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Recent transformer-based models, especially patch-based methods, have shown huge potentiality in vision tasks. However, the split fixed-size patches divide the input features into the same size patches, which ignores the fact that vision elements are often various and thus may destroy the semantic information. Also, the vanilla patch-based transformer cannot guarantee the information communication between patches, which will prevent the extraction of attention information with a global view. To circumvent those problems, we propose the Efficient Attention Pyramid Transformer (EAPT) in this paper. More specifically, we first propose Deformable Attention, which learns an offset for each position in patches. Therefore, even with split fixed-size patches, our method can still obtain non-fixed attention information that can cover various vision elements. Then, we design the Encode-Decode Communication module (En-DeC module), which can obtain communication information between all patches to get more complete global attention information. Finally, we also proposed a position encoding specifically for vision transformers, which can be used for patches of any dimensions and any lengths. Extensive experiments on the vision tasks of image classification, object detection, semantic segmentation demonstrate the effectiveness of our proposed model. Furthermore, we also conduct rigorous ablation studies to evaluate the key components of the proposed structure.

RGB-D salient object detection aims to detect visually distinctive objects or regions from a pair of the RGB image and the depth image. State-of-the-art RGB-D saliency detectors are mainly based on convolutional neural networks but almost suffer from an intrinsic limitation relying on the labeled data, thus degrading detection accuracy in complex cases. In this work, we present a self-supervised self-ensembling network (S $^3$ Net) for semi-supervised RGB-D salient object detection by leveraging the unlabeled data and exploring a self-supervised learning mechanism. To be specific, we first build a self-guided convolutional neural network (SG-CNN) as a baseline model by developing a series of three-layer cross-model feature fusion (TCF) modules to leverage complementary information among depth and RGB modalities and formulating an auxiliary task that predicts a self-supervised image rotation angle. After that, to further explore the knowledge from unlabeled data, we assign SG-CNN to a student network and a teacher network, and encourage the saliency predictions and self-supervised rotation predictions from these two networks to be consistent on the unlabeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our network quantitatively and qualitatively outperforms the state-of-the-art methods.

Background Chronic kidney disease (CKD) is an important contributor to morbidity and mortality from noncommunicable diseases. We aimed to examine the longitudinal trajectories in risk factors, estimate their impact on CKD burden in China from 1991 to 2011, and project trends in the next 20 years. Methods We used data from a cohort of the China Health and Nutrition Survey and applied the comparative risk assessment method to estimate the number of CKD events attributable to all non-optimal levels of each risk factors. Results In 2011, current smoking was the leading individual attributable factor for CKD burden in China responsible for 7.9 (95% CI, 7.5-8.3) million CKD cases with a population-attributable fraction of 8.7% (95% CI, 6.0-11.6), while the rates of smoking have reduced and may have mitigated the increase in CKD. High triglyceride (TG) and high systolic blood pressure (SBP) were the leading metabolic risk factors responsible for 6.8 (95% CI, 6.4-7.1) million and 5.8 (95% CI, 5.5-6.1) million CKD-attributable cases, respectively. Additionally, the number of CKD cases associated with high body mass index (BMI), high diastolic blood pressure (DBP), high plasma glucose, and low high-density lipoprotein cholesterol (HDL-C) was 5.4 (95% CI, 5.1-5.6), 3.9 (95% CI, 3.7-4.1), 3.0 (95% CI, 2.8-3.1) and 2.6 (95% CI, 2.5-2.8) million, respectively. Conclusions Current smoking, high TG, and high SBP were the top three risk factors that contributed to CKD burden in China. Increased BMI, DBP, plasma glucose, and decreased HDL-C were also associated with the increase in CKD burden.

Applying image-based processing methods to original videos on a framewise level breaks the temporal consistency between consecutive frames. Traditional video temporal consistency methods reconstruct an original frame containing flickers from corresponding nonflickering frames, but the inaccurate correspondence realized by optical flow restricts their practical use. In this article, we propose a temporally broad learning system (TBLS), an approach that enforces temporal consistency between frames. We establish the TBLS as a flat network comprising the input data, consisting of an original frame in an original video, a corresponding frame in the temporally inconsistent video on which the image-based technique was applied, and an output frame of the last original frame, as mapped features in feature nodes. Then, we refine extracted features by enhancing the mapped features as enhancement nodes with randomly generated weights. We then connect all extracted features to the output layer with a target weight vector. With the target weight vector, we can minimize the temporal information loss between consecutive frames and the video fidelity loss in the output videos. Finally, we remove the temporal inconsistency in the processed video and output a temporally consistent video. Besides, we propose an alternative incremental learning algorithm based on the increment of the mapped feature nodes, enhancement nodes, or input data to improve learning accuracy by a broad expansion. We demonstrate the superiority of our proposed TBLS by conducting extensive experiments.

Based on previous studies, we found that Bacillus Calmette-Guerin(BCG) vaccination may have a role in preventing SARS-CoV-2 infection. Thus, we conducted this meta-analysis to investigate this protective effect. We searched in Embase, PubMed, Web of Science, Cochrane Library, BioRxiv, and MedRxiv databases for studies that evaluated the relationship between BCG vaccination and SARS-CoV-2 infection or COVID-19 disease. The quality of all included studies was assessed through the Risk of Bias in Non-randomized Studies - of Interventions and the Agency for Healthcare Research and Quality. Review Manager (Version 5.3) was used for conducting all data analyses. A total of 8 studies were ultimately included in our meta-analysis. Our primary analysis found significantly lower SARS-CoV-2 infection rate in the BCG vaccination group compared to the control group, with a odds ratio (OR) of 0.61, (95% confidence interval (CI) 0.39 to 0.95, P = 0.03; I2 = 31%, and P = 0.21 for heterogeneity). Our study indicated that BCG vaccination can protect against SARS-CoV-2 infection. However, there is insufficient evidence that BCG vaccination can reduce the severity of COVID-19.

Aim: To investigate the effects of SKA3 on cell proliferation and metastasis in non-small-cell lung cancer (NSCLC) and its underlying mechanism. Methods: Immunohistochemistry was employed to analyze the expression of SKA3 in NSCLC. CCK-8 assay, EdU assay, Transwell assay and flow cytometry analysis were employed to assess cell proliferation, metastatic potential and apoptosis in vitro, respectively. A lung metastasis model was used to evaluate metastasis of NSCLC cells in vivo. A luciferase reporter gene assay was conducted to verify the targeting relationship. Results: SKA3 exhibited high expression in NSCLC tissues and cells. Overexpression of SKA3 remarkably accelerated cell proliferation and metastasis and suppressed apoptosis of NSCLC cells and promoted lung metastasis in a mouse model. miR-128-3p repressed SKA3 expression by targeting it. Conclusion: miR-128-3p inhibited the progression of NSCLC through targeting SKA3.

