[Objective] To analyse and predict the tendencies of using erythrocyte blood in Changsha based on the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) and ARIMA-LSTM combination model, so as to provide reliable basis for designing a feasible and effective blood inventory management strategy. [Methods] The data of erythrocyte usage from hospitals in Changsha between January 2012 and December 2023 were collected, and ARIMA model, LSTM model and ARIMA-LSTM combination model were established. The actual erythrocyte consumption from January to May 2024 were used to assess and verify the prediction effect of the models. The extrapolation prediction accuracy of the models were tested using two evaluation indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE), and then the prediction performance of the model was compared. [Results] The RMSE of LSTM model, optimal model ARIMA(1,1,1)(1,1,1)12 and ARIMA-LSTM combination model were respectively 5 206.66, 3 096.43 and 2 745.75, and the MAPE were 18.78%,11.54% and 9.76% respectively, which indicated that the ARIMA-LSTM combination model was more accurate than the ARIMA model and LSTM model, and the prediction results was basically consistent with the actual situation. [Conclusion] The ARIMA-LSTM model can better predict the clinical erythrocyte consumption in Changsha in the short term.

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.

Background: Intradermal microdroplet injections of botulinum toxin type-A (BoNT/A) effectively ameliorate rosacea-related angiogenesis, but the mechanism remains unclear. Objective: To explore the anti-angiogenesis of BoNT/A in the rosacea-like mouse model and to measure the secretion of vascular endothelial growth factor (VEGF) by mast cells. Methods: A rosacea-like mouse model was induced by LL37 in both Mas-related G-proteincoupled receptor B2 conditional knockout (MrgprB2 −/− ) mice and wild-type (WT) mice, then treated with BoNT/A and/or Apatinib. The abundance of endothelial cells and mast cells in mouse skin was determined using dual immunofluorescence staining. The VEGF levels in supernatants and cell lysates of laboratory of allergic disease 2 (LAD2) mast cells were assessed using reverse transcription quantitative polymerase chain reaction, western blots, and enzyme-linked immunosorbent assay. The effect of conditioned medium (CM) collected from LAD2 on human umbilical vein endothelial cells (HUVECs) was determined using tube formation assays. The number of proliferative cells was confirmed using the 5-ethynyl-2’-deoxyuridine incorporation assays.The effect of BoNT/A on the internalization of Mas-related G-protein-coupled receptor X2 (MRGPRX2) was detected using flow cytometry and immunofluorescence staining. Results: LL37-induced rosacea-like skin manifestations were significantly alleviated in MrgprB2 −/− mice compared to WT controls. BoNT/A mitigated the LL37-induced secretion of VEGF by LAD2. The CM from BoNT/A-treated LAD2 inhibited HUVEC proliferation and tube formation. The LAD2 cells co-treated with LL37 and BoNT/A exhibited dramatically enhanced MRGPRX2 internalization. Conclusion BoNT/A enhances LL37-mediated MRGPRX2 internalization in mast cells, thereby reducing VEGF secretion and neovascularization and improving facial flushing symptom in rosacea.

Objective To explore the clinical features,diagnosis and treatment of liposarcoma of spermatic cord.Methods The clinical data of 1 case with multiple recurrence of liposarcoma of spermatic cord were retrospectively analyzed,and the clinical diagnosis and treatment were discussed in combination with relevant literature.Results The patient underwent the first operation to examine the adipocytes in the right spermatic cord area.Postoperative examination revealed highly differentiated liposarcoma.Within 1 year of follow-up,radical resection of both testis and retroperitoneal tumor were performed respectively due to recurrence.Conclusion liposarcoma of spermatic cord is an extremely rare disease,and currently there is no standard treatment protocol.Radical surgical resection of localized lesions is the key,and surgical treatment is still the first choice for local recurrence.As it is unable to achieve R0 resection,the recurrence rate is very high.Since liposarcoma is not sensitive to radiotherapy and chemotherapy,more precise adjuvant therapy is highly expected.

Surgical operation is the main treatment of oral and maxillofacial tumors.Dysphagia is a common postoperative complication.Swal-lowing disorder can not only lead to mis-aspiration,malnutrition,aspiration pneumonia and other serious consequences,but also may cause psychological problems and social communication barriers,affecting the quality of life of the patients.At present,there is no systematic evalua-tion and rehabilitation management plan for the problem of swallowing disorder after oral and maxillofacial tumor surgery in China.Combining the characteristics of postoperative swallowing disorder in patients with oral and maxillofacial tumors,summarizing the clinical experience of ex-perts in the field of tumor and rehabilitation,reviewing and summarizing relevant literature at home and abroad,and through joint discussion and modification,a group of national experts reached this consensus including the core contents of the screening of swallowing disorders,the phased assessment of prognosis and complications,and the implementation plan of comprehensive management such as nutrition management,respiratory management,swallowing function recovery,psychology and nursing during rehabilitation treatment,in order to improve the evalua-tion and rehabilitation of swallowing disorder after oral and maxillofacial tumor surgery in clinic.

Objective To establish an optimal limiting dose for dose limiting rings placed in the anterior and posterior regions of the neck for reducing head and neck lymphedema under intensity-modulated radiation therapy(IMRT)for early nasopharyngeal carcinoma(NPC)from a dosimetric perspective.Method Fifteen newly diagnosed early-stage nasopharyngeal carcinoma patients who underwent CT localization for radiotherapy at the Cancer Hospital of Guangzhou Medical University from January to September 2022 were included in the study.Each case was designed with five sets of radiotherapy plans.Plan A consisted of conventional unlimited-field plans,while Plans B-E consisted of limited-field plans with dose constraints set at 20,18,16,and 14 Gy,respectively,with the remaining parameters consistent with Plan A.The impact on target coverage and organ-at-risk constraints was evaluated through variance analysis and pairwise multiple comparisons using a randomized block design to determine the optimal dose limits.Results The gradient of 16Gy was determined as the optimal dose limiting cutoff point for achieving the balance between target coverage and organ limiting dose.Compared with the conventional plan,The plans with the placement of a cervical anterior and posterior dose limiting ring(16Gy)did not change the target dose coverage(P>0.05),but only yielded a slight change in the homogeneity index(P<0.05).It did not cause any changes of the dosage in the inner ear,mandible,and brainstem(all P>0.05),but lead to statisti-cally significant reductions in the oral cavity,throat,and thyroid(all P<0.05).It caused a slight increase of the dose in the parotid gland and spinal cord(both P<0.05),but the increased dose was anyhow within the tolerance range.Conclusion The dosimetric investigation determines an optimal dose limit cutoff point for the cervical ante-rior and posterior dose limiting rings.It is expected to provide a design method for IMRT plans to reduce head and neck lymphedema after radiotherapy for early NPC.