Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

This study investigated the infection status,drug resistance,and molecular characteristics of Shiga toxin-producing Escherichia coli(STEC)in domestic goats in Chengkou county,Chongqing.In August 2023,283 fecal samples were collected from households in Chengkou county.After enrichment with EC broth and inoculation onto selective media,samples that tested positive for stx1/stx2 were selected for further isolation.The positive strains were investigated with antimicrobial susceptibility testing and whole genome sequencing.According to the whole genomic sequences,the stx subtypes,serotypes,multi-locus sequence types,virulence genes,drug resistance genes,and phylogenetic relationships of the STEC strains were analyzed.Forty-six strains of STEC were isolated from 283 goat fecal samples,thus resulting in a detection rate of 16.25%.The 46 STEC strains were categorized into 12 O∶H serotypes,among which O76∶H19 and O8∶H7 predominated,each represented by 9 strains.Five STEC strains were identified as serotype O157∶H7.The 46 STEC strains were categorized into 11 sequence types(STs),among which ST675 and ST196 predominated,each represented by nine strains,accounting for a 19.57%proportion.The strains were categorized into 7 stx subtypes,among which stx1c(26/46,56.52%),followed by stx2k(9/46,19.57%)predominated.All nine Stx2k-STEC strains were identified as serotype O8∶H7 and sequence type ST196.In antimicrobial susceptibility testing,2 STEC strains were resistant to ampicillin,one strain was resistant to ampicillin/sulbactam,one strain was resistant to cefazolin,and one strain was resistant to cefoxitin.Nine Stx2k-STEC strains were found to carry the beta-lactam resistance gene blaEC-18.Antimicrobial sensitivity tests revealed that the nine Stx2k-STEC strains were sensitive to all 15 tested antibiotics.Moreover,phylogenetic analysis indicated that the 9 Stx2k-STEC strains were remarkably similar but showed high genetic diversity with respect to that of the Stx2k-STEC strains isolated from other regions in China.Goatsare an important animal reservoir for STEC in theChengkou district of Chongqing,and novel sequence type Stx2k-STEC strains distinct from those found in other regions of China were identified in this region.

Objective:Based on a propensity score matchied analysis, the impact of neoadjuvant therapy, namely the transcatheter arterial chemoembolization (TACE) combined with the targeted and immunotherapy, on the prognosis of patients undergoing liver resection for hepatocellular carcinoma (HCC).Methods:Clinical data of 226 patients who underwent surgical resection for HCC of China Liver Cancer (CNLC) stage Ib, IIa, IIb, and IIIa at the Second Affiliated Hospital of Anhui Medical University from February 2020 to December 2024 were retrospectively analyzed, including 201 males and 25 females, aged 64.6±9.4 years. Patients were divided into the neoadjuvant therapy group ( n=25) and the direct surgery group ( n=201). Propensity score matching was used to analyze the liver fibrosis-4 score, platelet count, prothrombin time, activated partial thromboplastin time, and tumor number of the two groups. Postoperative pathological assessment of liver resection was performed. The Kaplan-Meier method was used to analyze the prognosis, and the log-rank test was used to compare the survival rates of the two groups. Results:After propensity score 1: 3 matching, there were no statistically significant differences (all P>0.05) regarding the baseline characteristics of the two groups. Pathological assessment after hepatectomy: the complete pathological response rate was 8% (2/25), and the major pathological response rate was 36% (9/25). The recurrence-free survival rates at 1, 2, and 3 years after surgery in the direct surgery group and the neoadjuvant therapy group were 52.0%, 48.0%, and 42.7% versus 76.0%, 72.0%, and 68.0%, respectively ( χ2=4.76, P=0.029). The overall survival rates at 1, 2, and 3 years after surgery in the direct surgery group and the neoadjuvant therapy group were 80.0%, 78.7%, and 77.3% versus 100.0%, 96.0%, and 96.0%, respectively ( χ2=4.31, P=0.038). Conclusion:Neoadjuvant therapy could reduce the risk of postoperative recurrence and prolong patients survival

Objective:This study aimed to evaluate the prognostic factors of clinical and histopathological parameters,including age,gender,tumor stage,tumor grade,tumor differentiation,lymph node metastasis(LNM),tumor frequency,and tumor count,in patients undergoing radical cystectomy(RC)combined with Mainz Pouch Ⅱ bladder reconstruction.Methods:A total of 237 bladder cancer patients(198 male and 39 female)who underwent RC combined with Mainz Pouch Ⅱ bladder reconstruction without chemotherapy or radiotherapy,from January 2004 to January 2023,were included in this study.Kaplan-Meier and Cox regression analyses were performed to assess the impact of age,tumor grade,tumor stage,tumor differentiation,LNM,tumor frequency,and tumor count on 5-year overall survival(OS)and 5-year cancer-specific survival(CSS).Results:The mean age at diagnosis was 59.8 years,with 198 male and 39 female patients.The mean follow-up duration was 47.8 months.In univariate analysis,patients younger than 65 years had significantly higher 5-year OS and 5-year CSS compared to those aged≥65 years.Patients with urothelial carcinoma showed better 5-year OS than those with non-urothelial carcinoma.Additionally,tumor stage,tumor grade,and LNM were negatively associated with 5-year OS and 5-year CSS.On multivariate analysis,only tumor grade and LNM remained statistically significant(P<0.05).Conclusions:Tumor grade and LNM were identified as independent prognostic risk factors for 5-year OS and 5-year CSS following RC combined with Mainz PouchⅡ bladder reconstruction.Moreover,the application of RC combined with Mainz Pouch Ⅱ bladder reconstruction should consider the patient's preferences and physical condition.

