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Abstract Background Emerging evidence has indicated a link between the gut microbiota and acute lymphoblastic leukaemia (ALL). However, the acute changes in gut microbiota during chemotherapy and the predictive value of baseline gut microbiota in infectious complication remain largely unknown. Methods Faecal samples (n = 126) from children with ALL (n = 49) undergoing induction chemotherapy were collected at three timepoints, i.e., initiation of chemotherapy (baseline, T0), 7 days (T1) and 33 days (T2) after initiation of chemotherapy. Gut microbiome profile was performed via metagenomic shotgun sequencing. The bioBakery3 pipeline (Kneaddata, Metaphlan 3 and HUMAnN) was performed to assign taxonomy and functional annotations. Gut microbiome at T0 were used to predict infection during chemotherapy. Results The microbial diversities and composition changed significantly during chemotherapy, with Escherichia coli, Klebsiella pneumoniae and Bifidobacterium longum being the most prominent species. The microbial metabolic pathways were also significantly altered during chemotherapy, including the pathway of pyruvate fermentation to acetate and lactate, and assimilatory sulfate reduction pathway. The receiver operating characteristic (ROC) models based on Bifidobacterium longum at T0 could predict infectious complications during the first month of chemotherapy with the area under the curve (AUC) of 0.720. Conclusions Our study provides new insights into the acute changes in microbial and functional characteristics in children with ALL during chemotherapy. The baseline gut microbiota could be potential biomarkers for infections during chemotherapy. Trial registration The study was approved by the Ethics Committee of Zhujiang Hospital, Southern Medical University (2021-KY-171-01) and registered on http://www.chictr.org.cn (ChiCTR2200065406, Registration Date: November 4, 2022).

Background: Although invasive mycosis is a common complication of hematologic malignancies, invasive Fusarium infection, especially systemic disseminated Fusarium infection is rare, which has a high mortality due to remarkable intrinsic drug resistance. Result: We reported a case of 5-year-old high-risk acute lymphocytic leukemia (ALL) female patient complicated with disseminated Fusarium solani infection during the course of chemotherapy. After diagnosis of ALL, the case was treated according to the South China Children's Leukemia Group-ALL-2016 protocol, and achieved a molecular complete response. On the seventh day of reinduction chemotherapy, she experienced endophthalmitis in the right eye along with recurrent high fever. Three days later, endophthalmitis spread to the left eye, blood culture and next-generation sequencing returned positive result for Fusarium solani. The patient was subsequently treated with antifungal therapy including Amphotericin B, Voriconazole and Posaconazole together with supportive treatments, but the effects were less satisfying. The patient then developed endocarditis, encephalitis, arthritis and other disseminated lesions caused by Fusarium solani infection, and eventually died of multiple organ failure. Conclusion: This case we reported highlights the lethality of disseminated Fusarium species infection in immunocompromised patients. Therefore, early diagnosis and timely anti-infective treatments are of utmost importance as that helps in controlling the spread of infection thereby leading to a favorable outcome.

BackgroundOverexpression of the cytokine receptor-like factor 2 (CRLF2) gene is the most common feature in the Philadelphia chromosome (Ph)-like subtype of B-cell acute lymphoblastic leukemia (B-ALL). However, the predictive value of CRLF2 overexpression for the prognosis of pediatric B-ALL patients remain controversial. The molecular mechanisms that upregulate CRLF2 expression level in patients has not been fully elucidated.MethodsIn this study, the prognostic impact of CRLF2 expression level on molecular types of B-ALL in pediatric patients from Zhujiang Hospital (n = 111) was retrospectively analyzed. Youden index analysis was used to categorize CRLF2 expression into 3 groups, and these categories more precisely described the differences in the prognosis of patients with varying expression levels of CRLF2 in both the Zhujiang Hospital cohort and the TARGET cohort.ResultsWe used the Zhujiang Hospital cohort as a discovery cohort to determine the cutoff value of CRLF2 expression. CRLF2-high patients accounted for approximately 6%. In addition, the percentage of bone marrow blast cells and initial white blood cell count in CRLF2-high patients were higher than those in CRLF2-low patients, and MRD turned negative slower. The results were validated in the TARGET cohort and indicated that CRLF2 overexpression could be subdivided by CRLF2 expression levels into 2 categories: CRLF2-high with a poor survival and CRLF2-medium with a good OS and EFS. Such heterogeneity was attributed to the different molecular mechanisms leading to CLRF2 upregulation, where the CRLF2 overexpression level was high in Ph-like B-ALL and medium in high hyperdiploid B-ALL.ConclusionThis study highlights the importance of the molecular mechanisms of the upregulation of CRLF2 expression in predicting the prognosis of pediatric B-ALL patients.

