An in vivo "turning model" reveals new RanBP9 interactions in lung macrophages.

The biological functions of the scaffold protein Ran Binding Protein 9 (RanBP9) remain elusive in macrophages or any other cell type where this protein is expressed together with its CTLH (C-terminal to LisH) complex partners. We have engineered a new mouse model, named RanBP9-TurnX, where RanBP9 fused to three copies of the HA tag (RanBP9-3xHA) can be turned into RanBP9-V5 tagged upon Cre-mediated recombination. We created this model to enable stringent biochemical studies at cell type specific level throughout the entire organism. Here, we have used this tool crossed with LysM-Cre transgenic mice to identify RanBP9 interactions in lung macrophages. We show that RanBP9-V5 and RanBP9-3xHA can be both co-immunoprecipitated with the known members of the CTLH complex from the same whole lung lysates. However, more than ninety percent of the proteins pulled down by RanBP9-V5 differ from those pulled-down by RanBP9-HA. The lung RanBP9-V5 associated proteome includes previously unknown interactions with macrophage-specific proteins as well as with players of the innate immune response, DNA damage response, metabolism, and mitochondrial function. This work provides the first lung specific RanBP9-associated interactome in physiological conditions and reveals that RanBP9 and the CTLH complex could be key regulators of macrophage bioenergetics and immune functions.

Oridonin represses lung cancer cell proliferation and migration by modulating AFAP1-AS1/IGF2BP1.

Oridonin belongs to a small molecule from the Chinese herb Rabdosia rubescens with potent anticancer activity. In spite of the lncRNA AFAP1-AS1 has been proven to exert promoting function in lung cancer, its relationship with oridonin in lung cancer is obscure. Therefore, our study planned to explore the potential of oridonin in lung cancer as well as unveil the regulatory mechanism of oridonin on AFAP1-AS1 in lung cancer cells. In the present study, oridonin inhibited lung cancer cell proliferation, migration, as well as invasion, as evidenced by MTT, wound healing, as well as transwell assays. Besides, we observed that oridonin could downregulate AFAP1-AS1 expression, and overexpressed AFAP1-AS1 could reverse the repressive effects of oridonin on lung cancer cell proliferation, migration, as well as invasion. More importantly, we found that AFAP1-AS1 could bind to IGF2BP1 through starBase prediction and RIP assay. The expression level of IGF2BP1 was also reduced by oridonin treatment but reversed after AFAP1-AS1 overexpression. Additionally, we proved that overexpressed IGF2BP1 could reverse the repressive impacts of oridonin on lung cancer cell proliferation, migration, as well as invasion. Further, in vivo experiments validated the repressive role of oridonin on tumor growth of lung cancer. Together, oridonin inhibits lung cancer cell proliferation as well as migration by modulating AFAP1-AS1/IGF2BP1, and AFAP1-AS1/IGF2BP1 possesses the potential to be a promising therapy targeting for lung cancer, especially in oridonin treatment.

Malignancy is increased in patients with antineutrophil cytoplasmic antibody-associated vasculitis in China.

OBJECTIVE: It has been reported that in western countries malignancy risk was higher in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) compared with that in the general population. In the current study, we investigated the incidence, spectrum and risk factors of malignancy in Chinese AAV patients. METHODS: AAV patients diagnosed from 1995 to 2021 in Peking University First Hospital with a follow-up more than 12 months were recruited. Standardized incidence ratios (SIR) were calculated to describe the risk of malignancy, adjusted for sex, age and follow-up time. RESULTS: A total of 552 AAV patients were recruited, among which 23 patients had malignancies either preceding or concurrent with AAV diagnosis, and 43 of the remaining 529 patients developed malignancies within 4.3 ± 4.2 years post AAV diagnosis (SIR: 2.24; 95% CI: 1.68-2.99; p < 0.001). Among these 66 patients, twenty different sites of malignancy were observed, lung cancer being most frequent. To get exactly expected malignancies for the calculation of SIR, 529 patients without preceding or concurrent malignancies were included in the following analysis. Lung cancer was still the leading malignancy diagnosis (SIR: 5.01; 95% CI: 3.29-7.62), followed by malignancies in the kidney, bladder, ureter and prostate. Male gender (HR:2.84; 95%CI:1.36-5.96; p = 0.006) and older age (per year, HR:1.04; 95%CI:1.00-1.07; p = 0.038) were significantly associated with increased risk of malignancy. For patients with malignancy developed beyond 5 years after the diagnosis of AAV, a significantly higher malignancy risk was observed in those with a cumulative cyclophosphamide dose over 20.0 g (SIR: 11.54; 95% CI: 4.77-27.93; p < 0.001). Within the first 2 years after the diagnosis of AAV, the risk of malignancy was still significantly higher than that in the general population, but the cumulative cyclophosphamide dose was not significantly associated with malignancy occurrence in this subgroup of patients. CONCLUSIONS: Malignancy risk is higher in Chinese AAV patients than that in the general population, with a different malignancy spectrum from western countries. Both the use of cyclophosphamide and AAV per se might be associated with higher incidence of malignancy occurrence.

