Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Rehabilitation is very important for postoperative patients with low back pain. However, the promotion of traditional clinic-based program is limited in developing countries like China, due to the maldistribution of medical resources. Smartphones may be a potential substitute for those who have no access to traditional rehabilitation. Objective: The aim of this study was to examine the efficacy of a smartphone based rehabilitation system in patients accepted lumbar spinal operation. Methods: Postoperative patients with low back pain were recruited and randomized to rehabilitation treatment provide by smartphone based e-health program (EH) or usual care treatment (UC) as control group. Primary outcomes were function and pain status assessed by Oswestry Disability Index (ODI) and visual analogue scale (VAS). Secondary outcomes were general mental health and life status (Likert scales, EQ-5D and SF-36). All the patients were assessed pre-operatively and then at 3, 6, 12 and 24 months post-operatively. Results: A total of 168 of the 863 eligible patients were included and randomized in this study. The analysis showed that the improvement of primary outcomes in EH group was superior to UC group at 24 months postoperatively (mean difference [SD]: ODI 7.02 [3.10], P<0.05; VAS 7.59 [3.42], P<0.05). No significant difference of primary outcomes was found at other time points. Subgroup analysis showed that the improvements of the primary outcomes were more significant in those completed 6 or more training each week throughout the trial (the highest compliance group, HC), compared with the UC group at 6 months (mean difference [SD]: ODI 17.94 [5.24], P<0.05; VAS 19.56 [5.27], P<0.05), 12 months (mean difference [SD]: ODI 13.39 [5.32], P<0.05; VAS 14.35 [5.23], P<0.05) and 24 months (mean difference[SD]: ODI 18.80 [5.22], P<0.05; VAS 21.56 [5.28], P<0.05). Conclusions: This research demonstrated that e-health, a smartphone based tele-rehabilitation system is effective in self-managed rehabilitation for postoperative patient with low back pain. The effectiveness of e-health was more evident in participants with higher compliance. Future research should focus on improving patient’s compliance. Clinical Trial: Chinese clinical trial registry, ChiCTR-TRC-13003314, http://www.chictr.org.cn/showproj.aspx?proj=6245

Original Article BAG3 promotes autophagy and suppresses NLRP3 inflammasome activation in Parkinson’s disease Zhong-Ming Ying, Qian-Kun Lv, Xiao-Yu Yao, An-Qi Dong, Ya-Ping Yang, Yu-Lan Cao, Fen Wang, Ai-Ping Gong, Chun-Feng Liu

Objectives: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. Methods: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. Results: The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874–1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817–0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone (P<0.05). Conclusions: The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians’ choices about whether performing biopsies in breast cancer diagnosis.

Sacituzumab govitecan (SG), a novel antibody-drug conjugate (ADC), shows promise in the treatment of breast cancer (BC); however, drug resistance limits its clinical application. Matrix metalloproteinase 1 (MMP1), which is overexpressed in many tumor types, plays a key role in tumor metastasis and drug resistance. The involvement of MMP1 in SG resistance in metastatic hormone receptor-positive (HR + ) BC has not been previously reported. In this study, we employed various in vitro and in vivo approaches to investigate the role of MMP1 in SG resistance in BC. MMP1 expression was manipulated in different BC cell lines through lentiviral transfection and small interfering RNA techniques. Key methodologies included Western blot, quantitative reverse transcription PCR, and RNA sequencing to assess marker expression and identify differentially expressed genes. Functional assays were conducted to evaluate cell viability, proliferation, invasion, and migration. In vivo, a cell-derived xenograft model in nude mice was utilized to assess tumor growth and drug response. Bioinformatics analyses further explored MMP1 expression and its clinical relevance across different cancer types. Our findings indicate that MMP1 is overexpressed by approximately 30-fold in HR + BC tissues and is associated with poorer prognosis among HR + BC patients. Furthermore, our analysis reveals that HR + BC with high MMP1 expression displays resistance to SG, supporting the hypothesis that MMP1 plays a key role in regulating ADC resistance. Mechanistic studies demonstrate that MMP1 can activate the NF-κB pathway, which subsequently influences the epithelial–mesenchymal transition, thereby contributing to SG resistance. Ultimately, our research underscores the potential of MMP1 as a therapeutic target and biomarker, facilitating personalized treatment strategies that could enhance patient outcomes in BC therapy.

Patients with hormone receptor (HR)-positive tumors breast cancer usually experience a relatively low pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Here, we derived a 10-microRNA risk score (10-miRNA RS)-based model with better performance in the prediction of pCR and validated its relation with the disease-free survival (DFS) in 755 HR-positive breast cancer patients (273, 265, and 217 in the training, internal, and external validation sets, respectively). This model, presented as a nomogram, included four parameters: the 10-miRNA RS found in our previous study, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and volume transfer constant (K^trans). Favorable calibration and discrimination of 10-miRNA RS-based model with areas under the curve (AUC) of 0.865, 0.811, and 0.804 were shown in the training, internal, and external validation sets, respectively. Patients who have higher nomogram score (>92.2) with NAC treatment would have longer DFS (hazard ratio=0.57; 95%CI: 0.39–0.83; P=0.004). In summary, our data showed the 10-miRNA RS-based model could precisely identify more patients who can attain pCR to NAC, which may help clinicians formulate the personalized initial treatment strategy and consequently achieves better clinical prognosis for patients with HR-positive breast cancer.