Whether specific genetic feature predicted immunotherapy efficacy: A case report.

RATIONALE: Blockade of programmed death protein 1 (PD-1), have been observed to have quite good efficacy in recurrent and metastatic cervical cancer. Generally, we believe that the biomarkers of PD-1 inhibitors are programmed cell death-ligand 1, tumor mutational burden, high microsatellite instability, or deficient mismatch repair. However, in the case reported below, we observed that the patient with negative existing predictive biomarkers have significant benefits after zimberelimab monotherapy, indicating that there were other biomarkers that may predict immunotherapy efficacy. However, currently, no one has explored and studied the other potential biomarkers of PD-1 inhibitors. PATIENT CONCERNS: A 51-year-old patient, diagnosed with cervical adenocarcinoma nearly 11 years ago, requested treatment. DIAGNOSES: The next-generation sequencing has shown PIK3CA E545K, SMAD4 1309-1G, and ALK E717K gene mutations, receptor tyrosine kinase 2 (ErbB-2) amplification, microsatellite stability, and low tumor mutational burden of 6.3 mutations per megabase. And immunohistochemistry revealed that the tumor was programmed cell death-ligand 1 negative. INTERVENTION: Zimberelimab monotherapy was accepted as third-line treatment. OUTCOMES: The patient had received zimberelimab for nearly 10 months, the best tumor response was PR (Response Evaluation Criteria in Solid Tumours) and no noticeable adverse reactions were observed. LESSONS: PIK3CA-E542K, ErbB2 amplification, and SMAD4 mutations could be potential biomarkers for PD-1 inhibitors, but a single instance is insufficient to validate the hypotheses. A larger number of patients or more clinical data will be necessary to determine whether these gene mutations are appropriate biomarkers for patients when treatment with PD-1 inhibitors.

Mechanisms from Academic Stress to Subjective Well-Being of Chinese Adolescents: The Roles of Academic Burnout and Internet Addiction.

Purpose: Academic stress is commonly known to affect adolescents' subjective well-being, but the influencing mechanisms are rarely investigated in the Chinese context. This study aims to investigate the psychological and behavioral factors operating as pathways between academic stress and adolescents' subjective well-being. Samples and Methods: A multi-stage cluster random sampling is used to select 1043 adolescents from junior and senior high schools in Jinhu County, Jiangsu Province, China (mean age=14.98 years; 51.1% boys). Structural equation modeling is used to test the direct and indirect effects. Results: Academic stress is significantly correlated with adolescents' subjective well-being after controlling for gender, grade, hukou, and socioeconomic status. In addition to partially mediating the link between academic stress and subjective well-being, academic burnout and Internet addiction can also operate as chain mediators in this mechanism. Adolescents' subjective well-being shows significant gender disparities, with boys having a greater degree of subjective well-being than girls. Father's income is found to have a significant positive association with adolescents' subjective well-being. Conclusion: The results indicate that academic stress is a significant predictor of academic burnout, which in turn points to a positive association with Internet addiction, thereby explaining low levels of adolescents' subjective well-being. The present study develops current knowledge and expands our understanding of the underlying mechanisms by which academic stress influences adolescents' subjective well-being. This can also illuminate the practical ramifications for policymakers and social workers to mitigate academic-related stress and burnout, prevent Internet addiction, and ultimately promote the well-being of adolescent students.

Fake news stance detection using selective features and FakeNET.

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.

Effect of the hospital-community-family (HCF) nutritional management on patients with esophageal and head and neck cancers undergoing radiotherapy: a randomized control trial.

Background: Malnutrition is particularly common in patients undergoing radiotherapy for head and neck cancers (HNC) and esophageal cancers (EC). Proper nutritional management plays an important role in improving the nutritional status and reducing complications in patients undergoing radiotherapy for malignancy. With most nutrition studies limited to the nutritional management of patients during hospitalization or after discharge, there is a lack of research evidence on the nutritional management of patients in combination with out-of-hospital. The aim of this study was to evaluate the effect of the hospital-community-family (HCF) nutritional management model on nutritional status and radiotherapy complications in EC and HNC radiotherapy patients. Methods: Between October 2019 and October 2021, a total of 116 EC and HNC radiotherapy patients were randomized into control group (conventional nutritional support) and experimental group (HCF-model nutritional management), and assessed weekly for 3 months. The primary endpoint was the patient's Nutrition Risk Screening 2002 (NRS2002) score, Scored Patient-Generated Subjective Global Assessment (PG-SGA), weight change, and Eastern Cooperative Oncology Group (ECOG) score from baseline level to 3 months after the end of treatment. The secondary endpoints were the incidence of albumin, hemoglobin, hematological parameters, and radiotherapy complications. Results: A total of 95 patients (47 in the control group and 48 in the experimental group) completed the study. At 3 months after treatment, NRS2002 (P=0.028) and PG-SGA (P=0.022) decreased, and albumin was higher (P=0.001) than at the beginning of treatment in HCF group. Weight decreased (P<0.001) and PG-SGA was higher after 3 months of treatment (P=0.012) in the control group. PG-SGA (P<0.001), NRS2002 (P<0.001), and ECOG (P=0.006) in the HCF group at the end of the 3-month treatment period were lower in the conventional group (P<0.05). The incidence of radiation mucositis (P=0.018)and radiation dermatitis (P=0.028) in the HCF nutrition management group was significantly reduced (P<0.05). Conclusions: HCF-model nutritional management significantly improved the nutritional status and reduced the incidence and severity of radiation mucositis and dermatitis for EC and HNC radiotherapy patients. These findings suggest that HCF-model nutritional management is a promising nutritional management model. Trial Registration: Chinese Clinical Trial Registry identifier: ChiCTR2300068399.

