CD1d affects the proliferation, migration, and apoptosis of human papillary thyroid carcinoma TPC-1 cells via regulating MAPK/NF-κB signaling pathway.

The study aimed to investigate the effect of CD1d down-regulation on the proliferation, migration, and apoptosis of papillary thyroid carcinoma cells and explore the underlying mechanism. CD1d expression was silenced in TPC-1 cells by transfection of CD1d siRNA lentivirus. The proliferation, apoptosis rate, and migration ability of TPC-1 cells were detected by CCK-8 assay, flow cytometry, and scratch assay, respectively. Western blot and qPCR analyses were performed to detect the expression of related proteins. CD1d was highly expressed in TPC-1 cells. Down-regulation of CD1d significantly decreased ALMS1, CDKN3, CDK6, Ki-67, Bcl2 expression, increased Bax and Caspase 3 expression (all P < 0.05), and decreased the migration ability of TPC-1 cells. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to identify the relevant signaling pathways. KEGG pathway enrichment analysis showed that the differentially expressed genes were mainly enriched in MAPK and NF-κB signaling pathways. Our findings suggest that CD1d down-regulation inhibited the proliferation and migration abilities of TPC-1 cells, increased cell apoptosis possibly via the MAPK/NF-κB signaling pathway.

Early postoperative acetylsalicylic acid administration does not increase the risk of postoperative intracranial bleeding in patients with spontaneous intracerebral hemorrhage.

Administration of acetylsalicylic acid (ASA) at early stage after surgery for spontaneous intracerebral hemorrhage (SICH) may increase the risk of postoperative intracranial bleeding (PIB), because of potential inhibition of platelet function. This study aimed to investigate whether early ASA administration after surgery was related to increased risk of PIB. This retrospective study enrolled SICH patients receiving surgery from September 2019 to December 2022 in seven medical institution. Based on postoperative ASA administration, patients who continuously received ASA more than three days within seven days post-surgery were identified as ASA users, otherwise as non-ASA users. The primary outcome was symptomatic PIB events within seven days after surgery. Incidence of PIB was compared between ASA users and non-ASA users using survival analysis. This study included 744 appropriate patients from 794 SICH patients. PIB occurred in 42 patients. Survival analysis showed no statistical difference between ASA users and non-ASA users in incidence of PIB (P = 0.900). Multivariate Cox analysis demonstrated current smoker (hazard ratio [HR], 2.50, 95%CI, 1.33-4.71, P = 0.005), dyslipidemia (HR = 3.03; 95%CI, 1.31-6.99; P = 0.010) and pre-hemorrhagic antiplatelet therapy (HR = 3.05; 95% CI, 1.64-5.68; P < 0.001) were associated with PIB. Subgroup analysis manifested no significant difference in incidence of PIB between ASA users and non-ASA users after controlling the effect from factors of PIB (i.e., sex, age, current smoker, regular drinker, dyslipidemia, pre-hemorrhagic antiplatelet therapy and hematoma location). This study revealed that early ASA administration to SICH patients after surgery was not related to increased risk of PIB.

The impact of hyperkalemia on ICU admission and mortality: a retrospective study of Chinese emergency department data.

