Real-world outcomes of chemotherapy plus immune checkpoint inhibitors versus chemotherapy alone in advanced, unresectable, and recurrent intrahepatic cholangiocarcinoma.

Background: There are limited treatment options available to improve the prognosis of patients with advanced or metastatic cholangiocarcinoma particularly intrahepatic cholangiocarcinoma (iCCA). This study aimed to evaluate the efficacy and safety of combining chemotherapy plus anti-PD-1/L1 drugs compared to chemotherapy alone in advanced, unresectable, and recurrent intrahepatic cholangiocarcinoma patients. Methods: Patients with advanced, unresectable, or recurrent iCCA who received chemotherapy combined with PD-1/PD-L1 inhibitors or chemotherapy alone were retrospectively screened and analyzed. The primary outcomes were overall survival (OS) and progression-free survival (PFS). The secondary outcomes were overall response rate (ORR), disease control rate (DCR), and safety. Results: 81 eligible patients were included in the study (chemotherapy plus anti-PD-1/L1 group n=51, and chemotherapy-alone group n=30). The median OS was 11 months for the chemotherapy plus anti-PD-1/L1 group, significantly longer than the 8 months in the chemotherapy-alone group, with a hazard ratio (HR) of 0.53 (95% CI 0.30-0.94, P = 0.008). The median PFS of 7 months in the chemotherapy plus anti-PD-1/L1 group was significantly longer than the 4 months in the chemotherapy-alone group, with HR of 0.48 (95% CI 0.27-0.87); P = 0.002). Similarly, the combined therapy group showed a higher ORR (29.4%) and DCR (78.4%) compared to 13.3% and 73.3% in the chemotherapy-alone group, respectively. More grade 3-4 treatment-related adverse effects were recorded in the chemotherapy plus anti-PD-1/L1 group (66.7%) compared to the chemotherapy-alone group (23.3%), however, they were manageable and tolerable. Conclusion: Chemotherapy plus anti-PD-1/L1 represents a more effective and tolerable treatment option for advanced, unresectable, and recurrent iCCA patients compared to chemotherapy alone.

ß, ß-Dimethylacrylshikonin potentiates paclitaxel activity, suppresses immune evasion and triple negative breast cancer progression via STAT3Y705 phosphorylation inhibition based on network pharmacology and transcriptomics analysis.

BACKGOUND: Triple negative breast cancer (TNBC) is an extremely aggressive and rapidly progressing cancer, wherein existing therapies provide little benefit to patients. ß, ß-Dimethylacrylshikonin (DMAS), an active naphthoquinone derived from comfrey root, has potent anticancer activity. However, the antitumor function of DMAS against TNBC remains to be proved. PURPOSE: Explore effects of DMAS on TNBC and clarify the mechanism. STUDY DESIGN: Network pharmacology, transcriptomics and various cell functional experiments were applied to TNBC cells to explore the effects of DMAS on TNBC. The conclusions were further validated in xenograft animal models. METHODS: MTT, EdU, transwell, scratch tests, flow cytometry, immunofluorescence, and immunoblot were utilized to assess the activity of DMAS on three TNBC cell lines. The anti-TNBC mechanism of DMAS was clarified by overexpression and knockdown of STAT3 in BT-549 cells. In vivo efficacy of DMAS was analysed using a xenograft mouse model. RESULTS: In vitro analysis revealed that DMAS inhibited the G2/M phase transition and suppressed TNBC proliferation. Additionally, DMAS triggered mitochondrial-dependent apoptosis and reduced cell migration by antagonizing epithelial-mesenchymal transition. Mechanistically, DMAS exerted its antitumour effects by inhibiting STAT3Y705 phosphorylation. STAT3 overexpression abolished the inhibitory effect of DMAS. Further studies showed that treatment with DMAS inhibited TNBC growth in a xenograft model. Notably, DMAS potentiated the sensitivity of TNBC to paclitaxel and inhibited immune evasion by downregulating the immune checkpoint PD-L1. CONCLUSIONS: For the first time, our study revealed that DMAS potentiates paclitaxel activity, suppresses immune evasion and TNBC progression by inhibiting STAT3 pathway. It has the potential as a promising agent for TNBC.

