Osteoporotic proximal humeral fracture (OPHF) is one of the common osteoporotic fractures in the aged, with an incidence only lower than vertebral compression fracture, hip fracture, and distal radius fracture. OPHF, secondary to osteoporosis and characterized by poor bone quality, comminuted fracture pattern, slow healing, and severely impaired shoulder joint function, poses a big challenge to the current clinical diagnosis and treatment. In the field of diagnosis, treatment, and rehabilitation of OPHF, traditional Chinese and Western medicine have accumulated rich experience and evidence from evidence-based medicine and achieved favorable outcomes. However, there is still a lack of guidance from a relevant consensus as to how to integrate the advantages of the two medical systems and achieve the integrated diagnosis and treatment. To promote the diagnosis and treatment of OPHF with integrated traditional Chinese and Western medicine, relevant experts from Orthopedic Expert Committee of Geriatric Branch of Chinese Association of Gerontology and Geriatrics, Youth Osteoporosis Group of Orthopedic Branch of Chinese Medical Association, Osteoporosis Group of Orthopedic Surgeon Branch of Chinese Medical Doctor Association, and Osteoporosis Committee of Shanghai Association of Integrated Traditional Chinese and Western Medicine have been organized to formulate Expert consensus on the diagnosis and treatment of osteoporotic proximal humeral fracture with integrated traditional Chinese and Western medicine ( version 2024) by searching related literatures and based on the evidences from evidence-based medicine. This consensus consists of 13 recommendations about the diagnosis, treatment and rehabilitation of OPHF with integrated traditional Chinese medicine and Western medicine, aimed at standardizing, systematizing, and personalizing the diagnosis and treatment of OPHF with integrated traditional Chinse and Western medicine to improve the patients ′ function.

Objective:To explore the expressions of zinc homeostasis-related proteins,G protein-coupled receptor 39(GPR39)and ANO1 mRNA in the sperm of patients with asthenozoospermia(AS),and analyze their correlation with sperm motility.Methods:We collected semen samples from 82 male subjects with PR+NP＜40％,PR＜32％and sperm concentration＞15 × 106/ml(the AS group,n=40)or PR+NP≥40％,PR≥32％and sperm concentration＞15 × 106/ml(the normal control group,n=42).We analyzed the routine semen parameters and measured the zinc content in the seminal plasma using the computer-assisted sperm analysis system,detected the expressions of zinc transporters(ZIP13,ZIP8 and ZNT10),metallothioneins(MT1G,MT1 and MTF),GPR39,and calcium-dependent chloride channel protein(ANO1)in the sperm by real-time quantitative PCR(RT qPCR),examined free zinc distribution in the sperm by laser confocal microscopy,and determined the expressions of GPR39 and MT1 proteins in the sperm by immunofluorescence staining,followed by Spearman rank correlation analysis of their correlation with semen parameters.Results:There was no statistically significant difference in the zinc concentration in the seminal plasma between the AS and normal control groups(P＞0.05).Compared with the controls,the AS patients showed a significantly reduced free zinc level(P＜0.05),relative expressions of MT1G,MTF,ZIP13,GPR39 and ANO1 mRNA(P＜0.05),and that of the GPR39 protein in the AS group(P＜0.05).No statistically significant differences were observed in the relative expression levels of ZIP8,ZNT10 and MT1 mRNA between the two groups(P＞0.05).The relative expression levels of GPR39,ANO1,MT1G and MTF mRNA were positively correlated with sperm motility and the percentage of progressively motile sperm(P＜0.05).Conclusion:The expressions of zinc homeostasis proteins(MT1G,MTF and ZIP13),GPR39 and ANO1 mRNA are downregulated in the sperm of asthenozoospermia pa-tients,and positively correlated with sperm motility.

OBJECTIVE: To investigate the factors related to renal impairment in patients with diabetic kidney disease (DKD) from the perspective of integrated Chinese and Western medicine. METHODS: Totally 492 patients with DKD in 8 Chinese hospitals from October 2017 to July 2019 were included. According to Kidney Disease Improving Global Outcomes (KDIGO) staging guidelines, patients were divided into a chronic kidney disease (CKD) 1-3 group and a CKD 4-5 group. Clinical data were collected, and logistic regression was used to analyze the factors related to different CKD stages in DKD patients. RESULTS: Demographically, male was a factor related to increased CKD staging in patients with DKD (OR=3.100, P=0.002). In clinical characteristics, course of diabetes >60 months (OR=3.562, P=0.010), anemia (OR=4.176, P<0.001), hyperuricemia (OR=3.352, P<0.001), massive albuminuria (OR=4.058, P=0.002), atherosclerosis (OR=2.153, P=0.007) and blood deficiency syndrome (OR=1.945, P=0.020) were factors related to increased CKD staging in patients with DKD. CONCLUSIONS Male, course of diabetes >60 months, anemia, hyperuricemia, massive proteinuria, atherosclerosis, and blood deficiency syndrome might indicate more severe degree of renal function damage in patients with DKD. (Registration No. NCT03865914).
Humans ; Male ; Diabetes Mellitus, Type 2 ; Diabetic Nephropathies ; Hyperuricemia ; Kidney ; Proteinuria ; Renal Insufficiency, Chronic/complications*

