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Abstract Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer. To enhance feasibility, MRP integrates cross-modal knowledge mining and temporal information embedding strategy to handle missing modalities and remain less affected by different NAT settings. We validated MRP through multi-center studies and multinational reader studies. MRP exhibited comparable robustness to breast radiologists, outperforming humans in predicting pathological complete response in the Pre-NAT phase (ΔAUROC 14% and 10% on in-house and external datasets, respectively). Furthermore, we assessed MRP’s clinical utility impact on treatment decision-making. MRP may have profound implications for enrolment into NAT trials and determining surgery extensiveness.

Abstract Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model’s representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks.

Abstract Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRDTR, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRDTR comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution. The performance of DeepMRDTR was evaluated for abnormality identification and disease diagnosis on data from two different institutions: one was an internal testing dataset from the same institution as the training data and the second was collected from an external institution to evaluate the model generalizability and robustness to an unrelated population dataset. In such a difficult multi-class diagnosis task, our system achieved the average area under the receiver operating characteristic curve (AUC) of 0.856 (95% confidence interval (CI):0.843–0.868) and 0.841 (95%CI:0.832–0.887) for abnormality identification, and 0.900 (95%CI:0.872–0.958) and 0.866 (95%CI:0.832–0.887) for major respiratory diseases’ diagnosis on CT and CXR datasets, respectively. Furthermore, to achieve a clinically actionable diagnosis, we deployed a preliminary version of DeepMRDTR into the clinical workflow, which was performed on par with senior experts in disease diagnosis, with an AUC of 0.890 and a Cohen’s k of 0.746–0.877 at a reasonable timescale; these findings demonstrate the potential to accelerate the medical workflow to facilitate early diagnosis as a triage tool for respiratory diseases which supports improved clinical diagnoses and decision-making.

Abstract Background Many patients with neurological disorders experience chronic fatigue, but the neural mechanisms involved are unclear. Objective Here we investigated whether the brain structural and functional connectivity alterations were involved in fatigue related to neuromyelitis optica spectrum disorder (NMOSD). Methods This prospective pilot study used structural and resting-state functional brain magnetic resonance imaging to compare total cortical thickness, cortical surface area, deep gray matter volume and functional connectivity (FC) between 33 patients with NMOSD and 20 healthy controls (HCs). Patients were subgrouped as low fatigue (LF) and high fatigue (HF). Results HF patients scored higher on the Hamilton Anxiety Rating Scale and Hamilton Rating Scale for Depression than LF patients and HCs. The two patient subgroups and HC group did not differ significantly in cortical thickness, cortical surface area and volumes of the bilateral caudate nucleus, bilateral putamen, bilateral amygdala, bilateral hippocampus, bilateral thalamus proper or right nucleus accumbens (p > 0.05). However, after correcting for age, sex, years of education, anxiety and depression, HF patients showed larger left pallidum than HCs (0.1573 ± 0.0214 vs 0.1372 ± 0.0145, p = 0.009). Meanwhile, both LF patients (0.0377 ± 0.0052 vs 0.0417 ± 0.0052, p = 0.009) and HF patients (0.0361 ± 0.0071 vs 0.0417 ± 0.0052, p = 0.013) showed smaller left nucleus accumbens than HCs.. Compared with LF patients, HF patients showed significantly decreased FC between the left pallidum and bilateral cerebellar posterior lobes. Conclusions This was the first evidence linking structural and functional alterations in the brain to fatigue in NMOSD, and in the future, long term follow-up was necessary.

[INLINE:1] An enriched environment is used as a behavioral intervention therapy that applies sensory, motor, and social stimulation, and has been used in basic and clinical research of various neurological diseases. In this study, we established mouse models of photothrombotic stroke and, 24 hours later, raised them in a standard, enriched, or isolated environment for 4 weeks. Compared with the mice raised in a standard environment, the cognitive function of mice raised in an enriched environment was better and the pathological damage in the hippocampal CA1 region was remarkably alleviated. Furthermore, protein expression levels of tumor necrosis factor receptor-associated factor 6, nuclear factor κB p65, interleukin-6, and tumor necrosis factor α, and the mRNA expression level of tumor necrosis factor receptor-associated factor 6 were greatly lower, while the expression level of miR-146a-5p was higher. Compared with the mice raised in a standard environment, changes in these indices in mice raised in an isolated environment were opposite to mice raised in an enriched environment. These findings suggest that different living environments affect the hippocampal inflammatory response and cognitive function in a mouse model of stroke. An enriched environment can improve cognitive function following stroke through up-regulation of miR-146a-5p expression and a reduction in the inflammatory response.