Gastroenteropancreatic neuroendocrine neoplasms: current development, challenges, and clinical perspectives.

Neuroendocrine neoplasms (NENs) are highly heterogeneous and potentially malignant tumors arising from secretory cells of the neuroendocrine system. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are the most common subtype of NENs. Historically, GEP-NENs have been regarded as infrequent and slow-growing malignancies; however, recent data have demonstrated that the worldwide prevalence and incidence of GEP-NENs have increased exponentially over the last three decades. In addition, an increasing number of studies have proven that GEP-NENs result in a limited life expectancy. These findings suggested that the natural biology of GEP-NENs is more aggressive than commonly assumed. Therefore, there is an urgent need for advanced researches focusing on the diagnosis and management of patients with GEP-NENs. In this review, we have summarized the limitations and recent advancements in our comprehension of the epidemiology, clinical presentations, pathology, molecular biology, diagnosis, and treatment of GEP-NETs to identify factors contributing to delays in diagnosis and timely treatment of these patients.

Collagen degradation assessment with an in vitro rotator cuff tendinopathy model using multiparametric ultrashort-TE magnetization transfer (UTE-MT) imaging.

PURPOSE: This study aims to assess ultrashort-TE magnetization transfer (UTE-MT) imaging of collagen degradation using an in vitro model of rotator cuff tendinopathy. METHODS: Thirty-six supraspinatus tendon specimens were divided into three groups and treated with 600 U collagenase (Group 1), 150 U collagenase (Group 2), and phosphate buffer saline (Group 3). UTE-MT imaging was performed to assess changes in macromolecular fraction (MMF), macromolecule transverse relaxation time (T2m), water longitudinal relaxation rate constant (R1m), the magnetization exchange rate from the macromolecular to water pool (Rm0 w) and from water to the macromolecular pool (Rm0 m), and magnetization transfer ratio (MTR) at baseline and following digestion and their differences between groups. Biochemical and histological studies were conducted to determine the extent of collagen degradation. Correlation analyses were performed with MMF, T2m, R1m, Rm0 w, Rm0 m, and MTR, respectively. Univariate and multivariate linear regression analyses were performed to evaluate combinations of UTE-MT parameters to predict collagen degradation. RESULTS: MMF, T2m, R1m, Rm0 m, and MTR decreased after digestion. MMF (r = -0.842, p < 0.001), MTR (r = -0.78, p < 0.001), and Rm0 m (r = -0.662, p < 0.001) were strongly negatively correlated with collagen degradation. The linear regression model of differences in MMF and Rm0 m before and after digestion explained 68.9% of collagen degradation variation in the tendon. The model of postdigestion in MMF and T2m and the model of MTR explained 54.2% and 52.3% of collagen degradation variation, respectively. CONCLUSION: This study highlighted the potential of UTE-MT parameters for evaluation of supraspinatus tendinopathy.

Efficient incorporation of highly migratory thallium into struvite structure: Unraveling the stabilization mechanisms from a mineralogical perspective.

