Near-Infrared-Light-Induced Iron(I) Dimer-Enabled Radical Cascade Reactions of Fluoroalkyl Bromides for the Synthesis of Ring-Fused Quinazolinones.

The use of an earth-abundant and inexpensive iron complex as a catalyst, coupled with near-infrared (NIR) light as the energy source, for radical reactions with alkyl halides has been far less developed. In this study, we report NIR light-mediated iron(I) dimer-catalyzed radical cascade reactions of fluoroalkyl bromides for the synthesis of ring-fused quinazolinones bearing a difluoromethyl group. In this process, the 3-bromo-1,10-phenanthroline ligand facilitates the reactivity of [CpFe(CO)2]2, thereby improving the efficiency of the reaction.

TRIM35 triggers cardiac remodeling by regulating SLC7A5-mediated amino acid transport and mTORC1 activation in fibroblasts.

BACKGROUND: Cardiac maladaptive remodeling is one of the leading causes of heart failure with highly complicated pathogeneses. The E3 ligase tripartite motif containing 35 (TRIM35) has been identified as a crucial regulator governing cellular growth, immune responses, and metabolism. Nonetheless, the role of TRIM35 in fibroblasts in cardiac remodeling remains elusive. METHODS: Heart tissues from human donors were used to verify tissue-specific expression of TRIM35. Fibroblast-specific Trim35 gene knockout mice (Trim35cKO) were used to investigate the function of TRIM35 in fibroblasts. Cardiac function, morphology, and molecular changes in the heart tissues were analyzed after transverse aortic constriction (TAC) surgery. The mechanisms by which TRIM35 regulates fibroblast phenotypes were elucidated using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA sequencing (RNA-Seq). These findings were further validated through the use of adenoviral and adeno-associated viral transfection systems, as well as the mTORC1 inhibitor Rapamycin. RESULTS: TRIM35 expression is primarily up-regulated in cardiac fibroblasts in both murine and human fibrotic hearts, and responds to TGF-ß1 stimulation. Specific deletion of TRIM35 in cardiac fibroblasts significantly improves cardiac fibrosis and hypertrophy. Consistently, the overexpression of TRIM35 promotes fibroblast proliferation, migration, and differentiation. Through paracrine signaling, it induces hypertrophic growth of cardiomyocytes. Mechanistically, we found that TRIM35 interacts with, ubiquitinates, and up-regulates the amino acid transporter SLC7A5, which enhances amino acid transport and activates the mTORC1 signaling pathway. Furthermore, overexpression of SLC7A5 significantly reverses the reduced cardiac fibrosis and hypertrophy caused by conditional knockout of TRIM35. CONCLUSION: Our findings demonstrate a novel role of fibroblast-TRIM35 in cardiac remodeling and uncover the mechanism underlying SLC7A5-mediated amino acid transport and mTORC1 activation. These results provide a potential novel therapeutic target for treating cardiac remodeling.

Geographic and environmental impacts on gut microbiome in Himalayan langurs (Semnopithecus schistaceus) and Xizang macaques (Macaca mulatta vestita).

Introduction: Gut microbiome plays a crucial role in the health of wild animals. Their structural and functional properties not only reflect the host's dietary habits and habitat conditions but also provide essential support for ecological adaptation in various environments. Methods: This study investigated the gut microbiome of Himalayan langurs (Semnopithecus schistaceus) and Xizang macaques (Macaca mulatta vestita) across different geographic regions using 16S rRNA gene and metagenomic sequencing. Results: Results showed distinct clustering patterns in gut microbiota based on geographic location. Soil had an insignificant impact on host gut microbiome. Himalayan langurs from mid-altitude regions exhibited higher levels of antibiotic resistance genes associated with multidrug resistance, while Xizang macaques from high-altitude regions showed a broader range of resistance genes. Variations in carbohydrate-active enzymes and KEGG pathways indicated unique metabolic adaptations to different environments. Discussion: These findings provide valuable insights into the health and conservation of these primates and the broader implications of microbial ecology and functional adaptations in extreme conditions.

Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists.

Background: Ultrasound is essential for detecting breast lesions. The American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) classification system is widely used, but its subjectivity can lead to inconsistency in diagnostic outcomes. Artificial intelligence (AI) models, such as ChatGPT-3.5, may potentially enhance diagnostic accuracy and efficiency in medical settings. This study aimed to assess the utility of the ChatGPT-3.5 model in generating BI-RADS classifications for breast ultrasound reports and its ability to replicate the "chain of thought" (CoT) in clinical decision-making to improve model interpretability. Methods: Breast ultrasound reports were collected, and ChatGPT-3.5 was used to generate diagnoses and treatment plans. We evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing test and a consistency analysis. To enhance the interpretability of the model, we applied the CoT method to deconstruct the decision-making chain of the GPT model. Results: A total of 131 patients were evaluated, with 57 doctors participating in the experiment. ChatGPT-3.5 showed promising performance in structure and organization (S&O), professional terminology and expression (PTE), treatment recommendations (TR), and clarity and comprehensibility (C&C). However, improvements are needed in BI-RADS classification, malignancy diagnosis (MD), likelihood of being written by a physician (LWBP), and ultrasound doctor artificial intelligence acceptance (UDAIA). Turing test results indicated that AI-generated reports convincingly resembled human-authored reports. Reproducibility experiments displayed consistent performance. Erroneous report analysis revealed issues related to incorrect diagnosis, inconsistencies, and overdiagnosis. The CoT investigation supports the potential of ChatGPT to replicate the clinical decision-making process and offers insights into AI interpretability. Conclusions: The ChatGPT-3.5 model holds potential as a valuable tool for assisting in the efficient determination of BI-RADS classifications and enhancing diagnostic performance.

Ruxolitinib-based senomorphic therapy mitigates cardiomyocyte senescence in septic cardiomyopathy by inhibiting the JAK2/STAT3 signaling pathway.

Background: Cellular senescence has emerged as a pivotal focus in cardiovascular research. This study investigates the previously unrecognized role of cellular senescence in septic cardiomyopathy (SCM) and evaluates senomorphic therapy using ruxolitinib (Rux) as a potential treatment option. Methods: We employed lipopolysaccharide (LPS)-induced neonatal rat cardiomyocytes (NRCMs) and two mouse models-LPS-induced and cecal ligation and puncture (CLP)-induced SCM models-to assess Rux's effects. RNA sequencing, western blotting (WB), quantitative polymerase chain reaction (qPCR), immunofluorescence, immunohistochemistry, senescence-associated ß-galactosidase (SA-ß-gal) assay, and other techniques were utilized to investigate underlying mechanisms. Results: Senescence-associated secretory phenotype (SASP) and cellular senescence markers were markedly elevated in LPS-induced NRCMs and SCM animal models, confirmed by the SA-ß-gal assay. Rux treatment attenuated SASP in vitro and in vivo, alongside downregulation of senescence markers. Moreover, Rux-based senomorphic therapy mitigated mitochondrial-mediated apoptosis, improved cardiac function in SCM mice, restored the balance of antioxidant system, and reduced reactive oxygen species (ROS) levels. Rux treatment restored mitochondrial membrane potential, mitigated mitochondrial morphological damage, and upregulated mitochondrial complex-related gene expression, thereby enhancing mitochondrial function. Additionally, Rux treatment ameliorated SCM-induced mitochondrial dynamic dysfunction and endoplasmic reticulum stress. Mechanistically, Rux inhibited JAK2-STAT3 signaling activation both in vitro and in vivo. Notably, low-dose Rux and ABT263 showed comparable efficacy in mitigating SCM. Conclusions: This study highlighted the potential significance of cellular senescence in SCM pathogenesis and suggested Rux-based senomorphic therapy as a promising therapeutic approach for SCM.

A Non-Canonical p75HER2 Signaling Pathway Underlying Trastuzumab Action and Resistance in Breast Cancer.

