A practical nomogram for preoperatively predicting lateral cervical lymph node metastasis in medullary thyroid carcinoma: a dual-center retrospective study.

Purpose: Lateral lymph node metastasis (LLNM) is very common in medullary thyroid carcinoma (MTC), but there is still controversy about how to manage cervical lateral lymph nodes, especially for clinically negative MTC. The aim of this study is to develop and validate a nomogram for predicting LLNM risk in MTC. Materials and methods: A total of 234 patients from two hospitals were retrospectively enrolled in this study and divided into LLNM positive group and LLNM negative group based on the pathology. The correlation between LLNM and preoperative clinical and ultrasound variables were evaluated by univariable and multivariable logistic regression analysis. A nomogram was generated to predict the risk of the LLNM of MTC patients, validated by external dataset, and evaluated in terms of discrimination, calibration, and clinical usefulness. Results: The training, internal, and external validation datasets included 152, 51, and 31 MTC patients, respectively. According to the multivariable logistic regression analysis, gender (male), relationship to thyroid capsule and serum calcitonin were independently associated with LLNM in the training dataset. The predictive nomogram model developed with the aforementioned variables showed favorable performance in estimating risk of LLNM, with the area under the ROC curve (AUC) of 0.826 in the training dataset, 0.816 in the internal validation dataset, and 0.846 in the external validation dataset. Conclusion: We developed and validated a model named MTC nomogram, utilizing available preoperative variables to predict the probability of LLNM in patients with MTC. This nomogram will be of great value for guiding the clinical diagnosis and treatment process of MTC patients.

Dual-Drug Loaded Nanobubbles Combined with Sonodynamic and Chemotherapy for Hepatocellular Carcinoma Therapy.

Purpose: Chemotherapy remains the primary therapeutic approach for advanced Hepatocellular Carcinoma (HCC). The therapeutic effect of chemotherapy is limited and the toxic side effects are serious. The aim of this study is to develop a nanobubble that is ultrasonically responsive to reduce the toxic side effects of direct chemotherapy. Methods: We developed curcumin/doxorubicin-cis-aconitic anhydride-polyethylene glycol nanobubble (C/DCNB) surface modified with acid-sensitive polyethylene glycol (PEG). And it is loaded with curcumin (CUR) and doxorubicin (DOX), as liposomes at the nanoscale for diagnosis and therapy of tumors. Results: In this study, the acid-sensitive PEG on the surface layer of nanobubbles serves to stabilize them in the blood circulatory system and in normal tissues, while peeling off in the acidic tumor microenvironment (pH 6.8). C/DCNB can identify tumor sites through contrast-enhanced ultrasound (CEUS). And ultrasound-mediated nanobubbles promote permeability of the tumor vascular, thus improving the enhanced permeability and retention (EPR) effects in the tumor, leading to the accumulation of nanobubbles in the tumor. After endocytosis of nanobubbles, drugs are released and curcumin generates reactive oxygen species (ROS) under ultrasound conditions. CUR can enhance the sensitivity of tumor cells to DOX by inhibiting the expression of P-glycoprotein. In vitro and vivo experiments demonstrate that C/DCNB can facilitate contrast-enhanced ultrasound imaging while simultaneously delivering drugs, enabling both imaging and treatment. Conclusion: The combination of C/DCNB and ultrasound provides an effective strategy for improving the efficiency of HCC therapy and imaging.

Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations.

A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19. IMPORTANCE: A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM.

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

Dynamic ultrasound molecular-targeted imaging of senescence in evaluation of lapatinib resistance in HER2-positive breast cancer.

BACKGROUND: Prolonged treatment of HER2+ breast cancer with lapatinib (LAP) causes cellular senescence and acquired drug resistance, which often associating with poor prognosis for patients. We aim to explore the correlation between cellular senescence and LAP resistance in HER2+ breast cancer, screen for molecular marker of reversible senescence, and construct targeted nanobubbles for ultrasound molecular imaging to dynamically evaluate LAP resistance. METHODS AND RESULTS: In this study, we established a new cellular model of reversible cellular senescence using LAP and HER2+ breast cancer cells and found that reversible senescence contributed to LAP resistance in HER2+ breast cancer. Then, we identified ecto-5'-nucleotidase (NT5E) as a marker of reversible senescence in HER2+ breast cancer. Based on this, we constructed NT5E-targeted nanobubbles (NT5E-FITC-NBs) as a new molecular imaging modality which could both target reversible senescent cells and be used for ultrasound imaging. NT5E-FITC-NBs showed excellent physical and imaging characteristics. As an ultrasound contrast agent, NT5E-FITC-NBs could accurately identify reversible senescent cells both in vitro and in vivo. CONCLUSIONS: Our data demonstrate that cellular senescence-based ultrasound-targeted imaging can identify reversible senescence and evaluate LAP resistance effectively in HER2+ breast cancer cells, which has the potential to improve cancer treatment outcomes by altering therapeutic strategies ahead of aggressive recurrences.

