Efficacy and safety comparison between axillary lymph node dissection with no axillary surgery in patients with sentinel node-positive breast cancer: a systematic review and meta-analysis.

BACKGROUND: This systematic review and meta-analysis aimed to study the evidence on the efficacy and safety of omitting axillary lymph node dissection (ALND) for patients with clinically node-negative but sentinel lymph node (SLN)-positive breast cancer using all the available evidence. METHODS: The Embase, Medline, and Cochrane Library databases were searched through February 25, 2023. Original trials that compared only the sentinel lymph node biopsy (SLNB) with ALND as the control group for patients with clinically node-negative but SLN-positive breast cancer were included. The primary outcomes were axillary recurrence rate, total recurrence rate, disease-free survival (DFS), and overall survival (OS). Meta-analyses were performed to compare the odds ratio (OR) in rates and the hazard ratios (HR) in time-to-event outcomes between both interventions. Based on different study designs, tools in the revised Cochrane risk of bias tool were used for randomized trials and the risk of bias in nonrandomized studies of interventions to assess the risk of bias for each included article. Funnel plots and Egger's test were used for the publication's bias assessment. RESULTS: In total, 30 reports from 26 studies were included in the systematic review (9 reports of RCTs, 21 reports of retrospective cohort studies). According to our analysis, omitting ALND in patients with clinically node-negative but SLN-positive breast cancer had a similar axillary recurrence rate (OR = 0.95, 95% confidence interval (CI): 0.76-1.20), DFS (HR = 1.02, 95% CI: 0.89-1.16), and OS (HR = 0.97, 95% CI: 0.92-1.03), but caused a significantly lower incidence of adverse events and benefited in locoregional recurrence rate (OR = 0.76, 95% CI: 0.59-0.97) compared with ALND. CONCLUSION: For patients with clinically node-negative but SLN-positive breast cancer (no matter the number of the positive SLN), this review showed that SLNB alone had a similar axillary recurrence rate, DFS, and OS, but caused a significantly lower incidence of adverse events and showed a benefit for the locoregional recurrence compared with ALND. An OS benefit was found in the Macro subset that used SLNB alone versus complete ALND. Therefore, omitting ALND is feasible in this setting. TRIAL REGISTRATION: CRD 42023397963.

A wireless sensor network node fault diagnosis model based on belief rule base with power set.

Wireless sensor network (WSN) is inevitably subject to node failures due to their harsh operating environments and extra-long working hours. In order to ensure reliable and correct data collection, WSN node fault diagnosis is necessary. Fault diagnosis of sensor nodes usually requires the extraction of data features from the original collected data. However, the data features of different types of faults sometimes have similarities, making it difficult to distinguish and represent the types of faults in the diagnosis results, these indistinguishable types of faults are called ambiguous information. Therefore, a belief rule base with power set (PBRB) fault diagnosis method is proposed. In this method, the power set identification framework is used to represent the fuzzy information, the evidential reasoning (ER) method is used as the reasoning process, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) is used as the parameter optimization algorithm. The results of the case study show that PBRB method has higher accuracy and better stability compared to other commonly used fault diagnosis methods. According to the research results, PBRB can not only represent the fault types that are difficult to distinguish, but also has the advantage of small sample training. This makes the model obtain high fault diagnosis accuracy and stability.

Understanding Quantitative Circadian Regulations Are Crucial Towards Advancing Chronotherapy.

Circadian rhythms have a deep impact on most aspects of physiology. In most organisms, especially mammals, the biological rhythms are maintained by the indigenous circadian clockwork around geophysical time (~24-h). These rhythms originate inside cells. Several core components are interconnected through transcriptional/translational feedback loops to generate molecular oscillations. They are tightly controlled over time. Also, they exert temporal controls over many fundamental physiological activities. This helps in coordinating the body's internal time with the external environments. The mammalian circadian clockwork is composed of a hierarchy of oscillators, which play roles at molecular, cellular, and higher levels. The master oscillation has been found to be developed at the hypothalamic suprachiasmatic nucleus in the brain. It acts as the core pacemaker and drives the transmission of the oscillation signals. These signals are distributed across different peripheral tissues through humoral and neural connections. The synchronization among the master oscillator and tissue-specific oscillators offer overall temporal stability to mammals. Recent technological advancements help us to study the circadian rhythms at dynamic scale and systems level. Here, we outline the current understanding of circadian clockwork in terms of molecular mechanisms and interdisciplinary concepts. We have also focused on the importance of the integrative approach to decode several crucial intricacies. This review indicates the emergence of such a comprehensive approach. It will essentially accelerate the circadian research with more innovative strategies, such as developing evidence-based chronotherapeutics to restore de-synchronized circadian rhythms.

[Effect of Paidu Baoshen Pill on renal fibrosis in 5/6 nephrectomized rats].

