Intratumoral and Peritumoral Radiomics for Predicting the Prognosis of High-grade Serous Ovarian Cancer Patients Receiving Platinum-Based Chemotherapy.

RATIONALE AND OBJECTIVES: This study aimed to develop a deep learning (DL) prognostic model to evaluate the significance of intra- and peritumoral radiomics in predicting outcomes for high-grade serous ovarian cancer (HGSOC) patients receiving platinum-based chemotherapy. MATERIALS AND METHODS: A DL model was trained and validated on retrospectively collected unenhanced computed tomography (CT) scans from 474 patients at two institutions, which were divided into a training set (N = 362), an internal test set (N = 86), and an external test set (N = 26). The model incorporated tumor segmentation and peritumoral region analysis, using various input configurations: original tumor regions of interest (ROIs), ROI subregions, and ROIs expanded by 1 and 3 pixels. Model performance was assessed via hazard ratios (HRs) and receiver operating characteristic (ROC) curves. Patients were stratified into high- and low-risk groups on the basis of the training set's optimal cutoff value. RESULTS: Among the input configurations, the model using an ROI with a 1-pixel peritumoral expansion achieved the highest predictive accuracy. The DL model exhibited robust performance for predicting progression-free survival, with HRs of 3.41 (95% CI: 2.85, 4.08; P < 0.001) in training set, 1.14 (95% CI: 1.03, 1.26; P = 0.012) in internal test set, and 1.32 (95% CI: 1.07, 1.63; P = 0.011) in external test set. KM survival analysis revealed significant differences between the high-risk and low-risk groups (P < 0.05). CONCLUSION: The DL model effectively predicts survival outcomes in HGSOC patients receiving platinum-based chemotherapy, offering valuable insights for prognostic assessment and personalized treatment planning.

[Retracted] Expression of caspase­3, Bax and Bcl­2 in hippocampus of rats with diabetes and subarachnoid hemorrhage.

[This retracts the article DOI: 10.3892/etm.2017.5438.].

Mitochondrial STED Imaging and Membrane Potential Monitoring with a Cationic Molecular Probe.

Mitochondria are essential organelles that not only undergo dynamic morphological changes but also exhibit functional activities such as mitochondrial membrane potential (MMP). While super-resolution techniques such as stimulated emission depletion (STED) nanoscopy can visualize the ultrastructure of mitochondria and the MMP probe can monitor mitochondria function, few dyes meet both demands. Here, a small molecule (MitoPDI-90) based on perylene diimide with cationic groups is reported and used for mitochondrial STED imaging and MMP indication. Characterized by excellent photostability, biocompatibility, and high quantum yield, MitoPDI-90 exhibits STED imaging compatibility, facilitating visualization of mitochondrial cristae and time-lapse imaging of highly dynamic mitochondria in living cells. Besides, MitoPDI-90 targets the mitochondria through electrical potential, also enabling live-cell MMP monitoring. MitoPDI-90 allows for super-resolution visualization and time-lapse imaging of mitochondria, and more importantly, indication of changes in MMP, providing insight into the functional activity of live-cell mitochondria.

Effort-reward imbalance and well-being among psychiatric nurses: the mediating role of burnout and decent work.

BACKGROUND: Psychiatric nurses face additional challenges due to negative perceptions, workplace violence, and a lack of respect, impacting their well-being and job satisfaction, which are crucial for improving psychiatric care and patient outcomes. OBJECTIVES: This study aims to examine the relationship between effort-reward imbalance, well-being, burnout, and decent work among psychiatric nurses. METHODS: This study used a cross-sectional design. Data were collected using a convenience sampling method in February 2024 from 397 psychiatric nurses at two psychiatric hospitals in Hangzhou and Huzhou, Zhejiang Province. The Effort-Reward Imbalance Scale, Decent Work Perception Scale, Maslach Burnout Inventory-General Survey, and General Well-Being Schedule Scale were used for data collection. Data analysis was conducted using SPSS 26.0 and the PROCESS macro. RESULTS: The study found that effort-reward imbalance among psychiatric nurses was negatively correlated with decent work (r = -0.564, p < 0.001) and well-being (r = -0.541, p < 0.001), and positively correlated with burnout (r = 0.603, p < 0.001). Burnout mediated the relationship between effort-reward imbalance and well-being (95% CI [-0.386, -0.257]), while decent work also served as a mediator (95% CI [-0.100, -0.012]). Additionally, burnout and decent work were found to mediate the relationship between effort-reward imbalance and well-being (95% CI [-0.050, -0.006]). CONCLUSION: This study highlights the impact of effort-reward imbalance on well-being, confirming that burnout and decent work serve as mediators. Enhancing support, fair compensation, reasonable work schedules, and professional development can promote psychiatric nurses' perception of decent work and improve their well-being.

