Objective To analyze the situation of rapid antiretroviral therapy (ART) and death of HIV/AIDS in Wuhan from 1994 to 2023, and to provide a scientific basis for further rapid initiation of ART and reduction of mortality rate. Methods According to the case follow-up and treatment database of China AIDS Prevention and Control Information System, data were obtained from all the cases reported from January 1, 1994 to December 31, 2023 with the current address in Wuhan City and the review status of the final review card. The data were analyzed using Kaplan-Meier and Cox proportional hazards models. Results The total mortality rate of HIV/AIDS in Wuhan from 1994 to 2023 was 12.76%. The proportion of receiving antiretroviral therapy within 30 days increased year by year, and the mortality rate decreased year by year. After adjusting for sex, age, occupation, ethnicity, education level, mobile population, history of STD, route of infection, source of sample, and first CD4 value, receiving antiretroviral therapy within 30 days (HR=0.08, 95%CI: 0.07-0.10) was a protective factor for HIV/AIDS mortality. Conclusion Rapid antiretroviral therapy can significantly reduce the risk of HIV/AIDS death. A sustainable model of rapid initiation of antiretroviral therapy should be further established to increase the proportion of rapid antiretroviral therapy for HIV/AIDS in Wuhan.

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.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To evaluate the effects and underlying mechanisms of metabolites derived from the kidney-reinforcing, blood circulation-activating, and collateral dredging decoction on the proliferation of multiple myeloma (MM) KM3 cells.Methods:MM KM3 cells in the logarithmic growth phase were treated with 3%, 6%, 9%, or 12% metabolites of kidney-reinforcing, blood circulation-activating, and collateral dredging decoction. Cell viability was assessed using the CCK-8 assay. Apoptosis and necrosis were evaluated using flow cytometry and TUNEL staining. Mitochondrial and cellular ultrastructural changes were examined using transmission electron microscopy. mRNA and protein expression levels of dynamin-related protein 1 (Drp1), mitochondrial fission protein 1 (Fis1), mitochondrial fission factor (MFF), PTEN-induced kinase 1 (Pink1), and E3 ubiquitin ligase (Parkin) were determined through quantitative real-time PCR and western blotting. High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) combined with network pharmacology, was utilized for reverse verification of the pharmacodynamic mechanisms and therapeutic targets underlying the anti-MM activity of this decoction.Results:The metabolites of the kidney-reinforcing, blood circulation-activating, and collateral dredging decoction inhibited KM3 cell proliferation and induced apoptosis in a dose-dependent manner. Transmission electron microscopy revealed increased mitochondrial fission and autophagic structures, with effects intensifying at higher metabolite concentrations. mRNA and protein expression of Drp1, Fis1, MFF, Pink1, and Parkin were significantly upregulated in treatment groups compared to controls ( P<0.05), with the most pronounced effects observed in the 12% metabolite group ( P<0.01). HPLC-MS/MS identified 121 bioactive compounds in BHTF, which shared 474 overlapping targets with MM. Enrichment analysis suggested that BHTF exerts antitumor effects primarily through apigenin, palmatine, and other key components by modulating TNF, NF-κB, and mitophagy pathways. Conclusion:The kidney-reinforcing and blood circulation-activating and collateral dredging decoction suppresses the proliferation of MM KM3 cells, potentially through mechanisms involving the regulation of mitochondrial dynamics and induction of autophagy.

Objective:To explore the genetic characteristics and pathogenesis for a child with mosaicism trisomy 21 and Congenital leukemia (CL).Methods:A child who was admitted to Ningbo Women and Children′s Hospital in March 2023 was selected as the study subject. A retrospective analysis was carried out on the clinical data, laboratory test results, immunophenotyping, and genetic characteristics of the child. This study was approved by the Medical Ethics Committee of the Hospital (Ethics No.: EC2024-063).Results:Whole genome sequencing (WGS) revealed that the child has mosaicism trisomy of chromosome 21, with a ratio of approximately 74%. In addition, pathogenic copy number variations involving multiple OMIM genes that could explain his clinical phenotype were detected and rated as pathogenic based on the guidelines from the American College of Medical Genetics and Genomics (ACMG). No pathogenic variant was detected with the GATA1 gene. Blood immune typing of the child conformed to the immunophenotype of acute myeloid leukemia. Conclusion:For children with trisomy 21, even in the absence of GATA1 gene variants, the occurrence of CL should be monitored, and early diagnosis and treatment are of great significance for improving the prognosis.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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.

ObjectiveTo assess the efficiency of a Sophora flavescens Ait (S. flavescens, Ku Shen)-soluble microneedle (SFA-MN) for improving skin lesion symptoms in mice with psoriasis.MethodsSFA-MNs were prepared using a two-mold molding process with 20% w/v polyvinylpyrrolidone and 15% w/v polyvinyl alcohol. The SFA-MNs were assessed for morphology, mechanical properties, in vitro dissolution, identification of components, and skin lesion improvement in imiquimod-induced psoriasis mice.ResultsThe SFA-MNs demonstrated good mechanical properties for efficiently penetrating the dermis, facilitating efficient drug delivery. Furthermore, they effectively inhibited mast cell levels in the dorsal lesion area of psoriasis mice and reduced the expression of the T-lymphocyte factor cluster of differentiation 3 and tumor necrosis factor-α. In addition, this system alleviated skin inflammation, splenic swelling, and thymic atrophy in the psoriasis-like mouse model. Seven major components were detected from SFA-MNs by comparison of the mass-to-nucleus ratios (m/z) of the secondary fragments N-methylcytisine, 5α, 9α-dihydroxymatrine, sophoramine, matrine, oxysophocarpine, oxymatrine, and kushenol O.ConclusionThe drug delivery strategy combining traditional herbal S. flavescens with soluble microneedle technology provides more targeted and effective immune regulation for treating psoriasis-like mice models, enabling enhanced therapeutic effects compared with the control group.

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.