Metagenomic Analyses Reveal Gut Microbial Profiles of Cnaphalocrocis medinalis Driven by the Infection of Baculovirus CnmeGV.

The composition of microbiota in the digestive tract gut is essential for insect physiology, homeostasis, and pathogen infection. Little is known about the interactions between microbiota load and oral infection with baculoviruses. CnmeGV is an obligative baculovirus to Cnaphalocrocis medinalis. We investigated the impact of CnmeGV infection on the structure of intestinal microbes of C. medinalis during the initial infection stage. The results revealed that the gut microbiota profiles were dynamically driven by pathogen infection of CnmeGV. The numbers of all the OTU counts were relatively higher at the early and later stages, while the microbial diversity significantly increased early but dropped sharply following the infection. The compositional abundance of domain bacteria Firmicutes developed substantially higher. The significantly enriched and depleted species can be divided into four groups at the species level. Fifteen of these species were ultimately predicted as the biomarkers of CnmeGV infection. CnmeGV infection induces significant enrichment of alterations in functional genes related to metabolism and the immune system, encompassing processes such as carbohydrate, amino acid, cofactor, and vitamin metabolism. Finally, the study may provide an in-depth analysis of the relationship between host microbiota, baculovirus infection, and pest control of C. medinalis.

CmHem, a hemolin-like gene identified from Cnaphalocrocis medinalis, involved in metamorphosis and baculovirus infection.

Background: As a member of the immunoglobulin superfamily, hemolins play a vital role in insect development and defense against pathogens. However, the innate immune response of hemolin to baculovirus infection varies among different insects. Methods and results: In this study, the hemolin-like gene from a Crambidae insect, Cnaphalocrocis medinalis, CmHem was cloned, and its role in insect development and baculovirus infection was analyzed. A 1,528 bp contig as potential hemolin-like gene of C. medinalis was reassembled from the transcriptome. Further, the complete hemolin sequence of C. medinalis (CmHem) was cloned and sequenced. The cDNA of CmHem was 1,515 bp in length and encoded 408 amino acids. The deduced amino acid of CmHem has relatively low identities (41.9-62.3%) to various insect hemolins. However, it contains four Ig domains similarity to other insect hemolins. The expression level of CmHem was the highest in eggs, followed by pupae and adults, and maintained a low expression level at larval stage. The synthesized siRNAs were injected into mature larvae, and the CmHem transcription decreased by 51.7%. Moreover, the abdominal somites of larvae became straightened, could not pupate normally, and then died. Infection with a baculovirus, C. medinalis granulovirus (CnmeGV), the expression levels of CmHem in the midgut and fat body of C. medinalis significantly increased at 12 and 24 h, respectively, and then soon returned to normal levels. Conclusions: Our results suggested that hemolin may be related to the metamorphosis of C. medinalis. Exposure to baculovirus induced the phased expression of hemolin gene in the midgut and fat body of C. medinalis, indicated that hemolin involved in the immune recognition of Crambidae insects to baculovirus.

Characterization of a novel cell wall hydrolase CwlE involved in Bacillus thuringiensis subsp. israelensis mother cell lysis.

Cell wall hydrolases are ubiquitous among spore-form bacteria and essential for mother cell lysis. In this study, a novel cell wall hydrolase gene cwlE involved in mother cell lysis was characterized from Bacillus thuringiensis subsp. israelensis (Bti) strain Bt-59. cwlE was specifically expressed in Bti and located in the large plasmid carrying the insecticidal genes. The encoded CwlE protein consists of a MurNAc-LAA domain and two highly conserved catalytic residues (E26 and E151). The recombinant CwlE-His protein was able to digest the cell wall of Bti, indicating that CwlE is an N-acetylmuramoyl-L-alanine amidase. Transcriptional analysis indicated that cwlE began to express at the early stage of stationary phase and was controlled by SigE. Single mutation of cwlE gene delayed Bti mother cell lysis, while double mutation of cwlE and sigK completely blocked Bti mother cell lysis. After exposure to UV light to deactivate the crystal proteins, the level of decrease of insecticidal activity against mosquito larvae of Bt-59 (ΔcwlE-sigK) was less than that observed for Bt-59. This study elucidates the mechanism of Bti mother cell lysis and provides an effective strategy for mosquito control using Bt products with increased persistence.

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion.

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

Matrine improves the hepatic microenvironment and reverses epithelial-mesenchymal transition in hepatocellular carcinoma by inhibiting the Wnt-1/ß-catenin signaling pathway.