Occluded person re-identification is a challenging task since the available data often suffers from information incompleteness and spatial misalignment. Most state-of-the-art occluded models rely on the external model to provide additional semantic information. However, for the time being, external models, such as the human parsing model and the pose estimation model cannot provide accurate semantic information under a complex occlusion environment and may introduce errors to the Re-ID model instead. In this paper, we propose an occluded person Re-ID model that mines the latent recognizable information of the person image itself, without the help of external models. Feature/Data uncertainty can reduce the influence of noisy samples in datasets and has been discussed in person Re-ID and face recognition, we extend the uncertainty to the micro feature level, and propose the spatial-wise and channel-wise feature uncertainty to constantly refine the features in the spatial domain and the channel domain respectively during feature construction by weakening the influence of noise features. Extensive experiments on the occluded datasets and holistic datasets have proved the effectiveness of our proposed methods.

Major depressive disorder is the most common mental disorder with significant economic burden and limited treatments. Acupuncture has emerged as a promising non-pharmacological treatment for reducing depressive symptoms. However, the potential mechanisms and clinical effectiveness of acupuncture are not fully understood. This review aimed to: (1) summarize the available evidence on the mechanisms and clinical effectiveness of acupuncture for depression, and then (2) compare with pharmacological interventions, guiding future studies. Studies with animal models of depression and patients have shown that acupuncture could increase hippocampal and network neuroplasticity and decrease brain inflammation, potentially to alleviating depressive disorders. Overall clinical studies indicated that acupuncture could relieve primary depression, particularly milder cases, and was helpful in the management of post-stroke depression, pain-related depression, and postpartum depression both as an isolated and adjunct treatment. It was emphasized that acupuncture combined with antidepressant pharmacological treatment not only enhanced the improvement of primary and secondary depressive symptoms but also reduced the side effects of the medical treatment, which is the main cause for high dropout rates with drug treatment. In summary, substantial evidence from animal and human researches supported the beneficial effect of acupuncture in depression. However, most clinical trials of acupuncture were small, and it is unclear whether their findings can be generalized, so more studies are needed.

Background: Manual segment target volumes were time-consuming and inter-observer variability couldn’t be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem. Objective: To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively. Methods and Materials: A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test. Results: The average Dice similarity coefficient (±standard deviation) given by the deep-learning- based and Atlas-based auto-segmentation were 0.84(±0.03) and 0.74(±0.04) for CTV2, 0.79(±0.02) and 0.68(±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55 mm and 9.34±3.39 mm for CTV2, 7.09±2.27 mm and 14.33±3.98 mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p Conclusions: Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze (PDRF). To solve the problem, the matched filter outputs of the PDRF under the action of target and jamming are analyzed. Then, the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF, and the time-frequency joint feature is constructed. Based on the time-frequency joint feature, the naive Bayesian classifier (NBC) with minimal risk is established for target and jamming recognition. To improve the adaptability of the proposed method in complex environments, an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed. The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio (SNR) decreases and the jamming-to-signal ratio (JSR) increases. Moreover, the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF.

Background: Multiple endocrine neoplasia type 2A (MEN 2A) is mainly caused by germline RET codon C634 mutation and is characterized by Medullary Thyroid Carcinoma (MTC), pheochromocytoma (PHEO), and hyperparathyroidism (HPTH). The early diagnosis and initial normative treatment are helpful for the long-term outcome of MEN2A. Methods: Three index cases and their 29 relatives from three families with MEN2A were included in this study. Genetic screening was performed on all participants. Demographic, clinical profiles, tumor histopathologic features, and follow-up records were systematically analyzed. Results: In total, RET C634Y mutation was identified in 10 individuals (10/32, 31.3%). Among them, 5 presented with MTC symptoms, whereas the other 5 did not show apparent clinical manifestation, and all were subjected to thyroidectomy with varying neck dissection. Compared to individuals in the former, the latter benefited greatly from RET screening with significantly younger age at diagnosis of MTC and surgery (18.1 ± 13.8 years vs. 39.0 ± 14.1 years, P =0.045), and lessaggressive MTC behavior (size: 0.74 vs. 2.82 cm, P =0.026; LN+/resected: 20.0% vs. 100.0%, P =0.048) and also lower recurrence rate of MTC (20.0% vs. 100.0%, P =0.048). The PHEO was identified in 6 of the 10 carriers (60.0%), and all had undergone adrenal-sparing surgery. During the 10 years of follow-up, one (16.7%) developed recurrence of PHEO. Conclusion: Integrated RET screening, serum calcitonin, and plasma metanephrine/ normetanephrine levels can facilitate the early diagnosis and standardized MTC/PHEO surgery to improve the prognosis of MEN2A. Laparoscopic adrenal-sparing surgery prior to the bilateral total thyroidectomy is a preferred surgical approach for PHEO.

We carry out an In0.53Ga0.47As/In0.52Al0.48As single photon avalanche diode which exhibits a single photon detection efficiency exceeding 60% at 1310 nm and neat temporal characteristic of 65 ps. A novel concept of dual multiplication layer is incorporated to avoid the tradeoff between dark count rate, afterpulsing and timing jitter, paving the possibility to improve the overall performance of a single photon detector. Based on this elevated device structure, we further optimize the detection efficiency and timing jitter by employing a delicate mesa structure to better confine the electric field distribution within the central multiplication region. For our detector operated under gated mode, a shorten gate width together with an increase of excess bias percentage leads to a significant improvement in the detection performance. We eventually achieve a single photon detection efficiency of 61.4% without the involvement of afterpulsing at the gating frequency of 10 kHz for 200 K.

Objectives : To explore the effectiveness of single injection of platelet-rich plasma with rehabilitation therapy for knee osteoarthritis combined with meniscus injury. Methods : Forty patients who met the inclusion criteria were randomly assigned to a rehabilitation group (REH group, 20 cases) receiving rehabilitation training, and a platelet-rich plasma group (PRP group, 20 cases) receiving an ultrasound-guided single injection of PRP in combination with rehabilitation training. Rehabilitation training in the two groups lasted for 2 weeks, and the patients were evaluated using the short-form McGill pain questionnaire (SF-MPQ), Western Ontario McMaster Universities index (WOMAC score), and infrared thermography (knee-joint mean temperature) before treatment, at 1 week, 1 month and 6 months after treatment. Results : Two patients were lost to follow-up in both the PRP group and the REH group. Significant reductions in pain scores, WOMAC scores and knee temperature were observed at 1 week and 1 month after treatment in both groups (p < 0.05). Significant lower SF-MPQ scores were observed in the PRP group than in the REH group at 6 months followup (p < 0.01). Similarly, the mean knee temperature was significantly lower in the PRP group than in the REH group at 6 months follow-up (p < 0.01). No severe complications occurred in either group. Conclusions: Compared to rehabilitation therapy alone, single injection of platelet-rich plasma in combination with rehabilitation therapy has beneficial effect on pain, knee function and mean knee temperature in patients with KOA combined with meniscus injury. Single injection of platelet-rich plasma combined with rehabilitation therapy has a good short-term effectiveness.