OBJECTIVE: This study assesses the influence of hyperkalemia on both disease severity and the risk of mortality among patients admitted to the emergency room. METHODS: This retrospective observational study utilized data from the Chinese Emergency Triage Assessment and Treatment database (CETAT, version 2.0), which was designed to evaluate and optimize management strategies for emergency room (ER) patients. Patients were systematically categorized based on serum potassium levels. Relationships between serum potassium levels, risk of mortality, and the severity of illness were then analyzed using multifactorial logistic regression and through Receiver Operating Characteristic (ROC) analysis. The effectiveness of various treatments at lowering potassium levels was also investigated. RESULTS: 12,799 emergency patients were enrolled, of whom 20.1% (n = 2,577) were hypokalemic and 2.98% (n = 381) were hyperkalemic. Among hyperkalemic patients, the leading reasons for visiting the ER were altered consciousness 23.88% (n = 91), cardiovascular symptoms 22.31% (n = 85), and gastrointestinal symptoms 20.47% (n = 78). Comparative analysis with patients exhibiting normal potassium levels revealed hyperkalemia as an independent factor associated with mortality in the ER. Mortality risk appears to positively correlate with increasing potassium levels, reaching peaks when blood potassium levels ranged between 6.5 and 7.0. Hyperkalemia emerged as a strong predictor of death in the ER, with an Area Under the Curve (AUC) of 0.89. The most frequently prescribed treatment for hyperkalemia patients was diuretics (57.32%, n = 188), followed by intravenous sodium bicarbonate (50.91%, n = 167), IV calcium (37.2%, n = 122), insulin combined with high glucose (27.74%, n = 91), and Continuous Renal Replacement Therapy (CRRT) for 19.82% (n = 65). Among these, CRRT appeared to be the most efficacious at reducing potassium levels. Diuretics appeared relatively ineffective, while high-glucose insulin, sodium bicarbonate, and calcium preparations having no significant effect on the rate of potassium decline. CONCLUSION: Hyperkalemia is common in emergency situations, especially among patients with altered consciousness. There is a strong positive correlation between the severity of hyperkalemia and mortality risk. CRRT appears to be the most effective potassium reducting strategy, while the use of diuretics should be approached with caution.

Security Management Method of Public Sentiment Analysis Based on Blockchain and Edge Computing.

The popularization of the Internet and the rise of social media enable people to share and access information rapidly, leading to a significant increase in the importance of public opinion analysis. However, public opinion analysis still needs help with issues, such as data security, data sharing, and data credibility, which may result in information leakage and inaccurate analysis results. This article proposes a secure management method for public opinion analysis to address these issues. First, a group key management framework is proposed, and then device grouping and mutual authentication methods are used to determine device identity, addressing the data security issue. A dynamic key generation algorithm is proposed to solve the data sharing problem, and a group key management algorithm is implemented to manage the entire lifecycle of group keys, addressing the data credibility issue and establishing a more secure data processing, and storage environment for public opinion analysis. This article utilizes OpenEthereum to build a blockchain network and conducts simulation experiments on the proposed solution. After comprehensive evaluation, it is confirmed that the solution is efficient and scalable.

Analysis on status quo and related factors of delays in diagnosis and treatment of breast cancer in Ningxia Hui Autonomous Region.

This study aimed to explore factors contributing to the delays in the diagnosis and treatment of breast cancer (BC) in Ningxia Hui Autonomous Region. We conducted a cohort analysis of 1012 patients with BC diagnosed at the General Hospital of Ningxia Medical University between January 2018 and December 2019. Sociodemographic data were collected through questionnaires, and clinical data were gathered and analyzed from relevant databases. Furthermore, observations were made regarding delays in the diagnosis and treatment of BC, followed by an analysis of the correlations between patient delay and both sociological factors within the population and clinical factors specific to patients with BC. Subsequently, the factors associated with patient delay and system delay were examined using Cox regression analysis, along with the inclusion of neoadjuvant therapy. In the prevention and treatment of BC in Ningxia, the patient delay rate was 33.20%, the diagnosis delay rate was 17.89%, the treatment delay rate was 0.0099% and the system delay rate was 41.60%. There was a higher proportion of patient delay and system delay in aged patients (age ≥ 61 years) with rural registered permanent residence (RPR), multiple clinical symptoms (such as nipple spillage, axillary abnormalities, etc), a T4 tumor size classification, and the initial use of neoadjuvant therapy. Besides, significant positive correlations were observed between patient delay and system delay time with BC stage. Patients aged 51 to 60 and those with molecular types (Limanal1B: ki-67 > 14%, Limanal1B: HER-2 positive) were prone to patient delay, whereas molecular characteristics influenced system delay, unrelated to sociodemographic factors. The study identifies significant age, residency, and tumor molecular subtype correlations with diagnostic and treatment delays in Ningxia's patients with BC, predominantly affecting women aged 41 to 60, especially urban dwellers. These findings underscore the need for targeted interventions to reduce delays and improve BC care in this region.

Nanobodies against African swine fever virus p72 and CD2v proteins as reagents for developing two cELISAs to detect viral antibodies.