The remediation of high-concentration thallium (Tl+) contaminated wastewater is a critical environmental concern. Current research emphasizes the effectiveness of adsorption and oxidation methods for Tl+ treatment, yet challenges persist in enhancing their performance. This study explores the feasibility of emergency Tl+ wastewater treatment and elucidates the mechanisms of Tl+ incorporation into mineral structures, with a focus on the struvite mineral as a framework for Tl+ integration via NH4+ ion exchange. To assess the efficacy and mechanisms of Tl+ immobilization, we utilized comprehensive analytical techniques, including X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), Thermogravimetric Analysis (TG), and Density Functional Theory (DFT) calculations. The findings reveal that struvite adsorbs Tl+ onto its surface, followed by an ion exchange process between monovalent cations (NH4+/K+) within the structure and Tl+. Ultimately, Tl+ is incorporated in the form of a (NH4,Tl)MgPO4 solid solution within the structure, achieving a remarkable maximum incorporation capacity of 320.56 mg/g, which significantly surpasses the capacity of typical adsorbents. The findings demonstrate significant Tl+ incorporation, validating the approach for emergency wastewater treatment and suggesting the potential of mineralogy in environmental remediation. This research contributes to advancing heavy metal wastewater treatment strategies, offering a foundation for further investigation.

Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

Global research progress of gut microbiota and epigenetics: bibliometrics and visualized analysis.

Background: Gut microbiota is an important factor affecting host health. With the further study of the mechanism of gut microbiota, significant progress has been made in the study of the link between gut microbiota and epigenetics. This study visualizes the body of knowledge and research priorities between the gut microbiota and epigenetics through bibliometrics. Methods: Publications related to gut microbiota and epigenetics were searched in the Web of Science Core Collection (WoSCC) database. Vosviewer 1.6.17 and CiteSpace 6.1.R2 were used for bibliometric analysis. Results: WoSCC includes 460 articles from 71 countries. The number of publications on gut microbiota and epigenetics has increased each year since 2011. The USA, PEOPLES R CHINA, and ITALY are at the center of this field of research. The University of California System, Harvard University, and the University of London are the main research institutions. Li, X, Yu, Q, Zhang, S X are the top authors in this research field. We found that current research hotspots and frontiers include short-chain fatty acids (SCFA) play an important role in gut microbiota and epigenetic mechanisms, gut microbiota and epigenetics play an important role in host obesity, diet, and metabolism. Gut microbiota and epigenetics are closely related to colorectal cancer, breast cancer, and inflammatory bowel disease. At the same time, we found that gut microbiota regulates epigenetics through the gut-brain axis and has an impact on psychiatric diseases. Therefore, probiotics can regulate gut microbiota, improve lifestyle, and reduce the occurrence and development of diseases. Conclusion: This is the first comprehensive and in-depth bibliometric study of trends and developments in the field of gut microbiota and epigenetics research. This study helps to guide the direction of research scholars in their current field of study.

Unveiling the Pharmacological Role of Human Deubiquitinating Enzymes in Temozolomide Response of Glioblastoma Cells.

Temozolomide (TMZ) stands as the primary chemotherapeutic drug utilized in clinical glioma treatment, particularly for high-grade glioblastoma (GBM). However, the emergence of TMZ resistance in GBM poses a significant hurdle to its clinical efficacy. Our objective was to elucidate the role of deubiquitinating enzymes (DUBs) in GBM cell resistance to TMZ. We employed the broad-spectrum DUBs inhibitor G5 to investigate the function of DUBs in TMZ cytotoxicity against GBM cells. Eighty-two GBM cell lines with specified DUBs knockout were generated and subjected to CCK-8 assays to assess cell proliferation and TMZ resistance. Furthermore, the association between DUBs and TMZ resistance in GBM cells, along with the modulation of autophagic flux, was examined. The pan-DUBs inhibitor G5 demonstrated the ability to induce cell death and enhance TMZ toxicity in GBM cells. Subsequently, we identified potential DUBs involved in regulating GBM cell proliferation and TMZ resistance. The impact of DUBs knockout on TMZ cytotoxicity was found to be associated with their regulation of TMZ-induced autophagy. In summary, our study provides primary insights into the role of DUBs in GBM cell proliferation and TMZ resistance, and contributes to a deeper understanding of the complex function of DUBs genes underlying TMZ resistance in GBM cells.

Construction and validation of nomogram model for predicting the risk of ventricular arrhythmia after emergency PCI in patients with acute myocardial infarction.

