Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

The testis,as one of the important reproductive organs in men,has two major functions of secreting androgens and producing sperm.Androgen and spermatogenesis are the key factors for the evaluation of the testicular function.The lack of androgen or the decline of spermatogenic function is both a symbolic manifestation and a"product"of testis aging.In order to gain a deeper in-sight into the relationship between testis aging and overall health,this article reviews the relevant literature based on the correlation of androgen deficiency with various systemic diseases and the belief in the impacts of testis aging on the health of the cardiovascular and nervous systems through different channels,the development and progression of metabolic diseases,orthopedic diseases,PCa,kidney disease,peptic ulcer and other diseases.All these suggest that adequate attention should be paid to the studies of male reproductive health and its impact on overall health,so as to provide some new ideas and evidence for clinical diagnosis and treatment of relevant conditions.

Objective To summarize the reasons and management strategies of reoperation after oblique lateral interbody fusion(OLIF),and put forward preventive measures.Methods From October 2015 to December 2019,23 patients who under-went reoperation after OLIF in four spine surgery centers were retrospectively analyzed.There were 9 males and 14 females with an average age of(61.89±8.80)years old ranging from 44 to 81 years old.The index diagnosis was degenerative lumbar intervertebral dics diseases in 3 cases,discogenie low back pain in 1 case,degenerative lumbar spondylolisthesis in 6 cases,lumbar spinal stenosis in 9 cases and degenerative lumbar spinal kyphoscoliosis in 4 cases.Sixteen patients were primarily treated with Stand-alone OLIF procedures and 7 cases were primarily treated with OLIF combined with posterior pedicle screw fixation.There were 17 cases of single fusion segment,2 of 2 fusion segments,4 of 3 fusion segments.All the cases underwent reoperation within 3 months after the initial surgery.The strategies of reoperation included supplementary posterior pedicle screw instrumentation in 16 cases;posterior laminectomy,cage adjustment and neurolysis in 2 cases,arthroplasty and neuroly-sis under endoscope in 1 case,posterior laminectomy and neurolysis in 1 case,pedicle screw adjustment in 1 case,exploration and decompression under percutaneous endoscopic in 1 case,interbody fusion cage and pedicle screw revision in 1 case.Visu-al analogue scale(VAS)and Oswestry disability index(ODI)index were used to evaluate and compare the recovery of low back pain and lumbar function before reoperation and at the last follow-up.During the follow-up process,the phenomenon of fusion cage settlement or re-displacement,as well as the condition of intervertebral fusion,were observed.The changes in in-tervertebral space height before the first operation,after the first operation,before the second operation,3 to 5 days after the second operation,6 months after the second operation,and at the latest follow-up were measured and compared.Results There was no skin necrosis and infection.All patients were followed up from 12 to 48 months with an average of(28.1±7.3)months.Nerve root injury symptoms were relieved within 3 to 6 months.No cage transverse shifting and no dislodgement,loosening or breakage of the instrumentation was observed in any patient during the follow-up period.Though the intervertebral disc height was obviously increased at the first postoperative,there was a rapid loss in the early stage,and still partially lost after reopera-tion.The VAS for back pain recovered from(6.20±1.69)points preoperatively to(1.60±0.71)points postoperatively(P＜0.05).The ODI recovered from(40.60±7.01)％preoperatively to(9.14±2.66)％postoperatively(P＜0.05).Conclusion There is a risk of reoperation due to failure after OLIF surgery.The reasons for reoperation include preoperative bone loss or osteoporosis the initial surgery was performed by Stand-alone,intraoperative endplate injury,significant subsidence of the fusion cage after surgery,postoperative fusion cage displacement,nerve damage,etc.As long as it is discovered in a timely manner and handled properly,further surgery after OLIF surgery can achieve better clinical results,but prevention still needs to be strengthened.