Abstract Immune checkpoint inhibitor (ICI)‐based therapies have shown promising advances for the first‐line treatment of advanced or metastatic esophageal cancer (EC). However, few studies concerning the identification of patients who achieve durable response from ICIs have been previously reported. In the present study, pre‐ and on‐treatment plasma circulating tumor DNA (ctDNA) were analyzed in 10 patients with advanced esophageal squamous cell cancer (ESCC) receiving first‐line chemoimmunotherapy. Patients with decreased molecular tumor burden index (mTBI) >7% experienced longer progression‐free survival (PFS) and durable clinical benefit (DCB, PFS ≥ 6 months). In addition, five patients showed stable disease at first scan, all three patients with decreased mTBI > 7% achieved DCB, while two cases with decreased mTBI ≤ 7% experienced non‐DCB. Our results demonstrate that ctDNA monitor might help identify which ESCC patients respond to chemoimmunotherapy.

The aim is to explore the predictive value of salivary bacteria for the presence of esophageal squamous cell carcinoma (ESCC). Saliva samples were obtained from 178 patients with ESCC and 101 healthy controls, and allocated to screening and verification cohorts, respectively. In the screening phase, after saliva DNA was extracted, 16S rRNA V4 regions of salivary bacteria were amplified by polymerase chain reaction (PCR) with high-throughput sequencing. Highly expressed target bacteria were screened by Operational Taxonomic Units clustering, species annotation and microbial diversity assessment. In the verification phase, the expression levels of target bacteria identified in the screening phase were verified by absolute quantitative PCR (Q-PCR). Receiver operating characteristic (ROC) curves were plotted to investigate the predictive value of target salivary bacteria. LEfSe analysis revealed higher proportions of Fusobacterium, Streptococcus and Porphyromonas, and Q-PCR assay showed significantly higher numbers of Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in patients with ESCC, when compared with healthy controls (all P

BackgroundImbalance of oral salivary microbiota has been linked to the pathogenesis of a variety of systemic diseases, and oral bacterial species have been shown to be useful biomarkers for systemic diseases.This study aimed to characterize the alterations of oral microbiota in patients with esophageal squamous cell carcinoma (ESCC) and to evaluate the diagnostic performance of oral microbial biomarkers for ESCC.MethodsThe relative abundance of flora in saliva samples was analyzed by 16S rDNA sequencing, and differences in the species present in samples from ESCC patients and healthy controls (HCs) were identified by analyzing species diversity and performing LEfSe analysis. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the diagnostic performance of the characteristic bacteria individually and in combination.ResultsDifferences in bacterial diversity indexes were observed for the saliva of ESCC patients versus HCs (P0.05). LEfSe analysis showed that Leptotrichia, Porphyromonas (Pg), Streptococcus, Rothia, Lactobacillus and Peptostreptococcus were more abundant in ESCC patient saliva than in HC saliva, whereas Haemophilus, Alloprevotella (All), Prevotella_7, Prevotella (Pre), Prevotella_6, Pasteurellaceae and Pasteurellales were significantly less abundant in ESCC patient saliva (P

In this study, atypical choroid plexus papilloma was treated with high-dose rapamycin for 17 days preoperatively in an infant. Rapamycin significantly reduced the blood supply to the tumor while reducing the tumor volume, and most of the tumor was resected successfully. However, the infant developed hyperglycemia related to the rapamycin dose, which was effectively controlled by adjusting the dose and applying insulin.

KDSR (3-ketodihydrosphingosine reductase) is a short-chain dehydrogenase located in the endoplasmic reticulum. Mutations in KDSR cause defects in ceramides, which play a key role in the biological processes of the skin and other tissues. Herein, we report a case of compound heterozygous mutations in KDSR that caused progressive keratodermia and thrombocytopenia in a 2-year-old male patient.

