Objective: To investigate the impact of RhE antigen-matched transfusion during liver transplantation on early postoperative recovery and complications. Methods: In this retrospective cohort study, ninety-five patients undergoing liver transplantation at Kunming First People's Hospital between January 2022 and July 2025 were enrolled. Patients were divided into two groups: Group 1 (RhE-mismatched transfusion, n=57) and Group 2 (RhE-matched transfusion, n=38). The baseline data, complete blood counts, hepatic and renal function, coagulation parameters, and complication rates between the two groups were compared at postoperative days 1, 3, 5, 7, and 10. Survival analysis was performed using the Kaplan-Meier method. Results: The baseline characteristics were well-balanced and comparable between the two groups (all P>0.05). The early postoperative mortality rate in the mismatched group (31.58%, 18/57) was significantly higher than that in the matched group (10.53%, 4/38) (P=0.017). The incidence of postoperative hepatic encephalopathy was significantly higher in the mismatched group (50.88%, 29/57) than in the matched group (10.53%, 4/38) (P<0.001). The incidence of postoperative haemorrhage in the mismatched group (24.56%, 14/57) was higher than that in the matched group (5.26%, 2/38), with a statistically significant difference (P=0.014). The incidence of perioperative infection in the mismatched group (28.07%, 16/57) was higher than that in the matched group (10.53%, 4/38), with a statistically significant difference (P=0.04). Corresponding odds ratios (OR) and 95% confidence intervals indicated a lower risk of these adverse events in the matched group. On postoperative day 1, the change in activated partial thromboplastin time (-1.6, 20.5) in the mismatched group was greater than in the matched group (-0.2, 5.5). The change in international normalised ratio (-0.56, 1.22) in the mismatched group was greater than in the matched group (-0.18, 0.32), while the change in albumin (-4.0, 4.8) was smaller in the mismatched group than in the matched group (-2.5, 8.8). On postoperative day 5, the change in albumin (-0.41±7.83) in the mismatched group was smaller than in the matched group (2.68±4.53). At postoperative day 7, the change in albumin in the mismatched group (-0.61±7.38) was smaller than that in the matched group (2.51±5.85), while the change in D-dimer in the mismatched group (0.73, 7.4) was greater than that in the matched group (-1.6, 4.3). On postoperative day 10, the mismatched group exhibited significantly higher fibrinogen levels (-1.21, 1.78) than the matched group (-0.49, 0.97), and significantly longer prothrombin times (-11.3, -2.7) than the matched group (-6.2, -0.8) (all P<0.05). The matched group exhibited a mean overall survival (OS) of 32.803 months (95% CI:29.171-36.436 months), significantly exceeding the mismatched group's 28.996 months (95% CI:24.202-33.790 months). The log-rank test yielded statistically significant results (χ =4.307, P=0.038). Conclusion: Implementing RhE blood group-matched transfusion during liver transplantation may help reduce early postoperative mortality and the incidence of major complication rates, promote faster recovery of coagulation and liver function, and thereby improve short-term patient outcomes.

BACKGROUND:Solute carrier family 1 member 5(SLC1A5)plays a potential role in a variety of diseases,but the exact mechanism of action is unclear.The construction of stable SLC1A5 overexpression and knockdown cell models can provide a powerful experimental tool for in-depth study of the exact role and mechanism of SLC1A5 in diseases and the discovery of potential therapeutic targets. OBJECTIVE:To construct lentiviral vectors for overexpression and knockdown of mouse SLC1A5 and establish stable transfected RAW264.7 cell lines,so as to provide an experimental foundation for further investigation of the role of SLC1A5 in inflammation. METHODS:Primers were designed and synthesized based on the SLC1A5 gene sequence,and the gene segment was amplified using polymerase chain reaction.Subsequently,the target gene segment was directionally inserted into the GV492 vector plasmid,which had been digested with AgeI/NheI enzymes,to construct recombinant lentiviral plasmids.Positive clones were further selected,and their sequences were confirmed.The pHelper1.0 plasmid vector and pHelper2.0 plasmid vector,along with the target plasmid vector,was co-cultured with 293T cells for transfection,resulting in the production and titration of lentiviral stocks.Furthermore,RAW264.7 cells were cultured in vitro,and the working concentration of puromycin was determined.Lentiviruses were separately co-cultured with RAW264.7 cells,and transfection efficiency was determined by measuring fluorescence intensity.Stable transfected cells were selected using puromycin,and real-time fluorescence quantitative PCR and western blot assay were employed to assess the gene and protein expression levels of SLC1A5 in stably transfected cell lines. RESULTS AND CONCLUSION:(1)Sequencing results indicated a perfect match between the sequencing and target sequences,confirming the successful construction of recombinant lentiviral vectors.