OBJECTIVE: To observe the anti-renal fibrosis effect of Paidu Baoshen Pill (PBP) on 5/6 nephrectomized rats and to explore its mechanism. METHODS: Totally 50 SD male healthy rats were randomly divided into the normal control group (n = 10), the sham-operation group (n = 10), and the nephrectomy model group (n = 30) according to the proportion of 1:1:3. Rats in the sham-operation group had their renal capsule isolated without nephrectomy. Rats in the nephrectomy model group had their kidneys 5/6 nephrectomized. Then 24 h urine was collected and 24 h urinary protein (24 h UP) detected. Serum blood urea nitrogen (BUN) and serum creatitine (SCr) were also tested. According to the SCr level 30 rats of the model group were further randomly divided into the model group, the PBP group, and the Niaoduqing Granule (NG) group, 10 in each group. Rats in the PBP group and the NG group were respectively administered with PBP (at the daily dose of 1.0 g/kg) and NG (at the daily dose of 3.33 g/kg) by gastrogavage (they were dissolved in distilled water). At the same time, 2 mL distilled water was administered by gastrogavage to rats in the normal control group, the sham-operation group, and the nephrectomy model group, once daily for 4 successive weeks. Mental conditions, activities, hair color, shape of stool, and the body weight were observed during administration. After 4 weeks, urine was collected to detect 24 h UP. Blood was sampled to detect SCr, BUN, transforming growth factor ß1 (TGF-ß1), type III procollagen (PC III), collagen type IV (Col IV), laminin (LN), and fibronectin (FN). After rats were killed, their left remnant renal tissues were collected for pathological examinations. The protein expression quantity of TGF-ß1 and FN was detected by immunohistochemical method. mRNA expression levels of TGF-ß1 and FN were detected using real time fluorescent quantitative PCR. RESULTS: There was no statistical difference in the above indices between the normal control group and the sham-operation group (P > 0.05). Compared with the sham-operation group, rats' general condition was poorer in the model group, their body weight grew slower, and 24 h UP increased; serum levels of BUN, SCr, TGF-ß1, PC III, Col IV, LN, and FN increased; the residual renal pathological lesion was serious; expression levels of TGF-ß1, TGF-ß1, mRNA, FN, and FN mRNA increased in the renal tissue (all P < 0.01). Compared with the model group, rats' general condition was better, their body weight grew faster, 24 h UP reduced (P < 0.05), blood levels of BUN and SCr decreased significantly (P < 0.01), serum levels of TGF-ß1, PC III, CoL IV, LN, and FN decreased (P < 0.05, P < 0.01); the residual renal pathological lesion was attenuated in the PBP group and the NG group; expression levels of TGF-ß1, TGF-ß1, mRNA, FN, and FN mRNA decreased (P < 0.01). Compared with the NG group, blood levels of SCr and FN, and expression levels of FN and FN mRNA decreased more in the PBP group (P < 0.05). CONCLUSIONS: PBP had the effect of anti-renal fibro- sis in 5/6 nephrectomized rats. Down-regulating expression levels of TGF-ß1, and FN from gene transcription and protein translation levels might be one of its mechanisms.

Error margin analysis for feature gene extraction.

BACKGROUND: Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it. RESULTS: In this paper, we propose a novel gene extraction method of error margin analysis to optimize the feature genes. The proposed algorithm has been tested upon one synthetic dataset and two real microarray datasets. Meanwhile, it has been compared with five existing gene extraction algorithms on each dataset. On the synthetic dataset, the results show that the feature set extracted by our algorithm is the closest to the actual gene set. For the two real datasets, our algorithm is superior in terms of balancing the size and the validation accuracy of the resultant gene set when comparing to other algorithms. CONCLUSION: Because of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.

Method of regulatory network that can explore protein regulations for disease classification.

OBJECTIVE: To develop regulatory network to explore and model the regulatory relationships of protein biomarkers and classify different disease groups. METHODS: Regulatory network is constructed to be a hopfield-like network with nodes representing biomarkers and directional connections to be regulations in between. The input to the network is the measured expression levels of biomarkers, and the output is the summation of regulatory strengths from other biomarkers. The network is optimized towards minimizing the energy function that is defined as the measure of the disagreement between the input and output of the network. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. RESULTS: Two datasets have been used as test beds, one dataset includes patients of nasopharyngeal carcinoma with different responses to chemotherapy drug, and the other consists of patients of severe acute respiratory syndrome, influenza, and control normals. The regulatory networks among protein biomarkers were reconstructed for different disease conditions in each dataset. We demonstrated our methods have better classification capability when comparing with conventional methods including Fisher linear discriminant (FLD), K-nearest neighborhood (KNN), linear support vector machines (linSVM) and radial basis function based support vector machines (rbfSVM). CONCLUSION: The derived networks can effectively capture the unique regulatory patterns of protein markers associated with different patient groups and hence can be used for disease classification. The discovered regulation relationships can potentially provide insights to revealing the molecular signaling pathways. In this paper, a novel technique of regulatory network is proposed on purpose of modeling biomarker regulations and classifying different disease groups. The network is composed of a certain number of nodes that are directionally connected in between in which nodes denote predictors and connections to be the regulation relationship. The network is optimized towards minimizing its energy function with biomarker expression data acquired from a specific patient group, thus the optimized network can model the regulatory relationship of biomarkers under the same circumstance. To simulate more complicated regulations, a sigmoid kernel function is imposed on each node to construct a non-linear regulatory network. The regulatory network can extract unique features of each disease condition, thus one immediate application of regulatory network is to classifying different diseases. We demonstrated that regulatory network is capable of performing disease classification through comparing with conventional methods including FLD, KNN, linSVM and rbfSVM on two protein datasets. We believe our method is promising in mining knowledge of protein regulations and be powerful for disease classification.