Background: Maternal subclinical hypothyroidism (SCH) has been associated with adverse pregnancy outcomes. This study aimed to explore whether SCH in the first trimester contributed to the developm...

This study aimed to assess the safety and effectiveness of modified endoscopic technique with a single portal from an external carpal tunnel approach for surgical operations in a suprafascial plane superficial to the transverse carpal ligament. Reversible nerve injury risk is threefold greater with a conventional endoscopic method than with open carpal tunnel release (OCTR), and this suprafascial plane endoscopic release (SPER) should circumvent the problem of hardware in the carpal tunnel encountered with the conventional endoscopic method and liable to cause iatrogenic damage to the median nerve. However, the surgical consequences of the new technique have not been studied. To fill this gap, a retrospective therapeutic study was conducted to compare negative outcomes versus open surgery. The Accordion Severity Grading System was used to grade complications from 0 to 3 according to necessity of treatment. Sequela and failure rates were also compared between the SPER and OCTR groups. Eighty-eight cases in 72 patients with idiopathic carpal tunnel syndrome (ICTS) met the inclusion criteria. SPER was performed in 28 hands in 27 patients, and OCTR in 60 hands in 49 patients. The results showed no significant difference in complication, sequela, or failure rates between groups (p > 0.05). Suprafascial plane endoscopic release, has certain advantages over the open method and was validated as a safe and effective method of treating ICTS.

Background The role of vitamin D in depression has been gaining increased research interest. However, little is known about the independent associations of serum 25-hydroxyvitamin D3 (25(OH)D3) and D2 (25(OH)D2) with depressive symptoms. The goal of this study was to examine the metabolites of vitamin D and their associations with depression. Methods This study was conducted in US females using data from the National Health and Nutrition Examination Survey 2011–2014. Depressive symptoms were assessed using a nine-item Patient Health Questionnaire, and serum 25(OH)D3 and 25(OH)D2 levels were measured using liquid chromatography-tandem mass spectrometry. Linear regression and generalized additive models were applied to evaluate the associations between 25(OH)D3, 25(OH)D2 and depression. Results A negative association between serum 25(OH)D3 and depressive symptoms was observed in the fully adjusted model. This association was also found among widowed, divorced, separated, and never-married individuals. The association between 25(OH)D2 and depressive symptoms was not statistically significant, but the dose-response analysis revealed an inverted U-shaped relationship between them with an inflection point at 56.2 nmol/L. To the left of the inflection point, we detected a positive association between 25(OH)D2 and depressive symptoms, whereas a negative association was observed to the right of the inflection point. Limitations The study used a cross-sectional approach, so causation cannot be determined. Conclusions Our study shows an inverse linear association between serum 25(OH)D3 and depressive symptoms in US females. The association between 25(OH)D2 and depression follows an inverted U-shaped curve with the inflection point at 56.2 nmol/L.

The construction of semiconductor heterojunction for photocatalytic H2 production from water splitting is an efficient and environment-friendly technology. In this work, ZnO/BiOCl (ZBC) and Sn-doped ZnO/BiOCl (ZBC-S) photocatalysts with Z-scheme heterojunction were successfully prepared by simple hydrothermal method. The photocatalytic H2 evolution from water splitting by the as-prepared photocatalysts was investigated. The formation of ZnO/BiOCl heterojunction reduces the recombination probability of the photogenerated carriers. The impurity levels originated from Sn doping reduce the band gap width of ZnO and BiOCl to some extent, thereby enhancing the light absorption ability. The ZBC-S composite exhibits the best photocatalytic activity. In addition, the photocatalytic efficiency of H2 production was improved by sensitization with Eosin Y (EY) dye. The H2 production rate under simulated sunlight reaches 4146.77 μmol g−1 h−1, which is 27 times higher than that of pure ZnO. Finally, the Z-scheme electron transfer route in ZnO/BiOCl heterojunction was determined, and the photocatalytic H2 production mechanism of EY sensitized ZBC-S was proposed.

Background/Aims: Anti-phospholipase A2 receptor (PLA2R) autoantibody is the main biomarker of idiopathic membranous nephropathy (IMN). We aimed to find a new cutoff value of anti-PLA2R for patients with IMN and to explore the relevance between this antibody and baseline clinical parameters.Methods: A total of 670 subjects including 374 IMN cases and 296 non-IMN controls were included between January 2017 and January 2020. All clinical parameters were collected at the time of renal biopsy. The levels of anti-PLA2R were detected by a commercial enzyme-linked immunosorbent assay (ELISA) kit. The optimal cutoff value was calculated by a receiver operating characteristic curve and compared in diagnostic efficiency.Results: The optimal cutoff value of anti-PLA2R for IMN was 7.45 RU/mL with the highest Youden index, and the corresponding sensitivity, specificity, positive predictive value and negative predictive value were 80.75%, 97.97%, 98.05% and 80.11%, respectively. Anti-PLA2R levels in IMN patients demonstrated a significant positive correlation with serum creatinine and 24-hour urinary protein, while they showed a negative correlation with serum albumin and estimated glomerular filtration rate.Conclusions: The recommended cutoff value of anti-PLA2R is 7.45 RU/mL using ELISA detection for distinguishing IMN from non-IMN nephropathy. The level of anti-PLA2R is related to baseline renal function in IMN. This new threshold can improve the diagnostic efficiency and facilitate early diagnosis of IMN.

Cable current detection plays a vital role in power grids toward improving transmission efficiency, relieving power congestion, and ensuring grid reliability. However, the sensors in existing current detection systems either are wired with measurement instruments or are wireless yet require batteries, which is inconvenient for monitoring overhead powerlines. Inspired by RF sensing tags, which are widely used for applications such as temperature and humidity measurements, in this article, we propose a distinctive cable current detection system that can avoid the constraint of wires and batteries. By detecting the ambient magnetic field produced by ac current flowing through a cable, the proposed system realizes a minimal measurement range of 5–17.5 A at 50/60 Hz with a maximum wireless distance of 5.2 m. This prototype can detect cable current with an extendable measurement range, providing a promising strategy for passive and wireless current detection.

Occluded person re-identification is a challenging task which suffers from various obstacles. However, existing occluded re-ID methods tend to exploit body detectors for pedestrian alignment, which are over-reliant on detection and lack of a flexible matching mechanism. To address this issue, we propose an Attribute Disentanglement and Registration (ADR) network to excavate non-occluded regions via attribute feature disentanglement, which can be matched effectively with a robust and soft attribute registration. The proposed ADR takes full advantages of pedestrian attributes’ high-level semantic concepts to alleviate the occlusion problem. First, the Attribute Disentanglement (AD) module obtains the representations of different attributes by localizing their spatial positions. Then the Attribute Registration (AR) module searches and matches these localized regions between different pedestrian images to conduct a registration, which allows the attribute features to be adaptively and efficiently matched. Extensive experiments on occluded, partial, and holistic Re-ID benchmarks demonstrate the effectiveness of the proposed ADR framework as well as its superiority over the existing state-of-the-art methods.