African swine fever virus (ASFV) poses a significant threat to the global swine industry. Currently, there are no effective vaccines or treatments available to combat ASFV infection in pigs. The primary means of controlling the spread of the disease is through rapid detection and subsequent elimination of infected pig. Recently, a lower virulent ASFV isolate with a deleted EP402R gene (CD2v-deleted) has been reported in China, which further complicates the control of ASFV infection in pig farms. Furthermore, an EP402R-deleted ASFV variant has been developed as a potential live attenuated vaccine candidate strain. Therefore, it is crucial to develop detection methods that can distinguish wild-type and EP402R-deleted ASFV infections. In this study, two recombinant ASFV-p72 and -CD2v proteins were expressed using a prokaryotic system and used to immunize Bactrian camels. Subsequently, eight nanobodies against ASFV-p72 and ten nanobodies against ASFV-CD2v were screened. Following the production of these nanobodies with horse radish peroxidase (HRP) fusion proteins, the ASFV-p72-Nb2-HRP and ASFV-CD2v-Nb22-HRP fusions were selected for the development of two competitive ELISAs (cELISAs) to detect anti-ASFV antibodies. The two cELISAs exhibited high sensitivity, good specificity, repeatability, and stability. The coincidence rate between the two cELISAs and commercial ELISA kits was 98.6% and 97.6%, respectively. Collectively, the two cELISA for detecting antibodies against ASFV demonstrated ease of operation, a low cost, and a simple production process. The two cELISAs could determine whether pigs were infected with wild-type or CD2v-deleted ASFV, and could play an important role in monitoring ASFV infections in pig farms.

Modeling and optimization of sporulation by Bacillus licheniformis BF-002 based on dynamics and recurrent neural networks.

Bacillus licheniformis is widely utilized in disease prevention and environmental remediation. Spore quantity is a critical factor in determining the quality of microbiological agents containing vegetative cells. To improve the understanding of Bacillus licheniformis BF-002 strain culture, a hybrid model integrating traditional dynamic modeling and recurrent neural network was developed. This model enabled the optimization of carbon/nitrogen source feeding rates, pH, temperature and agitation speed using genetic algorithms. Carbon and nitrogen source consumption in the optimal duplicate batches showed no significant difference compared to the control batch. However, the spore quantity in the broth increased by 16.2% and 35.2% in the respective duplicate batches. Overall, the hybrid model outperformed the traditional dynamic model in accurately tracking the cultivation dynamics of Bacillus licheniformis, leading to increased spore production when used for optimizing cultivation conditions.

Association between smoking and alcohol drinking and benign adrenal tumors: a Mendelian randomization study.

BACKGROUND: In recent years, the detection rate of adrenal tumors has increased, but it is unclear whether smoking and alcohol drinking are risk factors for benign adrenal tumors. The objective of this study is to employ Mendelian randomization (MR) analysis to explore the causal relationship between smoking, alcohol drinking and susceptibility to benign adrenal tumors. METHODS: We acquired large-scale data from publicly accessible databases on genome-wide association studies (GWAS) pertaining to smoking, alcohol drinking and benign adrenal tumors. A total of 11 sets of instrumental variables (IVs) and 281 associated single nucleotide polymorphic (SNP) loci were identified. The Mendelian randomization analyses were conducted using inverse variance weighting (IVW), MR-Egger regression and weighted median estimation (WME) methods, in addition to sensitivity analyses. RESULTS: There is no causal relationship between smoking status, alcohol drinking status, alcohol intake frequency, alcohol taken with meals, alcohol consumption and benign adrenal tumors, while pack years of smoking and cigarettes per day are risk factors for benign adrenal tumors. The IVW analysis revealed that both the pack years of smoking and cigarettes per day were positively associated with an increased risk of benign adrenal tumors (OR = 2.853, 95%CI = 1.384-5.878, p = 0.004; OR = 1.543, 95%CI = 1.147-2.076, p = 0.004). Two SNPs (rs8042849 in the analysis of pack years of smoking and rs8034191 in the analysis of cigarettes per day) significantly drove the observed causal effects. CONCLUSION: Two-sample Mendelian randomization analysis showed a causal effect between smoking but not alcohol consumption and benign adrenal tumors.