Overexpression of HER2 occurs in 25% of breast cancer. Targeting HER2 has proven to be an effective therapeutic strategy for HER2-positive breast cancer. While trastuzumab is the most commonly used HER2 targeting agent, which has significantly improved outcomes, the overall response rate is low. To develop novel therapies to boost trastuzumab efficacy, it is critical to identify the mechanisms underlying trastuzumab action and resistance. We recently showed that the inhibition of breast cancer cell growth by trastuzumab is not through the inhibition of HER2 canonical signaling. Here we report the identification of a novel non-canonical HER2 signaling pathway and its interference by trastuzumab. We showed that HER2 signaled through a non-canonical pathway, regulated intramembrane proteolysis (RIP). In this pathway, HER2 is first cleaved by metalloprotease ADAM10 to produce an extracellular domain (ECD) that is released and the p95HER2 that contains the transmembrane domain (TM) and intracellular domain (ICD). p95HER2, if further cleaved by an intramembrane protease, γ-secretase, produced a soluble ICD p75HER2 with nuclear localization signal (NLS). p75HER2 is phosphorylated and translocated to the nucleus. Nuclear p75HER2 promotes cell proliferation. Trastuzumab targets this non-canonical HER2 pathway via inhibition of the proteolytic cleavage of HER2 by both ADAM10 and γ-secretase. However, p75HER2 pathway also confers resistance to trastuzumab once aberrantly activated. Combination of trastuzumab with ADAM10 and γ-secretase inhibitors completely blocks p75HER2 production in both BT474 and SKBR3 cells. We concluded that HER2 signals through the RIP signaling pathway that promotes cell proliferation and is targeted by trastuzumab. The aberrant HER2 RIP signaling confers resistance to trastuzumab that could be overcome by the application of inhibitors to ADAM10 and γ-secretase.

Radical Replacement Process for Ligated Boryl Radical-Mediated Activation of Unactivated Alkyl Chlorides for C(sp3)-C(sp3) Bond Formation.

The ligated boryl radical (LBR) has emerged as a potent tool for activating alkyl halides in radical transformations through halogen-atom transfer (XAT). However, unactivated alkyl chlorides still present an open challenge for this strategy. We herein describe a new activation mode of the LBR for the activation of unactivated alkyl chlorides to construct a C(sp3)-C(sp3) bond. Mechanistic studies reveal that the success of the protocol relies on a radical replacement process between the LBR and unactivated alkyl chloride, forming an alkyl borane intermediate as the alkyl radical precursor. Aided with the additive K3PO4, the alkyl borane then undergoes one-electron oxidation, generating an alkyl radical. The incorporation of the radical replacement activation model to activate unactivated alkyl chlorides significantly enriches LBR chemistry, which has been applied to activate alkyl iodides, alkyl bromides, and activated alkyl chlorides via XAT.

Ultrasound Spectral Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastatic in Breast Cancer.

OBJECTIVES: To explore the clinical value of the nomogram based on ultrasound spectral combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. METHODS: We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. RESULTS: Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). CONCLUSION: Axillary ultrasound combined with Ki-67 and ultrasound spectral parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

BACKGROUND: The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status. PURPOSE: To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. MATERIAL AND METHODS: We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. RESULTS: The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. CONCLUSION: The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

Metagenomic Next-Generation Sequencing in the Diagnosis of Pulmonary Infections after Allogeneic Hematopoietic Stem Cell Transplantation.

