignment was identified in 59 APSO-treated patients. Rod fracture occurred in four cases and revision surgery was performed for one case. CONCLUSION: According to radiologic manifestations, coronal deformity caused by AS could be categorized into four patterns. APSO proved to be a feasible and effective procedure for correction of Pattern I to III patients. Coronal deformity pattern, apex location, sagittal profile of lumbar spine and preoperative hip function should be considered for osteotomy level selection in APSO.

Prevalence and molecular characterization of multi-resistant Escherichia coli isolates from clinical bovine mastitis in China.

Different antibiotics are used to treat mastitis in dairy cows that is caused by Escherichia coli (E. coli). Antimicrobial resistance in food-producing animals in China has been monitored since 2000. Surveillance data have shown that the prevalence of multiresistant E. coli in animals has increased significantly. This study aimed to investigate the occurrence and molecular characteristics of resistance determinants in E. coli strains (n = 105) obtained from lactating cows with clinical bovine mastitis (CBM) in China. A total of 220 cows with clinical mastitis, which has swollen mammary udder with reduced and red or gangrenous milk, were selected from 5000 cows. The results showed 94.3% of the isolates were recognized as multidrug resistant. The isolates (30.5%) were positive for the class I integrase gene along with seven gene cassettes that were accountable for resistance to trimethoprim resistance (dfrA17, dfr2d and dfrA1), aminoglycosides resistance (aadA1 and aadA5) and chloramphenicol resistance (catB3 and catB2), respectively. The blaTEM gene was present in all the isolates, and these carried the blaCTX gene. A double mutation in gyrA (i.e., Ser83Leu and Asp87Asn) was observed in all fluoroquinolone-resistant isolates. In total, nine fluoroquinolone-resistant E. coli isolates were identified with five different types of mutations in parC. In four (44.4%) isolates, Ser458Ala was present in parE, and in all nine (9/9) fluoroquinolone-resistant isolates, Pro385Ala was present in gyrB. Meanwhile, fluoroquinolone was observed as highly resistant, especially in isolates with gyrA and parC mutations. In summary, the findings of this research recognize the fluoroquinolone resistance mechanism and disclose integron prevalence and ESBLs in E. coli isolates from lactating cattle with CBM.

Osiris17 is indispensable for morphogenesis of intestinal tract in Locusta migratoria.

The Osiris gene family is believed to play important roles in insect biology. Previous studies mainly focused on the roles of Osiris in Drorophila, how Osiris operates during the development of other species remains largely unknown. Here, we investigated the role of LmOsi17 in development of the hemimetabolous insect Locusta migratoria. LmOsi17 was highly expressed in the intestinal tract of nymphs. Knockdown of LmOsi17 by RNA interference (RNAi) in nymphs resulted in growth defects. The dsLmOsi17-injected nymphs did not increase in body weight or size and eventually died. Immunohistochemical analysis showed that LmOsi17 was localized to the epithelial cells of the foregut and the gastric caecum. Histological observation and hematoxylin-eosin staining indicate that the foregut and gastric caecum are deformed in dsLmOsi17 treated nymphs, suggesting that LmOsi17 is involved in morphogenesis of foregut and gastric caecum. In addition, we observed a significant reduction in the thickness of the new cuticle in dsLmOsi17-injected nymphs compared to control nymphs. Taken together, these results suggest that LmOsi17 contributes to morphogenesis of intestinal tract that affects growth and development of nymphs in locusts.

Characteristics and risk differences of different tumor sizes on distant metastases of pancreatic neuroendocrine tumors: A retrospective study in the SEER database.