Inflammatory bowel disease (IBD) is a chronic inflammatory lesion of the intestine, mainly manifested by infiltration of intestinal inflammatory cells and imbalance of gut microbiota. Conventional treatments for IBD include antibiotics, immunosuppressive agents, 5-aminosalicylic acid, steroids and surgery, which have high toxic side effects. Resveratrol is a natural polyphenol, and its various derivatives have anti-oxidation and anti-inflammatory properties. In this paper, we comprehensively review the mechanism of resveratrol and its derivates to alleviate IBD by improving intestinal barrier, regulating the unbalanced gut microbiota, and targeting various inflammatory signaling pathways.

The broad learning system (BLS) is designed based on the technology of compressed sensing and pseudo-inverse theory, and consists of feature nodes and enhancement nodes, has been proposed recently. Compared with the popular deep learning structures, such as deep neural networks, BLS has the ability of rapid incremental learning and can remodel the system without the usual tedious retraining process. However, given that BLS is still in its infancy, it still needs analysis, improvements, and verification. In this article, we first analyze the principle of fast incremental learning ability of BLS in depth. Second, in order to provide an in-depth analysis of the BLS structure, according to the novel structure design concept of deep neural networks, we present four brand-new BLS variant networks and their incremental realizations. Third, based on our analysis of the effect of feature nodes and enhancement nodes, a new BLS structure with a semantic feature extraction layer has been proposed, which is called SFEBLS. The experimental results show that SFEBLS and its variants can increase the accuracy rate on the NORB dataset 6.18%, Fashion-MNIST dataset by 3.15%, ORL data by 5.00%, street view house number dataset by 12.88%, and CIFAR-10 dataset by 18.42%, respectively, and the four brand-new BLS variant networks also obviously outperform the original BLS.

Systematic and dynamic humoral immune reconstitution is little-known for patients with relapsed/refractory (R/R) multiple myeloma (MM) who received anti–B-cell maturation antigen (BCMA) chimeric antigen receptor (CAR) T-cell therapy. We investigated the kinetics of B-cell, normal plasma cell, and immunoglobulin recovery in 40 patients who achieved ongoing response after anti-BCMA CAR T-cell therapy. All patients developed B-cell aplasia and the median duration of B-cell aplasia was 70 days (range, 23-270). The B-cell count reached its nadir on median day 7 and returned to baseline level on median day 97. BCMA+ cells in bone marrow turned undetectable on median day 28 (13-159) in 94.87% (37 of 39) of patients. Normal plasma cells in bone marrow were first redetected on median day 212. All patients developed a significant decrease in serum IgG, IgA, and IgM on median day 60. At year 1, recovery of serum IgG, IgM, and IgA was observed in 53.33% (8 of 15; non-IgG MM), 73.08% (19 of 26; non-IgM MM), and 23.81% (5 of 21;non-IgA MM) of the patients, respectively. Median time to IgG, IgM, and IgA recovery were days 386, 254, and not reached during follow-up, respectively. Virus-specific IgG levels decreased with loss of protection. Twenty-three of 40 (57.5%) patients had a total of 44 infection events. There were no infection-related deaths. These results reveal a 7-month aplasia of bone marrow normal plasma cells and longer period of hypogammaglobulinemia, suggesting a profound and lasting humoral immune deficiency after anti-BCMA CAR T-cell therapy, especially for IgA.

Background: From December 2019, the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, China, and spread rapidly around the world. The pandemic also becomes a great challenge to radiation oncology departments since interruptions of radiation therapy increase the risks of cancer recurrence or the whole therapy failure. The present study aims to elucidate the influences of COVID-19 on the radiotherapy (RT) staff in China. Methods: The working staff members from different radiation oncology departments in China were retrospectively enrolled between December 1st, 2020 and March 9th, 2020 as many as possible. All the enrolled ones were invited to fill out a specific questionnaire to collect essential data. The basic information, anxiety levels, and workload of the staff were analyzed. Results: 1. A total of 1,755 questionnaires were sent back; 2. From December 1st , 2019 to March 9th, 2020, 7 out of 1,755 radiation oncology staff members contracted COVID-19 (P 0.05);3. The anxiety level of the RT staff about the COVID-19 outbreak (average score: 6.57) was higher than that of Wuhan medical staff (5.18) and higher than the national level (4.79); 71.43% of infected RT staff members expressed the need for psychological interventions; 4. During the epidemic, the departments of the 428 RT staff members shut down (24.39%). The 76.71% of the RT staff members felt workload relief during COVID-19 as compared to before. Conclusion: Among the 1,755 RT staff, the gender, age, and comorbidities were not related to COVID-19 infection. The geographic position and whether the person worked in a designated COVID-19 hospital were relevant factors with COVID-19 infection. The staff contracting COVID-19 experienced greater psychological pressure than that of the uninfected ones and therefore needed more psychological interventions. Funding Statement: This research was supported by grant No.2014CFB394, 2019CFB721 from the Natural Science Foundation of Hubei Province (CN) and No.WJ2017M027, No.WJ2019M200 from Health and Family Planning Commission of Hubei Province (CN). Declaration of Interests: None. Ethics Approval Statement: This study was approved by the Ethics Committee of Renmin Hospital of Wuhan University.

Background Recurrence of nasopharyngeal carcinoma (NPC) after chemoradiotherapy is common, but submucosal recurrence of NPC is rare. The final pathological results determine the optimal therapeutic schedule for treatment of NPC recurrence, but tissue retrieval from submucosal lesions is usually difficult. The present study aimed to assess the safety and efficacy of a novel approach of endonasopharyngeal ultrasound-guided transnasopharyngeal needle aspiration (ENUS-TNNA) for submucosal neoplasms in patients with suspected NPC recurrence. Methods Between March 2017 and June 2021, 11 post-chemoradiotherapy patients with suspected magnetic resonance imaging (MRI) findings of submucosal recurrence of NPC underwent ENUS-TNNA. The safety and effectiveness of using ENUS-TNNA to sample submucosal neoplasms were evaluated. Results Needle aspiration biopsies were performed without any incidences in all cases. Out of the 11 patients, nine were diagnosed with submucosal recurrence of NPC via histopathological or cytological evaluations. Of the two puncture-negative cases, one patient had atypical imaging findings and clinical manifestations and was therefore followed-up using MRI. After follow-up for 3 years, this patient was still considered to be cancer-free due to the shrinking diameters of the submucosal lesions. For the other puncture-negative patient, submucosal biopsy samples were obtained using a surgical method. Pathological examination of these biopsies revealed that an angiosarcoma had developed after radiotherapy. There were no severe complications that occurred during the ENUS-TNNA procedure. Conclusion ENUS-TNNA is a safe, effective, and minimally invasive approach to obtain tissue samples from the submucosal region of the nasopharynx for patients with suspected NPC recurrence.