OBJECTIVE: To make predictions about the risk of MVA (Malignant Ventricular Arrhythmia) after primary PCI (Percutaneous Coronary Intervention) in patients with AMI (Acute Myocardial Infarction) through constructing and validating the Nomogram model. METHODS: 311 AMI patients who suffered from emergency PCI in Hefei Second People's Hospital from January 2020 to May 2023 were selected as the training set; 253 patients suffering from the same symptom in Hefei First People's Hospital during the same period were selected as the validation set. Risk factors were further screened by means of multivariate logistic and stepwise regression. The nomogram model was constructed, and then validated by using C-index, ROC curve, decision curve and calibration curve. RESULTS: Multivariate logistic analysis revealed that urea, systolic pressure, hypertension, Killip class II-IV, as well as LVEF (Left Ventricular Ejection Fraction) were all unrelated hazards for MVA after emergency PCI for AMI (P<0.05); a risk prediction nomogram model was constructed. The C-index was calculated to evaluate the predictive ability of the model. Result showed that the index of the training and the validation set was 0.783 (95% CI: 0.726-0.84) and 0.717 (95% CI: 0.65-0.784) respectively, which suggested that the model discriminated well. Meanwhile, other tools including ROC curve, calibration curve and decision curve also proved that this nomogram plays an effective role in forecasting the risk for MVA after PCI in AMI patients. CONCLUSIONS: The study successfully built the nomogram model and made predictions for the development of MVA after PCI in AMI patients.

The clinical practice and dosimetric outcome of the manual adaptive planning during definitive radiotherapy for cervical cancer.

PROPOSE: To evaluate the advantage of the manual adaptive plans comparing to the scheduled plans, and explored clinical factors predicting patients suitable for adaptive strategy. METHODS AND MATERIALS: Eighty two patients with weekly online cone-beam computed tomography (CBCT) were enrolled. The re-CT simulation was performed after 15 fractions and a manual adaptive plan was developed if a significant deviation of the planning target volume (PTV) was found. To evaluate the dosimetric benefit, D98, homogeneity index (HI) and conformity index (CI) for the planning target volume (PTV), as well as D2cc of the bowel, bladder, sigmoid and rectum were compared between manual adaptive plans and scheduled ones. The clinical factors influencing target motion during radiotherapy were analyzed by chi-square test and logistic regression analysis. RESULTS: The CI and HI of the manual adaptive plans were significantly superior to the scheduled ones (P = 0.0002, 0.003, respectively), demonstrating a better dose coverage of the target volume. Compared to the scheduled plans, D98 of the manual adaptive plans increased by 3.3% (P = 0.0002), the average of D2cc to the rectum, bladder decreased 0.358 Gy (P = 0.000034) and 0.240 Gy (P = 0.03), respectively. In addition, the chi-square test demonstrated that age, primary tumor volume, and parametrial infiltration were the clinical factors influencing target motion during radiotherapy. Multivariate analysis further identified the large tumor volume (≥ 50cm3, OR = 3.254, P = 0.039) and parametrial infiltration (OR = 3.376, P = 0.018) as the independent risk factors. CONCLUSION: We found the most significant organ motion happened after 15 fractions during treatment. The manual adaptive plans improved the dose coverage and decreased the OAR doses. Patients with bulky mass or with parametrial infiltration were highly suggested to adaptive strategy during definitive radiotherapy due to the significant organ motion.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Active metabolites combination therapies: towards the next paradigm for more efficient and more scientific Chinese medicine.

Traditional Chinese medicine (TCM) formulae have been studied extensively in various human diseases and have proven to be effective due to their multi-component, multi-target advantage. However, its active metabolites are not clear and the specific mechanisms are not well established, which limits its scientific application. Recently, combination therapies are attracting increasing attention from the scientific community in the past few years and are considered as the next paradigm in drug discovery. Here, we tried to define a new concept of "active metabolites combination therapies (AMCT)" rules to elucidate how the bioactive metabolites from TCMs to produce their synergistic effects in this review. The AMCT rules integrate multidisciplinary technologies like molecular biology, biochemistry, pharmacology, analytical chemistry and pharmacodynamics, etc. Meanwhile, emerging technologies such as multi-omics combined analysis, network analysis, artificial intelligence conduce to better elucidate the mechanisms of these combination therapies in disease treatment, which provides new insights for the development of novel active metabolites combination drugs. AMCT rules will hopefully further guide the development of novel combination drugs that will promote the modernization and international needs of TCM.