Enhanced sporulation of B. licheniformis BF-002 through automatic co-feeding of carbon and nitrogen sources.

Bacillus licheniformis formulations are effective for environmental remediation, gut microbiota modulation, and soil improvement. An adequate spore quantity is crucial for the activity of B. licheniformis formulations. This study investigated the synergistic effects of carbon/nitrogen source consumption and concentration on B. licheniformis BF-002 cultivation, with the aim of developing an automatic co-feeding strategy to enhance spore production. Initial glucose (10 g/L) and amino nitrogen (1.5 g/L) concentrations promote cell growth, followed by reduced glucose (2.0 g/L) and amino nitrogen (0.5 g/L) concentrations for sustained spore generation. The spore quantity reached 2.59 × 1010 CFU/mL. An automatic co-feeding strategy was developed and implemented in 5 and 50 L cultivations, resulting in spore quantities of 2.35 × 1010 and 2.86 × 1010 CFU/mL, respectively, improving by 6.81% and 30.00% compared to that with a fixed glucose concentration (10.0 g/L). The culture broth obtained at both the 5 and 50 L scales was spray-dried, resulting in bacterial powder with cell viability rates of 85.94% and 82.68%, respectively. Even after exposure to harsh conditions involving high temperature and humidity, cell viability remained at 72.80% and 69.89%, respectively. Employing the automatic co-feeding strategy increased the transcription levels of the spore formation-related genes spo0A, spoIIGA, bofA, and spoIV by 7.42%, 8.46%, 8.87%, and 9.79%, respectively. The proposed strategy effectively promoted Bacillus growth and spore formation, thereby enhancing the quality of B. licheniformis formulations.

A study on a recommendation algorithm based on spectral clustering and GRU.

With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.

Multi-Omics Analysis Reveals the Role of Changes in Platelet Activation Pathways in the Survival Outcome of Patients with Esophageal Squamous Cell Carcinoma.

Objective: The objective of this study was to integrate metabolomics and transcriptomics data to identify key diagnostic and prognostic markers for esophageal squamous cell carcinoma (ESCC). Plasma samples were collected from 85 ESCC patients at different stages and 50 healthy volunteers for non-targeted metabolomic analysis. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for non-targeted metabolomic analysis. Subsequently, we integrated the metabolomic data with transcriptomic data from the Gene Expression Omnibus (GEO) and prognosis data from The Cancer Genome Atlas Program (TCGA) to perform pathway analysis. Our focus was on pathways that involve both metabolites and upstream genes, as they often exhibit higher accuracy. Results: Through the integration of metabolomics and transcriptomics, we identified significant alterations in the platelet activation pathway in ESCC. This pathway involves the participation of both metabolites and genes, making it a more accurate reflection of pathological changes associated with the disease. Notably, metabolite arachidonic acid (AA) and chemokine receptor type 2(CXCR2) were significantly downregulated in ESCC, while genes collagen type I alpha 1(COL1A1), collagen type I alpha 2(COL1A2), collagen type III alpha 1(COL3A1), type 3 inositol 1,4,5-trisphosphate receptor (ITPR3), and insulin-like growth factor II mRNA binding protein 3(IGF2BP3) were significantly upregulated, indicating the presence of tumor-induced platelet activation in ESCC. Further analysis of prognosis data revealed that high expression of COL1A1, IGF2BP3, and ITPR3 was associated with a favorable prognosis for ESCC, while high CXCR2 expression was linked to an adverse prognosis. In addition, we combined COL1A1, ITPR3, IGF2BP3, CXCR2, and AA to form a diagnostic biomarker panel. The receiver operating characteristic curve (ROC) demonstrated excellent diagnostic capability (AUC=0.987). Conclusion: Our study underscores the significant role of platelet activation pathways and related genes in the diagnosis and prognosis of ESCC patients. These findings offer promising insights for improving the clinical management of ESCC.

