Broad ligament hematoma is typically seen during cesarean section due to rupture of branches of uterine and vaginal vessels and it’s rare to be seen post-normal vaginal delivery. Addressing puerperal hematomas postpartum presents considerable challenges for obstetric care providers. While hematomas such as those affecting the vulva, vulvovaginal region, or paravaginal area are frequently encountered, retroperitoneal hematomas are rare and notably pose a greater risk to the life of the patient. The medical literature contains scant case reports on retroperitoneal hematomas, with no consensus on a definitive treatment approach. Pelvic arterial embolization has emerged as both a sensible and increasingly preferred method for treating these hematomas recently, but its application is contingent upon the patient maintaining hemodynamic stability and the availability of a specialized interventional embolization unit. In our case, we are presenting a very rare case of a 31-year-old primigravida female with a history of in vitro fertilization pregnancy. She delivered a normal vaginal delivery at 31 weeks gestation. Unfortunately, she experienced multiple complications intrapartum, including preeclampsia and placental abruption. These complications increased her risk of developing a broad ligament hematoma.
Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.
The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.
Hepatic encephalopathy(HE)is a formidable complication in patients with decompensated cirrhosis,often necessitating the administration of rifaximin(RFX)for effective management.RFX,is a gut-restricted,poorly-absorbable oral rifamycin derived antibiotic that can be used in addition to lactulose for the secondary prophylaxis of HE.It has shown notable reductions in infection,hospital readmission,duration of hospital stay,and mortality.However,limited data exist about the concurrent use of RFX with broad-spectrum antibiotics,because the patients are typically excluded from studies assessing RFX efficacy in HE.A pharmacist-driven quasi-experimental pilot study was done to address this gap.They argue against the necessity of RFX in HE during broad-spectrum antibiotic treatment,particularly in critically ill patients in intensive care unit(ICU).The potential for safe RFX discontinuation without adverse effects is clearly illuminated and valuable insight into the optimization of therapeutic strategies is offered.The findings also indicate that RFX discontinuation during broadspectrum antibiotic therapy was not associated with higher rates of delirium or coma,and this result remained robust after adjustment in multivariate analysis.Furthermore,rates of other secondary clinical and safety outcomes,including ICU mortality and 48-hour changes in vasopressor requirements,were comparable.However,since the activity of RFX is mainly confined to the modulation of gut microbiota,its potential utility in patients undergoing extensive systemic antibiotic therapy is debatable,given the overlapping antibiotic activity.Further,this suggests that the action of RFX on HE is class-specific(related to its activity on gut microbiota),rather than drug-specific.A recent double-blind randomized controlled(ARiE)trial provided further evidence-based support for RFX withdrawal in critically ill cirrhotic ICU patients receiving broad-spectrum antibiotics.Both studies prompt further discussion about optimal therapeutic strategy for patients facing
BACKGROUND Pediatric abdominal infection is a common but serious disease that requires timely and effective treatment.In surgical treatment,accurate diagnosis and rational application of antibiotics are the keys to improving treatment effects.AIM To investigate the effect of broad-spectrum bacterial detection on postoperative antibiotic therapy.METHODS A total of 100 children with abdominal infection who received surgical treatment in our hospital from September 2020 to July 2021 were grouped.The observation group collected blood samples upon admission and sent them for broad-spectrum bacterial infection nucleic acid testing,and collected pus or exudate during the operation for bacterial culture and drug sensitivity testing;the control group only sent bacterial culture and drug sensitivity testing during the operation.RESULTS White blood cell count,C-reactive protein,procalcitonin,3 days after surgery,showed better postoperative index than the control group(P<0.05).The hospital stay in the observation group was significantly shorter than that in the control group.The hospitalization cost in the observation group was significantly lower than that in the control group,and the difference between the two groups was statistically significant(P<0.05).CONCLUSION Early detection of broad-spectrum bacterial infection nucleic acids in pediatric abdominal infections can help identify pathogens sooner and guide the appropriate use of antibiotics,improving treatment outcomes and reducing medical costs to some extent.
The coronavirus disease 2019(COVID-19)pandemic,caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has claimedmillions of lives and caused innumerable economic losses worldwide.Unfortunately,state-of-the-art treatments still lag behind the continual emergence of new variants.Key to resolving this issue is developing antivirals to deactivate coronaviruses regardless of their structural evolution.Here,we report an innovative antiviral strategy involving extracellular disintegration of viral proteins with hyperanion-grafted enediyne(EDY)molecules.The core EDY generates reactive radical species and causes significant damage to the spike protein of coronavirus,while the hyperanion groups ensure negligible cytotoxicity of the molecules.The EDYs exhibit antiviral activity down to nanomolar concentrations,and the selectivity index of up to 20,000 against four kinds of human coronavirus,including the SARS-CoV-2 Omicron variant,suggesting the high potential of this new strategy in combating the COVID-19 pandemic and a future“disease X.”
Ke SunZhe DingXiaoying JiaHaonan ChengYingwen LiYan WuZhuoyu LiXiaohua HuangFangxu PuEntao LiGuiyou WangWei WangYun DingGary WongSandra ChiuJiaming LanAiguo Hu
High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.
Continual evolution of the severe acute respiratory syndrome coronavirus(SARS-CoV-2)virus has allowed for its gradual evasion of neutralizing antibodies(nAbs)produced in response to natural infection or vaccination.The rapid nature of these changes has incited a need for the development of superior broad nAbs(bnAbs)and/or the rational design of an antibody cocktail that can protect against the mutated virus strain.Here,we report two angiotensin-converting enzyme 2 competing nAbs—8H12 and 3E2—with synergistic neutralization but evaded by some Omicron subvariants.Cryo-electron microscopy reveals the two nAbs synergistic neutralizing virus through a rigorous pairing permitted by rearrangement of the 472-489 loop in the receptor-binding domain to avoid steric clashing.Bispecific antibodies based on these two nAbs tremendously extend the neutralizing breadth and restore neutralization against recent variants including currently dominant XBB.1.5.Together,these findings expand our understanding of the potential strategies for the neutralization of SARS-CoV-2 variants toward the design of broad-acting antibody therapeutics and vaccines.
