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.

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 paper proposed to understand the pathogenesis and provide syndrome differentiated treatment for anxiety and depression-related dry eyes from the perspective of the liver and the lung， in order to provide ideas for treatment of this disease with traditional Chinese medicine. It is believed that the occurrence and development of anxiety and depression-related dry eyes is related to the ethereal qi and blood damage and blocked circulation of qi and blood. The liver and the lung are the main located zang-fu （脏腑） organs of the disease， and the qi movement， sweat pores， meridians and collaterals abnormalities of the liver and the lung are the pathological basis. The basic pathogenesis is disharmony of the liver and the lung， loss nourishment of eyes， and loss calm of the mind. In clinical practice， the root treatment is to restore the functions of the liver governing ascent and the lung governing descent， and to open up the sweat pores， meridians and collaterals， while the branch treatment is to promote the production of body fluids， nourish yin and calm the mind. Both the root and the branch causes are treated to restore the physiological functions， and Danzhi Xiaoyao Powder （丹栀逍遥散） combined with Shengmai Powder （生脉散） with modification is often used as the basic prescription.

Objective:To explore the association between triglyceride glucose index (TyG)- waist to height ratio (WHtR)(TyG-WHtR) and cognitive impairment in middle-aged and elderly population.Methods:A cohort database was constructed using the data from the China Health and Retirement Longitudinal Study, with 8 946 participants in 2011 and 2015 as the baseline population. Cox proportional hazards regression models were used to estimate the association between TyG-WHtR levels at baseline and the risk of cognitive impairment in middle-aged and elderly population. The analysis was stratified by age and gender, respectively.Results:A total of 8 946 participants were included, with an average follow-up of 7.08 person-years and incidence density of cognitive impairment for 21.15 per 1 000 person-years. Compared with the Q1 level of TyG-WHtR, its Q3 and Q4 level increased the risk of cognitive impairment by 32% ( HR=1.32, 95% CI: 1.09-1.60) and 47% ( HR=1.47, 95% CI: 1.14-1.91), respectively. Trend test showed that the risk of cognitive impairment increased with the increase of TyG-WHtR level, and there was a dose-response relationship ( P=0.001). Stratified analysis showed that in the population aged 45-59 years, compared with the Q1 level of TyG-WHtR, its Q3 level increased the risk of cognitive impairment by 34% ( HR=1.34, 95% CI: 1.02-1.78). In the population aged 60 years and above, compared with the Q1 level, its Q3 and Q4 level increased the risk of cognitive impairment by 31% ( HR=1.31, 95% CI: 1.01-1.72) and 63% ( HR=1.63, 95% CI: 1.15-2.31), respectively. In the male group, there was no significant association between TyG-WHtR level and the risk of cognitive impairment ( P>0.05). In the female group, compared with the Q1 level of TyG-WHtR, its Q4 level increased the risk of cognitive impairment by 76% ( HR=1.76, 95% CI: 1.26-2.46). Conclusions:Middle-aged and elderly population with a higher TyG-WHtR level may increase the risk of cognitive impairment, and there were age and sex differences. Early cardiovascular health management and scientific and reasonable weight management are of great significance to preventing cognitive impairment.