OBJECTIVE: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality. Despite rapid progress in targeted therapy and immunotherapy for HCC over the past 10 years, the overall efficacy remains unsatisfactory. This is mainly due to the presence of an intrahepatic microenvironment of cirrhosis in HCC patients, leading to cancer recurrence and drug resistance. METHODS: In this study, we investigated the correlations between the Wnt-1/ß-catenin signaling pathway and the prognosis as well as liver function of HCC patients. Additionally, we conducted in vitro experiments using different concentrations of matrine on HuH-7 cells. Furthermore, we verified the associations between the Wnt-1/ß-catenin signaling pathway, inflammation, and epithelial-mesenchymal transition (EMT) in a rat model of pre-hepatocellular carcinoma. Finally, matrine was employed to treat pre-hepatocellular carcinoma in rats and patients with advanced hepatocellular carcinoma. RESULTS: The results demonstrated the activation of the Wnt-1/ß-catenin signaling pathway, the occurrence of EMT, and exacerbated inflammation in human HCC tissues. In HuH-7 cell experiments, matrine effectively downregulated the Wnt-1/ß-catenin pathway, reversed EMT, and suppressed migration and invasion of HCC cells. In the rat model of pre-hepatocellular carcinoma, matrine dose-dependently inhibited the activation of the Wnt-1/ß-catenin signaling pathway, reversed the occurrence of EMT, and alleviated liver inflammation. Matrine analogues exhibited promising hepatoprotective effects in patients with advanced HCC. CONCLUSIONS: Matrine can reverse EMT, alleviate intrahepatic inflammation, and counteract immune depletion by inhibiting the Wnt-1/ß-catenin signaling pathway in HCC.

Research on Error Correction Technology in Underwater SINS/DVL Integrated Positioning and Navigation.

Underwater vehicles are key carriers for underwater inspection and operation tasks, and the successful implementation of these tasks depends on the positioning and navigation equipment with corresponding accuracy. In practice, multiple positioning and navigation devices are often combined to integrate the advantages of each equipment. Currently, the most common method for integrated navigation is combination of the Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL). Various errors will occur when SINS and DVL are combined together, such as installation declination. In addition, DVL itself also has errors in the measurement of speed. These errors will affect the final accuracy of the combined positioning and navigation system. Therefore, error correction technology has great significance for underwater inspection and operation tasks. This paper takes the SINS/DVL integrated positioning and navigation system as the research object and deeply studies the DVL error correction technology in the integrated system.

EEG-Based Mental Workload Classification Method Based on Hybrid Deep Learning Model Under IoT.

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this paper, a method based on the spatial and time-frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time-frequency domain information is obtained based on wavelet transform. The spatial and time-frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

Molecular typing and mutational characterization of rectal neuroendocrine neoplasms.

BACKGROUND: Rectal neuroendocrine neoplasms (NENs) are rare neoplasms with limited understanding of its genomic alterations and molecular typing. METHODS: The paraffin-embedded tissue specimens of 38 patients with rectal NENs after surgery were subjected to whole gene sequencing (WGS), and mutation profilings were drawn to identify high-frequency mutation genes, copy-number variations (CNVs), tumor mutation burden (TMB), signal pathways, mutation signatures, DNA damage repair (DDR) genes, and molecular types. The differences of mutated genes and signaling pathways in different pathological grades and metastatic/non-metastatic groups were compared. It helped to search for potential targets. RESULTS: C > T and T > C transitions are the most common base substitutions in rectal NENs. DNA mismatch repair deficiency, DNA base modifications, smoking and exposure to ultraviolet light might play a role in the occurrence of rectal NENs. DAXX, KMT2C, BCL2L1, LTK, MERTK, SPEN, PKN1, FAT3, and LRP2 mutations were found in only low-grade rectal NETs, whereas APC, TP53, NF1, SOX9, and BRCA1 mutations were common in high-grade rectal NECs/MiNENs. These genes helped in distinguishing poorly-differentiated or well-differentiated rectal NENs. Alterations in P53, Wnt and TGFß signaling pathways were more pronounced in rectal NECs and MiNENs. Alterations in Wnt, MAPK and PI3K/AKT signaling pathways promoted metastases. Rectal NENs were classified into two molecular subtypes by cluster analysis based on the mutant genes and signaling pathways combined with clinicopathological features. Patients with mutations in the LRP2, DAXX, and PKN1 gene showed a trend of well-differentiated and early-stage tumors with less metastasis (p = 0.000). CONCLUSIONS: This study evaluated risk factors for regional lymphatic and/or distant metastases, identified high-frequency mutated genes, mutation signatures, altered signaling pathways through NGS. Rectal NENs were divided into two molecular types. This helps to evaluate the likelihood of metastasis, formulate follow-up strategies for patients and provide a target for future research on precision treatment of rectal NENs. PARP inhibitors, MEK inhibitors, mTOR/AKT/PI3K and Wnt signaling pathway inhibitors may be effective drugs for the treatment of metastatic rectal NENs.