Maximum pooling, average pooling, and strided convolution are three widely adopted down-sampling approaches in deep learning based 3D medical image analysis. However, these methods have their own pros and cons. Maximum pooling and strided convolution are advantageous in capturing the discriminative features but often lead to the aliasing problem. In comparison, average pooling anti-aliases the representations but produces less discriminative representations. To address such shortcoming, anti-aliased maximum pooling (MaxBlurPool) uses low-pass filters to mitigate the aliasing effect. However, these filters are designed to be fixed, making it difficult to adapt to various spatial positions. In this paper, we propose Position-aware Anti-aliaSing filterS (PASS) to learn spatially adaptive low-pass filters. Compared to maximum pooling, PASS integrates a one-layer local attention module, whose computational cost is minimal. Thus, PASS can be incorporated into existing network architecture with minor efforts. In comparison to previous anti-aliased counterparts, PASS brings consistent and clear performance gains on brain tumor segmentation, pulmonary nodule detection, and cerebral hemorrhage detection. Besides, PASS also greatly improves the model robustness under adversarial attack.

Abstract Background Studies on risk factors for carbapenem-resistant Klebsiella pneumoniae (CRKP) infection have provided inconsistent results, partly due to the choice of the control group. We conducted a systematic review and meta-analysis to assess the risk factors for CRKP infection by comparing CRKP-infected patients with two types of controls: patients infected with carbapenem-susceptible Klebsiella pneumoniae (comparison 1) or patients not infected with CRKP (comparison 2). Methods Data on potentially relevant risk factors for CRKP infection were extracted from studies indexed in PubMed, EMBASE, Web of Science or EBSCO databases from January 1996 to April 2019, and meta-analyzed based on the outcomes for each type of comparison. Results The meta-analysis included 18 studies for comparison 1 and 14 studies for comparison 2. The following eight risk factors were common to both comparisons: admission to intensive care unit (ICU; odds ratio, ORcomparison 1 = 3.20, ORcomparison 2 = 4.44), central venous catheter use (2.62, 3.85), mechanical ventilation (2.70, 4.78), tracheostomy (2.11, 8.48), urinary catheter use (1.99, 0.27), prior use of antibiotic (6.07, 1.61), exposure to carbapenems (4.16, 3.84) and exposure to aminoglycosides (1.85, 1.80). Another 10 risk factors were unique to comparison 1: longer length of hospital stay (OR = 15.28); prior hospitalization (within the previous 6 months) (OR = 1.91); renal dysfunction (OR = 2.17); neurological disorders (OR = 1.52); nasogastric tube use (OR = 2.62); dialysis (OR = 3.56); and exposure to quinolones (OR = 2.11), fluoroquinolones (OR = 2.03), glycopeptides (OR = 3.70) and vancomycin (OR = 2.82). Conclusions Eighteen factors may increase the risk of carbapenem resistance in K. pneumoniae infection; eight factors may be associated with both K. pneumoniae infections in general and CRKP in particular. The eight shared factors are likely to be ‘true’ risk factors for CRKP infection. Evaluation of risk factors in different situations may be helpful for empirical treatment and prevention of CRKP infections.

The mechanisms of age-associated memory impairment may be associated with glutamate receptor function and chromatin modification. To observe the effect of an enriched environment on the cognitive function of mice with age-associated memory impairment, 3-month-old C57BL/6 male mice (“young” mice) were raised in a standard environment, while 24-month-old C57BL/6 male mice with memory impairment (“age-associated memory impairment” mice) were raised in either a standard environment or an enriched environment. The enriched environment included a variety of stimuli involving movement and sensation. A water maze test was then used to measure cognitive function in the mice. Furthermore, quantitative real-time polymerase chain reaction and western blot assays were used to detect right hippocampal GluN2B mRNA as well as protein expression of GluN2B and CREB binding protein in all mice. In addition, chromatin immunoprecipitation was used to measure the extent of histone acetylation of the hippocampal GluN2B gene promoters. Compared with the young mice, the water maze performance of age-associated memory impairment mice in the standard environment was significantly decreased. In addition, there were significantly lower levels of total histone acetylation and expression of CREB binding protein in the hippocampus of age-associated memory impairment mice in the standard environment compared with the young mice. There were also significantly lower levels of histone acetylation, protein expression, and mRNA expression of GluN2B in the hippocampus of these mice. In contrast, in the age-associated memory impairment mice with the enriched environment intervention, the water maze performance and molecular biological indexes were significantly improved. These data confirm that an enriched environment can improve cognitive dysfunction in age-associated memory impairment mice, and suggest that the mechanisms may be related to the increased expression of CREB binding protein and the increased degree of total histone acetylation in the hippocampus of age-associated memory impairment mice, which may cause the increase of histone acetylation of GluN2B gene promoter and the enhancement of GluN2B mRNA transcription and protein expression in hippocampus. The animal experiment was approved by the Animal Ethics Committee of Yangzhou University, China (approval No. 20170312001) in March 2017.