Early and accurate identification of pathogens in pulmonary infections after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is critically important. The clinical usefulness of metagenomic next-generation sequencing (mNGS) in the diagnosis of pulmonary infections after allo-HSCT remains under discussion. This multicenter retrospective study was conducted to compare mNGS and conventional microbiological tests (CMTs) in identifying the pathogens of pulmonary infections in allo-HSCT recipients. One hundred forty allo-HSCT recipients with suspected pulmonary infections who underwent bronchoscopy were included. mNGS and CMTs performed on bronchoalveolar lavage fluid specimens showed 71.4% positivity on mNGS compared to 55.0% positivity on CMTs. mNGS identified 182 pathogens, including bacteria (n = 88), fungi (n = 35) and viruses (n = 59), compared to 106 pathogens detected by CMTs (bacteria, n = 31; fungi, n = 24; viruses, n = 51). Pulmonary infection was finally diagnosed in 98 patients, including 22 bacterial, 7 fungal, 18 viral, and 48 mixed infections and 3 infections with an unknown pathogen. Mixed infections were identified in 50.5% of the patients with pulmonary infection. The sensitivity of mNGS and CMTs for diagnosing pulmonary infections was 88.8% and 69.4%, respectively (P = .001), and the specificity were 81.0% and 85.7%, respectively (P = .688). Our findings suggest that mNGS may be a promising technology for diagnosing pulmonary infections in allo-HSCT recipients.

Comprehensive diagnostic model for osteosarcoma classification using CT imaging features.

Objective: The main objective of this study was to create and assess a detailed diagnostic model with an optimizing feature selection algorithm that combines computed tomography (CT) imaging characteristics, demographic information, and genetic markers to enhance the accuracy of benign and malignant classification of osteosarcoma. This research seeks to enhance the early identification and categorization of benign and malignant of osteosarcoma, ultimately enabling more personalized and efficient treatment approaches. Methods: Data from 225 patients diagnosed with osteosarcoma at two different medical institutions between June 2018 and June 2021 were gathered for this research study. A novel feature selection approach that combined Principal Component Analysis (PCA) with Improved Particle Swarm Optimization (IPSO) was utilized to analyze 1743 image-derived features. The performance of the resulting model was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), and compared to models developed using conventional feature selection methods. Results: The proposed model showed promising predictive performance with an AUC of 0.87, accuracy of 0.80, sensitivity of 0.75, and specificity of 0.85. These results suggest improved predictive ability compared to models built using traditional feature selection techniques, particularly in terms of accuracy and specificity. However, there is room for improvement in enhancing sensitivity. Conclusion: Our study introduces a novel predictive model for distinguishing between benign and malignant osteosarcoma., emphasizing its potential significance in clinical practice. Through the utilization of CT imaging features, our model shows improved accuracy and specificity, marking progress in the early detection and classification of osteosarcoma as either benign or malignant. Future investigations will concentrate on enhancing the model's sensitivity and validating its effectiveness on a larger dataset, aiming to boost its clinical relevance and support personalized treatment approaches for osteosarcoma.

A Sustainable Approach for Degradation of Alternariol by Peroxidase Extracted from Soybean Hulls: Performance, Pathway, and Toxicity Evaluation.

Alternariol (AOH), an emerging mycotoxin, inevitably exists widely in various food and feed commodities with cereals and fruits being particularly susceptible, raising global concerns over its harm to human and livestock health. The development of eco-friendly and efficient strategies to decontaminate AOH has been an urgent task. This study provided insight into the utilization of crude soybean hull peroxidase as a powerful biocatalyst for degrading AOH. The results confirmed that crude soybean hull peroxidase (SHP) could catalyze the oxidation of AOH by use of H2O2 as a co-substrate. The optimum reaction conditions for SHP-catalyzed AOH degradation were recorded at pH 4.0-8.0, at 42-57 °C, and at H2O2 concentration of 100-500 µM. Mass analysis elucidated the degradation of AOH through hydroxylation and methylation by crude SHP. Moreover, toxicological analysis indicated that crude SHP-catalyzed AOH degradation detoxified the hepatotoxicity of this mycotoxin. The performance of crude SHP to degrade AOH in food matrices was further evaluated, and it was found that the enzyme agent could achieve AOH degradation by 77% in wheat flour, 84% in corn flour, 34% in grape juice, and 26% in apple juice. Collectively, these findings establish crude SHP as a promising candidate for effective AOH degradation, with potential applications in the food and feed industry.

Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC), the predominant pathological type among thyroid malignancies, is responsible for the sharp increase in thyroid cancer. Although PTC is an indolent tumor with good prognosis, 60-70% of patients still have early cervical lymph node metastasis, typically in the central compartment. Whether there is central lymph node metastasis (CLNM) or not directly affects the formulation of preoperative surgical procedures, given that such metastases have been tied to compromised overall survival and local recurrence. However, detecting CLNM before operation can be challenging due to the limited sensitivity of preoperative approaches. Prophylactic central lymph node dissection (PCLND) in the absence of clinical evidence of CLNM poses additional surgical risks. This study aims to provide a comprehensive review of the risk factors related to CLNM in PTC patients. A key focus is on utilizing multimodal ultrasound (US) for accurate prognosis of preoperative CLNM and to highlight the distinctive role of US-based characteristics for predicting CLNM.

Photoinduced Single Electron Reduction of the 4-O-5 Linkage in Lignin Models for C-P Coupling Catalyzed by Bifunctional N-Heterocyclic Carbenes.

Catalytic activation of Caryl-O bonds is considered as a powerful strategy for the production of aromatics from lignin. However, due to the high reduction potentials of diaryl ether 4-O-5 linkage models, their single electron reduction remains a daunting challenge. This study presents the blue light-induced bifunctional N-heterocyclic carbene (NHC)-catalyzed one-electron reduction of diaryl ether 4-O-5 linkage models for the synthesis of trivalent phosphines. The H-bond between the newly devised bifunctional NHC and diaryl ethers is responsible for the success of the single electron transfer. Furthermore, this approach demonstrates selective one-electron reduction of unsymmetric diaryl ethers, oligomeric phenylene oxide, and lignin model.

Artificial neural network prediction of postoperative complications in papillary thyroid microcarcinoma based on preoperative ultrasonographic features.

OBJECTIVE: To predict post-thyroidectomy complications in papillary thyroid microcarcinoma (PTMC) patients using a deep learning model based on preoperative ultrasonographic features. This study addresses the global rise in PTMC incidence and the challenges in treatment decision-making with high-resolution ultrasonography. METHOD: This study enrolled 1638 patients with clinically staged cN0 PTMC who received surgical treatment from 1997 to 2019 at Beijing Friendship Hospital. Deep learning model was developed using fully connected neural network. Feature selection included 1000 iterations of Bootstrap sampling and Recursive Feature Elimination (RFE) to identify the top 10 features. Data preprocessing involved normalization and imputation for missing values. SMOTE addressed class imbalance. The model was trained and tested on random data split, with performance metrics including Accuracy (ACC), Area Under the Curve (AUC), Sensitivity (SEN), and Specificity (SPE), visualized through a ROC curve and confusion matrix. RESULTS: The fully connected deep neural network model demonstrated high accuracy (ACC 0.81), Area Under the Curve (AUC 0.74), sensitivity (SEN 0.65), and specificity (SPE 0.83) and visualized by ROC curve and confusion matrix. These results highlight the model's reliability and potential as an effective tool in predicting postoperative complications and assisting in clinical decision-making for PTMC patients. CONCLUSION: This study highlights the potential of deep learning in enhancing medical predictions and personalized healthcare. Despite promising results, limitations include a single-center data source and unconsidered factors like lifestyle and genetics. Future research should expand data sources, include more influencing factors, and refine algorithms to improve accuracy and applicability in thyroid cancer treatment.

Superior survival with allogeneic hematopoietic stem cell transplantation versus chemotherapy for high-risk adult acute lymphoblastic leukemia in a PDT-ALL-2016 pediatric-inspired cohort.

Not available.

Critical steps in the restoration of coal mine soils: Microbial-accelerated soil reconstruction.