BACKGROUND: The rate of distant metastasis in patients with pancreatic neuroendocrine tumors (PNETs) is 20%-50% at the time of initial diagnosis. However, whether tumor size can predict distant metastasis for PNETs remains unknown up to date. METHODS: We used Surveillance, Epidemiology, and End Results (SEER) population-based data to collect 6089 patients with PNETs from 2010 to 2019. The optimal cut-off point of tumor size to predict distant metastasis was calculated by Youden's index. Multivariate logistic regression analysis was used to figure out the association between tumor size and distant metastasis patterns. RESULTS: The most common metastatic site was liver (27.2%), followed by bone (3.0%), lung (2.3%) and brain (0.4%). Based on an optimal cut-off value of tumor size (25.5 mm) for predicting distant metastasis determined by Youden's index, patients were categorized into groups of tumor size < 25.5 mm and ≥ 25.5 mm. Multivariate logistic regression analyses showed that, compared with < 25.5 mm, tumor size ≥ 25.5 mm was an independent risk predictor of overall distant metastasis [odds ratio (OR) = 4.491, 95% confidence interval (CI): 3.724-5.416, P < 0.001] and liver metastasis (OR = 4.686, 95% CI: 3.886-5.651, P < 0.001). CONCLUSIONS: Tumor size ≥ 25.5 mm was significantly associated with more overall distant and liver metastases. Timely identification of distant metastasis for tumor size ≥ 25.5 mm may provide survival benefit for timely and precise treatment.

Free-hand technique of C7 pedicle screw insertion using a simply defined entry point without fluoroscopic guidance for cervical spondylotic myelopathy patients with C3 to C6 instrumented by lateral mass screws: a retrospective cohort study.

BACKGROUND: Nowadays, both lateral mass screw (LMS) and pedicle screw were effective instrumentation for posterior stabilization of cervical spine. This study aims to evaluate the feasibility of a new free-hand technique of C7 pedicle screw insertion without fluoroscopic guidance for cervical spondylotic myelopathy (CSM) patients with C3 to C6 instrumented by lateral mass screws. METHODS: A total of 53 CSM patients underwent lateral mass screws instrumentation at C3 to C6 levels and pedicle screw instrumentation at C7 level were included. The preoperative 3-dimenional computed tomography (CT) reconstruction images of cervical spine were used to determine 2 different C7 pedicle screw trajectories. Trajectory A passed through the axis of the C7 pedicle while trajectory B selected the midpoint of the base of C7 superior facet as the entry point. All these 53 patients had the C7 pedicle screw inserted through trajectory B by free-hand without fluoroscopic guidance and the postoperative CT images were obtained to evaluate the accuracy of C7 pedicle screw insertion. RESULTS: Trajectory B had smaller transverse angle, smaller screw length, and smaller screw width but both similar sagittal angle and similar pedicle height when compared with trajectory A. A total of 106 pedicle screws were inserted at C7 through trajectory B and only 8 screws were displaced with the accuracy of screw placement as high as 92.5%. CONCLUSION: In CSM patients with C3 to C6 instrumented by LMS, using trajectory B for C7 pedicle screw insertion is easy to both identify the entry point and facilitate the rod insertion.

Reliability and minimal detectable change of the Short Physical Performance Battery in older adults with mild cognitive impairment.

OBJECTIVES: Reliability of the Short Physical Performance Battery (SPPB) are rarely examined among older adults with mild cognitive impairment (MCI). This study aimed to investigate the test-retest reliability and minimal detectable change (MDC) of the SPPB in older adults with MCI. METHODS: Participants included 100 older adults with MCI. The SPPB was assessed with the first 2 assessments separated by a 20-min interval and the third separated by a 1-week interval. The intraclass correlation coefficient (ICC) and MDC values were estimated. RESULTS: The intraday ICC was 0.73 for the SPPB score, 0.90 for the 4-m walk time (4mwt), and 0.95 for the 5-times chair stand time (5cst); the corresponding interday ICC values were 0.76, 0.89, and 0.91, respectively. The MDC values ranged from 1.1 to 1.2 for the SPPB score, from 0.77 to 0.80 s for the 4mwt, and from 1.32 to 1.77 for the 5cst. CONCLUSIONS: The SPPB had satisfactory reliability among older adults with MCI. The test-retest reliability of the SPPB is sufficient (>0.7) for group comparisons. Moreover, the test-retest reliability for the 4mwt and 5cst subscale performances is acceptable (> 0.9) for individual-level measurements over time.

Switching GoÌ -Martini for Investigating Protein Conformational Transitions and Associated Protein-Lipid Interactions.

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and ß2-adrenergic receptor (ß2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport.

NCSP-PLM: An ensemble learning framework for predicting non-classical secreted proteins based on protein language models and deep learning.