Objective:To explore the clinical application and triage management value of using blood circulating cell-free DNA (cfDNA) (cysteine dioxygenase type 1 gene, CDO1, and Homeobox protein A9 gene, HOXA9) hypermethylation level to detect and diagnose ovarian cancer.Methods:A case-control study was conducted on patients who went for surgery at Chengdu Womens and Childrens Central Hospital from November 2022 to October 2023. Blood samples were collected before surgery for evaluation of cancer antigen 125 (CA125), human epididymis protein 4 (HE4), risk of ovarian malignancy algorithm (ROMA) score, and DNA methylation testing. The basic clinical information, biomarkers, and transvaginal ultrasound (TVS) information were collected simultaneously. Information from a total of 151 patients was collected, including 122 cases with benign pathology and 29 ovarian cancer cases. The pathologic diagnosis of ovarian tissue was defined as the gold standard. The multivariate logistic regression analysis was used to identify high-risk factors for ovarian cancer. The clinical efficacy of DNA methylation detection for ovarian cancer was analyzed using the area under curve (AUC).Results:The results showed that the age, menopausal status, CA125 and HE4 detection, ROMA score, positivity rate of CDO1 gene and HOXA9 gene single or combined testing in ovarian cancer patients were higher than those in the benign group and showed significant differences ( P<0.05). Among these detection protocols, the AUC of CDO1 and HOXA9 dual gene methylation testing for ovarian cancer was the highest at 0.936 (95% CI, 0.878-0.994), with 89.7% (95% CI 73.6%-96.4%) sensitivity and 97.5% (95% CI 93.0%-99.2%) specificity, respectively. The positive detection rate of CDO1 and HOXA9 dual gene methylation in early ovarian cancer FOGO I-II stage is 12/14 higher than other tests. Conclusion:Blood cfDNA methylation detection, a simple, non-invasive, and highly sensitive detection method, is superior to the current ovarian cancer testing in the risk assessment and early detection.

Objective:To investigate the value of contrast-enhanced ultrasound (CEUS) as a preoperative planning strategy for lymphaticovenous anastomosis (LVA) in improving the quality of LVA and the outcome of short-term limb volume reduction in patients with secondary upper limb lymphedema.Methods:Patients with breast cancer-related upper extremity lymphedema who underwent LVA at the Department of General Surgery Cancer Center Division of Breast Surgery of Zhejiang Provincial People’s Hospital from August 2021 to August 2023 were enrolled retrospectively. According to whether preoperative ultrasound lymphography was performed, the patients were divided into CEUS assisted group and control group. In the CEUS assisted group, preoperative CEUS lymphography combined with high-frequency ultrasound color Doppler imaging was utilized for precise localization of lymphatic vessels and recipient veins, as well as surgical target planning for LVA. In the control group, preoperative indocyanine green lymphography was employed to guide surgical exploration. Mann-Whitney U test was used to compare the number of LVA surgical exploration incisions per limb and the number of successful anastomoses per limb between the two groups. The success rate of anastomosis (total number of successful anastomoses/total number of surgical exploration incisions) was compared by the chi-square test. The duration of single anastomosis, mean arm circumference, and the difference between preoperative and postoperative mean arm circumference were compared by independent sample t-test. Paired-sample t-test was used to compare the improvement of the mean arm circumference of the operated limb of the two groups after 3 months of follow-up. P < 0.05 was considered statistically significant. Results:A total of 47 female patients were enrolled, including 27 patients in the CEUS assisted group, with an average age of (57.1±9.0) years and a median edema course of 2 years. There were 20 cases in the control group, with an average age of (58.1±9.6) years and a median duration of edema of 2 years. The CEUS group, compared with the control group, exhibited a higher number of surgical exploration incisions per limb [6.0 (4.0, 7.0) cases vs. 5.0 (3.0, 6.0) cases], a greater number of successful anastomoses per limb [5.0 (3.0, 6v0) cases vs. 3.0 (2.0, 3.0) cases], and a significantly increased overall success rate of anastomosis [82.8% (125/151) vs. 61.4% (54/88)]. Additionally, there was a significant increase in the preoperative and postoperative mean arm circumference difference [(6.2±3.3) cm vs. (3.9±1.9) cm]. The duration of single anastomosis was significantly shortened [(57.4±16.0) min vs. (92.8±18.5) min], with statistically significant differences observed in all comparisons (all P < 0.05). The preoperative and postoperative mean arm circumference were compared between the CEUS group [(31.4±4.6) cm vs. (25.3±4.7) cm] and the control group [(31.3±4.3) cm vs. (27.5±3.8) cm], respectively, with statistically significant differences observed in both groups (both P < 0.01). Conclusion:CEUS lymphography, as a preoperative planning strategy for LVA, can significantly increase the number and success rate of LVA anastomosis in patients with secondary upper limb lymphedema, shorten the duration of single anastomosis, and improve the short-term effect of limb volume reduction after LVA.