Soil reconstruction is a critical step in the restoration of environments affected by mining activities. This paper provides a comprehensive review of the significant role that microbial processes play in expediting soil structure formation, particularly within the context of mining environment restoration. Coal gangue and flotation tailings, despite their low carbon content and large production volumes, present potential substrates for soil reclamation. These coal-based solid waste materials can be utilized as substrates to produce high-quality soil and serve as an essential carbon source to enhance poor soil conditions. However, extracting active organic carbon components from coal-based solid waste presents a significant challenge due to its complex mineral composition. This article offers a thorough review of the soilization process of coal-based solid waste under the influence of microorganisms. It begins by briefly introducing the primary role of in situ microbial remediation technology in the soilization process. It then elaborates on various improvements to soil structure under the influence of microorganisms, including the enhancement of soil aggregate structure and soil nutrients. The article concludes with future recommendations aimed at improving the efficiency of soil reconstruction and restoration, reducing environmental risks, and promoting its application in complex environments. This will provide both theoretical and practical support for more effective environmental restoration strategies.

Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study.

Objective: To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS. Methods: 183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings. Results: LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations. Conclusion: Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.

A multi-Task Learning based applicable AI model simultaneously predicts stage, histology, grade and LNM for cervical cancer before surgery.

PURPOSE: To build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery. METHODS: This retrospective and prospective cohort study was conducted from January 2001 to March 2014 for the training set and from January 2018 to November 2021 for the validation set at Beijing Chaoyang Hospital, Capital Medical University. Preoperative clinical information of cervical cancer patients was used. An Artificial Neural Network (ANN) algorithm was used to build the MTL-based AI model. Accuracy and weighted F1 scores were calculated as evaluation indicators. The performance of the MTL model was compared with Single-Task Learning (STL) models. Additionally, a Turing test was performed by 20 gynecologists and compared with this AI model. RESULTS: A total of 223 cervical cancer cases were retrospectively enrolled into the training set, and 58 cases were prospectively collected as independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01-0.01) per 20 patients. The mean time consumption doctor and doctor with AI were 581.1s (95%CI: 300.0-900.0) per 20 patients and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P < 0.05) The time consumption of AI was significantly less than that of doctors' prediction and AI-assisted doctors' results. (P < 0.05 CONCLUSION: A multi-task learning AI model can simultaneously predict stage, histology, grade, and LNM for cervical cancer preoperatively with minimal time consumption. To improve the conditions and use of the beneficiaries, the model should be integrated into routine clinical workflows, offering a decision-support tool for gynecologists. Future studies should focus on refining the model for broader clinical applications, increasing the diversity of the training datasets, and enhancing its adaptability to various clinical settings. Additionally, continuous feedback from clinical practice should be incorporated to ensure the model's accuracy and reliability, ultimately improving personalized patient care and treatment outcomes.

The impact of the donors' COVID-19 status on the outcomes of allogeneic hematopoietic stem cell transplantation: a multi-center retrospective study.

Introduction: To explore the impact of donors' COVID-19 status on allogeneic stem cell transplantation (allo-HSCT), we compared the transplant outcomes of 74 participants. Methods: This multi-center retrospective study included nine participants receiving grafts from COVID-19 positive donors (CPD), 45 from COVID-19 experienced donors (CED), and 20 from COVID-19 naive donors (CND). We evaluated engraftment, complications, and survival rates among the three groups. Results: All apheresis procedures were successful with no significant differences in CD34+ cells or lymphocytes in grafts among the three groups. All patients achieved engraftment by day 30 post-HSCT. The incidence of grade II-IV acute graft-versus-host disease (aGVHD) was 55.6%, 20%, and 10% in the CPD, CED, and CND groups, respectively (p = 0.024). Multivariate analysis indicated that COVID-19 positivity in donors at the time of apheresis was an independent risk factor for II-IV aGVHD (p = 0.020, OR = 12.159, 95% CI 1.783 -135.760). No differences were observed among the groups in terms of chronic GVHD, viral infection, or sinusoidal obstruction syndrome. The 6-month overall survival and disease-free survival rates were also similar among the three groups. Discussion: Our results suggest that the COVID-19-positive status of donors might not impact graft collection, engraftment, or short-term survival of allo-HSCT recipients but might increase the risk of aGVHD. Further research is needed to explore the influence of donors' COVID-19 status on long-term complications and survival in allo-HSCT recipients.