Variants at the GTF2I repeat domain containing 1 (GTF2IRD1)–GTF2I locus are associated with primary Sjögren’s syndrome, systemic lupus erythematosus, and rheumatoid arthritis. Numerous studies have indicated that this susceptibility locus is shared by multiple autoimmune diseases. However, until now there were no studies of the correlation between GTF2IRD1–GTF2I polymorphisms and neuromyelitis optica spectrum disorders (NMOSD). This case control study assessed this association by recruiting 305 participants with neuromyelitis optica spectrum disorders and 487 healthy controls at the Department of Neurology, from September 2014 to April 2017. Peripheral blood was collected, DNA extracteds and the genetic association between GTF2IRD1–GTF2I polymorphisms and neuromyelitis optica spectrum disorders in the Chinese Han population was analyzed by genotyping. We found that the T allele of rs117026326 was associated with an increased risk of neuromyelitis optica spectrum disorders (odds ratio (OR) = 1.364, 95% confidence interval (CI) 1.019–1.828; P = 0.037). This association persisted after stratification analysis for aquaporin-4 immunoglobulin G antibodies (AQP4-IgG) positivity (OR = 1.397, 95% CI 1.021–1.912; P = 0.036) and stratification according to coexisting autoimmune diseases (OR = 1.446, 95% CI 1.072–1.952; P = 0.015). Furthermore, the CC genotype of rs73366469 was frequent in AQP4-IgG-seropositive patients (OR = 3.15, 95% CI 1.183–8.393, P = 0.022). In conclusion, the T allele of rs117026326 was associated with susceptibility to neuromyelitis optica spectrum disorders, and the CC genotype of rs73366469 conferred susceptibility to AQP4-IgG-seropositivity in Han Chinese patients. The protocol was approved by the Ethics Committee of West China Hospital of Sichuan University, China (approval number: 2016-31) on March 2, 2016.

Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder–decoder architecture for spatial–temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial–temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial–temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results.

Introduction and aim. Studies suggest that entecavir and lamivudine are useful as prophylactics against hepatitis B virus (HBV) reactivation in patients undergoing chemotherapy or immunosuppressive therapy, but which drug is more effective is unclear. Here we meta-analyzed available evidence on relative efficacy of prophylactic entecavir or lamivudine therapy in patients with chronic or resolved hepatitis B infection who were undergoing chemotherapy or immunosuppressive therapy.Material and methods. Two reviewers searched PubMed, EMBASE and Google Scholar as well as reference lists in relevant articles to find studies published between January 2005 and May 2015 that met inclusion and exclusion criteria. Data on HBV reactivation, HBV-related hepatitis and all-cause mortality were extracted from the studies and meta-analyzed.Results. A total of eight studies involving 593 patients were included in the meta-analysis, which was performed using a fixed-effect model since no significant heterogeneity was found. Entecavir was associated with significantly lower risk of HBV reactivation than lamivudine (RR 0.29, 95% CI 0.17 to 0.52) as well as lower risk of HBV-related hepatitis (RR 0.11, 95% CI 0.03 to 0.40). The two drugs were associated with similar risk of all-cause mortality (RR 1.12, 95% CI 0.54 to 2.35). Egger’s test suggested no significant publication bias in the meta-analysis.Conclusions. The available evidence suggests that entecavir is more effective than lamivudine for preventing HBV reactivation and HBV-related hepatitis in patients with chronic or resolved HBV infection who are undergoing chemotherapy or immunosuppressive therapy.