In-Depth Structure and Function Characterization of Heterogeneous Interchain Cysteine-Conjugated Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs), integrating high specificity of antigen-targeting antibodies and high potency of cell-killing chemical drugs, have become one of the most rapidly expanding therapeutic biologics in oncology. Although ADCs were widely studied from multiple aspects, overall structural elucidation with comprehensive understanding of variants is scarcely reported. Here, for the first time, we present a holistic and in-depth characterization of an interchain cysteine-conjugated ADC, focusing on conjugation and charge heterogeneity, and in vitro biological activities. Conjugation mapping utilized a bottom-up approach, unraveled positional isomer composition, provided insights into the conjugation process, and elucidated how conjugation affects the physicochemical and biological properties of an ADC. Charge profiling combined bottom-up and top-down approaches to interrogate the origin of charge heterogeneity, its impact on function, and best practice for characterization. Specifically, we pioneered the utilization of capillary isoelectric focusing-mass spectrometry to decode not only critical post-translational modifications but also drug load and positional isomer distribution. The study design provides general guidance for in-depth characterization of ADCs, and the analytical findings in turn benefit the discovery and development of future ADCs.

Identification of Breast Cancer Immune-related Prognostic Characteristics in Tumor Microenvironment.

BACKGROUND: Accumulated evidence suggest that tumor microenvironment (TME) plays a crucial role in breast cancer (BRCA) progression and therapeutic effects. OBJECTIVE: This study aimed to characterize immune-related BRCA subtypes in TME, and identify genes with prognostic value. METHODS: RNA sequencing profiles with corresponding clinical data from The Cancer Genome Atlas (TCGA) database of BRCA patients were downloaded to evaluate immune infiltration using the single-sample gene set enrichment (ssGAEA) algorithm. Further, BRCA was clustered according to immune infiltration status by consensus clustering analysis. Using Venn analysis, differentially expressed genes (DEGs) were overlapped to obtain candidate genes. Kaplan-Meier (K-M) analysis was performed to identify prognostic genes, and the results were verified in the GEO and METABRIC datasets. RT-qPCR was conducted to detect the mRNA expression of prognostic genes. RESULTS: In the TCGA database, 3 immune-related BRCA subtypes were identified [cluster1 (C1), cluster2 (C2), and cluster3 (C2)]. The C2 subtype had better overall survival (OS) compared to the C1 subtype. Higher levels of immune markers and checkpoint protein were found in the C2 subtype than in others. By combining DEGs between BRCA and normal tissues, with the C1 and C2 subtypes associated with different OS, 25 BRCA candidate genes were identified. Among these, 8 genes were identified as prognostic genes for BRCA. RT-qPCR showed that the expressions of 2 genes were significantly elevated in BRCA tissues, while that of other genes were decreased. CONCLUSION: Three BRCA subtypes were identified with the immune index, which may help design advanced treatment of BRCA. The data code used for the analysis in this article was available on GitHub (https://github.com/tangzhn/BRCA1.git).

Serum Interleukin-1 Levels Are Associated with Intracranial Aneurysm Instability.

Serum interleukin-1 (IL-1) are possibly indicative of the inflammation in the intracranial aneurysm (IA) wall. This study aimed to investigate whether IL-1 could discriminate the unstable IAs (ruptured intracranial aneurysms (RIAs) and symptomatic unruptured intracranial aneurysms (UIAs)) from stable, asymptomatic UIAs. IA tissues and blood samples from 35 RIA patients and 35 UIA patients were collected between January 2017 and June 2020 as the derivation cohort. Blood samples from 211 patients with UIAs were collected between January 2021 and June 2022 as the validation cohort (including 63 symptomatic UIAs). Blood samples from 35 non-cerebral-edema meningioma patients (non-inflammatory control) and 19 patients with unknown-cause subarachnoid hemorrhage (hemorrhagic control) were also collected. IL-1ß and IL-1.ra (IL-1 receptor antagonist) were measured in serum and IA tissues, and the IL-1 ratio was calculated as log10 (IL-1.ra/IL-1ß). Based on the derivation cohort, multivariate logistic analysis showed that IL-1ß (odds ratio, 1.48, P = 0.001) and IL-1.ra (odds ratio, 0.74, P = 0.005) were associated with RIAs. The IL-1 ratio showed an excellent diagnostic accuracy for RIAs (c-statistic, 0.91). Histological analysis confirmed the significant correlation of IL-1 between serum and aneurysm tissues. IL-1 ratio could discriminate UIAs from non-inflammatory controls (c-statistic, 0.84), and RIAs from hemorrhagic controls (c-statistic, 0.95). Based on the validation cohort, the combination of IL-1 ratio and PHASES score had better diagnostic accuracy for symptomatic UIAs than PHASES score alone (c-statistic, 0.88 vs 0.80, P < 0.001). Serum IL-1 levels correlate with aneurysm tissue IL-1 levels and unstable aneurysm status, and could serve as a potential biomarker for IA instability.

