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Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research achievements, Chinese medical concept normalization (CMCN) remains insufficiently explored due to its complex syntactic structure and the paucity of Chinese medical semantic and ontology resources. In recent years, deep learning has been extensively applied across numerous natural language processing tasks, owing to its robust learning capabilities, adaptability, and transferability. It has proven to be well suited for intricate and specialized knowledge discovery research in the biomedical field. In this study, we conduct research on CMCN through the lens of deep learning. Specifically, our research introduces a model that leverages polymorphic semantic information and knowledge enhanced through multi-task learning and retain more important medical features through continual learning. As the cornerstone of CMCN, disease names are the main focus of this research. We evaluated various methodologies on Chinese disease dataset built by ourselves, finally achieving 76.12 % on Accuracy@1, 87.20 % on Accuracy@5 and 90.02 % on Accuracy@10 with our best-performing model GCBM-BSCL. This research not only advances the fields of knowledge mining and medical concept normalization but also enhances the integration and application of artificial intelligence in the medical and health field. We have published the source code and results on https://github.com/BearLiX/CMCN.

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Inpatient falls are common adverse events especially for patients with hematologic malignancies. A fall-risk prediction model for patients with hematologic malignancies are still needed. Here we conducted a multicenter study that prospectively included 516 hospitalized patients with hematologic malignancies, and developed a nomogram for fall risk prediction. Patients were divided into the modeling group (n = 389) and the validation group (n = 127). A questionnaire containing sociodemographic factors, general health factors, disease-related factors, medication factors, and physical activity factors was administered to all patients. Logistic regression analysis revealed that peripheral neuropathy, pain intensity, Morse fall scale score, chemotherapy courses, and myelosuppression days were risk factors for falls in patients with hematologic malignancies. The nomogram model had a sensitivity of 0.790 and specificity of 0.800. The calibration curves demonstrated acceptable agreement between the predicted and observed outcomes. Therefore, the nomogram model has promising accuracy in predicting fall risk in patients with hematologic malignancies.

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The dirhodium(II) complexes with bridging phosphine and OAc ligands showed high reactivity and selectivities in olefin dehydrosilylation. In order to determine the structure of the actual catalyst which cannot be determined experimentally, the geometries of the dirhodium catalyst, the detailed catalytic mechanism, and the stereo- and chemo-selectivities of the title reaction were studied using DFT calculations. The results showed that one OAc group is monodentate and the other is bidentate in the dirhodium catalyst C'. The determined catalytic cycle consists of four processes: Rh-H bond activation in C', Si-H bond activation in alkoxysilane, alkylene insertion into the Rh-Si bond, followed by ß-H elimination or σ-metathesis reaction. Among them, the alkylene insertion process is the rate-determining step. The stereoselectivity of the title reaction is controlled by the steric effect and orbital interactions between the alkyene and dirhodium catalysts in the ß-H elimination process. The chemoselectivity is regulated by the presence of the axial ligand in the dirhodium catalyst, when there is an axial ligand coordinated to the Rh atom, E-alkene is the main product, whereas alkane would be obtained in the absence of an axial ligand. Our work determines the structure of the actual catalyst, and provides explanations and predictions for the activity, and chemo- and stereo-selectivity control of olefin dehydrosilylation.

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BACKGROUND: For patients with sepsis receiving non-invasive ventilation (NIV), early rehabilitation is crucial. The Sitting Baduanjin (SBE) is an efficient early rehabilitation exercise suitable for bed patients. There is no consensus about the effect of SBE on the early rehabilitation of septic patients with NIV. This study focused on how the SBE affected the early rehabilitation of sepsis patients with NIV. METHODS: 96 sepsis patients with NIV were randomly assigned to either an Baduanjin group that received the SBE based on the routine rehabilitation exercise (n = 48) or a control group (n = 48) that received routine rehabilitation exercise. The primary outcome was the Medical Research Council(MRC)score, and the Barthel Index score, the duration of NIV, length of ICU stay, length of total stay, hospitalization expense as secondary outcomes. RESULTS: A total of 245 sepsis patients were screened, with 96 randomly assigned. The study was completed by 90 patients out of the 96 participants.Results revealed that the MRC score increased in both groups, but the improvement of muscle strength in Baduanjin group was more obvious, with statistical significance (p < 0.001).There was statistically significantly difference between the two groups in Barthel Index at the day of transfer out of ICU(P = 0.028).The patients in the Baduanjin group had an average reduction of 24.09 h in the duration of NIV and 3.35 days in total length of hospital stay compared with the control group (p < 0.05).Of note, the Baduanjin group had significantly reduction the total hospitalization expense. No serious adverse events occurred during the intervention period. CONCLUSIONS: In patients with sepsis, the SBE appears to improve muscle strength and activities of daily living (ADL), and lowed the duration of NIV, the length of the total stay, and the hospitalization expense. TRIAL REGISTRATION: The study registered on the Chinese Clinical Trial Registry ( www.chictr.org.cn ), Clinical Trials identifier ChiCTR1800015011 (28/02/2018).