Gestational diabetes mellitus (GDM) is the most common cause of hyperglycemia during pregnancy, and its prevalence has increased over the past decades. GDM is directly related to the recent obstetric outcomes and long-term maternal and child health, which can be greatly improved by early identification and diagnosis of GDM. However, the prediction of the disease has always been a difficult problem due to the lack of simple and practical serological markers. Despite the controversy, recent studies have identified that circulating inflammatory cells and platelets, routinely included in the obstetric blood tests, are related to the development of GDM and adverse pregnancy outcomes. In this review, we summarized the studies in this field based on the recent literature. The inflammatory cell components we included were the total number of white blood cells, neutrophils, lymphocytes, monocytes and platelets, which were routinely examined in the blood tests in pregnancy. The aim of this review is not only to enrich our understanding of the pathogenesis of GDM but also to provide evidence for the value of these novel and practical serological markers in early identification of GDM and the prevention and its adverse outcomes.

With the rapid popularization of new energy vehicles, the demands for high energy density power batteries and related cathode materials are becoming increasingly urgent. Layered Li–Ni–Co–Mn–O cathode materials are attracting much attention because of their high specific capacity; however, their cycling and high voltage performance need to be further improved. Herein, single crystal structure LiNi0.8Co0.1Mn0.1O2 materials, prepared using a multiple-step calcination process, are further controllably doped with fluorine to improve the Li storage performance. Fluorine doping not only improves the crystal space but also increases the Co3+ and lattice oxygen content, therefore promoting the lithium ion diffusion and crystal structure stability of layered LiNi0.8Co0.1Mn0.1O2. With an appropriate amount of fluorine doping, the LiNi0.8Co0.1Mn0.1O2 single crystal material demonstrates a remarkable specific capacity of 202.7 mAh g−1 (0.1C) and a high capacity retention of 86.6% after 100 cycles (1.0C), higher than the control sample and promising for application in lithium-ion batteries.

This paper substantially advances upon state-of-the-art to enhance liver vessels segmentation accuracy by leveraging advantages of synthetic PET-CT (SPET-CT) images in addition to computed tomography angiography (CTA) volumes. Our setup makes a hybrid solution of modified GAN-cAED combining synthetic ability of generative adversarial network (GAN) to deliver SPET-CT images with generative ability of convolutional autoencoder (cAED) network in terms of latent learning to more refined segmentation of major liver vessels. We improve time complexity through a novel concept of controlled segmentation by introducing a threshold metric to stop segmentation up-to a desired level. The innovative concept of controlled vessel segmentation with a stopping criterion via variant threshold levels will help surgeons to avoid unintentional major blood vessels cutting, reducing the risk of excessive blood loss. Clinically, such solutions offer computer-aided liver surgeries and drug treatment evaluation in a CTA-only environment, shorten the requirement of radioactive and expensive fused PET-CT images.

For achieving more effective mechanical energy conversion, based on low-cost and flexible polydimethylsiloxane (PDMS) and polyvinylidene fluoride (PVDF) polymer film as triboelectric and piezoelectric function layer respectively, a polymer-based hybrid piezoelectric-triboelectric nanogenerator (PT-NG) with a structure of Al/PVDF/Cu-PDMS/indium tin oxide (ITO)/polyethylene terephthalate (PET) has been demonstrated. The device has realized the simultaneous triboelectric and piezoelectric conversions in one structure. In addition, when PDMS and PVDF are further modified by graphene quantum dot (GQD) and titanium dioxide (TiO2) nanoparticles respectively, both triboelectric and piezoelectric outputs of the modified device are greatly enhanced synchronously. The experimental results have indicated that the increase of triboelectric output is due to the improvement of dielectric properties of PDMS film doped with conductive GQDs as well as the enhancement of the effective contact caused by the change of PDMS surface microstructure. While the promotion of piezoelectric output is mainly attributed to the fact that PVDF film after TiO2 modification induces more polarized β phase with a polarization-free process. Accordingly, the modified device converts mechanical energy into electricity more effectively, which shows a promising prospect in the fields of flexibility display, electronic skin, wearable electronic products and self-powered sensors.

BACKGROUND Fiberoptic bronchoscopy has been widely used in the diagnosis and treatment of respiratory diseases. Numerous major and minor complications have been reported following this procedure. The incidence of major postoperative complications is approximately 0.5% and includes respiratory depression, pneumothorax, pulmonary edema, pneumonia, airway obstruction and cardiorespiratory arrest. Minor complications include vasovagal reactions, cardiac arrhythmias, hemorrhage, pneumothorax, aphonia, nausea, vomiting and fever. However, to our knowledge, a case of atrial fibrillation (AF) concomitant with fatal arterial embolism in the upper extremities following diagnostic bronchoscopy has never been reported. CASE SUMMARY A 70-year-old female patient presented with a history of rheumatic heart disease beginning at 10 years of age and an approximately 10-year history of hypertension. The patient was transferred from the cardiology department to the respiratory department due to recurrent coughing, pneumonia, and fever. She underwent fiberoptic bronchoscopy in the respiratory department. Approximately 2 h after completion of bronchoscopy, she complained of left arm numbness and weakness. Physical examination detected cyanosis of the left upper extremity, grade III weakened limb muscle strength, and undetectable left brachial artery pulsation. Auscultation indicated AF. B-mode ultrasound examination of the blood vessels showed hyperechoic material in the left subclavian, axillary and brachial arteries, and parallel veins. As our hospital has no vascular surgery capability, the patient was transferred to a specialized hospital for emergency thrombectomy that day. A tracking investigation found that the patient’s conditions improved after successful thrombectomy. CONCLUSION Thromboembolism following bronchoscopy is rare, and only a few cases of cerebral air embolism after bronchoscopy have been reported.