KRAS mutation-induced Ras activation plays an important role in the pathogenesis of pancreatic cancer, but the role of wild-type Ras and Ras GTPase-activating proteins remains unclear. The present study was designed to determine the expression spectra of Ras GTPase-activating proteins genes in pancreatic cancer cells, and the role of DAB2IP, a Ras GTPase-activating proteins gene, in the development and progression of pancreatic cancer. Following the analyses of the expression profiles of 16 Ras GTPase-activating proteins in 6 pancreatic cancer cell lines including Bxpc-3 (with wild-type KRAS), Capan-2, Sw1990, Aspc-1, CFPAC-1, and Panc-1 (with mutant KRAS) and 1 normal human pancreatic ductal epithelial cell line, H6C7, the expression of DAB2IP messenger RNA was further analyzed by quantitative real-time polymerase chain reaction. The role of DAB2IP in pancreatic cancer was further investigated in vitro and in vivo by upregulating DAB2IP in Bxpc-3 cells through transfection of DAB2IP into Bxpc-3 cells with recombinant lentivirus. The DAB2IP expression in pancreatic cancer cells and tissues with wild-type KRAS was significantly lower than that in cells and tissues with mutant KRAS ( P < .05). In Bxpc-3 cells with wild-type KRAS, overexpression of DAB2IP decreased the expression of P-AKT and P-ERK and the Ras activity; increased the expression of P-JNK and caspase 3; inhibited cell proliferation, invasiveness, and migration; and increased the cell sensitivity to cetuximab. Overexpression of DAB2IP inhibited tumor progression in a mouse model. In conclusion, DAB2IP downregulates Ras activity in wild-type pancreatic cancer cells. Overexpression of DAB2IP decreases the Ras activity, inhibits cell proliferation, and increases sensitivity to cetuximab in wild-type pancreatic cancer cells. In conclusion, DAB2IP may serve as a potential molecular therapeutic target for the treatment of pancreatic cancer.

This paper presents a two-level integrated optimization system for use in generating the candidate set of optimal evacuation plans that serve as the input for simulation-based evacuation systems. In the proposed system, the high-level optimization aims to maximize the throughput during the specified evacuation duration, and the low-level optimization is intended to minimize the total travel time as well as the waiting time for the entire operation if the specified duration is sufficient for meeting all evacuation demands. To effectively represent traffic flow relations with mathematical formulations, this paper employs the cell transmission concept, but with a revised formulation for large-scale network applications. The performance of the proposed models and their applicability has been tested with a microscopic simulation program that replicates the Ocean City evacuation network. Evaluation results from these numerical studies have demonstrated the promising properties of the proposed integrated optimization system. [ABSTRACT FROM AUTHOR]

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout (Tinitial) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model Tinitial and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller–switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time.

Federated learning (FL) is a type of distributed machine learning approacs that trains global models through the collaboration of participants. It protects data privacy as participants only contribute local models instead of sharing private local data. However, the performance of FL highly relies on the number of participants and their contributions. When applying FL over conventional computer networks, attracting more participants, encouraging participants to contribute more local resources, and enabling efficient and effective collaboration among participants become very challenging. As software-defined networks (SDNs) enable open and flexible networking architecture with separate control and data planes, SDNs provide standardized protocols and specifications to enable fine-grained collaborations among devices. Applying FL approaches over SDNs can take use such advantages to address challenges. A SDN control plane can have multiple controllers organized in layers; the controllers in the lower layer can be placed in the network edge to deal with the asymmetries in the attached switches and hosts, and the controller in the upper layer can supervise the whole network centrally and globally. Applying FL in SDNs with a layered-distributed control plane may be able to protect the data privacy of each participant while improving collaboration among participants to produce higher-quality models over asymmetric networks. Accordingly, this paper aims to make a comprehensive survey on the related mechanisms and solutions that enable FL in SDNs. It highlights three major challenges, an incentive mechanism, privacy and security, and model aggregation, which affect the quality and quantity of participants, the security and privacy in model transferring, and the performance of the global model, respectively. The state of the art in mechanisms and solutions that can be applied to address such challenges in the current literature are categorized based on the challenges they face, followed by suggestions of future research directions. To the best of our knowledge, this work is the first effort in surveying the state of the art in combining FL with SDNs.

To meet the requirements of 5G on enabling higher capacity, faster rates, more connectivity, higher reliability, lower latency, greater versatility, and application-domain specific topologies, new concepts and design approaches are in great need. Current standards for 4G may influence the introduction of radio features and network solutions for 5G systems. New network architectures extending beyond heterogeneous networks and exploiting new frequency spectra (e.g., millimeter wave) are emerging from research laboratories around the world. In addition to the network side, advanced terminals and receivers are being developed to optimize network performances. Splitting the control and data planes [currently studied in 3G Partnership Project (3GPP)] is an interesting paradigm for 5G, together with massive multi-input multi-output (MIMO), advanced antenna systems, software-defined networking (SDN), network functions virtualization (NFV), the Internet of Things, and cloud computing. Also, new radio protocols enabling heterogeneous traffics are required.