CFI-Net: A Choquet Fuzzy Integral based Ensemble Network with PSO-Optimized Fuzzy Measures for Diagnosing Multiple Skin Diseases Including Mpox.

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98.06% and 94.81% for Mpox detection, 99.06% for foot ulcer detection, and an impressive 99.61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.

Enhanced degradation and methane production of food waste anaerobic digestate using an integrated system of anaerobic digestion and microbial electrolysis cells for long-term operation.

The integrated system of anaerobic digestion and microbial electrolysis cells (AD-MEC) was a novel approach to enhance the degradation of food waste anaerobic digestate and recover methane. Through long-term operation, the start-up method, organic loading, and methane production mechanism of the digestate have been investigated. At an organic loading rate of 4000 mg/L, AD-MEC increased methane production by 3-4 times and soluble chemical oxygen demand (SCOD) removal by 20.3% compared with anaerobic digestion (AD). The abundance of bacteria Fastidiosipila and Geobacter, which participated in the acid degradation and direct electron transfer in the AD-MEC, increased dramatically compared to that in the AD. The dominant methanogenic archaea in the AD-MEC and AD were Methanobacterium (44.4-56.3%) and Methanocalculus (70.05%), respectively. Geobacter and Methanobacterium were dominant in the AD-MEC by direct electron transfer of organic matter into synthetic methane intermediates. AD-MEC showed a perfect SCOD removal efficiency of the digestate, while methane as clean energy was obtained. Therefore, AD-MEC was a promising technology for deep energy transformation from digestate.

CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection.

The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.

Metaheuristics optimization-based ensemble of deep neural networks for Mpox disease detection.

The rising number of cases of human Mpox has emerged as a major global concern due to the daily increase of cases in several countries. The disease presents various skin symptoms in infected individuals, making it crucial to promptly identify and isolate them to prevent widespread community transmission. Rapid determination and isolation of infected individuals are therefore essential to curb the spread of the disease. Most research in the detection of Mpox disease has utilized convolutional neural network (CNN) models and ensemble methods. However, to the best of our knowledge, none have utilized a meta-heuristic-based ensemble approach. To address this gap, we propose a novel metaheuristics optimization-based weighted average ensemble model (MO-WAE) for detecting Mpox disease. We first train three transfer learning (TL)-based CNNs (DenseNet201, MobileNet, and DenseNet169) by adding additional layers to improve their classification strength. Next, we use a weighted average ensemble technique to fuse the predictions from each individual model, and the particle swarm optimization (PSO) algorithm is utilized to assign optimized weights to each model during the ensembling process. By using this approach, we obtain more accurate predictions than individual models. To gain a better understanding of the regions indicating the onset of Mpox, we performed a Gradient Class Activation Mapping (Grad-CAM) analysis to explain our model's predictions. Our proposed MO-WAE ensemble model was evaluated on a publicly available Mpox dataset and achieved an impressive accuracy of 97.78%. This outperforms state-of-the-art (SOTA) methods on the same dataset, thereby providing further evidence of the efficacy of our proposed model.

StoneNet: An Efficient Lightweight Model Based on Depthwise Separable Convolutions for Kidney Stone Detection from CT Images.

Kidney stone disease is one of the most common and serious health problems in much of the world, leading to many hospitalizations with severe pain. Detecting small stones is difficult and time-consuming, so an early diagnosis of kidney disease is needed to prevent the loss of kidney failure. Recent advances in artificial intelligence (AI) found to be very successful in the diagnosis of various diseases in the biomedical field. However, existing models using deep networks have several problems, such as high computational cost, long training time, and huge parameters. Providing a low-cost solution for diagnosing kidney stones in a medical decision support system is of paramount importance. Therefore, in this study, we propose "StoneNet", a lightweight and high-performance model for the detection of kidney stones based on MobileNet using depthwise separable convolution. The proposed model includes a combination of global average pooling (GAP), batch normalization, dropout layer, and dense layers. Our study shows that using GAP instead of flattening layers greatly improves the robustness of the model by significantly reducing the parameters. The developed model is benchmarked against four pre-trained models as well as the state-of-the-art heavy model. The results show that the proposed model can achieve the highest accuracy of 97.98%, and only requires training and testing time of 996.88 s and 14.62 s. Several parameters, such as different batch sizes and optimizers, were considered to validate the proposed model. The proposed model is computationally faster and provides optimal performance than other considered models. Experiments on a large kidney dataset of 1799 CT images show that StoneNet has superior performance in terms of higher accuracy and lower complexity. The proposed model can assist the radiologist in faster diagnosis of kidney stones and has great potential for deployment in real-time applications.

