The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

Objective To investigate the species of ticks in Suizhou City, Hubei Province, so as to provide insights into management of ticks and tick-borne diseases. Methods During the period between May 2023 and June 2024, livestock breeding farms and vegetation neighboring the place of residence of confirmed and suspected patients with tick-borne disease were selected as sampling points in rural areas from Yindian Township, Gaocheng Township, Wanhe Township, Wushan Township, Xiaolin Township, Xihe Township, Hedian Township and Beijiao Street in Suizhou City, Hubei Province, where confirmed and suspected cases with tick-borne diseases had been reported. The parasitic ticks on the body surface of free-range livestock were captured with tweezers in livestock breeding farms, and free ticks on the vegetation surface were captured with the flagging method. Morphological identification of tick samples was performed under a microscope, and the gender and developmental stage of ticks were determined. One engorged adult tick, 2 to 3 blood-feeding but non-engorged adult ticks, 10 to 15 unfed female ticks, 15 to 20 unfed male ticks, and 30 to 40 tick nymphs or larvae were assigned into a group, respectively. Genomic DNA was extracted from tick samples in each group, and mitochondrial 16S rRNA gene was amplified. Sequence analysis was performed with the DNASTAR software, and phylogenetic analysis was performed using the software MEGA 7.0. In addition, the phylogenetic tree was generated using the maximum likelihood method based on the Kimura 2 parameter model. Results A total of 2 438 ticks were captured from Suizhou City, Hubei Province during the period between May 2023 and June 2024, including 595 free ticks and 1 483 parasitic ticks. Three developmental stages of ticks were captured, including larvae, nymphs, and adults, and 75.18% (1 899/2 438) of captured ticks were adult, in which 79.04% (1 501/1 899) were female. Morphological and molecular biological assays identified one family, three genera and four species of captured ticks, including 2 425 Haemaphysalis longicornis ticks (99.47%) and one H. flava tick (0.04%) of the genus Haemaphysalis, 11 Rhipicephalus microplus ticks (0.45%) of the genus Rhipicephalus, and one Ixodes sinensis tick (0.04%) of the genus Ixodes in the family Ixodidae. Phylogenetic analysis revealed that the H. longicornis sequence (SZ49) in this study was clustered with sequences from Yunnan Province (GenBank accession number: MH024510.1), Hebei Province (GenBank accession number: MK450606.1) and Henan Province (GenBank accession number: MZ230645.1) into a clade, and the H. flava sequence (SZ19) in this study was clustered with sequences from Japan (GenBank accession number: MW064044.1), South Korea (GenBank accession number: ON629585.1), and Jiangsu Province (GenBank accession number: PP494741.1) and Hebei Province of China (GenBank accession number: MH520685.1) into a clade, while the R. microplus sequence (SZ8) in this study was clustered with the sequences from India (GenBank accession number: MK621328.1), and Henan Province (GenBank accession number: MT555307.1) and Guizhou Province of China (GenBank accession number: PP446801.1) into a clade. The sequence of I. sinensis (SZ23) in this study had 99.51% homology with that (GenBank accession number: OM368265.1) of ticks sampled from Wuhan City, Hubei Province. Conclusion There are four tick species of H. longicornis, H. flava, R. microplus and I. sinensis in Suizhou City, Hubei Province, and H. longicornis is the dominant species. H. flava is firstly recorded in Suizhou City.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