Transarterial Chemoembolization Combined With PD-1 Inhibitors Plus Lenvatinib Showed Improved Efficacy for Treatment of Unresectable Hepatocellular Carcinoma Compared With PD-1 Inhibitors Plus Lenvatinib.

Background: Programmed cell death protein-1 inhibitors combined with lenvatinib have become a popular treatment option for patients with unresectable hepatocellular carcinoma. Transarterial chemoembolization combined with programmed cell death protein-1 inhibitors and lenvatinib has also shown preliminary efficacy in the unresectable hepatocellular carcinoma. We conducted this observational, retrospective, cohort study to compare the clinical outcomes and safety of transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib versus programmed cell death protein-1 inhibitors plus lenvatinib in patients with unresectable hepatocellular carcinoma. Methods: Between November 2019 and November 2021, patients who were diagnosed with unresectable hepatocellular carcinoma and received transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib or programmed cell death protein-1 inhibitors plus lenvatinib treatment were reviewed for eligibility. The primary endpoints included objective response rate, overall survival, and progression-free survival. The secondary endpoint was the frequency of key adverse events. Results: In total, 105 patients were eligible for the present study, and they were divided into the transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group (n = 46) and the programmed cell death protein-1 inhibitors plus lenvatinib group (n = 59). The patient cohort after a one-to-one propensity score matching (n = 86) was also analyzed. The transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group had a higher objective response rate both in the patient cohort before propensity score matching (54.3% vs 25.4%, P = .002) and after propensity score matching (55.8% vs 30.2%, P = .017). The patients in the transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group had prolonged overall survival (median, 20.5 vs 12.6 months, P = .015) and progression-free survival (median, 10.2 vs 7.4 months, P = .035). For patient cohort- propensity score matching, the overall survival (20.5 vs 12.8 months, P = .013) and progression-free survival (12.1 vs 7.8 months, P = .030) were also significantly better in the transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group than in the programmed cell death protein-1 inhibitors plus lenvatinib group. There were no significant differences between the 2 groups concerning adverse reactions caused by immunotherapy and lenvatinib. The adverse reactions caused by transarterial chemoembolization were transient and were quickly reversed. Conclusions: Compared to programmed cell death protein-1 inhibitors plus lenvatinib, transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib may provide better treatment response and survival benefits for patients with unresectable hepatocellular carcinoma, and the adverse events were manageable.

PD-1/PD-L1 Inhibitor Plus Chemotherapy Versus PD-1/PD-L1 Inhibitor in Microsatellite Instability Gastrointestinal Cancers: A Multicenter Retrospective Study.

PURPOSE: To investigate the efficacy of PD-1/PD-L1 inhibitors plus chemotherapy versus anti-PD-1/PD-L1 monotherapy in advanced microsatellite instability (MSI)/mismatch repair-deficient (dMMR) gastrointestinal cancers. METHODS: We retrospectively recruited patients with MSI/dMMR gastrointestinal cancer who received anti-PD-1/PD-L1 with or without chemotherapy and compared objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) of PD-1/PD-L1 inhibitor plus chemotherapy (chemo-anti-PD-1/PD-L1 group) and PD-1/PD-L1 inhibitor alone (anti-PD-1/PD-L1 group). Propensity score-based overlap weighting analysis was conducted to adjust the baseline covariable imbalance. Sensitivity analysis was performed to confirm the stability of the results by propensity score matching and multivariable Cox and logistic regression models. RESULTS: A total of 256 patients were eligible, with 68 and 188 receiving chemo-anti-PD-1/PD-L1 and anti-PD-1/PD-L1, respectively. The chemo-anti-PD-1/PD-L1 group showed significant improvements versus the anti-PD-1/PD-L1 group in ORR (61.8% v 38.8%; P = .001), DCR (92.6% v 74.5%; P = .002), PFS (median PFS [mPFS], not reached [NR] v 27.9 months; P = .004), and OS (median OS [mOS], NR v NR; P = .014). After overlap weighting, the improvements tended to be more significant with chemo-anti-PD-1/PD-L1 versus anti-PD-1/PD-L1 in ORR (62.5% v. 38.3%; P < .001), DCR (93.8% v 74.2%; P < .001), PFS (mPFS, NR v 26.0 months; P = .004), and OS (mOS, NR v NR; P = .010). These results were solidified through sensitivity analysis. CONCLUSION: Chemo-anti-PD-1/PD-L1 is superior to anti-PD-1/PD-L1 in MSI/dMMR gastrointestinal cancers with improved efficacy.