The Effects of Exercise for Cognitive Function in Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Physical exercise can slow down the decline of the cognitive function of the older adults, yet the review evidence is not conclusive. The purpose of this study was to compare the effects of aerobic and resistance training on cognitive ability. METHODS: A computerized literature search was carried out using PubMed, Cochrane Library, Embase SCOPUS, Web of Science, CNKI (China National Knowledge Infrastructure), Wanfang, and VIP database to identify relevant articles from inception through to 1 October 2022. Based on a preliminary search of the database and the references cited, 10,338 records were identified. For the measured values of the research results, the standardized mean difference (SMD) and 95% confidence interval (CI) were used to synthesize the effect size. RESULTS: Finally, 10 studies were included in this meta-analysis. Since the outcome indicators of each literature are different in evaluating the old cognitive ability, a subgroup analysis was performed on the included literature. The study of results suggests that aerobic or resistance training interventions significantly improved cognitive ability in older adults compared with control interventions with the Mini-Mental State Examination (MD 2.76; 95% CI 2.52 to 3.00), the Montreal Cognitive Assessment (MD 2.64; 95% CI 2.33 to 2.94), the Wechsler Adult Intelligence Scale (MD 2.86; 95% CI 2.25 to 3.47), the Wechsler Memory Scale (MD 9.33; 95% CI 7.12 to 11.54), the Wisconsin Card Sorting Test (MD 5.31; 95% CI 1.20 to 9.43), the Trail Making Tests (MD -8.94; 95% CI -9.81 to -8.07), and the Stroop Color and Word Test (MD -5.20; 95% CI -7.89 to -2.51). CONCLUSION: Physical exercise improved the cognitive function of the older adults in all mental states. To improve cognitive ability, this meta-analysis recommended that patients perform at least moderate-intensity aerobic exercise and resistance exercise on as many days as possible in the week to comply with current exercise guidelines while providing evidence for clinicians.

Revisiting [Formula: see text]-wavelet compressed-sensing MRI in the era of deep learning.

Following their success in numerous imaging and computer vision applications, deep-learning (DL) techniques have emerged as one of the most prominent strategies for accelerated MRI reconstruction. These methods have been shown to outperform conventional regularized methods based on compressed sensing (CS). However, in most comparisons, CS is implemented with two or three hand-tuned parameters, while DL methods enjoy a plethora of advanced data science tools. In this work, we revisit [Formula: see text]-wavelet CS reconstruction using these modern tools. Using ideas such as algorithm unrolling and advanced optimization methods over large databases that DL algorithms utilize, along with conventional insights from wavelet representations and CS theory, we show that [Formula: see text]-wavelet CS can be fine-tuned to a level close to DL reconstruction for accelerated MRI. The optimized [Formula: see text]-wavelet CS method uses only 128 parameters compared to >500,000 for DL, employs a convex reconstruction at inference time, and performs within <1% of a DL approach that has been used in multiple studies in terms of quantitative quality metrics.

Risk Factors of Flatfoot in Children: A Systematic Review and Meta-Analysis.

Background: This study aimed to explore the risk factors for flatfoot in children and adolescents to provide a reference basis for studying foot growth and development in children and adolescents. Methods: We examined the cross-sectional research literature regarding flatfoot in children and adolescents published in the past 20 years, from 2001 to 2021, in four electronic databases: PubMed, Web of Science, EBSCO, and Cochrane Library. Two researchers independently searched the literature according to the inclusion and exclusion criteria and evaluated the literature quality of the selected research; from this, a total of 20 articles were included in our review. After the relevant data were extracted, the data were reviewed using Manager 5.4 software (The Cochrane Collaboration, Copenhagen, Denmark), and the detection rate and risk factors for flatfoot in children were analyzed. Results: In total, 3602 children with flatfoot from 15 studies were included in the analysis. The meta-analysis results showed that being male (OR = 1.33, 95% CI: 1.09, 1.62, p = 0.005), being aged <9 years (age <6, OR = 3.11, 95% CI: 2.47, 3.90, p < 0.001; age 6−9 years, OR = 0.54, 95% CI: 0.41, 0.70, p < 0.001), joint relaxation (OR = 4.82, 95% CI: 1.19, 19.41, p = 0.03), wearing sports shoes (OR = 2.97, 95% CI: 1.46, 6.03, p = 0.003), being a child living in an urban environment (OR = 2.10, 95% CI: 1.66, 2.64, p < 0.001) and doing less exercise (OR = 0.25, 95% CI: 0.08, 0.80, p = 0.02) were risk factors for the detection of flatfoot. Conclusion: In summary, the detection rate of flatfoot in children in the past 20 years was found to be 25% through a meta-analysis. Among the children included, boys were more prone to flatfoot than girls, and the proportion of flatfoot decreased with age.

Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.

Self-supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self-supervised learning methods for physics-guided reconstruction networks split acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network, while the other is used to define the training loss. In this study, we propose an improved self-supervised learning strategy that more efficiently uses the acquired data to train a physics-guided reconstruction network without a database of fully sampled data. The proposed multi-mask self-supervised learning via data undersampling (SSDU) applies a holdout masking operation on the acquired measurements to split them into multiple pairs of disjoint sets for each training sample, while using one of these pairs for DC units and the other for defining loss, thereby more efficiently using the undersampled data. Multi-mask SSDU is applied on fully sampled 3D knee and prospectively undersampled 3D brain MRI datasets, for various acceleration rates and patterns, and compared with the parallel imaging method, CG-SENSE, and single-mask SSDU DL-MRI, as well as supervised DL-MRI when fully sampled data are available. The results on knee MRI show that the proposed multi-mask SSDU outperforms SSDU and performs as well as supervised DL-MRI. A clinical reader study further ranks the multi-mask SSDU higher than supervised DL-MRI in terms of signal-to-noise ratio and aliasing artifacts. Results on brain MRI show that multi-mask SSDU achieves better reconstruction quality compared with SSDU. The reader study demonstrates that multi-mask SSDU at R = 8 significantly improves reconstruction compared with single-mask SSDU at R = 8, as well as CG-SENSE at R = 2.