Little information is available on the association between brachial-ankle pulse wave velocity (baPWV) and the risk of stroke in Chinese H-type hypertension patients. Therefore, our study aimed to assess this association between baseline baPWV and short-term risk of first stroke and to propose a cutoff value of baPWV that could predict near cerebrovascular events. A total of 9787 hypertension patients without preexisting stroke who underwent baPWV measurement were included. The primary end points were first symptomatic stroke. Secondary end points were first ischemic stroke and first hemorrhagic stroke. During a median follow-up of 20.8 months, there was a total of 138 first strokes including 123 first ischemic strokes and 15 first hemorrhagic strokes. When baPWV was categorized in quartiles, the higher risks of first stroke (HR = 1.52; 95% CI: 1.05-2.21) and first ischemic stroke (HR = 1.53; 95% CI: 1.03-2.26) were found in participants in quartile 4 (≥21.31 m/s), compared with those in quartile 1-3 (

Dorzagliatin is a novel allosteric glucokinase activator targeting both pancreatic and hepatic glucokinase currently under clinical investigation for treatment of type 2 diabetes (T2D). This study aimed to investigate the effect of renal impairment (RI) on dorzagliatin’s pharmacokinetics (PKs) and safety, and to guide appropriate clinical dosing in patients with diabetic kidney disease, including end‐stage renal disease (ESRD). Based on the results from physiologically‐based pharmacokinetic modeling, the predicted outcome of RI on dorzagliatin PK property would be minimum that the plasma exposure area under concentration (AUC) of dorzagliatin in patients with ESRD would increase at about 30% with minimal change in peak concentration (Cmax) comparing to those in healthy volunteers (HVs). To definitively confirm the prediction, a two‐part RI study was designed and conducted based on regulatory guidance starting with the patients with ESRD matched with HVs. Results of the RI study showed minimum difference between patients with ESRD and HVs with respect to dorzagliatin exposure with geometric mean ratio of ESRD to HV at 0.81 for Cmax and 1.11 for AUC. The elimination half‐life, volume of distribution, and systemic clearance for dorzagliatin were similar between the two groups. Dorzagliatin was well‐tolerated in patients with ESRD during the study. Therefore, RI showed no significant impact on dorzagliatin PK, suggesting that dorzagliatin can be readily used in patients with T2D at all stages of RI without need for dose adjustment.

UbiquitinC-terminal hydrolase-L1 (UCH-L1) is a cysteine hydrolase. It functions as a ubiquitin hydrolase, stabilizes the ubiquitin monomer, and affects cell division through cell cycle protein deubiquitination. Abnormal UCH-L1 expression is closely related to the occurrence and development of several tumors. Although some in vitro studies have demonstrated the significance of UCH-L1 in non-small-cell lung cancer (NSCLC), only few clinical studies have focused on the UCH-L1 expression in NSCLC, and the results are controversial and non-uniform. We investigated the UCH-L1 expression in 401 cases of surgically resected NSCLC, including 286 cases of adenocarcinoma (ADC) and 65 cases of squamous cell carcinoma. The associations between the UCH-L1 expression and clinicopathological features, programmed cell death-ligand 1 (PD-L1) expression, and prognostic significance were analyzed. For NSCLC, the UCH-L1 expression is associated with sex, smoking history, tumor size (>3 cm), lymphocyte infiltration, advanced pathological stages, and shortened overall survival (OS; 89.72 vs. 114.55 months; P = 0.005), but not PD-L1 expression. The UCH-L1 expression in ADC is associated with advanced pathological stages, pleural invasion, and shortened OS (90.38 vs. 118.55 months; P = 0.010). Multivariate analysis confirmed that UCH-L1 expression was an independent poor prognostic factor for NSCLC (OS: hazard ratio [HR], 1.854; 95% confidence interval [CI], 1.132-3.038; P = 0.014). Our results suggest that the UCH-L1 expression differs across tumors with different clinicopathological features, and it is related to poor prognosis.

The neurodegenerative disease is a vital threat to the elders, the incidence is increasing but the efficient therapy is rare. A feasible strategy to treat neurodegenerative diseases is dietary prevention. Among them, anthocyanins are a group of phytochemicals that have shown rising evidences of ameliorating neurodegenerative disease. This review summarized the neuroprotective effect of anthocyanins in vitro and in vivo and discussed the related pathways. Anthocyanins alleviated neurological dysfunction such as cognitive and memory functions via the protection of neurons, glial cells and hippocampal nerve cells against the damage of Aβ-proteins, glutamate, and lipopolysaccharides. In addition, anthocyanins reduced oxidative stress, suppressed inflammation, inhibited apoptosis in nerve cells. The neuroprotection of anthocyanin is also related to the intestinal metabolites. The human study is inadequate but the accumulated evidences indicated that anthocyanins could be the potential dietary supplement to prevent against neurodegenerative diseases.

Background Common chronic wounds include diabetic ulcers, venous ulcers, and pressure ulcers. The traditional Chinese medicine Huiyang Shengji decoction (HYSJD) has been shown to promote the healing of diabetic chronic wounds, however, its pharmacological mechanism is still unclear. Purpose This study aimed to determine the mechanism of HYSJD in promoting the healing of diabetic chronic skin ulcers. Methods Ultra-performance liquid chromatography was combined with tandem mass spectrometry (UPLC-MS/MS) to analyze the main components of HYSJD and the absorbed components in mouse serum at 30 min after oral administration of HYSJD. db/db mouse models for chronic skin ulcers were constructed by full-thickness skin resection. Wound tissues at day 7 post wound formation were used to perform microarray analysis of growth factors and chemokine expression. GO and KEGG enrichment analysis was performed on differentially expressed proteins. ELISA assays were used to measure differential expressed cytokines in the serum and Western blot analysis was used to determine the expression levels of related pathway proteins in the skin wounds. Results UPLC-MS/MS analysis showed that the main chemical components of HYSJD were flavonoids, terpenes, alkaloids, phenylpropanoids, and carbohydrates. At 30 min after oral administration of HYSJD, five absorbed components were detected in the serum, these included formononetin, calycosin, hypaconitine, calycosin-7-glucoside, and sinapic acid. HYSJD was found to increase the wound healing rate in chronic skin ulcers in db/db mice at days 3, 7, and 14 post wound formation, and promote the proliferation of epidermal cells. Two proteins that were differentially expressed between the different groups, i.e., IGF-1 and EGFR, were further validated. Serum ELISA assays showed that serum EGFR in the HYSJD treatment group was significantly increased. KEGG pathway analysis suggested that the PI3K/AKT pathway involved in HYSJD promoting the proliferation of epidermal cells in chronic wounds in db/db mice. Experimental verification showed that HYSJD activated the PI3K/AKT signaling pathway in mouse wound skin. Conclusion HYSJD promotes the proliferation of epidermal cells in chronic diabetic wounds by increasing EGFR expression in the wounds and activating the PI3K/AKT signaling pathway. Our study provides an experimental basis for the pharmacological mechanism of HYSJD.

Zhi Dong,1,&ast; Yingyu Lin,1,&ast; Fangzeng Lin,2,&ast; Xuyi Luo,3 Zhi Lin,1 Yinhong Zhang,1 Lujie Li,1 Zi-Ping Li,1 Shi-Ting Feng,1 Huasong Cai,1 Zhenpeng Peng1 1Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People’s Republic of China; 2Department of Interventional Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510080, People’s Republic of China; 3Department of Emergency, Guangzhou First People’s Hospital, Guangzhou, Guangdong, 510180, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Huasong Cai; Zhenpeng PengDepartment of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58th, The Second Zhongshan Road, Guangzhou, Guangdong, People’s Republic of ChinaTel +86 20-87755766, extension 8471Fax +86 20-87615805Email caihuas@mail.sysu.edu.cn; pengzhp@mail.sysu.edu.cnPurpose: The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment.Patients and Methods: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model.Results: Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively.Conclusion: Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.Keywords: hepatocellular carcinoma, transarterial chemoembolization, machine learning, prediction model, treatment response

With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking.