BACKGROUND:At present,the pathogenesis of osteoarthritis is still unclear,and there is a lack of effective means to control the disease.Research on osteoarthritis is mostly concentrated in the field of immunity,and there are few studies in the field of oxidative stress. OBJECTIVE:To explore the roles of oxidative stress and immune infiltration in osteoarthritis and to predict related miRNAs and therapeutic agents. METHODS:The GSE55235 dataset(10 samples of osteoarthritis and 10 healthy control samples)and the GSE55457 dataset(10 samples of osteoarthritis and 10 healthy control samples)were obtained from the GEO database for merging to obtain their differentially expressed genes that were combined with oxidative stress genes to get the differentially expressed genes of oxidative stress.The differentially expressed genes of oxidative stress were analyzed for KEGG and GO enrichment,and the osteoarthritis pathways and biological processes were evaluated using GSEA enrichment analysis.The protein-protein interaction network was constructed using the STRING online website and Cytoscape software,and the Degree algorithm was run to get the key genes.The GSE1919 dataset was obtained from the GEO database as a validation dataset,and the key genes were analyzed by variance analysis and receiver operating characteristic curve analysis to get the core genes.In addition,immune infiltration was evaluated by CIBERSORT and the correlation between core genes and immune cells was explored.miRNA prediction of core genes was performed using TargetScan and target drugs were predicted using the DSigDB database. RESULTS AND CONCLUSION:Sixty-five differentially expressed genes and five core genes(IL1B,CXCL8,MYC,NFKBIA,JUN)associated with oxidative stress were identified.Enrichment analysis showed that differentially expressed genes associated with oxidative stress were concentrated in the pathways of oxidative stress,interleukin-17,osteoclast differentiation,fluid shear stress and atherosclerosis.The area under the receiver operating characteristic curve for the five core genes exceeded 0.85,indicating their excellent specificity and sensitivity in diagnosing bone and joint conditions,as well as their close association with immune cells.The predicted miRNA was has-miR-3937,and the therapeutic small-molecule drugs were metformin,ionomycin and celecoxib.To conclude,oxidative stress and immune infiltration exist in osteoarthritis,and immune infiltration is involved in activating oxidative stress.The core genes and predicted miRNAs can be used as novel markers for the diagnosis of osteoarthritis,and small molecule drugs are predicted to treat osteoarthritis.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

BACKGROUND: The purpose of this study was to evaluate the safety and efficacy of subsequent radiotherapy (RT) following first-line treatment with durvalumab plus chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC). METHODS: A total of 122 patients with ES-SCLC from three hospitals during July 2019 to December 2021 were retrospectively analyzed. Inverse probability of treatment weighting (IPTW) analysis was performed to address potential confounding factors. The primary focus of our evaluation was to assess the impact of RT on progression-free survival (PFS) and overall survival (OS). RESULTS: After IPTW analysis, 49 patients received durvalumab plus platinum-etoposide (EP) chemotherapy followed by RT (Durva + EP + RT) and 72 patients received immunochemotherapy (Durva + EP). The median OS was 17.2 months vs . 12.3 months (hazard ratio [HR]: 0.38, 95% confidence interval [CI]: 0.17-0.85, P = 0.020), and the median PFS was 8.9 months vs . 5.9 months (HR: 0.56, 95% CI: 0.32-0.97, P = 0.030) in Durva + EP + RT and Durva + EP groups, respectively. Thoracic radiation therapy (TRT) resulted in longer OS (17.2 months vs . 14.7 months) and PFS (9.1 months vs . 7.2 months) compared to RT directed to other metastatic sites. Among patients with oligo-metastasis, RT also showed significant benefits, with a median OS of 17.4 months vs . 13.7 months and median PFS of 9.8 months vs . 5.9 months compared to no RT. Continuous durvalumab treatment beyond progression (TBP) prolonged OS compared to patients without TBP, in both the Durva + EP + RT (NA vs . 15.8 months, HR: 0.48, 95% CI: 0.14-1.63, P = 0.238) and Durva + EP groups (12.3 months vs . 4.3 months, HR: 0.29, 95% CI: 0.10-0.81, P = 0.018). Grade 3 or 4 adverse events occurred in 13 (26.5%) and 13 (18.1%) patients, respectively, in the two groups; pneumonitis was mostly low-grade. CONCLUSION Addition of RT after first-line immunochemotherapy significantly improved survival outcomes with manageable toxicity in ES-SCLC.
Humans ; Small Cell Lung Carcinoma/therapy* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Lung Neoplasms/therapy* ; Aged ; Antibodies, Monoclonal/therapeutic use* ; Adult ; Immunotherapy/methods* ; Aged, 80 and over

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.