BMRI-NET: A Deep Stacked Ensemble Model for Multi-class Brain Tumor Classification from MRI Images.

Brain tumors are one of the most dangerous health problems for adults and children in many countries. Any failure in the diagnosis of brain tumors may lead to shortening of human life. Accurate and timely diagnosis of brain tumors provides appropriate treatment to increase the patient's chances of survival. Due to the different characteristics of tumors, one of the challenging problems is the classification of three types of brain tumors. With the advent of deep learning (DL) models, three classes of brain tumor classification have been addressed. However, the accuracy of these methods requires significant improvements in brain image classification. The main goal of this article is to design a new method for classifying the three types of brain tumors with extremely high accuracy. In this paper, we propose a novel deep stacked ensemble model called "BMRI-NET" that can detect brain tumors from MR images with high accuracy and recall. The stacked ensemble proposed in this article adapts three pre-trained models, namely DenseNe201, ResNet152V2, and InceptionResNetV2, to improve the generalization capability. We combine decisions from the three models using the stacking technique to obtain final results that are much more accurate than individual models for detecting brain tumors. The efficacy of the proposed model is evaluated on the Figshare brain MRI dataset of three types of brain tumors consisting of 3064 images. The experimental results clearly highlight the robustness of the proposed BMRI-NET model by achieving an overall classification of 98.69% and an average recall, F1-score and MCC of 98.33%, 98.40, and 97.95%, respectively. The results indicate that the proposed BMRI-NET model is superior to existing methods and can assist healthcare professionals in the diagnosis of brain tumors.

Development of a rapid startup method of direct electron transfer-dominant methanogenic microbial electrosynthesis.

The rapid startup of carbon dioxide reduction-methanogenic microbial electrosynthesis is crucial for its industrial application, and the development of cathode biofilm is the key to its industrialization. Based on the new discovery that biofilm formed by placing graphite felt in an anaerobic reactor was electroactive, with strong direct electron transfer and methanogenesis ability (24.52 mL/L/d), a new startup method was developed. The startup time was shortened by at least 20 days and charge transfer resistance was reduced by 4.45-10.78 times than common startup methods (inoculating cathode effluent or granular sludge into the cathode chamber). The new method enriched electroactive bacteria. Methanobacterium and Methanosaeta accounted for 62.04% and 34.96%, respectively. The common methods inoculating cathode effluent or granular sludge enriched hydrogenotrophic microorganisms (>95%) or Methanosaeta (54.10%) due to the local environments of cathode. This new rapid and easy startup method may support the scale-up of microbial electrosynthesis.

A deep learning-based framework for detecting COVID-19 patients using chest X-rays.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused outbreaks of new coronavirus disease (COVID-19) around the world. Rapid and accurate detection of COVID-19 coronavirus is an important step in limiting the spread of the COVID-19 epidemic. To solve this problem, radiography techniques (such as chest X-rays and computed tomography (CT)) can play an important role in the early prediction of COVID-19 patients, which will help to treat patients in a timely manner. We aimed to quickly develop a highly efficient lightweight CNN architecture for detecting COVID-19-infected patients. The purpose of this paper is to propose a robust deep learning-based system for reliably detecting COVID-19 from chest X-ray images. First, we evaluate the performance of various pre-trained deep learning models (InceptionV3, Xception, MobileNetV2, NasNet and DenseNet201) recently proposed for medical image classification. Second, a lightweight shallow convolutional neural network (CNN) architecture is proposed for classifying X-ray images of a patient with a low false-negative rate. The data set used in this work contains 2,541 chest X-rays from two different public databases, which have confirmed COVID-19 positive and healthy cases. The performance of the proposed model is compared with the performance of pre-trained deep learning models. The results show that the proposed shallow CNN provides a maximum accuracy of 99.68% and more importantly sensitivity, specificity and AUC of 99.66%, 99.70% and 99.98%. The proposed model has fewer parameters and low complexity compared to other deep learning models. The experimental results of our proposed method show that it is superior to the existing state-of-the-art methods. We believe that this model can help healthcare professionals to treat COVID-19 patients through improved and faster patient screening.

rs895819 in microRNA-27a increase stomach neoplasms risk in China: A meta-analysis.