Non-classical secreted proteins (NCSPs) refer to a group of proteins that are located in the extracellular environment despite the absence of signal peptides and motifs. They usually play different roles in intercellular communication. Therefore, the accurate prediction of NCSPs is a critical step to understanding in depth their associated secretion mechanisms. Since the experimental recognition of NCSPs is often costly and time-consuming, computational methods are desired. In this study, we proposed an ensemble learning framework, termed NCSP-PLM, for the identification of NCSPs by extracting feature embeddings from pre-trained protein language models (PLMs) as input to several fine-tuned deep learning models. First, we compared the performance of nine PLM embeddings by training three neural networks: Multi-layer perceptron (MLP), attention mechanism and bidirectional long short-term memory network (BiLSTM) and selected the best network model for each PLM embedding. Then, four models were excluded due to their below-average accuracies, and the remaining five models were integrated to perform the prediction of NCSPs based on the weighted voting. Finally, the 5-fold cross validation and the independent test were conducted to evaluate the performance of NCSP-PLM on the benchmark datasets. Based on the same independent dataset, the sensitivity and specificity of NCSP-PLM were 91.18% and 97.06%, respectively. Particularly, the overall accuracy of our model achieved 94.12%, which was 7~16% higher than that of the existing state-of-the-art predictors. It indicated that NCSP-PLM could serve as a useful tool for the annotation of NCSPs.

Prognostic factors for surgical site infection in patients with spinal metastases and following surgical treatment.

There were few articles reviewed prognostic factors of surgical site infection (SSI) in patients with spinal metastases following surgery. The purpose of the present study was to systematically: (1) investigate the incidence rates of SSI following spinal metastases surgery; (2) identify the factors which were independently associated with postoperative wound infection. One hundred sixty-seven consecutive adult patients with spinal metastases and underwent surgical treatment were retrospectively enrolled from January 2011 to February 2022. Demographic data, disease and operation-related indicators were extracted and analyzed. Univariate and multivariate logistic analysis model were performed respectively to determine independent risk factors of SSI. 17 cases infection were collected in this study. The overall incidence of SSI after surgery of spinal metastases patients was 10.2%. Univariate regression analysis showed that age (P = .028), preoperative ALB level (P = .024), operation time (P = .041), intraoperative blood loss (P = .030), Karnofsky Performance Status score (P = .000), body mass index (P = .013), American Society of Anesthesiologists > 2 (P = .010), Tobacco consumption (P = .035), and number of spinal levels involved in surgical procedure (P = .007) were associated with wound infection. Finally, the multivariate logistic model demonstrated that body mass index (P = .043; OR = 1.038), preoperative ALB level (P = .018; OR = 1.124), and number of spinal levels (P = .003; OR = 1.753) were associated with SSI occurrence. Surgery on multiple vertebral levels for spinal metastases significantly increases the risk of SSI and weight management, nutritional support and palliative surgery have the positive significance in reducing wound complications. Orthopedist should focus on identifying such high-risk patients and decrease the incidence of wound infection by formulating comprehensive and multi-disciplinary care strategy.

Sea Cucumber Polysaccharide from Stichopus japonicu and Its Photocatalytic Degradation Product Alleviate Acute Alcoholic Liver Injury in Mice.

To prevent alcoholic liver disease, the addition of bioactive substances to the alcoholic drink Baijiu has been considered a feasible option. In the present study, the hepatoprotective effects of a sea cucumber sulfated polysaccharide (SCSP) isolated from Stichopus japonicu were investigated. Moreover, in order to enhance its solubility in an alcohol solution, it was depolymerized using a photocatalytic reaction, and the photocatalytic degradation products (dSCSPs) with an average molecular weight of less than 2 kDa were studied and compared with SCSP. They were characterized by a series of chemical and spectroscopy methods and the oligosaccharide fragments in the dSCSP were further identified by HPLC-MSn analysis. Then, the in vivo experiment showed that the addition of SCSP or dSCSP to Baijiu could alleviate alcoholic liver injury in mice. Further analysis also revealed their protective effect in reducing oxidative stress damage and their regulation of the metabolism of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) in the liver. Of note, dSCSP was more effective at reducing the level of malondialdehyde in the liver. These findings indicate that the addition of sea cucumber polysaccharide or its low-molecular-weight derivative in Baijiu has the potential to alleviate alcoholic liver injury.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.