Clinical features of COVID-19 infection in patients with myasthenia gravis: a real-world retrospective study.

Objective: We investigated the risk factors associated with severe or critical Coronavirus disease 2019 (COVID-19) infection due to the Omicron variant in patients with myasthenia gravis (MG) and determined the potential effect of COVID-19 on myasthenic exacerbation during the Omicron pandemic. Methods: This retrospective study included 287 patients with MG in Tianjin, China. Clinical data of the patients were collected using electronic questionnaires, databases, and clinical records. Results: The overall infection rate was 84.7%. Advanced age, comorbidities, generalized phenotype, and MG instability were drivers of COVID-19 severity, and post-COVID-19 myasthenic exacerbation. The concurrent use of a steroid-sparing agent did not affect COVID-19 susceptibility or severity. It did lower the risk of myasthenic exacerbation after COVID-19 infection. Patients with severe COVID-19 experienced myasthenic exacerbation earlier than patients with non-severe infection (p < 0.001). The severity of COVID-19 (Hazards Ratio = 3.04, 95% CI: 1.41-6.54, p = 0.004) and the clinical phenotype (Hazards Ratio = 3.29, 95% CI: 1.63-6.63, p < 0.001) emerged as independent risk factors for early MG exacerbation. Conclusion: Generally, patients with MG appear to be susceptible to the Omicron strains. Immunotherapy for MG did not increase COVID-19 susceptibility or severity. We do not advocate an immediate cessation of ongoing immunosuppressive treatments once a COVID-19 infection is diagnosed. Instead, a judicious evaluation of the risks and benefits, tailored to each individual, is recommended.

Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning.

Epithelial ovarian cancer (EOC) presents considerable difficulties in prognostication and treatment strategy development. Bevacizumab, an anti-angiogenic medication, has demonstrated potential in enhancing progression-free survival (PFS) in EOC patients. Nevertheless, the identification of individuals at elevated risk of disease progression following treatment remains a challenging task. This study was to develop and validate a deep learning (DL) model using retrospectively collected computed tomography (CT) plain scans of inoperable and recurrent EOC patients receiving bevacizumab treatment diagnosed between January 2013 and January 2024. A total of 525 patients from three different institutions were retrospectively included in the study and divided into training set (N = 400), internal test set (N = 97) and external test set (N = 28). The model's performance was evaluated using Harrell's C-index. Patients were categorized into high-risk and low-risk group based on a predetermined cutoff in the training set. Additionally, a multimodal model was evaluated, incorporating the risk score generated by the DL model and the pretreatment level of carbohydrate antigen 125 as input variables. The Net Reclassification Improvement (NRI) metric quantified the reclassification performance of our optimal model in comparison to the International Federation of Gynecology and Obstetrics (FIGO) staging model. The results indicated that DL model achieved a PFS predictive C-index of 0.73 in the internal test set and a C-index of 0.61 in the external test set, along with hazard ratios of 34.24 in the training set (95% CI: 21.7, 54.1; P < 0.001) and 8.16 in the internal test set (95% CI: 2.5, 26.8; P < 0.001). The multimodal model demonstrated a C-index of 0.76 in the internal test set and a C-index of 0.64 in the external test set. Comparative analysis against FIGO staging revealed an NRI of 0.06 (P < 0.001) for the multimodal model. The model presents opportunities for prognostic assessment, treatment strategizing, and ongoing patient monitoring.

Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms.