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This study aims to to establish a diagnosis model based on simple clinical features for children with cervical histiocytic necrotizing lymphadenitis or malignant lymphoma. Simple clinical features of pediatric patients were analyzed to develop a diagnosis model based on a comparison of classical machine-learning algorithms. This was a single-center retrospective study in a tertiary pediatrics hospital. Pediatric patients treated for cervical histiocytic necrotizing lymphadenitis or malignant lymphoma treated at our institution in recent 5 years were included. Demographic data and laboratory values were recorded and binary logistics regression analysis was applied to select possible predictors to develop diagnostic models with different algorithms. The diagnostic efficiency and stability of each algorithm were evaluated to select the best one to help establish the final model. Eighty-three children were included with 45 cases of histiocytic necrotizing lymphadenitis and 38 cases of malignant lymphoma. Peak temperature, white blood cell count, monocyte percentage, and urea value were selected as possible predictors based on the binary logistics regression analysis, together with imaging features already reported (size, boundary, and distribution of mass). In the ten-round random testing sets, the discriminant analysis algorithm achieved the best performance with an average accuracy of 89.0% (95% CI 86.2-93.6%) and an average AUC value of 0.971 (95% CI 0.957-0.995). CONCLUSION: A discriminant analysis model based on simple clinical features can be effective in differential diagnosis of cervical histiocytic necrotizing lymphadenitis and malignant lymphoma in children. Peak body temperature, white blood cell count, and short diameter of the largest mass are significant predictors. WHAT IS KNOWN: • Several multivariate diagnostic models for HNL and ML have been proposed based on B-ultrasound or CT features in adults. • The differences between children and adults are nonnegligible in the clinical featues of HNL. WHAT IS NEW: • The study firstly report a large-sample diagnostic model between the HNL and MLin pediatric patients. • Non-imaging clinical features has also been proven with quite good diagnostic value.

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OBJECTIVES: To study the risk factors and prognostic characteristics of pediatric silent lupus nephritis (SLN) with class Ⅲ to V. METHODS: A retrospective study was conducted to collect clinical data from 30 children diagnosed with SLN at the Department of Pediatrics, Second Xiangya Hospital, Central South University, from May 2007 to April 2023. Based on renal pathological classification, the patients were divided into a class Ⅱ group (12 cases) and a class Ⅲ to Ⅴ group (18 cases). The risk factors for the occurrence of class Ⅲ to Ⅴ SLN were analyzed, and the prognostic characteristics were summarized. RESULTS: Among the 30 SLN patients, the median follow-up time was 61.50 months. There were no statistically significant differences in the proportions of patients who discontinued glucocorticoids or achieved low disease activity status, nor in the annual decline rate of estimated glomerular filtration rate (eGFR) between the class Ⅱ and class Ⅲ to V groups (P>0.05). However, three patients in the class Ⅱ group progressed to stage 1 chronic kidney disease (CKD), while eight patients in the class III to V group reached stage 1 CKD, and four patients reached stage 2 CKD. Among the 26 female SLN patients, serum complement C3 levels in the class III to V group were lower than those in the class Ⅱ group (P<0.05). Serum C3 levels in SLN patients, as well as in female SLN patients, were negatively correlated with the fluorescence intensity of IgA, IgG, and C3 immune complexes in the kidneys (P<0.05). Additionally, serum C3 levels in female SLN patients were negatively correlated with the renal pathological activity index (P<0.05). Binary logistic regression analysis indicated that being female and having low serum complement C3 levels were risk factors for the occurrence of class Ⅲ to V SLN in children (P<0.05). CONCLUSIONS: Class Ⅲ to V SLN is not uncommon among SLN children, and there remains a risk of long-term renal function progression. Being female and having low serum complement C3 levels are identified as risk factors for class Ⅲ to V SLN in children.

