[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.

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.

BACKGROUND:Inhibitory control and drug craving are the core elements of evaluating drug withdrawal in methamphetamine addicts,which has attracted much attention in academic circles.As we all know,in order to achieve complete abstinence from drug addiction,the key is to restore the damaged inhibition and control function of drug addicts and effectively reduce the craving for drugs. OBJECTIVE:To systematically analyze the relationship between exercise and methamphetamine abstinence inhibitory control and drug craving,to find out an effective exercise intervention scheme that can promote methamphetamine abstinence,and to further explore the internal mechanism of exercise,in order to provide theoretical support and applied reference for the future use of exercise in drug withdrawal. METHODS:CNKI,WanFang,VIP,Web of Science,and PubMed databases were searched for relevant literature using the keywords of"exercise,physical activity,methamphetamine,inhibitory function,craving,addiction"in Chinese and"sport*,exercise,methamphetamine,drug craving,executive function,addiction"in English.According to the inclusion and exclusion criteria,86 documents were finally included for review. RESULTS AND CONCLUSION:In terms of inhibitory control in methamphetamine abstinent individuals,either acute and long-term moderate-intensity aerobic exercise or acute high-intensity interval training can significantly improve the inhibitory control capacity of methamphetamine abstinent individuals.For long-term aerobic exercise,aerobic group exercise or full-body comprehensive exercise is more effective.If the exercise format is power cycling,it is recommended to increase the frequency of exercise intervention.In terms of the drug craving intensity in methamphetamine abstinent individuals,acute moderate-intensity aerobic exercise and resistance training,as well as long-term moderate-intensity,high-intensity,or progressive load aerobic and resistance training,can effectively reduce the drug craving in methamphetamine abstinent individuals.Exercise exerts intrinsic regulatory effects on methamphetamine-mediated addiction.Exercise can influence the expression of tyrosine hydroxylase in the brain's ventral tegmental area,thereby stimulating the expression of dopamine receptor coupling proteins and promoting dopamine synthesis in the brain's reward regions,thereby compensating for dopamine depletion caused by methamphetamine addiction.Furthermore,exercise can also regulate protein kinase A inhibitors,affecting the protein kinase A signaling pathway mediated by dopamine D1 receptors,by inhibiting protein kinase A,thus affecting cAMP response element-binding protein and regulating methamphetamine addiction.Additionally,exercise can also,at the genetic level,affect the expression of the c-fos gene in the brain's nucleus accumbens region,activate a subset of glutamatergic neurons in this area,generate a rewarding effect,and thus improve methamphetamine addiction.Although current research has confirmed the relationship between exercise and methamphetamine addiction and has clarified the brain mechanisms underlying the effects of exercise,whether there are other brain regulatory pathways for the effects of exercise remains to be explored through more scientifically rigorous animal or human experiments,starting from the cellular or molecular level.

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.

Objective:To investigate the efficacy and mechanism of static progressive stretch (SPS) with different parameters in the treatment of stiff knee in rats.Methods:Fifty-six male 8-week SD rats were randomly divided into an operation group ( n=48) and a blank group ( n=8, normal feeding rats without any treatment). The knee joints of the rats in the operation group were fixed with Kirschner wire for 4 weeks to create models of right knee flexion stiffness. The 42 rats with successful modeling were randomly divided into 6 groups ( n=7): the model group was executed and sampled after successful modeling, the spontaneous recovery group was not given any treatment after successful modeling, group T1 was given SPS treatment for 20 min once per day, group T2 was given SPS treatment for 30 min once per day, group T3 was given SPS treatment for 20 min once every 2 days, and group T4 was given SPS treatment for 30 min once every 2 days. After 16 days, the range of knee motion, number of myofibroblasts, and positive proportion of transforming growth factor- β1 (TGF- β1) in the joint capsule were detected and compared between groups. Results:The ranges of knee motion in the spontaneous recovery group and the 4 SPS treatment groups were significantly greater than those before treatment ( P<0.05), and the improvements in the range of knee motion in the 4 SPS treatment groups were significantly greater than that in the spontaneous recovery group ( P<0.05). The range of knee motion in group T2 (112.29°±1.89°) was improved the most significantly. The number of myofibroblasts was (23.72±10.75)/HP, which was significantly smaller than that in T3 group [(55.72±33.56)/HP] or in T4 group [(50.72±33.34)/HP] ( P<0.05). The positive proportions of TGF- β1 in the joint capsule in the 4 SPS treatment groups were significantly lower than that in the model group, and the positive proportion of TGF- β1 in the joint capsule in group T2 (0.51%±0.38%) was significantly lower than those in group T3 and T4 ( P<0.05). Conclusions:As SPS treatment can reduce the expression of TGF- β1 in the joint and inhibit the excessive proliferation of myofibroblasts to alleviate the pathological changes in a stiff knee, it has a significant effect on the stiff knee in rats. The SPS treatment for 30 minutes and once per day may lead to the best efficacy.