AUV-Aided Optical-Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning.

Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

Knowledge Sharing for Pulmonary Nodule Detection in Medical Cyber-Physical Systems.

With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been humanity's best dream through the ages. This paper explores a novel knowledge-sharing model in MCPS and takes a pulmonary nodule detection task as a significant case for building an Unet-based mask generator. A Classification-guided Module (CGM)-based discriminator with knowledge from EMRs is set against a generator to offer a promising result for each mask from the inexperienced participant of federated ML. After an iterative communication between the federated server and its clients for knowledge sharing, the segmented sub-image owns a coincident attribute distribution with that of the EMRs from the experts. Besides, the adversarial network augment the data to normalize the data distribution for all the clients as a remission for none independent identically distributed (non-IID) data problem. We implement a detection framework on the simulated EI environment following an existing adaptive synchronization strategy based on data sharing and median loss function. On 1304 scans of the merged dataset, our proposed framework can help boost the detection performance for most of the existing methods of pulmonary nodule detection.

Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network.

In this paper, we study data transmission in the Terrestrial-Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial-satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space-time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space-time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial-satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn-Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

A Malicious Code Detection Method Based on FF-MICNN in the Internet of Things.

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion-malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.

The Gut Microbiota Composition of Cnaphalocrocis medinalis and Their Predicted Contribution to Larval Nutrition.

Intestinal bacterial flora plays an important role in the nutrition, physiology, and behavior of herbivorous insects. The composition of gut microbiota may also be affected by the food consumed. Cnaphalocrocis medinalis is an oligophagous pest, feeds on rice leaves almost exclusively and causes serious damage to rice in Asian countries. Using antibiotic treatment and metagenome sequencing, we investigated the influence of the food sources (rice and maize seedlings) on the structure and functions of intestinal bacteria of C. medinalis. Firstly, food utilization indices, relative growth rate (RGR), relative consumption rate (RCR), efficiency of conversion of ingested food (ECI), and efficiency of conversion of digested food (ECD), were all significantly adversely affected in the antibiotic treatment eliminating gut bacteria, showing that the microbiota loading in the gut were essential for the larva growth and development of C. medinalis. Further, metagenome sequencing revealed that different diets caused a variation in gut microbiota composition of C. medinalis, indicating that the gut microbiota were in part driven by the diet provided. However, the larvae of C. medinalis hosted a core microbial community in the gut, which was independent from the diets changing. The dominant bacteria in the two feeding groups were highly consistent in the gut of C. medinalis larvae, with the gut bacterial community dominated by Firmicutes at the phylum level, Enterococcus at the genus level, Enterococcus sp. FDAARGOS-375, E. casseliflavus, E. gallinarum, and E. sp. CR-Ec1 accounted for more than 96% of the gut microbiota. Functional prediction analysis demonstrated that gut bacteria encoded a series of metabolism-related enzymes involved in carbohydrate metabolism and amino acid synthesis. Carbohydrate metabolism was the most enriched function in both groups and was more abundant in rice feeding group than in maize feeding group. The core dominant Enterococcus species possessed complete pathways of 14 carbohydrates metabolism, 11 amino acids biosynthesis, and two vitamins synthesize, implied to contribute an essential role to the nutrition intake and development of C. medinalis. Finally, the study may provide an in-depth analysis of the symbiont-host co-adaptation and new insights into the management of C. medinalis.

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

Prognosis of hepatocellular carcinoma and its association with immune cells using systemic inflammatory response index.