Semi-supervised network representation learning is becoming a hotspot in graph mining community, which aims to learn low-dimensional vector representations of vertices using partial label information. In particular, graph neural networks integrate structural information and other side information like vertex attributes to learn node representations. Although the existing semi-supervised graph learning performs well on limited labeled data, it is still often hampered when labeled dataset is quite small. To mitigate this issue, we propose PMNRL, a pseudo-multitask learning framework for semi-supervised network representation learning to boost the expression power of graph networks such as vanilla GCN (Graph Convolutional Networks) and GAT (Graph Attention Networks). In PMNRL, by leveraging the community structures in networks, we create a pseudo task that classifies nodes’ community affiliation, and conduct a joint learning of two tasks (i.e., the original task and the pseudo task). Our proposed scheme can take advantage of the inherent connection between structural proximity and label similarity to improve the performance without the need to resort to more labels. The proposed framework is implemented in two ways: two-stage method and end-to-end method. For two-stage method, communities are first detected and then the community affiliations are used as “labels” along with original labels to train the joint model. In end-to-end method, the unsupervised community learning is combined into the representation learning process by shared layers and task-specific layers, so as to encourage the common features and specific features for different tasks at the same time. The experimental results on three real-world benchmark networks demonstrate the performance improvement of the vanilla models using our framework without any additional labels, especially when there are quite few labels.

Etching the grating on the magnetic film surface is able to increase the film effective longitudinal permeability ( $\mu _{\textit {eL}}$ ) significantly, which has been demonstrated by a magnetic sensor consisting of a solenoid and grooved grating patterned film. The feasibility that the magnetic polarization field generated by the groove end faces compensates for the shape demagnetization field and intensifies $\mu _{\textit {eL}}$ is verified theoretically. After the optimal structural parameters are obtained, the planar and patterned films are manufactured with the microfabrication process. The effects of groove width, sensor dimensions, and excitation frequency on the sensor performance are systematically investigated. For the prototype with the patterned magnetic film of $10~\mu \text{m}$ groove width, the impedance ratio and impedance sensitivity are 4049% and 347 %/Oe at 1 MHz. Compared with the prototype with the same thickness planar smooth magnetic film core, they are improved by 436% and 659%, respectively. It is revealed that the grooved grating patterned films offer the possibility of developing film-based microsensors, making their performance comparable to that of wire-based sensors.

Nonalcoholic fatty liver disease (NAFLD) has emerged as a public health concern. To date, the mechanism of NAFLD progression remains unclear, and pharmacological treatment options are scarce. Traditional animal NAFLD models are limited in helping address these problems due to interspecies differences. Liver chips are promising for modeling NAFLD. However, pre-existing liver chips cannot reproduce complex physicochemical microenvironments of the liver effectively; thus, NAFLD modeling based on these chips is incomplete. Herein, we develop a biomimetic liver lobule chip (LC) and then establish a more accurate on-chip NAFLD model. The self-developed LC achieves dual blood supply through the designed hepatic portal vein and hepatic artery and the microtissue cultured on the LC forms multiple structures similar to in vivo liver. Based on the LC, NAFLD is modeled. Steatosis is successfully induced and more importantly, changing lipid zonation in a liver lobule with the progression of NAFLD is demonstrated for the first time on a microfluidic chip. In addition, the application of the induced NAFLD model has been preliminarily demonstrated in the prevention and reversibility of promising drugs. This study provides a promising platform to understand NAFLD progression and identify drugs for treating NAFLD. Statement of significance Liver chips are promising for modeling nonalcoholic fatty liver disease. However, on-chip replicating liver physicochemical microenvironments is still a challenge. Herein, we developed a liver lobule chip with dual blood supply, achieving self-organized liver microtissue that is similar to in vivo tissue. Based on the chip, we successfully modeled NAFLD under physiologically differentiated nutrient supplies. For the first time, the changing lipid zonation in a single liver lobule with the early-stage progression of NAFLD was demonstrated on a liver chip. This study provides a promising platform for modeling liver-related diseases.

Renal cell carcinoma (RCC) is the second most common cancer of the urinary system, accounting for approximately 10–15% of kidney cancers in the world. Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with the highest mortality. Surgical resection or puncture of tumor tissue is still an important clinical treatment and diagnosis of ccRCC, but its high recurrence rate and poor prognosis often lead to the short survival period of patients. Hence, the development of novel molecular biomarkers is of great clinical importance. miRNAs are endogenous non-coding small RNAs with a length of 19–24 nt. A growing number of studies have reported that miRNAs, as proto-oncogenes or tumor suppressor genes, play a key role in the development of ccRCC and might be effective diagnostic and prognostic biomarkers. In addition, miRNAs can also predict the efficacy of treatment drug, thus improving the accuracy of clinical medication. Furthermore, non-invasive detection of miRNAs or extracellular vesicles (EV) in body fluids has better convenience and repeatability, which shows remarkable advantages compared with tissue detection. In this review, we summarized the typical miRNAs reported in recent years and place emphasis on evaluating miRNAs in different body fluids to provide reference for the clinical diagnosis and prognosis of ccRCC in the future.

Acute fatty liver of pregnancy (AFLP) is a rare but critical obstetric-specific disease with a high fatality rate, posing a serious threat to the safety of mothers and infants. These guidelines were specially formulated to standardize AFLP clinical pathways and to improve maternal and infant outcomes. Based on a two-round questionnaire survey, the guideline development team identified the following nine clinical issues that clinicians were most concerned about, and developed recommendations for each of them: prenatal outpatient screening for AFLP, diagnosis, preoperative risk assessment, delivery modes and timing, anesthesia methods, perinatal complications, selecting AFLP patients for artificial liver treatment, prognostic assessment, and monitoring during treatment. The guidelines cover the key issues related to AFLP diagnosis and treatment that concern clinicians.

Preparing large amounts of training data is the key to the success of machine learning. Due to the public’s concern about individual privacy, different techniques are proposed to achieve privacy preserving machine learning. Homomorphic encryption enables calculation on encrypted data in the cloud. However, current schemes either focus on single key or a specific algorithm. Cooperation between different institutions is quite common in this era of big data. Encrypting data from different institutions under one single key is a risk to data privacy. Moreover, constructing secure scheme for a specific machine learning algorithm lacks universality. Based on an additively homomorphic encryption supporting one multiplication, we propose a general multikey computing framework to execute common arithmetic operations on encrypted data such as addition, multiplication, comparison, sorting, division and etc. Our scheme can be used to run different machine learning algorithms. Our scheme is proven to be secure against semi-honest attackers and the experimental evaluations demonstrate the practicality of our computing framework.