A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations.

Skin cancer remains one of the deadliest kinds of cancer, with a survival rate of about 18-20%. Early diagnosis and segmentation of the most lethal kind of cancer, melanoma, is a challenging and critical task. To diagnose medicinal conditions of melanoma lesions, different researchers proposed automatic and traditional approaches to accurately segment the lesions. However, visual similarity among lesions and intraclass differences are very high, which leads to low-performance accuracy. Furthermore, traditional segmentation algorithms often require human inputs and cannot be utilized in automated systems. To address all of these issues, we provide an improved segmentation model based on depthwise separable convolutions that act on each spatial dimension of the image to segment the lesions. The fundamental idea behind these convolutions is to divide the feature learning steps into two simpler parts that are spatial learning of features and a step for channel combination. Besides this, we employ parallel multidilated filters to encode multiple parallel features and broaden the view of filters with dilations. Moreover, for performance evaluation, the proposed approach is evaluated on three different datasets including DermIS, DermQuest, and ISIC2016. The finding indicates that the suggested segmentation model has achieved the Dice score of 97% for DermIS and DermQuest and 94.7% for the ISBI2016 dataset, respectively.

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification.

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation.

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach.

Perceived discrimination, integration, depression and behavioral problems among Chinese migrant children: Examining the moderating role of migration pattern in the rural-urban dual system.

INTRODUCTION: The purpose of this study is to investigate the influence of perceived discrimination on children's depression and behavioral problems via the mediator of integration among Chinese migrant children. Rural-urban differences in the proposed relationships are also examined. METHODS: The sample included 484 migrant children (Mean age = 11.65 years; 52.9% girls), which was collected through multi-stage cluster random sampling in Kunming, Southwest China. Structural equation modelling (SEM) was adopted for data analysis. RESULTS: Results indicate that perceived discrimination reduces the integration of Chinese migrant children, which in turn, leads to their higher levels of depression and more behavioral problems. The multi-group analysis on rural-urban differences reveals that the effects of discrimination on depression and behavioral problems are significant among rural-urban migrants but not among urban-urban ones. CONCLUSIONS: This study contributes to current knowledge by revealing the mechanisms among perceived discrimination, integration, depression and behavioral problems of Chinese migrant children. The migration pattern differences in terms of their depression and behavioral problems are also highlighted.

Efficient, Revocable, and Privacy-Preserving Fine-Grained Data Sharing With Keyword Search for the Cloud-Assisted Medical IoT System.

The cloud-assisted medical Internet of Things (MIoT) has played a revolutionary role in promoting the quality of public medical services. However, the practical deployment of cloud-assisted MIoT in an open healthcare scenario raises the concern on data security and user's privacy. Despite endeavors by academic and industrial community to eliminate this concern by cryptographic methods, resource-constrained devices in MIoT may be subject to the heavy computational overheads of cryptographic computations. To address this issue, this paper proposes an efficient, revocable, privacy-preserving fine-grained data sharing with keyword search (ERPF-DS-KS) scheme, which realizes the efficient and fine-grained access control and ciphertext keyword search, and enables the flexible indirect revocation to malicious data users. A pseudo identity-based signature mechanism is designed to provide the data authenticity. We analyze the security properties of our proposed scheme, and via the theoretical comparison and experimental results we demonstrate that for the resource-constrained devices in the patient and doctor side of MIoT, in comparison with other related schemes, ERPF-DS-KS just consumes the lightweight and constant size communication/storage as well as computational time cost. For the keyword search, compared with related schemes, the cloud can quickly check whether a ciphertext contains the specified keyword with slight computations in the online phase. This further demonstrates that ERPF-DS-KS is efficient and practical in the cloud-assisted MIoT scenario.