[Expression and identification of gene Loa22 from the virulent strain of serovar Lai of L. interrogans and its cytotoxicity to macrophages].

OBJECTIVE: To study the function of Loa22 gene from virulent serovar Lai. L. interrogans by expressing its protein. METHODS: The recombinant plasmid with Loa22 was constructed and its expression was induced by IPTG. The expression product was identified by SDS-PAGE and Western Blotting and purified with GST-affinity chromatography. The purified protein was applied to mice macrophages ANA-1 cells purified by GST-affinity column and exerted to ANA-1 cells. The cytotoxicity of purified protein to ANA-1 was evaluated by detecting LDH activity in culture medium, XTT aborbtion and apoptosis ration. RESULTS: The recombinant plasmid with Loa22 mature peptide was constructed successfully and the protein was purified subsequently. Significant higher level of LDH activity and apoptosis ratio and lower XTT absorption were observed by comparison of expression protein treated cells and those untreated. CONCLUSION: Loa22 had a toxic effect on ANA-1 and the gene Loa22 might be a virulent-related gene of L. interrogans.

[Construction of recombinant Mycobacterium smegmatis expressing ESAT-6 and its effects on macrophages].

OBJECTIVE: Objective To construct recombinant Mycobacterium smegmatis expressing ESAT-6 of the human pathogen Mycobacterium tuberculosis. METHODS: ESAT-6 gene was amplified from M. tuberculosis genomic DNA and inserted into an E.coli-mycobacterium shuttle vector under the control of HSP60 promoter. The recombinant vector was transformed into M. smegmatis by electroporation. To assess the ability of recombinant M. smegmatis to activate macrophage, mouse macrophage ANA-1 was cocultured with recombinant M. smegmatis. The apoptosis of ANA-1 cells was detected by flow cytometry and iNOS mRNA expression of the cells was detected by reverse transcription-polymerase chain reaction (RT-PCR). The survival of M. smegmatis strains in ANA-1 cells was evaluated. RESULTS: The recombinant vector was verified by restriction endonuclease digestion and DNA sequencing. ESAT-6 protein was expressed in M. smegmatis in response to heat shock and the molecular weight of the expression product was identical to the expected value. The growth curve of the new recombinant M. smegmatis was consistent with that of the wild-type strain, suggesting the absence of ESAT-6 protein toxicity against M. smegmatis. The recombinant M. smegmatis did not induce significant changes in mouse macrophage ANA-1 apoptosis. Coculture of the macrophages with recombinant M. smegmatis for 4 to 24 h could induce iNOS expression in the former, and the CFU of recombination M. smegmatis grown in ANA-1 cells was much less than that of the control bacteria. CONCLUSION: The recombinant M. smegmatis expressing M. tuberculosis ESAT-6 gene possess immunogenicity, which provides experimental evidence for the development of novel M. smegmatis-based vaccine against tuberculosis.

[Protein-protein interaction between ESAT-6 and CFP-10 of mycobacterium tuberculosis].

OBJECTIVE: To investigate the protein-protein interaction between ESAT-6 and CFP-10 of mycobacterium tuberculosis. METHODS: ESAT-6 gene and CFP-10 gene were amplified from mycobacterium tuberculosis genome DNA. The ESAT-6 gene was subcloned into pGBKT7 and the CFP-10 gene was subcloned into pGADT7-Rec. After being verified with restriction endonuclease digestion and DNA sequencing, the recombinant vectors were transformed into yeast cell AH109 by lithium acetate method. RESULTS: The yeast cells co-transformed with pGBKT7-ESAT-6 and pGADT7-CFP-10 grew on SD/-Ade/-His/-Leu/-Trp plates, and beta-galactosidase activity assays showed positive results. CONCLUSION: ESAT-6 and CFP-10 protein could interact with each other in yeast cells.