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies.

Colletotrichum keratitis: An important fungal infection of nine human eyes.

Colletotrichum is a relatively uncommon cause of human infection. Previous findings on Colletotrichum keratitis were scarce, and most diagnoses were based on morphological distinction, perhaps underestimating the incidence of Colletotrichum species. This research describes the clinical characteristics and treatment outcomes of 9 cases of Colletotrichum keratitis discovered in our hospital using next-generation sequencing (NGS).We reviewed 78 patients with NGS-proven fungal keratitis between September 1, 2021 and May 31, 2023, 9 patients (11.5 %) were verified as infected with Colletotrichum species, and their medical records were reviewed to identify the clinical characteristics. NGS revealed that 3 patients were infected with C. truncatum, 3 patients with C. gloeosporioides, and the other 3 patients with C. fructicola. Seven patients had a history of corneal plant trauma (all three patients with C. fructicola had corneal injury history due to chestnut burrs), one patient was infected by mosquitoes flying into the eye, and one patient had an unknown origin. Seven patients underwent penetrating keratoplasty, while two patients underwent lamellar keratoplasty. Eight patients healed after keratoplasty, but one required evisceration due to recurrence of fungal infection in the anterior chamber and intractable discomfort.In conclusion NGS allows for more precise diagnosis and enhances epidemiological awareness of Colletotrichum keratitis, which is not as rare as previously reported.

A Versatile Strategy toward Donor-Acceptor Nanofibers with Tunable Length/Composition and Enhanced Photocatalytic Activity.

Living crystallization-driven self-assembly (CDSA) has emerged as an efficient strategy to generate nanofibers of π-conjugated polymers (CPNFs) in a controlled fashion. However, reports of donor-acceptor (D-A) heterojunction CPNFs are extremely rare. The preparation of these materials remains a challenge due to the lack of rational design guidelines for the D-A π-conjugated units. Herein, we report a versatile CDSA strategy based upon carefully designed D-A-co-oligomers in which electron-deficient benzothiadiazole (BT) or dibenzo[b,d]thiophene 5,5-dioxide (FSO) units are attached to the two ends of an oligo(p-phenylene ethynylene) heptamer [BT-OPE7-BT, FSO-OPE7-FSO]. This arrangement with the electron-deficient groups at the two ends of the oligomer enhances the stacking interaction of the A-D-A π-conjugated structure. In contrast, D-A-D structures with a single BT in the middle of a string of OPE units disrupt the packing. We employed oligomers with a terminal alkyne to synthesize diblock copolymers BT-OPE7-BT-b-P2VP and BT-OPE7-BT-b-PNIPAM (P2VP = poly(2-vinylpyridine), PNIPAM = poly(N-isopropylacrylamide)) and FSO-OPE7-FSO-b-P2VP and FSO-OPE7-FSO-b-PNIPAM. CDSA experiments with these copolymers in ethanol were able to generate CPNFs of controlled length by both self-seeding and seeded growth as well as block comicelles with precisely tunable length and composition. Furthermore, the D-A CPNFs with a BT-OPE7-BT-based core demonstrate photocatalytic activity for the photooxidation of sulfide to sulfoxide and benzylamine to N-benzylidenebenzylamine. Given the scope of the oligomer compositions examined and the range of structures formed, we believe that the living CDSA strategy with D-A-based co-oligomers opens future opportunities for the creation of D-A CPNFs with programmable architectures as well as diverse functionalities and applications.