(2)The titer for the overexpression SLC1A5 lentivirus was 1×109 TU/mL,while the titer for the knockdown SLC1A5 lentivirus was 3×109 TU/mL.(3)The working concentration of puromycin for RAW264.7 cells was determined to be 3 μg/mL.(4)The optimal conditions for transfecting RAW264.7 cells with overexpression/knockdown expression of SLC1A5 lentivirus involved the use of HiTransG P transfection enhancer with a multiplicity of infection value of 50.(5)A significant upregulation of the gene and protein expression levels of SLC1A5 was detected in cell lines stably overexpressing SLC1A5,while gene and protein expression levels of SLC1A5 were significantly decreased in the knockdown stable cell lines.These findings indicate that lentiviral vectors for mouse SLC1A5 overexpression and knockdown have been successfully constructed and a stably transfected RAW264.7 cell line has been obtained.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

ObjectiveTo explore the potential mechanism of Huoxue Xiaoyi Formula （活血消异方， HXF） in treating ovarian endometriosis （OEM） from the perspective of T follicular helper （Tfh） cells and the Janus kinase/signal transducer and activator of transcription （JAK/STAT） signaling pathway. MethodsForty-five female SD rats with normal estrous cycles were randomly divided into three groups， HXF group， model group， and normal group， with 15 rats in each group. A rat model of OEM was established by autologous endometrial tissue implantation. After successful modeling， the treatment group received HXF at 5.85 g/（kg·d） by gavage for 14 consecutive days. The model group and normal group received 1 mL/d of normal saline by gavage. RNA-sequencing data from human proliferative-phase endometriotic and normal endometrial tissues were downloaded from the GEO database. Transcriptomic sequencing was used to analyze gene expression in rat ovarian ectopic tissues and normal uterine tissues， and comparisons were made with human data to verify JAK/STAT pathway activation in proliferative-phase ectopic tissues. Immunohistochemistry was used to detect the positive expression of CXC chemokine receptor 5 （CXCR5） and interleukin-21 （IL-21） in rat ovarian ectopic and normal uterine tissues. Western Blotting was performed to detect the protein levels of IL-21， IL-21 receptor （IL-21R）， Janus kinase 1 （JAK1）， signal transducer and activator of transcription 6 （STAT6）， and B-cell lymphoma 2 （Bcl-2）. Tfh cell infiltration was analyzed using immune cell infiltration methods. ResultsGene set enrichment analysis showed that the JAK/STAT pathway was significantly activated in human proliferative-phase endometriotic tissues compared to normal endometrial tissues. Similarly， the JAK/STAT pathway was markedly activated in rat ovarian ectopic tissues in the model group compared to the normal group， but suppressed in the HXF group compared to the model group. Compared with normal uterine tissues， ovarian ectopic tissues in the model group showed increased Tfh cell infiltration scores， higher CXCR5 and IL-21 expression， and elevated levels of IL-21， IL-21R， JAK1， STAT6， and Bcl-2 proteins. Compared with the model group， HXF group showed reduced CXCR5 and IL-21 expression and decreased protein levels of IL-21， IL-21R， JAK1， STAT6， and Bcl-2. ConclusionHXF may suppress activation of the JAK/STAT signaling pathway in ovarian endometriotic tissues by inhibiting IL-21 secretion from Tfh cells.

Objective To investigate the suitable habitats of Phlebotomus chinensis in Gansu Province, so as provide insights into effective management of mountain-type zoonotic visceral leishmaniasis (MT-ZVL). Methods The geographical coordinates of locations where MT-ZVL cases were reported were retrieved in Gansu Province from 2015 to 2023, and data pertaining to 26 environmental variables were captured, including 19 climatic variables (annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter), five geographical variables (elevation, annual normalized difference vegetation index, vegetation type, landform type and land use type), and two population and economic variables (population distribution and gross domestic product). Twelve species distribution models were built using the biomod2 package in R project, including surface range envelope (SRE) model, generalized linear model (GLM), generalized additive model (GAM), multivariate adaptive regression splines (MARS) model, generalized boosted model (GBM), classification tree analysis (CTA) model, flexible discriminant analysis (FDA) model, maximum entropy (MaxEnt) model, optimized maximum entropy (MAXNET) model, artificial neural network (ANN) model, random forest (RF) model, and extreme gradient boosting (XGBOOST) model. The performance of 12 models was evaluated using the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS), and Kappa coefficient, and single models with high performance was selected to build the optimal ensemble models. Factors affecting the survival of Ph. chinensis were identified based on climatic, geographical, population and economic variables. In addition, the suitable distribution areas of Ph. chinensis were predicted in Gansu Province under shared socioeconomic pathway 126 (SSP126), SSP370 and SSP585 scenarios based on climatic data during the