Aim: To explore the prognostic value of the systemic inflammatory response index (SIRI) and peripheral blood T-cell subsets in patients with hepatocellular carcinoma (HCC) and the relationship between them. Materials & methods: We treated 352 patients with HCC with sorafenib and/or immune checkpoint inhibitors (ICIs) and analyzed SIRI and peripheral blood T cells. Results: SIRI was an independent prognostic factor for patients with HCC receiving systemic therapy. Patients with high SIRI and low baseline peripheral blood T-cell counts showed a poor response to ICIs. SIRI was significantly and negatively correlated with CD3+, CD4+ and CD8+ T-cell counts. Conclusion: SIRI markers can be employed to noninvasively assess the presence of cancer-promoting inflammation in the tumor microenvironment and predict the efficacy of targeted therapy and immunotherapy.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. The change of immune microenvironment plays an important role in the occurrence and development of HCC. Recently, targeted therapy and immunotherapy have brought new hope to patients with advanced HCC. However, owing to the complexity of the immune microenvironment, not all patients can benefit from it. This study explores a simple, non-invasive method based on blood cell count to assess the immune microenvironment of HCC and predict the efficacy of treatment.

Sarcopenia and Systemic Inflammation Response Index Predict Response to Systemic Therapy for Hepatocellular Carcinoma and Are Associated With Immune Cells.

Background: Systemic therapies, including immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs), have challenged the use of conventional therapies for hepatocellular carcinoma (HCC). It is crucial to determine which patients could benefit most from combination therapy. This study aims to examine the associations of sarcopenia and systemic inflammation response index (SIRI) with the treatment responses and efficacies in patients with HCC treated with ICIs and tyrosine kinase inhibitors TKIs, as well as investigate the correlation between sarcopenia and inflammatory or immune states. Methods: We reviewed 160 patients with HCC treated with TKIs and ICIs. The patients' psoas muscle size was measured on axial computed tomography scans and normalized for the patients' height squared. This value was referred to as the psoas muscle index (PMI). Sarcopenia was determined from PMI and their relationships with patients' clinicopathological characteristics, inflammation indexes, peripheral blood T-cell subsets and survival were evaluated. Results: Sarcopenia and systemic inflammation response index (SIRI) were independent predictors for overall survival and progression-free survival. Patients with high PMI and low SIRI demonstrated significantly better median overall survival and progression-free survival (36.0 months and 9.6 months, respectively) than those with either low PMI or high SIRI (20.8 months and 6.0 months, respectively) and those with both high SIRI and low PMI (18.6 months and 3.0 months, respectively). Portal vein tumor thrombus (P=0.003), eastern cooperative oncology group performance status score of 1 (P=0.048), high alkaline phosphatase (P=0.037), high neutrophil-to-lymphocyte ratio (NLR) (P=0.012), low lymphocyte-to-monocyte ratio (LMR) (P=0.031), high platelet-to-lymphocyte ratio (PLR) (P=0.022) and high SIRI (P=0.012) were closely associated with an increased incidence of sarcopenia. PMI was negatively correlated with SIRI (r = -0.175, P=0.003), NLR (r = -0.169, P=0.036), and PLR (r = -0.328, P=0.000) and was significantly positively correlated with LMR (r = 0.232, P=0.004). The CD3+ and CD4+ T-cell counts of the high PMI group were significantly higher than those of the low PMI group. Conclusion: Sarcopenia and high SIRI were associated with reduced survival in patients with HCC treated with ICIs and TKIs. Sarcopenia could affect inflammatory states and the immune microenvironment.

Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT.

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular and cerebrovascular diseases that seriously affect the lives and health of people. The development of Internet of Medical Things (IoMT) has enabled the remote diagnosis of OSA. The physiological signals of human sleep are sent to the cloud or medical facilities through Internet of Things, after which diagnostic models are employed for OSA detection. In order to improve the detection accuracy of OSA, in this study, a novel OSA detection system based on manually generated features and utilizing a parallel heterogeneous deep learning model in the context of IoMT is proposed, and the accuracy of the proposed diagnostic model is investigated. The OSA recognition scheme used in our model is based on short-term heart rate variability (HRV) signals extracted from ECG signals. First, the HRV signals and the linear and nonlinear features of HRV are combined into a one-dimensional (1-D) sequence. Simultaneously, a two-dimensional (2-D) HRV time-frequency spectrum image is obtained. The 1-D data sequences and 2-D images are coded in different branches of the proposed deep learning network for OSA diagnosis. To validate the performance of the proposed scheme, the Physionet Apnea-ECG public database is used. The proposed scheme outperforms the existing methods in terms of accuracy and provides a novel direction for OSA recognition.

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing.

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.