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The immunosuppressive tumor microenvironment (TME) remains a major obstacle to tumor control and causes suboptimal responses to immune checkpoint blockade (ICB) therapy. Thus, developing feasible therapeutic strategies that trigger inflammatory responses in the TME could improve the ICB efficacy. Mitochondria play an essential role in inflammation regulation and tumor immunogenicity induction. Herein, we report the discovery and characterization of a class of small molecules that can recapitulate aqueous self-assembly behavior, specifically target cellular organelles (e.g., mitochondria), and invigorate tumor cell immunogenicity. Mechanistically, this nanoassembly platform dynamically rewires mitochondria, induces endoplasmic reticulum stress, and causes apoptosis/paraptosis-associated immunogenic cell death. After treatment, stressed and dying tumor cells can act as prophylactic or therapeutic cancer vaccines. In preclinical mouse models of cancers with intrinsic or acquired resistance to PD-1 blockade, the local administration of nanoassemblies inflames the immunologically silent TME and synergizes with ICB therapy, generating potent antitumor immunity. This chemically programmed small-molecule immune enhancer acts distinctly from regular cytotoxic therapeutics and offers a promising strategy for synchronous and dynamic tailoring of innate immunity to achieve traceless cancer therapy and overcome immunosuppression in cancers.

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Azobenzene moieties can serve as active fragments in antimicrobials and exert trans/cis conversions of molecules. Herein, a series of novel nicotinamide derivatives (NTMs) were developed by employing a two-step strategy, including azo-incorporating and bioisosteric replacement. Azo-incorporation can conveniently provide compounds that can be easily optically interconverted between trans/cis isomers, enhancing the structural diversity of azo compounds. It is noteworthy that the replacement of the azo bond with a 1,2,4-oxadiazole motif through further bioisosteric replacement led to the discovery of a novel compound, NTM18, which made a breakthrough in preventing rice sheath blight disease. A control effect value of 94.44% against Rhizoctonia solani could be observed on NTM18, while only 11.11% was determined for boscalid at 200 mg·L-1. Further mechanism validations were conducted, and the molecular docking analysis demonstrated that compound NTM18 might have a tight binding with SDH via an extra π-π interaction between the oxadiazole ring and residue of D_Y586. This work sets up a typical case for the united applications of azo-incorporating and bioisosteric replacement in fungicide design, posing an innovative approach in structural diversity-based development of pesticides.

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Bioactive coatings play a crucial role in enhancing the osseointegration of titanium implants for bone repair. Electrodeposition offers a versatile and efficient technique to deposit uniform coatings onto titanium surfaces, endowing implants with antibacterial properties, controlled drug release, enhanced osteoblast adhesion, and even smart responsiveness. This review summarizes the recent advancements in bioactive coatings for titanium implants used in bone repair, focusing on various electrodeposition strategies based on material-structure synergy. Firstly, it outlines different titanium implant materials and bioactive coating materials suitable for bone repair. Then, it introduces various electrodeposition methods, including electrophoretic deposition, anodization, micro-arc oxidation, electrochemical etching, electrochemical polymerization, and electrochemical deposition, discussing their applications in antibacterial, osteogenic, drug delivery, and smart responsiveness. Finally, it discusses the challenges encountered in the electrodeposition of coatings for titanium implants in bone repair and potential solutions.