Millijoule Terahertz Radiation from Laser Wakefields in Nonuniform Plasmas.

We report the experimental measurement of millijoule terahertz (THz) radiation emitted in the backward direction from laser wakefields driven by a femtosecond laser pulse of few joules interacting with a gas target. By utilizing frequency-resolved energy measurement, it is found that the THz spectrum exhibits two peaks located at about 4.5 and 9.0 THz, respectively. In particular, the high frequency component emerges when the drive laser energy exceeds 1.26 J, at which electron acceleration in the forward direction is detected simultaneously. Theoretical analysis and particle-in-cell simulations indicate that the THz radiation is generated via mode conversion from the laser wakefields excited in plasma with an up-ramp profile, where radiations both at the local electron plasma frequency and its harmonics are produced. Such intense THz sources may find many applications in ultrafast science, e.g., manipulating the transient states of matter.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Plasma Metabolic Profiles-Based Prediction of Induction Chemotherapy Efficacy in Nasopharyngeal Carcinoma: Results of a Bidirectional Clinical Trial.

PURPOSE: The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier utilizing plasma metabolomics profiling. EXPERIMENTAL DESIGN: A total of 166 NPC patients enrolled in the clinical trial NCT05682703 and undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-NMR before and after IC treatment. An AI-assisted radiomics method was developed to effectively evaluate the efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable scenario in real-world. RESULTS: Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Utilizing machine learning techniques, it was determined that the XGB model exhibited notable efficacy, attaining an Area Under the Curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability with an AUC of 0.786 (95%CI, 0.533-0.922). CONCLUSION: In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile as good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.

The ANTsX Ecosystem for Mapping the Mouse Brain.

Precision mapping techniques coupled with high resolution image acquisition of the mouse brain permit the study of the spatial organization of gene expression and their mutual interaction for a comprehensive view of salient structural/functional relationships. Such research is facilitated by standardized anatomical coordinate systems, such as the well-known Allen Common Coordinate Framework (AllenCCFv3), and the ability to spatially map to such standardized spaces. The Advanced Normalization Tools Ecosystem is a comprehensive open-source software toolkit for generalized quantitative imaging with applicability to multiple organ systems, modalities, and animal species. Herein, we illustrate the utility of ANTsX for generating precision spatial mappings of the mouse brain and potential subsequent quantitation. We describe ANTsX-based workflows for mapping domain-specific image data to AllenCCFv3 accounting for common artefacts and other confounds. Novel contributions include ANTsX functionality for velocity flow-based mapping spanning the spatiotemporal domain of a longitudinal trajectory which we apply to the Developmental Common Coordinate Framework. Additionally, we present an automated structural morphological pipeline for determining volumetric and cortical thickness measurements analogous to the well-utilized ANTsX pipeline for human neuroanatomical structural morphology which illustrates a general open-source framework for tailored brain parcellations.

Salmonella Grumpensis Causing Diarrhea in Children - Shanghai Municipality, China, 2023.

What is already known about this topic?: Foodborne diseases are a growing public health concern with a notable disease burden in China. What is added by this report?: Two children with diarrhea visited a healthcare facility within 24 hours on August 1 and 2, 2023. Salmonella Grumpensis was detected in their stool samples by the public health laboratory. Whole genome sequencing (WGS) analysis revealed characteristics typical of outbreak strains. Although the origin of the outbreak was unknown, the possibility of a hidden shared infection was deemed feasible. What are the implications for public health practice?: It underscores the importance of thorough genomic surveillance to promptly detect emerging pathogens. Public health laboratories play a crucial role by utilizing advanced genomic technologies for accurate pathogen identification and timely warning systems.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (µPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on µPADs can further facilitate the realization of smartphone µPADs platforms for efficient disease detection. RESULTS: This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on µPADs. Our platform successfully applied sandwich c-ELISA for detecting the ß-amyloid peptide 1-42 (Aß 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE: This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aß 1-42, particularly in areas with low resources and limited communication infrastructure.