Retinal microcirculatory disorder is a key factor in the occurrence and development of diabetic retinopathy （DR）， and also an important link in the prevention and treatment of DR. The theory of "Gaozhuo" holds that the microcirculatory disorder in DR is based on the deficiency of spleen Qi and is characterized by the obstruction caused by "Gaozhuo" and blood stasis. The deficiency of spleen Qi is an essential precondition for the endogenous formation and accumulation of Gaozhuo， while Gaozhuo invasion is the direct cause of microcirculatory disorders in DR. The deficiency of spleen Qi and the endogenous formation of Gaozhuo mean the process in which glucose metabolism dysfunction induces an excessive production of inflammatory factors and lipid metabolites. The obstruction caused by "Gaozhuo" and blood stasis is the direct pathogenesis of microcirculatory disorders in DR， encompassing two stages： Gaozhuo obstruction and turbidity and stasis stagnation. Gaozhuo obstruction and turbidity and stasis stagnation represent the process in which inflammatory factors and lipid metabolites damage the retinal microcirculation and induce thrombosis， thus mediating microcirculatory disorders. Turbidity and stasis stagnation and blood extravasation outside the vessels reveal the progression to microvascular rupture and hemorrhage resulting from the microcirculatory disorders. According to the pathogenesis evolution of the theory of "Gaozhuo"， microcirculatory disorders in DR can be divided into deficiency of spleen Qi with Gaozhuo obstruction， deficiency of spleen Qi with turbidity and stasis stagnation， and turbidity and stasis stagnation with blood extravasation outside the vessels. Clinically， treatment principles should focus on strengthening the spleen and benefiting Qi， resolving turbidity， and dispersing stasis. Different syndrome patterns should be addressed with tailored therapies， such as enhancing the spleen and benefiting Qi while regulating Qi and reducing turbidity， strengthening the spleen and benefiting Qi while resolving turbidity and dispelling stasis， and strengthening the spleen and resolving turbidity while removing stasis and stopping bleeding. Representative prescriptions include modified Wendantang， modified Buyang Huanwutang， modified Danggui Buxuetang， Zhuixue Mingmu decoction， Tangmuqing， Shengqing Jiangzhuo Tongluo Mingmu prescription， Danhong Huayu decoction， and Yiqi Yangyin Huoxue Lishui formula.

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.

OBJECTIVE: To study the clinical effects of pomalidomide-based regimen in the treatment of relapsed and refractory multiple myeloma (RRMM). METHODS: 60 patients with RRMM in hematology department of the First Affiliated Hospital of Xinxiang Medical University from November 2020 to January 2023 were selected. Among them, 15 cases were treated with PDD regimen (pomalidomide + daratumumab + dexamethasone), and 45 cases were treated with PCD regimen (pomalidomide + cyclophosphamide + dexamethasone). The clinical effects were evaluated. RESULTS: The median number of treatment cycles for the entire cohort was 5 (2-11), with an overall response rate (ORR) of 75.0%. The ORR of patients treated with PDD regimen was 73.3%, while the ORR of patients treated with PCD regimen was 75.6%. The ORR of 46 patients with non high-risk cytogenetic abnormalities (non-HRCA) was 86.9%, significantly higher than the 35.7% of 14 patients with HRCA (χ² =15.031, P < 0.05). The median PFS for all patients was 8.0(95%CI : 6.8-9.1) months and the median OS was 14.0 (95%CI : 11.3-16.7) months. Among patients treated with PDD regimen, the PFS and OS of patients with non-HRCA were significantly higher than those of patients with HRCA [PFS: 7.0(95%CI : 4.6-9.3) months vs 4.0(95%CI : 3.1-4.8) months, χ² =5.120, P < 0.05; OS: not reached vs 6.0(95%CI : 1.1-10.9) months, χ² =9.870, P < 0.05]. Among patients treated with PCD regimen, the PFS and OS of patients with non-HRCA were significantly higher than those of patients with HRCA [PFS: 9.0(95%CI : 6.2-11.8) months vs 6.0(95%CI : 5.4-6.6) months, χ²=14.396, P < 0.05; OS: not reached vs 11.0(95%CI : 6.4-15.6) months, χ² =7.471, P < 0.05]. CONCLUSION The pomalidomide-based regimen has a good clinical effect and safety in the treatment of RRMM.
Humans ; Multiple Myeloma/drug therapy* ; Thalidomide/administration & dosage* ; Dexamethasone/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Female ; Male ; Middle Aged ; Recurrence ; Aged ; Cyclophosphamide/therapeutic use* ; Treatment Outcome ; Antibodies, Monoclonal