Research Hotspots of the Rehabilitation Medicine Use of sEMG in Recent 12 Years: A Bibliometric Analysis.

Objective: Surface electromyography (sEMG) has been widely applied to rehabilitation medicine. However, the bibliometric analysis of the rehabilitation medicine use of sEMG is vastly unknown. Therefore, this research aimed to investigate the current trends of the rehabilitation medicine use of sEMG in the recent 12 years by using CiteSpace (5.8). Methods: Literature relating to rehabilitation medicine use of sEMG from 2010 to 2021 was retrieved from the Web of Science. CiteSpace analyzed country, institution, cited journals, authors, cited references and keywords. An analysis of counts and centrality was used to reveal publication outputs, countries, institutions, core journals, active authors, foundation references, hot topics and frontiers. Results: A total of 1949 publications were retrieved from 2010 to 2021. The total number of publications continually increased over the past 12 years, and the most active countries, institutions, journals and authors in rehabilitation medicine use of sEMG were identified. The most productive country and institution in this field were America (484) and the University of Sao Paulo (36). Andersen LL (28) was the most prolific author, and Dario Farina ranked first among the cited authors. Besides, there were three main frontiers in keywords for sEMG research, including "activation", "exercise", and "strength". Conclusion: The findings from this bibliometric study provide the current status and trends in clinical research of rehabilitation medicine use of sEMG over the past ten years, which may help researchers identify hot topics and explore new directions for future research in this field.

A Novel Nomogram Model to Identify Candidates and Predict the Possibility of Benefit From Primary Tumor Resection Among Female Patients With Metastatic Infiltrating Duct Carcinoma of the Breast: A Large Cohort Study.

BACKGROUND: The impact of primary site surgery on survival remains controversial in female patients with stage IV breast cancer. The purpose of this study was to investigate the role of primary tumor surgery in patients with stage IV breast cancer and concurrently develop a nomogram to identify which patients will benefit from surgery. METHODS: We retrospectively searched the SEER database for female patients newly diagnosed with stage IV breast infiltrating duct carcinoma (BIDC) between 2010 and 2015 and then divided them into surgery and non-surgery groups. The propensity score matching (PSM) method was implemented to eliminate the bias, and Kaplan-Meier survival analysis was generated to compare the overall survival (OS) and cancer-specific survival (CSS) between the two groups. After PSM, Cox regression analyses were performed to determine the independent protective value of primary tumor surgery, while logistic regression analyses were utilized to uncover significant predictors of surgical benefit and establish a screening nomogram for female patients with stage IV BIDC. Nomogram performance was evaluated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). RESULT: 5,475 patients with stage IV BIDC were included in this study, and 2,375 patients (43.38%) received primary tumor surgery. After PSM, the median CSS was 53 months (95% CI: 46.84-59.16) in the surgery group compared with only 33 months (95% CI: 30.05-35.95) in the non-surgery group. We further found that primary tumor surgery was an independent protective factor for patients with stage IV BIDC. The independent factors affecting the benefit of locoregional surgery in patients with stage IV BIDC included histological grade, T stage, molecular subtype, lung metastasis, liver metastasis, brain metastasis, and marital status. The AUC of the nomogram was 0.785 in the training set and 0.761 in the testing set. The calibration curves and DCA confirmed that the nomogram could precisely predict the possibility of benefit from primary tumor resection. CONCLUSION: Our study suggested that primary tumor surgery improved the prognosis of female patients with stage IV BIDC and developed a nomogram to quantify the probability of surgical benefit to help identify surgical candidates clinically.

Compressed Sensing MRI with ℓ1-Wavelet Reconstruction Revisited Using Modern Data Science Tools.

Deep learning (DL) has emerged as a powerful tool for improving the reconstruction quality of accelerated MRI. These methods usually show enhanced performance compared to conventional methods, such as compressed sensing (CS) and parallel imaging. However, in most scenarios, CS is implemented with two or three empirically-tuned hyperparameters, while a plethora of advanced data science tools are used in DL. In this work, we revisit ℓ1 -wavelet CS for accelerated MRI using modern data science tools. By using tools like algorithm unrolling and end-to-end training with stochastic gradient descent over large databases that DL algorithms utilize, and combining these with conventional concepts like wavelet sub-band processing and reweighted ℓ1 minimization, we show that ℓ1-wavelet CS can be fine-tuned to a level comparable to DL methods. While DL uses hundreds of thousands of parameters, the proposed optimized ℓ1-wavelet CS with sub-band training and reweighting uses only 128 parameters, and employs a fully-explainable convex reconstruction model.