As a novel and promising antitumor target, AXL plays an important role in tumor growth, metastasis, immunosuppression and drug resistance of various malignancies, which has attracted extensive research interest in recent years. In this study, by employing the structure-based drug design and bioisosterism strategies, we designed and synthesized in total 54 novel AXL inhibitors featuring a fused-pyrazolone carboxamide scaffold, of which up to 20 compounds exhibited excellent AXL kinase and BaF3/TEL-AXL cell viability inhibitions. Notably, compound 59 showed a desirable AXL kinase inhibitory activity (IC₅₀: 3.5 nmol/L) as well as good kinase selectivity, and it effectively blocked the cellular AXL signaling. In turn, compound 59 could potently inhibit BaF3/TEL-AXL cell viability (IC₅₀: 1.5 nmol/L) and significantly suppress GAS6/AXL-mediated cancer cell invasion, migration and wound healing at the nanomolar level. More importantly, compound 59 oral administration showed good pharmacokinetic profile and in vivo antitumor efficiency, in which we observed significant AXL phosphorylation suppression, and its antitumor efficacy at 20 mg/kg (qd) was comparable to that of BGB324 at 50 mg/kg (bid), the most advanced AXL inhibitor. Taken together, this work provided a valuable lead compound as a potential AXL inhibitor for the further antitumor drug development.

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Histiocytosis, Langerhans-Cell/surgery* ; Humans ; Stomach

Objective: To investigate whether atrial fibrillation (AF) before transcatheter aortic valve implantation (TAVI) will affect the prognosis of patients post TAVI. Methods: This is a single center retrospective study. A total of 115 patients with severe aortic stenosis (AS) who were admitted to General Hospital of Northern Theater Command from May 2016 to November 2020 and successfully received TAVI treatment were included. According to absence or accompaniment of AF pre-TAVI, they were divided into AF group (21 cases) and non-AF group (94 cases). The patients were followed up for postoperative antithrombotic treatment and the occurrence of the net adverse clinical and cerebrovascular events (NACCE) at 12 months post TAVI, including cardiogenic death, readmission to hospital for heart failure, nonfatal myocardial infarction, ischemic stroke and severe bleeding (BARC levels 3-5). Univariate logistic regression was used to analyze the related factors of NACCE. Results: Among the 115 selected patients, age was (73.8±6.9) years, there were 63 males. And 21 cases (18.2%) were diagnosed as AFbefore TAVI. In terms of postoperative antithrombotic therapy, 48.9% (46/94) of the patients in the non-AF group received monotherapy and 47.9% (45/94) received dual antiplatelet therapy. In the AF group, 47.6% (10/21) received anticoagulants and 33.3% (7/21) received dual antiplatelet therapy. The proportion of patients in the AF group taking non-vitamin K antagonist oral anticoagulants (NOAC) was higher than that in the non-AF group (38.1% (8/21) vs. 2.1% (2/94), P<0.001). Patients in both groups were followed up to 12 months after TAVI. During the 12 months follow-up, the incidence of NACCE after TAVI was 14.3% (3/21) in the AF group, which was numerically higher than that in the non-AF group (6.4% (6/94)), but the difference was not statistically significant (P=0.441). The incidence of severe bleeding was significantly higher in the AF group than in the non-AF group (9.5% (2/21) vs. 0, P=0.032). Univariate logistic regression analysis showed that hypertension was associated with the risk of NACCE (OR=8.308, P=0.050), while AF was not associated with the risk of NACCE (P=0.235). Conclusion: The incidence of severe bleeding after TAVI is higher in patients with AF than in patients without AF prior TAVI, and there is a trend of increased risk of NACCE post TAVI in AF patients.
Aged ; Aged, 80 and over ; Anticoagulants ; Aortic Valve ; Aortic Valve Stenosis/surgery* ; Atrial Fibrillation/drug therapy* ; Follow-Up Studies ; Humans ; Male ; Retrospective Studies ; Risk Factors ; Transcatheter Aortic Valve Replacement ; Treatment Outcome

Objective:To identify the classification characteristics and quality of life (QOL) of breast cancer (BC) patients during chemotherapy, so as to provide basis for improving the sleep and QOL of this group.Methods:A cross-sectional investigation was completed among 421 BC patients in 5 tertiary hospitals in Shanghai, Wuhan, Tangshan and Nanning in 1-12 months of 2016 using validated instruments including self-made general information questionnaire, Pittsburgh Sleep Quality Index (PSQI) and Functional Assessment of Cancer Therapy-Breast (FACT-B).Results:Four latent class of patients were identified through latent profile analysis (LPA), named by badly worse sleep quality(SQ) (C1, n=23), medium-SQ with difficulty to fall asleep (C2, n=127), medium-SQ with worse sleeping process (C3, n=30), none sleep disorders (C4, n=241). Total points of SQ among C1-C4 had significant difference ( χ2 value was 309.28, P<0.05). Age, BMI, job status, whether had surgery and course of chemotherapy between classes had statistically significant differences ( χ2 values were 9.57-25.28, all P<0.05). It had significant difference between C2 and C3, C2 and C4, C3 and C1, C3 and C4 on QOL ( χ2 values were 5.96-52.73, all P<0.05). Conclusion:SQ of BC patients during chemotherapy has heterogeneity among population. Different features of SQ of BC patients have different performance on QOL. Health professionals should keep an eye on patients with features of older age, high BMI, in job status, already received surgery and during early-stage chemotherapy, provide personal nursing intervention to improve SQ and QOL.

Anxiety/epidemiology* ; Depression/epidemiology* ; Humans ; Sleep ; Sleep Quality ; Students ; Surveys and Questionnaires ; Universities