Objective To explore the curative effect and parameter settings of vagus nerve stimulation (VNS) therapy in refractory epilepsy. Methods VNS was performed in 71 patients with drug-resistant epilepsy, and McHugh classification was used to evaluate the curative effect of VNS. Programmable pulse parameters included output current, frequency, pulse width, and On/Off times. The optimum settings for each patient should balance the goals of maximizing efficacy, minimizing side effects, and preserving battery life. Results After operation, all patients were followed up for 3-56 months (median 22 months), and experienced a significantly reduced seizure frequency compared with before operation [18 (3, 92) vs 30 (5, 145) times/month; Z = 826.500, P = 0.000]. In these patients, 20 cases (28.17% ) had < 50% reduction of seizure frequency, and 51 cases (71.83% ) had ≥ 50% reduction of seizure frequency, including 27 cases (38.03%) with ≥ 80% reduction and 14 cases (19.72%) with seizure free. Seizure termination or seizure diminution was reported in 8 patients (11.27% ) after using magnet. Conclusions VNS is a safe and effective therapy in medically refractory epilepsy. Optimized output current is generally 1.00-1.75 mA, however, 0.75 mA is also an efficient current. Besides, parameter settings should be considered to reach the maximizing efficacy. DOI: 10.3969/j.issn.1672-6731.2015.09.008

Objective. To discuss the effects of genistein on the metabolism of celecoxib in vitro and in vivo. Method. In vitro, the effects of genistein on the metabolism of celecoxib were studied using rat and human liver microsomes. In vivo, pharmacokinetics of celecoxib was evaluated in rats with or without genistein. Fifteen Sprague-Dawley (SD) rats were randomized into three groups: celecoxib (A group), celecoxib and 50 mg/kg genistein (B group), and celecoxib and 100 mg/kg genistein (C group). Single dose of 33.3 mg/kg celecoxib was orally administered 30 min after genistein ig. At 0.5, 1, 2, 3, 4, 6, 8, 10, 12, and 24 h after celecoxib administration, 300–400 µl blood samples were collected and the concentration of celecoxib was analyzed by ultrahigh-performance liquid chromatography-tandem mass spectrometry system. Result. Genistein showed notable inhibitory effects on three microsomes. It affected pharmacokinetics of celecoxib in vivo experiments. Genistein had dramatically ability to suppress CYP2C9∗1 and ∗3. After pretreatment with genistein, AUC and Cmax of the C group were higher than B group. CLz/F of C group was lower than the B group. Conclusion. Genistein inhibits the conversion of celecoxib in vitro and in vivo. So, the dosage of celecoxib should be adjusted if it was used associated with genistein.

11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) and type 2 (11β-HSD2) are expressed in rat testis, where they regulate the local concentrations of glucocorticoids. Here, we investigated the expression and localization of 11β-HSD in rat testis during postnatal development, and the regulation of these genes by luteinizing hormone (LH) and androgens. mRNA and protein levels were analyzed by quantitative real-time-polymerase chain reaction and western blotting, respectively, in testes collected from rats at postnatal day (PND) 7, 14, 21, 35, and 90, and from rats treated with LH, 7α-methyl-19-nortestosterone (MENT) and testosterone at PND 21 and PND 90. Immunohistochemical staining was used to identify the localization of the 11β-HSD in rat testis at PND 7, 14, and 90. We found that 11β-HSD1 expression was restricted to the interstitial areas, and that its levels increased during rat testis development. In contrast, whereas 11β-HSD2 was expressed in both the interstitial areas and seminiferous tubules at PND 7, it was present only in the interstitial areas at PND 90, and its levels declined during testicular development. Moreover, 11β-HSD1 mRNA was induced by LH in both the PND 21 and 90 testes and by MENT at PND 21, whereas 11β-HSD2 mRNA was induced by testosterone and MENT in the PND 21 testis and by LH in the PND 90 testis. In conclusion, our study indicates that the 11β-HSD1 and 11β-HSD2 genes have distinct patterns of spatiotemporal expression and hormonal regulation during postnatal development of the rat testis.