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OBJECTIVE: To investigate the clinical features of bladder endometriosis and factors associated with urinary symptoms, pregnancy outcomes, and long-term effects of symptom relief and recurrence. DESIGN: A single-center retrospective cohort study. SETTING: A tertiary referral hospital. PARTICIPANTS: Forty-seven patients who were surgically confirmed to have bladder endometriosis at Peking Union Medical College Hospital between January 2012 and December 2023 were included in this study. INTERVENTIONS: A retrospective study of the clinical and pathological features and reproductive outcomes in patients with bladder endometriosis. MEASUREMENTS AND MAIN RESULTS: Among 47 patients with bladder endometriosis, 27 (57.4%) presented with urinary symptoms, including urinary frequency, urgency, dysuria, and hematuria. Patients with urinary symptoms were more likely to have previous cesarean sections (odds ratio [OR] 4.5, 95% confidence interval [CI] 1.1-19.2, p=0.032) and experience dysmenorrhea compared to those without (p=0.008, OR 5.3, 95%CI 1.5-18.8). Anterior compartment obliteration was another factor associated with urinary symptoms (OR 7.2, 95%CI 1.3-40.4, p=0.016). Bladder lesions located within 1 cm of the ureteral orifice (OR 7.2, 95%CI 1.3-40.4, p=0.020) and the deeper invasive layer of lesions (mucosal layer, OR 6.1, 95%CI 1.4-25.8, p=0.009) were also found to be associated with symptoms. Regarding reproductive outcomes, 12 patients desired to conceive. Of the patients who desired pregnancy, 66.7% achieved pregnancy; 5 spontaneously and 3 after IVF treatment. The miscarriage rate among patients with bladder endometriosis was 25.0% in the age range of 27-40 years. Additionally, all patients experienced symptom relief after one year of follow-up. Only two patients experienced bladder endometriosis recurrence. CONCLUSIONS: Previous cesarean section, dysmenorrhea, anterior compartment obliteration, lesion in the trigone, and mucosal layer invasion were identified as factors associated with urinary symptoms. Although some patients conceived successfully after surgery, disentangling the independent effect of bladder endometriosis on fertility remains challenging.

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Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

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ALKBH5 is one of the demethylases involved in the regulation of RNA m6A modification. In addition to its role in the dynamic regulation of RNA m6A modification, ALKBH5 has been found to play important roles in various tissues fibrosis processes in recent years. However, the mechanisms and effects of ALKBH5 in fibrosis have been reported inconsistently. Multiple cell types, including parenchymal cells, immune cells (neutrophils and T cells), macrophages, endothelial cells, and fibroblasts, play roles in various stages of fibrosis. Therefore, this review analyzes the mechanisms by which ALKBH5 regulates these cells, its impact on their functions, and the outcomes of fibrosis. Furthermore, this review summarizes the role of ALKBH5 in fibrotic diseases such as pulmonary fibrosis, liver fibrosis, cardiac fibrosis, and renal fibrosis, and discusses various ALKBH5 inhibitors that have been discovered to date, exploring the potential of ALKBH5 as a clinical target for fibrosis.

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ObjectiveTo observe the effect of light walking combined with arthroscopic surgery on the efficacy and Bone Mineral Density (BMD) of patients with ankle arthritis. Methods 180 patients with ankle arthritis were retrospectively selected, who were divided into a control group (Group A) and an experimental group (Group B) according to treatment methods, with 90 patients in each group. Group A was treated with conventional open surgery and Group B was treated with light walking combined with arthroscopic surgery. The two groups were compared before and after treatment on scales such as the AOFAS ankle hindfoot score. Meanwhile, the patients' BMD and levels of inflammatory factors such as tumor cell necrosis factor-α (TNF-α) were compared before and after treatment. Results The Kofoed and AOFAS scale scores were higher for mild walking combined with arthroscopic surgery than for conventional treatment (P < 0.05), and their pain visual analogue VAS scores were lower than for conventional treatment (P < 0.05). In Group B, the postoperative BMD increase was significantly higher (P < 0.05). The R-value between PDGF bone growth factor and BMD was 0.957 and the R-value between VEGF growth factor and BMD was 0.903. The R-value between patient age and BMD was -0.936 and the R-value between patient BMI and BMD was -0.913. The treatment efficiency in Group B was 96.7 %. Conclusion The results prove that light walking combined with arthroscopic surgery is suitable for the surgical treatment of patients with ankle arthritis because it has a better therapeutic effect and makes patients' BMD level improved.