Effects of exogenous steroid hormones on growth, body color, and gonadal development in the Opsariichthys bidens.

To investigate the effects of exogenous steroid hormones on growth, body color, and gonadal development in the Opsariichthys bidens (O. bidens), synthetic methyltestosterone (MT) and 17ß-estradiol (E2) were used for 28 days' treatment of 4-month-old O. bidens before the breeding season. Our results suggested that MT had a significant growth-promoting effect (P < 0.05), whereas E2 played an inhibitory role. On the body surface, the females in the MT group showed gray stripes, and the fish in other groups showed no obvious stripes. The males with MT treatment displayed brighter blue-green stripes compared to the CK and E2 groups. The histological analysis showed that the MT significantly promoted testes development in males, blocked oocyte development, and caused massive apoptosis in females, whereas the E2 group promoted ovarian development and inhibited testes development. Based on qRT-PCR analysis, in females, the expression of igf-1, dmrt1, and cyp19a1a genes revealed that E2 treatment resulted in down-regulation of igf-1 expression and up-regulation of cyp19a1a expression. In males, igf-1 and dmrt1 were significantly up-regulated after MT treatment, and E2 treatment led to down-regulation of igf-1. Therefore, this study demonstrates that MT and E2 play an important role in reversing the morphological sex characteristics of females and males.

A decision tree model to help treatment decision-making for severe spontaneous intracerebral hemorrhage.

BACKGROUND: Surgical treatment demonstrated a reduction in mortality among patients suffering from severe spontaneous intracerebral hemorrhage (SSICH). However, which SSICH patients could benefit from surgical treatment was unclear. This study aimed to establish and validate a decision tree (DT) model to help determine which SSICH patients could benefit from surgical treatment. MATERIALS AND METHODS: SSICH patients from a prospective, multicenter cohort study were analyzed retrospectively. The primary outcome was the incidence of neurological poor outcome (modified Rankin scale as 4-6) on the 180th day posthemorrhage. Then, surgically-treated SSICH patients were set as the derivation cohort (from a referring hospital) and validation cohort (from multiple hospitals). A DT model to evaluate the risk of 180-day poor outcome was developed within the derivation cohort and validated within the validation cohort. The performance of clinicians in identifying patients with poor outcome before and after the help of the DT model was compared using the area under curve (AUC). RESULTS: One thousand two hundred sixty SSICH patients were included in this study (middle age as 56, and 984 male patients). Surgically-treated patients had a lower incidence of 180-day poor outcome compared to conservatively-treated patients (147/794 vs. 128/466, P <0.001). Based on 794 surgically-treated patients, multivariate logistic analysis revealed the ischemic cerebro-cardiovascular disease history, renal dysfunction, dual antiplatelet therapy, hematoma volume, and Glasgow coma score at admission as poor outcome factors. The DT model, incorporating these above factors, was highly predictive of 180-day poor outcome within the derivation cohort (AUC, 0.94) and validation cohort (AUC, 0.92). Within 794 surgically-treated patients, the DT improved junior clinicians' performance to identify patients at risk for poor outcomes (AUC from 0.81 to 0.89, P <0.001). CONCLUSIONS: This study provided a DT model for predicting the poor outcome of SSICH patients postsurgically, which may serve as a useful tool assisting clinicians in treatment decision-making for SSICH.

Robust All-Solid-State Lithium Metal Batteries Enabled by a Composite Lithium Anode with Improved Bulk Li Diffusion Kinetics Properties.