Objective To explore the effects of standardized environmental enrichment(EE)on cognitive function and serum brain-derived neurotrophic factor(BDNF)levels in patients with post-stroke dementia.Methods A prospective study was conducted,including 80 patients with post-stroke dementia admitted to Department of Traditional Chinese Medicine Rehabilitation,910th Hospital of the Joint Logistics Support Force of Chinese PLA from January 2021 to May 2023.Patients were randomly divided into control group,cognitive training(COG)group,aerobic exercise training(AE)group and environmental enrichment(EE)group,with 20 cases in each group.All patients received routine treatment,with COG group receiving additional cognitive function training(30 minutes each time),AE group receiving additional aerobic exercise training(30 minutes each time),and EE group receiving both aerobic exercise and cognitive function training(15 minutes of aerobic exercise training and 15 minutes of cognitive training each time).The training was conducted once a day,5 days a week,for a total of 8 weeks.The patients'mini-mental state scale(MMSE),modified Barthel index(MBI),Hamilton depression scale(HAMD),stroke-specific quality of life(SS-QOL)score and serum levels of BDNF were assessed before treatment,at 4 weeks and 8 weeks of treatment,respectively.Results Before treatment,there were no significant differences in general information,MMSE,MBI,HAMD,SS-QOL scores,and serum levels of BDNF among the four groups(P＞0.05).After 4 and 8 weeks of treatment,the above indicators of the four groups were improved compared with those before treatment,with all differences being statistically significant(P＜0.05).Inter-group comparison showed that after 4 and 8 weeks of treatment,MMSE,MBI,SS-QOL scores,and serum BDNF levels in COG,AE and EE groups were significantly higher than those in control group,and HAMD scores were significantly lower than those in control group(P＜0.05).In addition,MMSE,MBI,SS-QOL scores and BDNF levels of group EE were better than those of other 3 groups,while HAMD scores were lower than those of other 3 groups,with all differences being statistically significant(P＜0.05).There was no significant difference in above outcome indicators between COG group and AE group after 4 and 8 weeks of treatment(P＞0.05).Conclusion Standardized enrichment environment can significantly enhance cognitive function,daily living abilities of post-stroke dementia patients,alleviate depression symptoms,and improve the quality of life,which may be related to the increase in serum BDNF levels.

Purpose::Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.Methods::This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q 1, Q 3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. Results::According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval ( CI): 2.08 -25.42, p = 0.002), 2.85 (95% CI: 1.11 -7.31, p = 0.030), 2.62 (95% CI: 1.12 -6.13, p = 0.027), 2.44 (95% CI: 1.25 -4.76, p = 0.009), and 1.5 (95% CI: 1.10 -2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ 2= 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ 2= 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. Conclusion::Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Objective:To investigate the clinical characteristics and prognosis of primary bone marrow lymphoma.Methods:The clinical data of 6 patients with primary bone marrow lymphoma admitted to Gansu Provincial People's Hospital from February 2011 to March 2023 were collected,and their clinical characteristics and prognosis were retrospectively analyzed and summarized.Results:The median age of 6 patients was 61(52-74)years old.There were 2 males and 4 females.All patients had fever and abnormal blood routine examination.Physical examination and imaging examination showed no lymphadenopathy,no extranodal lesions in lung,gastrointestinal,liver and spleen,skin,etc.After strict exclusion of systemic lymphoma involvement in the bone marrow,the diagnosis was confirmed by bone marrow examination,5 cases were primary myeloid diffuse large B-cell lymphoma and 1 case was primary myeloid peripheral T-cell lymphoma(NOS).1 case abandoned treatment,5 cases received CHOP-like or combined R regimen,including 1 case of autologous stem cell transplantation.4 cases died and 2 case survived.The median OS was 5.5(1-36)months.Conclusion:The prognosis of primary marrow lymphoma is poor,and bone marrow-related examination is an important means of diagnosis.Diffuse large B-cell lymphoma is the most common histomorphologic and immune subtype,and autologous hematopoietic stem cell transplantation may improve the prognosis.

Objective To investigate the diagnostic value of three-dimensional power Doppler ultrasound(3D-PDUS)in fetal growth restriction(FGR).Methods A total of 120 pregnant women in the third trimester who were given birth in Wenzhou People's Hospital from September 2021 to December 2023 were selected as study objects,50 pregnant women with FGR confirmed by clinical and ultrasound were included in case group,and 70 pregnant women with normal fetal development were included in control group.The renal volume and renal blood flow parameters of the fetuses in two groups were compared.The pregnancy outcomes and perinatal conditions of two groups were compared.Receiver operating characteristic(ROC)curve was plotted to calculate the area under the curve(AUC),and the diagnostic efficacy of various blood flow parameters for FGR was evaluated.Results The renal volume/gestational week,renal vascularization index,vascularization flow index and renal artery peak systolic velocity of the fetuses in case group were significantly lower than those in control group,while renal artery peak systolic velocity/end diastolic velocity,pulsation index and resistance index were significantly higher than those in control group(P＜0.05).There was no significant difference in renal flow index between two groups(P＞0.05).ROC curve results showed that the diagnostic efficacy of renal volume/gestational week and renal artery peak systolic velocity were higher,while the diagnostic efficacy of combined application was the highest,with an AUC of 0.89.The rate of low birth weight infants in case group was significantly higher than that in control group,and neonatal Apgar score was significantly lower than that in control group(P＜0.01).Conclusion 3D-PDUS quantitative analysis parameters evaluated renal volume and blood perfusion could predict FGR,which is conducive to early diagnosis of FGR and guide clinical intervention,and effectively reduce adverse pregnancy outcomes.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.