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Atomic Fe in N-doped C (Fe-N-C) catalysts provide the most promising non-precious metal O2 reduction activity at the cathodes of proton exchange membrane fuel cells. However, one of the biggest remaining challenges to address towards their implementation in fuel cells is their limited durability. Fe demetallation has been suggested as the primary initial degradation mechanism. However, the fate of Fe under different operating conditions varies. Here, we monitor operando Fe dissolution of a highly porous and >50% FeN x electrochemical utilization Fe-N-C catalyst in 0.1 M HClO4, under O2 and Ar at different temperatures, in both flow cell and gas diffusion electrode (GDE) half-cell coupled to inductively coupled plasma mass spectrometry (ICP-MS). By combining these results with pre- and post-mortem analyses, we demonstrate that in the absence of oxygen, Fe cations diffuse away within the liquid phase. Conversely, at -15 mA cm-2 geo and more negative O2 reduction currents, the Fe cations reprecipitate as Fe-oxides. We support our conclusions with a microkinetic model, revealing that the local pH in the catalyst layer predominantly accounts for the observed trend. Even at a moderate O2 reduction current density of -15 mA cm-2 geo at 25 °C, a significant H+ consumption and therefore pH increase (pH = 8-9) within the bulk Fe-N-C layer facilitate precipitation of Fe cations. This work provides a unified view on the Fe dissolution degradation mechanism for a model Fe-N-C in both high-throughput flow cell and practical operating GDE conditions, underscoring the crucial role of local pH in regulating the stability of the active sites.

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Malic enzymes (MEs) are metabolic enzymes that catalyze the oxidation of malate to pyruvate and NAD(P)H. While researchers have well established the physiological metabolic roles of MEs in organisms, recent research has revealed a link between MEs and carcinogenesis. This review collates evidence of the molecular mechanisms by which MEs promote cancer occurrence, including transcriptional regulation, post-transcriptional regulation, post-translational protein modifications, and protein-protein interactions. Additionally, we highlight the roles of MEs in reprogramming energy metabolism, suppressing senescence, and modulating the tumor immune microenvironment. We also discuss the involvement of these enzymes in mediating tumor resistance and how the development of novel small-molecule inhibitors targeting MEs might be a good therapeutic approach. Insights through this review are expected to provide a comprehensive understanding of the intricate relationship between MEs and cancer, while facilitating future research on the potential therapeutic applications of targeting MEs in cancer management.

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Surface Enhanced Resonance Raman (SERS) is a powerful optical technique, which can help enhance the sensitivity of Raman spectroscopy aided by noble metal nanoparticles (NPs). However, current SERS-NPs are often suboptimal, which can aggregate under physiological conditions with much reduced SERS enhancement. Herein, a robust one-pot method has been developed to synthesize SERS-NPs with more uniform core diameters of 50 nm, which is applicable to both non-resonant and resonant Raman dyes. The resulting SERS-NPs are colloidally stable and bright, enabling NP detection with low-femtomolar sensitivity. An algorithm has been established, which can accurately unmix multiple types of SERS-NPs enabling potential multiplex detection. Furthermore, a new liposome-based approach has been developed to install a targeting carbohydrate ligand, i.e., hyaluronan, onto the SERS-NPs bestowing significantly enhanced binding affinity to its biological receptor CD44 overexpressed on tumor cell surface. The liposomal HA-SERS-NPs enabled visualization of spontaneously developed breast cancer in mice in real time guiding complete surgical removal of the tumor, highlighting the translational potential of these new glyco-SERS-NPs.

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BACKGROUND: Chronic hepatitis B virus (HBV) infection is a serious human health threat given its high morbidity and mortality. Timely and effective antiviral treatment can postpone liver disease progression and reduce the occurrence of HBV-related end-stage liver disease. At present, the antiviral treatment criteria are mainly based on alanine transaminase (ALT) levels, HBV DNA levels and HBV e antigen levels according to the American Association for the Study of Liver Diseases treatment guidelines. However, some chronic hepatitis B (CHB) patients not meeting the above criteria still experience liver disease progression without antiviral treatment. It is urgent to identify a more comprehensive tool to screen out more antiviral treatment candidates as soon as possible. METHODS: Considering the vital role of the immune response in the development of HBV infection and CHB cure, we collected data from 335 treatment-naïve CHB patients and comprehensively analysed their clinical and immune traits (including innate and adaptive responses). The immune parameters were obtained by flow cytometry. Finally, we established a model that can better distinguished CHB patients who need treatment through machine learning and LASSO regression of serological and immune parameters. RESULTS: Through a series of analyses, we selected four important clinical parameters (ALT, HBV DNA, the Fibroscan value, and the A/G ratio) and four immune indicators (NKbright + NKp44+, NKbright + NKG2A+, NKT+GranzymeB+, and CD3 + CD107a + ) from more than 200 variables and then successfully established a mathematical model with high sensitivity and specificity to better screen out antiviral treatment candidates from all CHB patients. CONCLUSIONS: Our results developed a refined model to better screen out antiviral treatment candidates from all CHB patients by combining common clinical parameters and important immune indicators, including innate and adaptive immunity. These findings provide more information for improving treatment guidelines and have potential implications for the timing of antiviral therapy to achieve better virus control and reduce the occurrence of end-stage liver disease.