BACKGROUND: Across the globe, gastric cancer is a significant public health problem. This meta-analysis was conducted to investigate the association of microRNA-27a (miRNA-27a) rs895819 with gastric cancer risk. METHODS: The search of databases updated on October 10, 2018 included Pubmed, Embase, Cochrane Library and Web of science. Odds ratio (ORs) and 95% confidence interval (CIs) were calculated to assess the risk of tumor. RESULTS: Overall meta-analysis suggested the miRNA-27a rs895819 was not related to the gastric carcinogenesis among all model including allele contrast (G vs A, pooled OR: 1.096, 95% CI: 0.962-1.249, P = 0.196), codominant model (GG vs AA, pooled OR: 1.124, 95% CI: 0.794-1.592, P = 0.590; AG vs AA, pooled OR: 1.101, 95% CI: 0.966-1.217, P = 0.060), dominant model (AG + GG vs AA, pooled OR: 1.123, 95% CI: 0.964-1.307, P = 0.136) and recessive model (GG vs AG + AA, pooled OR: 0.927, 95% CI: 0.673-1.278, P = 0.644). Interestingly, among different ethnicity group, significant relation between rs895819 and gastric cancer was observed in co-dominant model among Chinese population (AG vs AA, pooled OR: 1.158, 95% CI: 1.038-1.291, P = 0.008) but not some regions of European population (AG vs AA, pooled OR: 0.852, 95% CI: 0.632-1.148, P = 0.179). CONCLUSIONS: Our results find that rs895819 contributed to occurrence of gastric cancer in co-dominant model in Chinese population.

Centrifugal micro-channel array droplet generation for highly parallel digital PCR.

Stable water-in-oil emulsion is essential to digital PCR and many other bioanalytical reactions that employ droplets as microreactors. We developed a novel technology to produce monodisperse emulsion droplets with high efficiency and high throughput using a bench-top centrifuge. Upon centrifugal spinning, the continuous aqueous phase is dispersed into monodisperse droplet jets in air through a micro-channel array (MiCA) and then submerged into oil as a stable emulsion. We performed dPCR reactions with a high dynamic range through the MiCA approach, and demonstrated that this cost-effective method not only eliminates the usage of complex microfluidic devices and control systems, but also greatly suppresses the loss of materials and cross-contamination. MiCA-enabled highly parallel emulsion generation combines both easiness and robustness of picoliter droplet production, and breaks the technical challenges by using conventional lab equipment and supplies.

Meta-Analysis for the Therapeutic Effect of Neoadjuvant Therapy in Resectable Esophageal Cancer.

We aimed to review the therapeutic effects of neoadjuvant chemoradiotherapy (NCRT), chemotherapy (NCT), and radiotherapy (NRT) on patients with resectable Esophageal cancer (EsC) by comparison with surgery alone (SA). PubMed, EMBASE and Cochrane were searched for eligible studies published up to March 2015. Cochrane reviews were used for quality assessment. Eight primary outcomes were analyzed. Risk ratios (RRs)/ hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) were calculated using the random- or fixed- effects model. Heterogeneity was assessed using the Chi-square-based Q statistic and the I 2 test. Publication bias was examined by the Begg's funnel plot. Totally 24 articles including 4718 EsC cases were eligible for this meta-analysis. The quality of the literatures was relatively high. Significant difference was found in five-year survival rate (RR = 1.45, 95% CI: 1.17-1.79, P < 0.01) between patients treated with NCT and SA, while the eight enrolled primary outcomes were all statistically different between NCRT and SA, and significant difference was identified in three-year survival between NCRT and NCT (RR = 1.35, 95% CI: 1.14-1.60, P < 0.01). No obvious publication bias was observed. NCRT and NCT provide an obvious benefit for EsC treatment over SA, and NCRT possesses a clear advantage compared with NCT.

[Three-dimensional computed tomography in postoperative evaluation of the knees after anterior cruciate ligament reconstruction].

This study evaluated the clinical value of three-dimensional computed tomography (3D-CT) images in the knees following arthroscopic anterior cruciate ligament (ACL) reconstruction. Sixty-five consecutive patients underwent arthroscopic ACL reconstruction with single-incision and single-tunnel techniques. Preoperative and postoperative (12 months in between) clinical evaluation were performed using the Lysholm knee score and a KT-1000 arthrometer (side to side). Computed tomography (CT) of the knees was performed in a week after operation in all cases and at mean follow-up of 12 months. All of the clinical evaluation scales performed showed an overall improvement. 3D-CT images can display not only the bone tunnels of the knees including femoral and tibia very distinctly, but also the contour of the reconstructed ACL including adjacent structures. The average femoral tunnel diameter increased significantly (3%) from (9.15 +/- 0.03) mm postoperatively to (9.48 +/- 0.5) mm after 12 months; tibial tunnel increased significantly (12%) from (9.11 +/- 0.09) mm to (10.2 +/- 0.3) mm. There was no statistical difference between tunnel enlargements. So multi-slices spiral CT can evaluate the contour and changes of contour and changes of the knee after ACL reconstruction, which will be helpful in the intraoperative location and postoperative assessment of the knees.