Background MicroRNA (miRNA) can be used as a biomarker for the early diagnosis of diabetic nephropathy (DN). The purpose of this study was to evaluate the diagnostic value of miR-638 in DN and to analyse its regulatory effect on inflammation. Methods This retrospective study involved 98 subjects, including non-diabetic healthy controls (n = 30), patients with type 2 diabetes (T2DM, n = 36) without complications and patients with DN (n = 32). After the anthropometric and biochemical evaluation, serum miR-638 levels were assessed by real-time reverse transcription-polymerase chain reaction (qRT-PCR). The levels of inflammatory cytokines (interleukin [IL]-1β, IL-6, and tumor necrosis factor-alpha [TNF-α]) were detected using enzyme-linked immunosorbent assay. The Spearman correlations were used to analyze the correlation between miR-638 and urinary albumin excretion (UAE), estimated glomerular filtration rate (eGFR), and inflammatory factors. Furthermore, the receiver operating characteristic (ROC) curve was used to measure the diagnostic value of miR-638 in DN. Human mesangial cells (HMCs) were treated with normal glucose (NG, 5.5 mM glucose), high glucose (HG, 30 mM glucose), or high osmotic pressure solution (HO, 5.5 mM glucose + 24.5 mM mannitol) in vitro to simulate the hyperglycamic state in vivo. Subsequently, the HMCs were transfected with miR-638 mimics to regulate the level of miR-638 in the cells and detect its regulation on cell inflammation and proliferation. Results Compared with healthy controls and patients with T2DM, serum miR-638 in patients with DN was significantly lower. The reduced miR-638 expression has a significant diagnostic value, which can significantly distinguish patients with DN from healthy controls or patients with T2DM. Inflammatory factors were significantly upregulated in patients with DN and negatively correlated with miR-638 levels. In addition, miR-638 was negatively correlated with UAE and positively correlated with eGFR. HG decreased the level of miR-638 and promoted the expression of inflammatory factors and proliferation in HMCs. However, miR-638 mimic significantly decreased the levels of inflammatory factors and inhibited the proliferative ability induced by HG. Conclusions Serum miR-638 expression was low in DN and can be a potentially valuable biomarker for DN. This miRNA seems to influence inflammatory responses and participate in the progression of DN by regulating proliferation.

Objective Many studies have focused on the severity and prevalence of menopausal symptoms among middle-aged women, which are limited by heterogeneity and diversity of subtypes. Subtyping facilitates the adaptation to prevention and clinical intervention strategies that target women. To determine the existence of significant subgroups of women with similar menopausal symptoms, a person-centered approach was used to identify potential profiles of women during the menopausal transition. In addition, we aimed to examine the association between latent subtypes and individual factors. Methods This cross-sectional study included 797 middle-aged women, aged 40 to 60 years, who were recruited from Shandong Province, China, between December 2017 and August 2018. We identified the subtypes in menopausal symptoms by performing a latent class analysis according to the self-reported Menopause Rating Scale and evaluated the robustness of our identified subtypes using a sensitivity analysis. Multinomial logistic regression was performed to explore the association between emergent latent subtypes and sociodemographic, clinical, and psychosocial characteristics. Results The mean age of participants was 49.83 ± 5.05 years. (1) Four potential classes were identified in middle-aged women: "severe symptoms" (14.9%), "dominant sleep-emotion symptoms" (31.4%), "physical/mental exhaustion symptoms" (32.5%), and "no symptoms" (21.2%). The four classes were also verified using a sensitivity analysis according to age and menopause status subgroups, which revealed the robust subtypes of menopausal symptoms. (2) The odds ratio of neuroticism, chronic diseases, and gynecological diseases were significantly higher for the "severe symptoms," "dominant sleep-emotion symptoms," and "physical/mental exhaustion symptoms" classes, compared to the "no symptoms" class, while the odds ratio of mindfulness and social support were lower. Conclusions A person-centered approach for middle-aged women could address the unmet need to understand the heterogeneity of menopausal symptoms. Subtyping facilitates the identification of the potential causes of menopausal symptoms and the development of personalized interventions. Video Summary:http://links.lww.com/MENO/A830.

The present study aimed to assess the correlation between ambient air pollutants and Streptococcus pneumoniae (S. pneumoniae)-induced pneumonia in children and retrospectively reviewed the daily data regarding S. pneumoniae from children with pneumonia in a tertiary hospital of Hangzhou City, between January 1st, 2018, and December 31st, 2018. The excess risk (ER) of NO2 with regard to the daily number of S. pneumoniae isolates obtained from the respiratory tract specimens of children with pneumonia was 13.31% (95% confidence interval [CI]: 3.12–24.51%, P = 0.010) in the single-pollutant model. An increase of 10 μg/m3 in NO2 exposure was associated with a 23.30% increased risk for the acquisition of S. pneumoniae-induced pneumonia in children (95% CI: 2.02–49.02%; P = 0.031) according to the multi-pollutant model. The ER of NO2 with regard to the daily number of S. pneumoniae isolates (1 μg/ml ≤ minimum inhibitory concentration (MIC) to penicillin ≤ 2 μg/ml) obtained from the respiratory tract specimens of children with pneumonia was 15.80% (95% CI: 2.02–31.45%; P = 0.024) in the single-pollutant model. According to the multi-pollutant model, the ER of NO2 with regard to the daily number of S. pneumoniae isolates (1 μg/ml ≤ MIC to penicillin ≤ 2 μg/ml) obtained from the respiratory tract specimens of children with pneumonia was 37.09% (95% CI: 5.70–77.81%; P = 0.018). In conclusion, ambient NO2 is associated with S. pneumoniae-induced pneumonia in children. More importantly, NO2 exposure is associated with the increased MICs of penicillin against S. pneumoniae from children with pneumonia.

Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Hence, it is necessary to study BOF steelmaking modeling. In this paper, a novel regression algorithm is proposed by using nonparallel support vector regression with weight information (WNPSVR) for the end-point prediction of BOF steelmaking. The weight information is excavated by K-nearest neighbors (KNNs) algorithm. Since the whale optimization algorithm (WOA) has the characteristics of fast convergence speed and a few adjustment parameters, WOA is applied to optimize the parameters in the objective function of WNPSVR. Compared with traditional prediction models, WNPSVR-WOA is not easy to fall into local minimum values and is insensitive to noise. Thus, the prediction and control of molten steel end-point information are more accurate. Experimental results verify the effectiveness and feasibility of the proposed model. Within different error bounds (0.005 wt.% for carbon content model and 10°C for temperature model), the hit rates of carbon content and temperature are 89% and 95%, respectively. Meanwhile, a double hit rate of 85% is achieved. The above results conclude that our WNPSVR-WOA has important reference value for actual BOF application and can improve the steel product quality. Moreover, WNPSVR-WOA can also be used to other fields.