Complex PTSD in Chinese Adolescents Exposed to Childhood Trauma: A Latent Profile Analysis.

Although numerous studies have supported the idea that complex posttraumatic stress disorder (CPTSD) is a distinct disorder from posttraumatic stress disorder (PTSD) and demonstrated that childhood interpersonal trauma is an important risk factor for CPTSD, few studies have examined the validity of CPTSD in adolescents, especially in non-Western contexts. Moreover, the question of which form of child maltreatment plays the most important role in predicting CPTSD, and how CPTSD is associated with psychological health, physical health, and social function among adolescents, is not clear. The present study used a Chinese high school student sample with childhood trauma experiences (N = 395) to address these questions. Latent profile analysis indicated that there were four subgroups in our sample: Low symptoms (54.43%), Disturbance of self-organization (DSO, 18.99%), PTSD (15.95%), and CPTSD (10.63%). Further analysis revealed that emotional abuse was an important risk factor for CPTSD. In addition, the CPTSD class showed the highest levels of depression, anxiety, and stress, as well as the lowest levels of life satisfaction and physical health. This study revealed that CPTSD is a distinct disorder from PTSD in Chinese adolescents exposed to childhood trauma. It provides evidence that emotional abuse might be an important risk factor for CPTSD, and demonstrates that CPTSD is accompanied by serious psychological and physical consequences in adolescents. We suggest that parents and educators should focus more on adolescents' emotional needs, avoid using negative ways such as verbal violence to express love, and pay more attention to adolescents' DSO symptoms in parenting, teaching practices and clinical interventions.

Acquired ALK Resistance Mutations Identified from Liquid Biopsy in an ALK-Rearranged Squamous Cell Lung Cancer Patient Treated with Sequential ALK TKI Therapy: A Case Report.

Anaplastic lymphoma kinase (ALK) rearrangement is extremely rare in lung squamous cell carcinoma (LSCC), and it remains controversial as to whether LSCC patients with ALK rearrangement can benefit from ALK tyrosine kinase inhibitors (TKIs). Here, we report an LSCC patient with ALK rearrangement who was treated with sequential ALK TKI therapies and experienced a clinical benefit of 35 months. Although the use of ALK TKIs showed clinical benefits, targeted next-generation sequencing (NGS) for dynamic monitoring of circulating tumor DNA (ctDNA) from patient plasma revealed the accumulation of ALK resistance mutations, which could provide valuable information in designing the treatment strategy. Our study highlights the importance of dynamic monitoring of ctDNA using NGS to discover tumor evolution to guide treatment decision-making and provides meaningful insights into the potential treatment options for ALK-positive LSCC patients.

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.

AIM: COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. METHODS: In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. RESULTS: The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. CONCLUSION: This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

Having Infants in the Family Group Promotes Altruistic Behavior of Marmoset Monkeys.

The common marmoset (Callithrix jacchus) has attracted much attention as a useful model for studying social behaviors [1-3]. They naturally live in a monogamous family group and exhibit cooperative breeding [4], in which parents and older siblings help to carry infants less than 2 months old [5-7]. Marmoset parents also transfer foods to their offspring, a process that may help them learn the food diet [8]. Furthermore, marmosets show spontaneous altruistic behaviors, such as providing food to non-reciprocating and genetically unrelated individuals [9]. These social habits indicate that marmosets may be a useful non-human primate model for studying parenting and altruistic behaviors, as well as underlying neural mechanisms. Using a novel rescue paradigm, we found that marmoset parents and older siblings showed strong motivation to rescue trapped young infants but not juvenile marmosets beyond 2 months of age, and infant calls alone could trigger these parents' rescue behaviors. The marmoset parents showed little rescue of each other, but young infants or infant calls could also induce such parents' mutual rescue. Moreover, all these infant- and mate-rescue behaviors depended on currently having young infants in the family group. Functional MRI studies on awake adult marmosets showed that calls from young infants, but not juvenile marmosets, elicited a large-scale activation of specific brain areas including auditory and insular cortices, and such activation was absent in marmosets not living with infants. Thus, such infant-induced modification of neural activity offers a window for examining the neural basis of altruistic behaviors in marmoset monkeys.

An Android Inline Hooking Framework for the Securing Transmitted Data.

Information leaks can occur through many Android applications, including unauthorized access to sensors data. Hooking is an important technique for protecting Android applications and add security features to them even without its source code. Various hooking frameworks are developed to intercept events and process their own specific events. The hooking tools for Java methods are varied, however, the native hook has few methods. Besides, the commonly used Android hook frameworks cannot meet the requirement of hooking the native methods in shared libraries on non-root devices. Even though some approaches are able to hook these methods, they have limitations or are complicated to implement. In the paper, a feasible hooking approach for Android native methods is proposed and implemented, which does not need any modifications to both the Android framework and app's code. In this approach, the method's reference address is modified and control flow is redirected. Beyond that, this study combines this approach with VirtualXposed which aims to run it without root privileges. This hooking framework can be used to enforce security policies and monitor sensitive methods in shared objects. The evaluation of the scheme demonstrates its capability to perform hook operation without a significant runtime performance overhead on real devices and it is compatible and functional for the native hook.