period from 1991 to 2020, from 2041 to 2060 (2050s), and from 2081 to 2100 (2090s) . Results A total of 11 species distribution models were successfully built for prediction of potential distribution areas of Ph. chinensis in Gansu Province, and the RF model had the highest predictive accuracy (AUC = 0.998). The ensemble model built based on the RF model, XGBOOST model, GLM, and MARS model had an increased predictive accuracy (AUC = 0.999) relative to single models. Among the 26 environmental factors, precipitation of the wettest quarter (12.00%), maximum temperature of the warmest month (11.58%), and annual normalized difference vegetation index (11.29%) had the greatest contributions to suitable habitats distribution of Ph. sinensis. Under the climatic conditions from 1991 to 2020, the potential suitable habitat area for Ph. chinensis in Gansu Province was approximately 5.80 × 10⁴ km², of which the highly suitable area was 1.42 × 10⁴ km², and primarily concentrated in the southernmost region of Gansu Province. By the 2050s, the unsuitable and lowly suitable areas for Ph. chinensis in Gansu Province had decreased by varying degrees compared to that of 1991 to 2020 period, while the moderately and highly suitable areas exhibited expansion and migration. By the 2090s, under the SSP126 scenario, the suitable habitat area for Ph. chinensis increased significantly, and under the SSP585 scenario, the highly suitable areas transformed into extremely suitable areas, also showing substantial growth. Future global warming is conducive to the survival and reproduction of Ph. chinensis. From the 2050s to the 2090s, the highly suitable areas for Ph. chinensis in Gansu Province will be projected to expand northward. Under the SSP126 scenario, the suitable habitat area for Ph. chinensis in Gansu Province is expected to increase by 194.75% and 204.79% in the 2050s and 2090s, respectively, compared to that of the 1991 to 2020 period. Under the SSP370 scenario, the moderately and highly suitable areas will be projected to increase by 164.40% and 209.03% in the 2050s and 2090s, respectively, while under the SSP585 scenario, they are expected to increase by 195.98% and 211.66%, respectively. Conclusions The distribution of potential suitable habitats of Ph. sinensis gradually shifts with climatic changes. Intensified surveillance and management of Ph. sinensis is recommended in central and eastern parts of Gansu Province to support early warning of MT-ZVL.

Qingzao Jiufeitang is a famous classical formula for treating lung injury caused by warm and dryness， included in the Catalogue of Ancient Famous Classical Formulas（The First Batch）. By systematically organizing ancient and modern literature on this formula， this study analyzed and verified the origin， medicinal composition， original plants and processing， dosage and decoction method， efficacy and application of this formula. According to the research， Qingzao Jiufeitang was first recorded in Yimen Falyu in the Qing dynasty， and its creation was mainly inspired by the Ming dynasty physician MIAO Xiyong's idea of the moisturizing drugs with sweet flavour and cold nature. Based on the 2020 edition of the Pharmacopoeia of the People's Republic of China（hereinafter referred to as the Chinese Pharmacopoeia） and the textual research results of modern scholars on traditional Chinese herbal medicines， the botanical sources and processing methods of the herbs in this formula are basically clarified. Among them， Mori Folium， Gypsum Fibrosum， Ginseng Radix et Rhizoma， Sesami Semen Nigrum， Asini Corii Colla， Ophiopogonis Radix and Eriobotryae Folium are consistent with the 2020 edition of the Chinese Pharmacopoeia. The primary source of Glycyrrhizae Radix et Rhizoma is the dried roots and rhizomes of Glycyrrhiza uralensis， family Leguminosae， while the primary source of Armeniacae Semen Amarum is the dried mature seeds of Prunus armeniaca， family Rosaceae. It is recommended to use Gypsum Ustum， stir-fried Sesami Semen Nigrum， stir-fried Armeniacae Semen Amarum， Asini Corii Colla bead， and honey-fried Eriobotryae Folium， and the rest of the raw products. According to the conversion of ancient and modern doses， the recommended dosages are 11.19 g for Mori Folium， 9.33 g for Gypsum Fibrosum， 3.73 g for Glycyrrhizae Radix et Rhizoma， 2.61 g for Ginseng Radix et Rhizoma， 3.73 g for Sesami Semen Nigrum， 4.48 g for Ophiopogonis Radix， 2.61 g for Armeniacae Semen Amarum， 3.73 g for Eriobotryae Folium. The decoction method is to add 300 mL of water， decoct it down to 180 mL， remove the residue， and then add 2.98 g of Asini Corii Colla into the decoction. Take it warm after meals， two to three times a day. Qingzao Jiufeitang has the effects of clearing dryness and moistening the lungs， nourishing Yin and invigorating Qi. In ancient times， it was mainly used to treat stagnation and depression of various Qi， as well as paralysis， asthma and vomiting. In modern clinical practice， it is mostly used to treat diseases in respiratory system， otolaryngology， skin system and digestive system caused by warm-dry impairing lung， deficiency of both Qi and Yin. The above research results can provide a reference for the later development of Qingzao Jiufeitang.