Enhancing Outcome Prediction in Intracerebral Hemorrhage Through Deep Learning: A Retrospective Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to employ deep learning techniques to analyze and validate an automatic prognostic biomarker for predicting outcomes following intracerebral hemorrhage (ICH). MATERIALS AND METHODS: This study included patients with ICH whose onset-to-imaging time (OIT) was less than 6 h. Patients were randomly divided into training and test sets at a 7:3 ratio. Using the Resnet50 deep learning method, we extracted features from the hematoma and perihematomal edema (PHE) areas and constructed a 90-day prognosis prediction model using logistic regression. To evaluate predictive efficacy and clinical significance, we employed logistic regression to train three models: Clinical, Deep Score, and the combined Clinical-Deep Learning (Merge). RESULTS: Our study comprised 1098 patients (652 male, 446 female), with a mean Glasgow Coma Scale (GCS) score of 10. Univariate and multivariate analyses identified age, intraventricular hemorrhage (IVH), hematoma and PHE volume, and admission GCS score as independent prognostic factors. Additionally, 15 deep learning features were retained through LASSO regression. In the training set, the AUC values for the three models were as follows: Clinical model (0.88), Deep Score (0.91), and Merge model (0.94). In the test set, the Merge model exhibited a significantly higher AUC value than the other models. Calibration curves revealed satisfactory calibration of the Merge model nomogram in both training and test sets. CONCLUSION: Our Merge model nomogram is an objective and effective prognostic tool, offering personalized risk assessments for 90-day functional outcomes in patients with ICH.

Large-scale genome sequencing of giant pandas improves the understanding of population structure and future conservation initiatives.

The extinction risk of the giant panda has been demoted from "endangered" to "vulnerable" on the International Union for Conservation of Nature Red List, but its habitat is more fragmented than ever before, resulting in 33 isolated giant panda populations according to the fourth national survey released by the Chinese government. Further comprehensive investigations of the genetic background and in-depth assessments of the conservation status of wild populations are still necessary and urgently needed. Here, we sequenced the genomes of 612 giant pandas with an average depth of ~26× and generated a high-resolution map of genomic variation with more than 20 million variants covering wild individuals from six mountain ranges and captive representatives in China. We identified distinct genetic clusters within the Minshan population by performing a fine-grained genetic structure. The estimation of inbreeding and genetic load associated with historical population dynamics suggested that future conservation efforts should pay special attention to the Qinling and Liangshan populations. Releasing captive individuals with a genetic background similar to the recipient population appears to be an advantageous genetic rescue strategy for recovering the wild giant panda populations, as this approach introduces fewer deleterious mutations into the wild population than mating with differentiated lineages. These findings emphasize the superiority of large-scale population genomics to provide precise guidelines for future conservation of the giant panda.

Dual Dynamic Cross-Linked Epoxy Vitrimers Used for Strong, Detachable, and Reworkable Adhesives.

Novel reprocessable thermosetting adhesives (RTAs), which combine high adhesive strength, reusability, disassembly, and recyclability features, have attracted increasing attention. However, developing RTAs with a rapidly adhesive rate while ensuring high adhesive strength and self-healing ability is still a significant challenge. Here, we prepared a novel vitrimer called DAx-DTSAy, which can be used as an RTA. First, by adjusting the ratio of rigid and flexible segments, maximum tensile strength reached 35.92 MPa. Second, the combined effect of dynamic hydroxyl ester bonds and dynamic disulfide bonds resulted in a rapid stress relaxation behavior, with a complete relaxation time 13.6 times shorter than a vitrimer only cross-linked with hydroxy ester bonds. This feature endowed its good self-healing and reprocessing capabilities. After self-healing at 180 °C, the maximum healing rate of mechanical properties was 91.8%. After three reprocesses, the maximum recovery rate of tensile strength was 120.2%. Furthermore, the combination of rigid and flexible segments and the synergistic effect of dual dynamic covalent bonds made DAx-DTSAy capable of use as a high-performance RTA. The lap shear strength of a DAx-DTSAy film on stainless steel reached 18.18 MPa after 15 min, with a recovery rate of 91.9% after 5 rebonding cycles. Additionally, DAx-DTSAy can be disassembled in chemical agents and exhibited better insulation properties compared to traditional epoxy resins. DAx-DTSAy can be employed as a novel high-performance adhesive in applications such as electronic devices and transportation, contributing to the development of thermosetting adhesives toward recyclability and sustainability.

Genome-wide identification and expression analysis of the PP2C gene family in Apocynum venetum and Apocynum hendersonii.