Objective:To investigate the predictive role of intrapartum ultrasound indicators on the difficulty of vacuum-assisted delivery.Methods:A prospective cohort study was conducted. The study subjects were singleton pregnant women hospitalized for delivery at the Third Affiliated Hospital of Sun Yat-sen University from February 2021 to December 2022, who had indications for vacuum-assisted delivery, and completed intrapartum ultrasound examination within 10 minutes before the procedure. Intrapartum ultrasound indicators included fetal position, angle of progression (AOP), and head-perineum distance (HPD). Based on the difficulty of vacuum-assisted delivery, the subjects were divided into easy and difficult groups. The fetal position, AOP, and HPD before vacuum-assisted delivery and delivery outcomes were compared between the two groups to explore the correlation and predictive value of intrapartum ultrasound indicators on the difficulty of vacuum-assisted delivery. Statistical and predictive value analyses were performed using independent-sample t-test, U-test, Chi-square (or Fisher's exact) test, logistic regression analysis, and receiver operating characteristic (ROC) curve. Results:A total of 162 cases were included in the study, with 120 in the easy and 42 in the difficult groups. The age of the 162 pregnant women ranged from 20 to 44 years, with an average of (30.6±3.9) years; 107 cases (66.0%) were first pregnancies, and 139 cases (85.8%) were primipara. (1) The fetal head stations in the difficult and easy groups were 2.3 (2.0-2.5) cm and 2.0 (2.0-2.5) cm, respectively, with no statistically significant difference ( P>0.05). The AOP during the interval and contraction periods and the ΔAOP in the difficult group were smaller than those in the easy group [ (138.1±8.8) vs. (143.8±7.9), t=3.89; (148.7±9.3) vs. (157.9±8.9), t=5.67; and (10.6±6.4) vs. (14.1±6.3), t=3.08; all P<0.01], while the HPD during the interval and contraction periods and ΔHPD in the difficult group were greater than those in the easy group [(3.4±0.5) cm vs. (3.2±0.4) cm, t=－2.69; (2.8±0.5) cm vs. (2.4±0.5) cm, t=－4.70; (－0.5±0.4) cm vs. (－0.8±0.5) cm, t=－2.83; all P<0.01]. (2) In the difficult group, seven cases required forceps delivery after 2-3 vacuum cup detachments; in the easy group, all cases were successfully delivered after 1-2 vacuum tractions. The duration of vacuum extraction was longer in the difficult group [7.0 (6.0-10.0) min vs. 3.0 (2.0-3.0) min, Z=9.65] (all P<0.001). (3) In the difficult group, four cases had severe maternal and neonatal delivery complications, including two cases of shoulder dystocia, one case of vesicovaginal fistula after failed vacuum extraction converted to forceps delivery, and one case of third-degree perineal tear after failed vacuum extraction converted to forceps delivery. In the easy group, one case had shoulder dystocia with mild neonatal asphyxia. The rate of vaginal tears in the difficult group was higher than in the easy group [47.6% (20/42) vs. 29.2% (35/120)] ( χ2=4.72, P=0.030). The incidence of postpartum hemorrhage in the difficult and easy groups was 11.9% (5/42) and 8.3% (10/120), respectively, with no statistically significant difference (Fisher's exact test, P=0.539). No cases required cesarean section after failed vacuum extraction. The incidence of scalp hematoma was higher in the difficult group than in the easy group [28.6% (12/42) vs. 11.7% (14/120), χ2=6.60, P=0.010]. The two groups had no statistically significant differences in the incidence of other neonatal complications. (4) Multivariate logistic regression analysis identified three independent variables associated with difficult vacuum extraction: maternal age, AOP and HPD during contraction. The ROC curve was used to test the predictive value of the multivariate model for difficult vacuum extraction, with an area under the curve of 0.808 (95% CI: 0.734-0.882) ( P<0.001). When the maximum Youden index (0.487) was taken, the sensitivity and specificity of the model in predicting difficult vacuum extraction were 0.762 (95% CI: 0.696-0.828) and 0.725 (95% CI: 0.656-0.794), respectively. Conclusions:AOP and HPD are related to the difficulty of vacuum extraction. The risk of difficult vacuum extraction increases with advanced maternal age, smaller AOP and larger HPD during the contraction phase.

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.

The MXene/MF microspheres were prepared by coating MXene nanosheets on melamine formaldehyde(MF)resin microspheres.Co(NO3)2 was adsorbed on the surface of the microspheres by impregnation,and then calcined at high temperature in an argon atmosphere.MF pyrolysis generated reducing gases such as CO and NH3,reducing Co2+to elemental Co,which was then used as a catalyst for in situ growth of carbon nanotube(CNT)through chemical vapor deposition(CVD),forming MXene-Co-CNT microspheres(MXene-Co-CNT MS).During this process,the pyrolysis of MF microspheres had dual effects.On one hand,the template was sacrificed to produce an internal hollow structure,and on the other hand,the generated gas worked as carbon source to generate CNT,forming an external sea urchin-like structure.Both of them promoted the formation of a novel structure,which combined the advantages of large specific surface area and good conductivity,thus possessing excellent electrocatalytic activity.The MXene-Co-CNT MS was modified on glassy carbon electrode(GCE)and further used in highly sensitive detection of nitroaromatic compounds(NACs).The detection limits of MXene-Co-CNT MS/GCE for 2,4,6-trinitrotoluene(TNT),1,3,5-nitrobenzamide(TNB),2,4-dinitrotoluene(DNT),1,3-dinitrobenzene(DNB),1-Cl-2,4-dinitrotoluene(Cl-DNB),and 4-nitrophenol(4-NP)were 26.84,31.60,35.03,54.14,43.86 and 28.67 nmol/L,respectively.It also had excellent anti-interference ability,and was used to detect NACs in environmental water samples accurately.