Early Onset Immune-Related Adverse Event to Identify Pseudo-Progression in a Patient With Ovarian Cancer Treated With Nivolumab: A Case Report and Review of the Literature.

Background: Immune checkpoint inhibitors (ICIs) have shown clinical benefit in many advanced tumors, however, pseudo-progression is a noted phenomenon of ICIs characterized by radiologic enlargement of the tumor burden, followed by regression. How to differentiate pseudo-progression from progression remains a critical clinical issue. Recent studies have demonstrated the association between immune-related adverse events (irAEs) and efficacy of ICIs. Here we demonstrated an ovarian cancer patient treated with nivolumab in whom the early-onset irAE was identified to differentiate pseudo-progression from progression. Case presentation: Here we present the case of a 47-years-old woman with histopathologically confirmed diagnosis of metastatic ovarian cystadenocarcinoma. Immunohistochemical examination showed 10% of tumor cells to express the programmed cell death receptor ligand 1 (PD-L1) and a 381-gene panel sequencing in a College of American Pathologists (CAP) certificated lab revealed a moderate mutational tumor burden with 5.7 Mutants/Mb. The patient received nivolumab 100 mg every 2 weeks as a fourth line treatment. Within the first 2 months, the tumor volume increased by 23.6%. However, the patient experienced an elevation of Alanine aminotransferase (ALT) and Aspartate aminotransmerase (AST), which was diagnosed as the immune-related hepatitis. Thus, the treatment continued and afterwards, the patient reached a partial response (PR) to nivolumab and the progression-free survival was 9 months. Conclusion: To our knowledge, this is the first case describing early-onset immune-related adverse events to identify pseudo-progression in a patient with ovarian cancer treated with nivolumab, and PD-L1 expression level may be a predictive biomarker in the immunotherapy of ovarian cancer.

Gefitinib induces non-small cell lung cancer H1650 cell apoptosis through downregulating tumor necrosis factor-related apoptosis-inducing ligand expression levels.

Non-small cell lung cancer (NSCLC) presents severe threats to the lives of patients. Gefitinib is one of the first-line drugs available for the treatment of NSCLC in the clinical setting. The present study investigated the effects of gefitinib on NSCLC H1650 cell viability and apoptosis via MTT assays and flow cytometry. Western blot analysis was employed to detect tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) expression levels in H1650 cells. In the present study, H1650 cells were treated with TRAIL siRNA or an empty plasmid vector control, followed by gefitinib treatment to investigate apoptosis. Gefitinib treatment markedly inhibited H1650 cell viability, induced apoptosis and reduced TRAIL expression levels. TRAIL interference enhanced H1650 cell apoptosis induced by gefitinib. TRAIL overexpression suppressed gefitinib-induced H1650 cell apoptosis. In addition, gefitinib induced NSCLC H1650 cell apoptosis by downregulating TRAIL expression levels.

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information.

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.

Upregulation of miR-146a increases cisplatin sensitivity of the non-small cell lung cancer A549 cell line by targeting JNK-2.

The aim of the present study was to investigate the effects of microRNA (miR-)146a on the cisplatin sensitivity of the non-small cell lung cancer (NSCLC) A549 cell line and study the underlying molecular mechanism. The differences in expression of miRNAs between A549 and A549/cisplatin (A549/DDP) cells were determined, and miR-146a was selected to study its effect on cisplatin sensitivity of A549/DDP cells. miR-146a mimic and inhibitor transient transfection systems were constructed using vectors, and A549/DDP cells were infected with miR-146a mimic and inhibitor to investigate growth, apoptosis and migration. The directed target of miR-146a was determined and the underlying molecular mechanism was validated in the present study. The results of the present study demonstrated that miR-146a was downregulated in NSCLC A549/DDP cells, compared with A549 cells. The overexpression of miR-146a induced apoptosis and inhibited the growth and invasion of A549/DDP cells, which resulted in increased cisplatin sensitivity in NSCLC cells. The JNK2 gene was determined as the direct target of miR-146a, and may be activated by the overexpression of miR-146a. Additionally, JNK2 activated the expression of p53 and inhibited B cell lymphoma 2. The upregulation of miR-146a increased cisplatin sensitivity of the A549 cell line by targeting JNK2, which may provide a novel method for treating NSCLC cisplatin resistance.