All-solid-state batteries (ASSBs) with a Li metal anode are expected to be one of the most promising energy storage systems to achieve high energy density. However, the interfacial instability between the Li metal anode and solid-state electrolyte (SSE) limits the rate capability and cycling stability of ASSBs. The main issue is the formation of voids at the Li/SSE interface during Li stripping due to the slow diffusion of Li within the bulk Li metal, then increasing internal cell resistance and inducing the formation of lithium dendrites. To address these issues, a composite Li anode (LAO) composed by Li-Ag alloy and Li2O is constructed by mixing the stoichiometric metal Li and Ag2O directly. LAO anode is capable of improving bulk Li diffusion kinetics and inhibiting the formation of interfacial voids effectively, achieving a high critical current density over 1.5 mA cm-2 and long stable cycling over 1000 h at 1 mA cm-2. The ASSBs, employing LAO as the anode, Li6PS5Cl as the SSE, and LiCoO2 as the cathode, exhibit superior rate capability and stable cycling over 4000 cycles at 5 C. Moreover, ASSBs can operate stably with a high LiCoO2 loading of 17.8 mg cm-2 for more than 100 cycles at 0.2 C.

Resumption of production operations during the silent disposal period of African swine fever outbreaks: a case study.

BACKGROUND: Batch production, a widely implemented production model in large-scale pig farms, was characterized by its long-term duration, cost-effectiveness, and efficiency. Nevertheless, the recent occurrence of African swine fever (ASF) outbreaks in China has necessitated the implementation of discreet mating operations within this model, leading to disruptions in production cycles and substantial indirect losses. CASE PRESENTATION: This study implemented a novel operational procedure, which involved the division of risk areas for zone management and allowed mating operations, in 12 farms experiencing ASF outbreaks. Another 12 farms were used as a control group, employing the old procedure. Subsequently, the prognoses of both the old and new procedures were calculated and analyzed. The findings indicate that the new method resulted in an enhanced retention rate and reduced non-productive days (NPD), without impacting the positive detection rate and disposal time. Consequently, this approach significantly mitigated economic losses (p < 0.05). CONCLUSION: The efficacy of the novel procedure in mitigating the indirect economic losses stemming from ASF outbreaks, through the reduction of NPD while maintaining retention rates and disposition days, has been substantiated. This methodology has demonstrated feasibility in extensive pig farming operations and exhibits promise for broader application.

A deep-learning system to help make the surgical planning of coil embolization for unruptured intracranial aneurysms.

BACKGROUND: Coil embolization is a common method for treating unruptured intracranial aneurysms (UIAs). To effectively perform coil embolization for UIAs, clinicians must undergo extensive training with the assistance of senior physicians over an extended period. This study aimed to establish a deep-learning system for measuring the morphological features of UIAs and help the surgical planning of coil embolization for UIAs. METHODS: Preoperative computational tomography angiography (CTA) data and surgical data from UIA patients receiving coil embolization in our medical institution were retrospectively reviewed. A convolutional neural network (CNN) model was trained on the preoperative CTA data, and the morphological features of UIAs were measured automatically using this CNN model. The intraclass correlation coefficient (ICC) was utilized to examine the similarity between the morphologies measured by the CNN model and those determined by experienced clinicians. A deep neural network model to determine the diameter of first coil was further established based on the CNN model within the derivation set (75% of all patients) using neural factorization machines (NFM) model and was validated using a validation set (25% of all patients). The general match ratio (the difference was within ± 1 mm) between the predicted diameter of first coil by model and that used in practical scenario was calculated. RESULTS: One-hundred fifty-three UIA patients were enrolled in this study. The CNN model could diagnose UIAs with an accuracy of 0.97. The performance of this CNN model in measuring the morphological features of UIAs (i.e., size, height, neck diameter, dome diameter, and volume) was comparable to the accuracy of senior clinicians (all ICC > 0.85). The diameter of first coil predicted by the model established based on CNN model and the diameter of first coil used actually exhibited a high general match ratio (0.90) within the derivation set. Moreover, the model performed well in recommending the diameter of first coil within the validation set (general match ratio as 0.91). CONCLUSION: This study presents a deep-learning system which can help to improve surgical planning of coil embolization for UIAs.

Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles. METHODS: First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups. RESULTS: We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy. Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity. CONCLUSION: Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment. And this work also revealed ISG20 exhibits promising potential as a therapeutic target for ccRCC.