Objective To assess the efficacy and safety of interferon-beta (IFN-β) as monotherapy versus placebo for patients with relapsing-remitting multiple sclerosis (RRMS). Methods We searched Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE, CINAHL, LILACS, PEDRO, China Biology Medicine Disc (CBMDisc), as well as clinical trial registries and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP, retrieval deadline: June 2014). Furthermore, we checked reference lists of published reviews and retrieved articles, and communicated personally with investigators and biotechnology companies participating in trials of IFN-β in an effort to identify further studies or unpublished data. Two review authors independently screened studies, extracted data and evaluated the risk of bias. Formal Meta-analysis were conducted by using Review Manager software (Version 5.3.3) and the impacts of limitations in study design or execution (risk of bias), inconsistency in results, imprecision of results, indirectness of evidence and publication bias on the quality of the body of evidence were assessed. Results A total of 576 articles were retrieved. After screening of titles and abstracts, 26 studies were provisionally selected. The full text of papers were obtained for further assessment of eligibility. Finally, 5 studies were included, involving 2129 patients with RRMS (high-dose IFN-β group: N = 1076; placebo group: N = 1053). All studies were randomized, double-blind, controlled, parallel-group clinical trials with a follow-up for at least one year, evaluating IFN-β versus placebo as monotherapy for patients with RRMS. Most studies had methodological limitations, mainly on a high risk of attrition bias. Moreover, the intention to treat (ITT) principle was not used in data analysis. Data from only 919 patients (43.17%) were available to calculate the primary outcomes at 2 years of follow-up. Meta-analysis indicated IFN-β slightly reduced the number of patients with at least one relapse [risk ratio (RR) = 0.810, 95%CI: 0.740-0.890; P = 0.000] and the number of patients with disability progression during the first 2 years of follow-up (RR = 0.700, 95%CI: 0.550-0.880; P = 0.002). However, the sensitivity analysis (worst-case scenario analysis) showed no treatment effect (RR = 1.110, 95%CI: 0.730-1.680, P = 0.620; RR = 1.310, 95%CI: 0.600-2.890, P = 0.500, respectively). Data from 1581 patients (74.26%) were available to analyze the number of patients with at least one relapse during the first year of follow-up (RR = 0.740, 95% CI: 0.590-0.930; P = 0.010). Absolute risk reduction (ARR) was 13.24% and number needed to treat (NNT) was 8, which meant 8 patients were needed to treat to prevent one patient against relapse. However, the pooled results showed no treatment effect on the annualized relapse rate. The adverse events frequently caused by IFN-β included injection-site reactions, chills, pyrexia, myalgia, influenza-like symptoms, headache, increased alanine aminotransferase (ALT) and increased aspartate aminotransferase (AST). However, the incidences of lymphocytopenia, leucopenia, depression and committed or attempted suicide were not significantly increased by IFN-β. Conclusions There is high-quality evidence to support that IFN-β slightly reduces the number of patients with RRMS having relapse during the first year of follow-up, but the clinical effect beyond one year is uncertain. There is insufficient evidence to determine the efficacy of IFN-β in reducing the number of patients with disability progression. New randomized controlled trials of high quality are needed to assess the long-term efficacy. doi: 10.3969/j.issn.1672-6731.2014.09.007

BACKGROUND: 11β-Hydroxysteroid dehydrogenase 1 (11β-HSD1) activates glucocorticoid locally in liver and fat tissues to aggravate metabolic syndrome. 11β-HSD1 selective inhibitor can be used to treat metabolic syndrome. Curcumin and its derivatives as selective inhibitors of 11β-HSD1 have not been reported. METHODOLOGY: Curcumin and its 12 derivatives were tested for their potencies of inhibitory effects on human and rat 11β-HSD1 with selectivity against 11β-HSD2. 200 mg/kg curcumin was gavaged to adult male Sprague-Dawley rats with high-fat-diet-induced metabolic syndrome for 2 months. RESULTS AND CONCLUSIONS: Curcumin exhibited inhibitory potency against human and rat 11β-HSD1 in intact cells with IC50 values of 2.29 and 5.79 µM, respectively, with selectivity against 11β-HSD2 (IC50, 14.56 and 11.92 µM). Curcumin was a competitive inhibitor of human and rat 11β-HSD1. Curcumin reduced serum glucose, cholesterol, triglyceride, low density lipoprotein levels in high-fat-diet-induced obese rats. Four curcumin derivatives had much higher potencies for Inhibition of 11β-HSD1. One of them is (1E,4E)-1,5-bis(thiophen-2-yl) penta-1,4-dien-3-one (compound 6), which had IC50 values of 93 and 184 nM for human and rat 11β-HSD1, respectively. Compound 6 did not inhibit human and rat kidney 11β-HSD2 at 100 µM. In conclusion, curcumin is effective for the treatment of metabolic syndrome and four novel curcumin derivatives had high potencies for inhibition of human 11β-HSD1 with selectivity against 11β-HSD2.