BACKGROUND: Protein phosphatase class 2 C (PP2C) is the largest protein phosphatase family in plants. Members of the PP2C gene family are involved in a variety of physiological pathways in plants, including the abscisic acid signalling pathway, the regulation of plant growth and development, etc., and are capable of responding to a wide range of biotic and abiotic stresses, and play an important role in plant growth, development, and response to stress. Apocynum is a perennial persistent herb, divided into Apocynum venetum and Apocynum hendersonii. It mainly grows in saline soil, deserts and other harsh environments, and is widely used in saline soil improvement, ecological restoration, textiles and medicine. A. hendersonii was found to be more tolerant to adverse conditions. The main purpose of this study was to investigate the PP2C gene family and its expression pattern under salt stress and to identify important candidate genes related to salt tolerance. RESULTS: In this study, 68 AvPP2C genes and 68 AhPP2C genes were identified from the genomes of A. venetum and A. hendersonii, respectively. They were classified into 13 subgroups based on their phylogenetic relationships and were further analyzed for their subcellular locations, gene structures, conserved structural domains, and cis-acting elements. The results of qRT-PCR analyses of seven AvPP2C genes and seven AhPP2C genes proved that they differed significantly in gene expression under salt stress. It has been observed that the PP2C genes in A. venetum and A. hendersonii exhibit different expression patterns. Specifically, AvPP2C2, 6, 24, 27, 41 and AhPP2C2, 6, 24, 27, 42 have shown significant differences in expression under salt stress. This indicates that these genes may play a crucial role in the salt tolerance mechanism of A. venetum and A. hendersonii. CONCLUSIONS: In this study, we conducted a genome-wide analysis of the AvPP2C and AhPP2C gene families in Apocynum, which provided a reference for further understanding the functional characteristics of these genes.

Case report: management of a young male patient with diabetic ketoacidosis and thyroid storm.

This report describes a case of concomitant diabetic ketoacidosis (DKA) and thyroid storm (TS) in a 20-year-old male patient that presented both diagnostic and management challenges owing to their intricate interrelationship in endocrine-metabolic disorders. The patient, previously diagnosed with type 1 diabetes mellitus (T1DM) and hyperthyroidism, was admitted to the emergency department with symptoms of DKA and progressive exacerbation of TS. Initial treatment focused on correcting DKA; as the disease progressed to TS, it was promptly recognized and treated. This case emphasizes the rarity of simultaneous occurrence of DKA and TS, as well as the challenges in clinical diagnosis posed by the interacting pathophysiological processes and overlapping clinical manifestations of DKA and TS. The patient's treatment process involved multiple disciplines, and after treatment, the patient's critical condition of both endocrine metabolic diseases was alleviated, after which he recovered and was eventually discharged from the hospital. This case report aims to emphasize the need for heightened awareness in patients with complex clinical presentations, stress the possibility of concurrent complications, and underscore the importance of prompt and collaborative treatment strategies.

Application of fluorescent carbon quantum dots functional modified carboxymethyl cellulose film in toluene gas specific detection.

With the increasing consumption of organic solvents in chemical and pharmaceutical industries, the environment pollution of volatile organic compounds (VOCs) has become an urgent problem. Therefore, the rapid-visual detection method is of great significance in the analysis of VOCs. Based on the fluorescence quenching/enhancement mechanism of carbon quantum dots (CQDs), with the help of carboxymethyl cellulose membrane with porous and large specific surface area structure, a series of green CQDs@carboxymethyl cellulose composite film (CQDs@CMC composite film) was prepared in this study. In the typical-targeted pollutants (toluene) detection application, a fluorescence spectroscopy method was established which could achieve the high sensitivity and strong specificity detection. The mainly conclusions were as follows: The fluorescence spectrometric detection method for toluene: A kind of hydrophobic Lmi/Bet CQDs@CMC composite film was prepared and characterized with imidazole/betaine CQDs and porous carboxymethyl cellulose composite film as raw materials. The toluene detection performance was studied, and the recognition mechanism was explored. The results showed that toluene enhanced the fluorescence of Lmi/Bet CQDs@CMC composite film. The fluorescence intensity of composite films was proportional to toluene concentration when the toluene concentration ranged from 200 to 2200 mg/L. The detection limit of toluene was 1.169 mg/L, which provides a theoretical basis for the detection of toluene by fluorescence spectrometry.