Objective To make an epidemiological investigation on traditional Chinese medicine(TCM)dampness syndrome manifestations in the population at risk of cerebrovascular diseases in Guangdong area.Methods A cross-sectional study was conducted to analyze the clinical data related to the risk of cerebrovascular diseases in 330 Guangdong permanent residents.The diagnosis of dampness syndrome,quantitative scoring of dampness syndrome and rating of the risk of stroke were performed for the investigation of the distribution pattern of dampness syndrome and its influencing factors.Results(1)A total of 306(92.73%)study subjects were diagnosed as dampness syndrome.The percentage of dampness syndrome in the risk group was 93.82%(258/275),which was slightly higher than that of the healthy group(48/55,87.27%),but the difference was not statistically significant(χ2 = 2.91,P = 0.112).The quantitative score of dampness syndrome in the risk group was higher than that of the healthy group,and the difference was statistically significance(Z =-2.24,P = 0.025).(2)Among the study subjects at risk of cerebrovascular disease,evaluation time(χ2 = 26.11,P = 0.001),stroke risk grading(χ2= 8.85,P = 0.031),and history of stroke or transient ischemic attack(TIA)(χ2 = 9.28,P = 0.015)were the factors influencing the grading of dampness syndrome in the population at risk of cerebrovascular disease.Conclusion Dampness syndrome is the common TCM syndrome in the population of Guangdong area.The manifestations of dampness syndrome are more obvious in the population with risk factors of cerebrovascular disease,especially in the population at high risk of stroke,and in the population with a history of stroke or TIA.The assessment and intervention of dampness syndrome should be taken into account for future project of stroke prevention in Guangdong.

Objective:To investigate the effectiveness and feasibility of a 3D U-Net in conjunction with a three-phase CT image segmentation model in the automatic segmentation of GTVnx and GTVnd in nasopharyngeal carcinoma.Methods:A total of 645 sets of computed tomography (CT) images were retrospectively collected from 215 nasopharyngeal carcinoma cases, including three phases: plain scan (CT), contrast-enhanced CT (CTC), and delayed CT (CTD). The dataset was grouped into a training set consisting of 172 cases and a test set comprising 43 cases using the random number table method. Meanwhile, six experimental groups, A1, A2, A3, A4, B1, and B2, were established. Among them, the former four groups used only CT, only CTC, only CTD, and all three phases, respectively. The B1 and B2 groups used phase fine-tuning CTC models. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) served as quantitative evaluation indicators.Results:Compared to only monophasic CT (group A1/A2/A3), triphasic CT (group A4) yielded better result in the automatic segmentation of GTVnd (DSC: 0.67 vs. 0.61, 0.64, 0.64; t = 7.48, 3.27, 4.84, P < 0.01; HD95: 36.45 vs. 79.23, 59.55, 65.17; t = 5.24, 2.99, 3.89, P < 0.01), with statistically significant differences ( P < 0.01). However, triphasic CT (group A4) showed no significant enhancement in the automatic segmentation of GTVnx compared to monophasic CT (group A1/A2/A3) (DSC: 0.73 vs. 0.74, 0.74, 0.73; HD95: 14.17 mm vs. 8.06, 8.11, 8.10 mm), with no statistically significant difference ( P > 0.05). For the automatic segmentation of GTVnd, group B1/B2 showed higher automatic segmentation accuracy compared to group A1 (DSC: 0.63, 0.63 vs. 0.61, t = 4.10, 3.03, P<0.01; HD95: 58.11, 50.31 mm vs. 79.23 mm, t = 2.75, 3.10, P < 0.01). Conclusions:Triphasic CT scanning can improve the automatic segmentation of the GTVnd in nasopharyngeal carcinoma. Additionally, phase fine-tuning models can enhance the automatic segmentation accuracy of the GTVnd on plain CT images.