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The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis.

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OBJECTIVES: Mechanisms of non-typhoidal Salmonella (NTS) resistance to azithromycin have rarely been reported. Here we investigate the epidemiology and genetic features of 10 azithromycin-resistant NTS isolates. METHODS: A total of 457 NTS isolates were collected from a tertiary hospital in Guangzhou. We performed antimicrobial susceptibility tests, conjugation experiments, efflux pump expression tests, whole-genome sequencing and bioinformatics analysis to conduct the study. RESULTS: The results showed that 10 NTS isolates (2.8%) were resistant to azithromycin with minimum inhibitory concentration values ranging from 128 to 512 mg/L and exhibited multidrug resistance. The phylogenetic tree revealed that 5 S. London isolates (AR1-AR5) recognized at different times and departments were closely related [3-74 single-nucleotide polymorphisms (SNPs)] and 2 S. Typhimurium isolates (AR7 and AR8) were clones (<3 SNPs) at 3-month intervals. The azithromycin resistance was conferred by mph(A) gene found on different plasmids, including IncFIB, IncHI2, InFII, IncC and IncI plasmids. Among them, IncFIB, InFII and IncHI2 plasmids carried different IS26-class 1 integron (intI1) arrangement patterns that mediated multidrug resistance transmission. Conjugative IncC plasmid encoded resistance to ciprofloxacin, ceftriaxone and azithromycin. Furthermore, phylogenetic analysis demonstrated that mph(A)-positive plasmids closely related to 10 plasmids in this study were mainly discovered from NTS, Escherichia coli, Klebsiella pneumonia and Enterobacter hormaechei. The genetic environment of mph(A) in 10 NTS isolates was IS26-mph(A)-mrx(A)-mphR(A)-IS6100/IS26 that co-arranged with intI1 harbour multidrug-resistant (MDR) gene cassettes on diverse plasmids. CONCLUSIONS: These findings highlighted that the dissemination of these plasmids carrying mph(A) and various intI1 MDR gene cassettes would seriously restrict the availability of essential antimicrobial agents for treating NTS infections.

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Urban flooding is among the costliest natural disasters worldwide. Timely and effective rescue path planning is crucial for minimizing loss of life and property. However, current research on path planning often fails to adequately consider the need to assess area risk uncertainties and bypass complex obstacles in flood rescue scenarios, presenting significant challenges for developing optimal rescue paths. This study proposes a deep reinforcement learning (RL) algorithm incorporating four main mechanisms to address these issues. Dual-priority experience replays and backtrack punishment mechanisms enhance the precise estimation of area risks. Concurrently, random noisy networks and dynamic exploration techniques encourage the agent to explore unknown areas in the environment, thereby improving sampling and optimizing strategies for bypassing complex obstacles. The study constructed multiple grid simulation scenarios based on real-world rescue operations in major urban flood disasters. These scenarios included uncertain risk values for all passable areas and an increased presence of complex elements, such as narrow passages, C-shaped barriers, and jagged paths, significantly raising the challenge of path planning. The comparative analysis demonstrated that only the proposed algorithm could bypass all obstacles and plan the optimal rescue path across nine scenarios. This research advances the theoretical progress for urban flood rescue path planning by extending the scale of scenarios to unprecedented levels. It also develops RL mechanisms adaptable to various extremely complex obstacles in path planning. Additionally, it provides methodological insights into artificial intelligence to enhance real-world risk management.