Impact of lipoprotein(a) and fibrinogen on prognosis in patients with coronary artery disease: A retrospective cohort study.

BACKGROUND: Despite the considerable progress made in preventative methods, medication, and interventional therapies, it remains evident that cardiovascular events (CVEs) continue to be the primary cause of both death and morbidity among individuals diagnosed with coronary artery disease (CAD). OBJECTIVE: To compare the connection between lipoprotein a (Lp[a]), fibrinogen (Fib), and both parameters combined with all-cause mortality to detect their value as prognostic biomarkers. METHODS: This is a retrospective study. Patients diagnosed with CAD between January 2007 and December 2020 at the Guangdong Provincial People's Hospital (China) were involved in the study. 43,367 patients met the eligibility criteria. The Lp(a) and Fib levels were distributed into three tertile groups (low, medium, and high). All of the patients included in the study were followed up for all-cause mortality. Kaplan-Meier and Cox regression were performed to determine the relationship between Lp(a), Fib, and all-cause mortality. A concordance statistics model was developed to detect the impact of Fib and Lp(a) in terms of anticipating poor outcomes in patients with CAD. RESULTS: Throughout a median follow-up of 67.0 months, 6,883 (15.9%) patients died. Participants with high Lp(a) (above 27.60 mg/dL) levels had a significantly higher risk for all-cause mortality than individuals with low Lp(a) levels (below 11.13 mg/dL; adjusted hazard ratio [aHR] 1.219, 95% confidence interval [CI]: 1.141-1.304, p< 0.001). Similarly, patients with high Fib levels (above 4.32 g/L) had a significantly greater risk of developing all-cause mortality compared with those with reduced Fib levels (below 3.41 g/L; aHR 1.415, 95% CI: 1.323-1.514, p< 0.001). Patients with raised Lp(a) and Fib levels had the maximum risk for all-cause mortality (aHR 1.702; 95% CI: 1.558-1.859, p< 0.001). When considered together, Lp(a) and Fib caused a significant elevation of the concordance statistic by 0.009 (p< 0.05), suggesting a higher value for predicting mortality when combining the two indicators. CONCLUSION: High Lp(a) and Fib levels could be used as predictive biomarkers for all-cause mortality in individuals with CAD. The prediction accuracy for all-cause mortality improved after combining the two parameters.

Ru doping and interface engineering synergistically boost the electrocatalytic performance of a WP/WP2 nanosheet array for an efficient hydrogen evolution reaction.

The surface electronic structure and morphology of catalysts have a crucial impact on the electrocatalytic hydrogen evolution reaction performance. This work reports on the fabrication of a Ru-doped WP/WP2 heterojunction nanosheet array electrode via a one-step phosphating treatment of a Ru-doped WO3 precursor. Benefitting from the large electrochemical active surface of nanosheet arrays, rich WP/WP2 heterojunction interface, and trace Ru atom doping, the catalyst has a fairly low overpotential of 58.0 mV at 10 mA cm-2 and a Tafel slope of 50.71 mV dec-1 in acid solution toward the electrocatalytic HER. Further, theoretical calculations unveil that Ru atom doping and interface effect synergistically optimized the electronic structure of the catalyst and hence weakened the adsorption capacity of the catalyst surface toward hydrogen (H), which lowered the Gibbs free energy (ΔGH*) and consequently effectively improved the HER performance. This work may open new avenues for developing advanced nanoarray electrodes with efficient electrochemical energy conversion.

Mechanism of Guigan Longmu Decoction in the Treatment of Arrhythmias Based on Network Pharmacology and Untargeted Metabolomics Assays.

BACKGROUND: Guigan longmu decoction (GGLM), a traditional Chinese medicine compound, has demonstrated efficacy in treating rapid arrhythmia clinically. Nevertheless, its mechanism of action remains elusive. This study aims to elucidate the molecular mechanism underlying the efficacy of GGLM in treating arrhythmia utilizing non-targeted metabolomics, widely-targeted metabolomics, and network pharmacology, subsequently validated through animal experiments. METHODS: Initially, network pharmacology analysis and widely-targeted metabolomics were performed on GGLM. Subsequent to that, rats were administered GGLM intervention, and nontargeted metabolomics assays were utilized to identify metabolites in rat plasma postadministration. The primary signaling pathways, core targets, and key active ingredients of GGLM influencing arrhythmia were identified. Additionally, to validate the therapeutic efficacy of GGLM on arrhythmia rat models, a rat model of rapid arrhythmia was induced via subcutaneous injection of isoproterenol, and alterations in pertinent pathogenic pathways and proteins in the rat model were assessed through qRT-PCR and Western blot following GGLM administration. RESULTS: The results of network pharmacology showed that 99 active ingredients in GGLM acted on 249 targets and 201 signaling pathways, which may be key to treating arrhythmia. Widelytargeted metabolic quantification analysis detected a total of 448 active ingredients in GGLM, while non-targeted metabolomics identified 279 different metabolites and 10 major metabolic pathways in rats. A comprehensive analysis of the above results revealed that the core key active ingredients of GGLM in treating arrhythmia include calycosin, licochalcone B, glabridin, naringenin, medicarpin, formononetin, quercetin, isoliquiritigenin, and resveratrol. These active ingredients mainly act on the relevant molecules and proteins upstream and downstream of the MAPK pathway to delay the onset of arrhythmia. Animal experimental results showed that the heart rate of rats in the model group increased significantly, and the mRNA and protein expression of p38, MAPK, JNK, ERK, NF-kb, IL-1ß, and IL-12 in myocardial tissue also increased significantly. However, after intervention with GGLM, the heart rate of rats in the drug group decreased significantly, while the mRNA and protein expression of p38 MAPK, JNK, ERK1, NF-kb, IL-1ß, and IL-12 in myocardial tissue decreased significantly. CONCLUSION: GGLM, as an adjunctive therapy in traditional Chinese medicine, exhibits favorable therapeutic efficacy against arrhythmia. This can be attributed to the abundant presence of bioactive compounds in the formulation, including verminin, glycyrrhizin B, glabridine, naringenin, ononin, quercetin, isorhamnetin, and kaempferol. The metabolites derived from these active ingredients have the potential to mitigate myocardial inflammation and decelerate heart rate by modulating the expression of proteins associated with the MAPK signaling pathway in vivo.

Caregiving information needs of family caregivers of adolescent patients with suicide attempts: a qualitative study in China.

BACKGROUND: In the cultural milieu of China, family caregivers assume a pivotal role in the post-adolescent suicide attempt recovery journey. Nevertheless, they frequently encounter a dearth of requisite knowledge and information pertaining to the appropriate caregiving protocols for these adolescents. Notwithstanding, scholarly investigation into the informational requisites of this demographic concerning caregiving remains significantly constrained. METHODS: Between September and December 2023, a phenomenological approach was applied in qualitative research. Semi-structured interviews were undertaken with 15 family caregivers of adolescents who had experienced suicide attempts. The amassed data underwent systematic organization and analysis through the utilization of the Colaizzi method. RESULTS: Four primary themes were identified: (1) negative emotional encounters; (2) requirements for addressing dilemmas; (3) addressing the needs of the unknown; and (4) insufficient access to support. CONCLUSIONS: Family caregivers experience complex negative emotions upon learning about a teenager's suicide attempt. Throughout the caregiving process, they face numerous challenges, with apparent lack of external support, leading to an increased urgent need for caregiving information. Healthcare professionals, especially nurses, should actively identify and respond to the informational needs of family caregivers when caring for adolescents who have attempted suicide. This includes providing education on various coping mechanisms and support strategies, as well as assisting them in better understanding how to effectively manage the stress and challenges of caregiving. By doing so, healthcare professionals can help alleviate the psychological and emotional burden on family caregivers, thereby enhancing their caregiving abilities and overall well-being.

Accurate Recognition of Vascular Lumen Region from 2D Ultrasound Cine Loops for Bubble Detection.

BACKGROUND: Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness. OBJECTIVES: To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence. METHODS: This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm. Results A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82. CONCLUSION: It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.