Aided diagnosis of thyroid nodules based on an all-optical diffraction neural network.

Background: Thyroid cancer is the most common malignancy in the endocrine system, with its early manifestation being the presence of thyroid nodules. With the advantages of convenience, noninvasiveness, and a lack of radiation, ultrasound is currently the first-line screening tool for the clinical diagnosis of thyroid nodules. The use of artificial intelligence to assist diagnosis is an emerging technology. This paper proposes the use optical neural networks for potential application in the auxiliary diagnosis of thyroid nodules. Methods: Ultrasound images obtained from January 2013 to December 2018 at the Institute and Hospital of Oncology, Tianjin Medical University, were included in a dataset. Patients who consecutively underwent thyroid ultrasound diagnosis and follow-up procedures were included. We developed an all-optical diffraction neural network to assist in the diagnosis of thyroid nodules. The network is composed of 5 diffraction layers and 1 detection plane. The input image is placed 10 mm away from the first diffraction layer. The input of the diffractive neural network is light at a wavelength of 632.8 nm, and the output of this network is determined by the amplitude and light intensity obtained from the detection region. Results: The all-optical neural network was used to assist in the diagnosis of thyroid nodules. In the classification task of benign and malignant thyroid nodules, the accuracy of classification on the test set was 97.79%, with an area under the curve value of 99.8%. In the task of detecting thyroid nodules, we first trained the model to determine whether any nodules were present and achieved an accuracy of 84.92% on the test set. Conclusions: Our study demonstrates the potential of all-optical neural networks in the field of medical image processing. The performance of the models based on optical neural networks is comparable to other widely used network models in the field of image classification.

Identification and validation of an epithelial-mesenchymal transition-related lncRNA pairs prognostic model for gastric cancer.

Background: Gastric cancer (GC) is a common malignancy. A mounting body of evidence has demonstrated the correlation between GC prognosis and epithelial-mesenchymal transition (EMT)-related biomarkers. This research constructed an available model using EMT-related long noncoding RNA (lncRNA) pairs to predict the survival for GC patients. Methods: The transcriptome data along with clinical information on GC samples were derived from The Cancer Genome Atlas (TCGA). Differentially expressed EMT-related lncRNAs were acquired and paired. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied to filter lncRNA pairs, and the risk model was built to investigate its effect on the prognosis of GC patients. Then, the areas under the receiver operating characteristic curves (AUCs) were calculated and the cutoff point for distinguishing low- or high-risk GC patients was identified. And the predictive ability of this model was tested in the GSE62254. Furthermore, the model was evaluated from the perspectives of survival time, clinicopathological parameters, infiltration of immunocytes, and functional enrichment analysis. Results: The risk model was built by using the identified twenty EMT-related lncRNA pairs, and it was not necessary to know the specific expression level of each lncRNA. Survival analysis pointed out that GC patients with high risk had poorer outcomes. Additionally, this model could be an independent prognostic variable for GC patients. The accuracy of the model was also verified in the testing set. Conclusions: The new predictive model constructed here is composed of EMT-related lncRNA pairs, with reliable prognostic values, and can be utilized to predict the survival of GC.

The role of BHLHE40 in clinical features and prognosis value of PDAC by comprehensive analysis and in vitro validation.

Pancreatic ductal adenocarcinoma (PDAC) is the leading cause of cancer-related mortality, primarily due to the abundance of cancer-associated fibroblasts (CAFs), depleted effector T cells, and increased tumor cell stemness; hence, there is an urgent need for efficient biomarkers with prognostic and therapeutic potential. Here, we identified BHLHE40 as a promising target for PDAC through comprehensive analysis and weighted gene coexpression network analysis of RNA sequencing data and public databases, taking into account the unique characteristics of PDAC such as cancer-associated fibroblasts, infiltration of effector T cells, and tumor cell stemness. Additionally, we developed a prognostic risk model based on BHLHE40 and three other candidate genes (ITGA2, ITGA3, and ADAM9) to predict outcomes in PDAC patients. Furthermore, we found that the overexpression of BHLHE40 was significantly associated with T stage, lymph node metastasis, and American Joint Committee on Cancer (AJCC) stage in a cohort of 61 PDAC patients. Moreover, elevated expression levels of BHLHE40 were validated to promote epithelial-mesenchymal transition (EMT) and stemness-related proteins in BXPC3 cell lines. Compared to the parent cells, BXPC3 cells with BHLHE40 overexpression showed resistance to anti-tumor immunity when co-cultured with CD8+ T cells. In summary, these findings suggest that BHLHE40 is a highly effective biomarker for predicting prognosis in PDAC and holds great promise as a target for cancer therapy.

Nomogram for preoperative estimation risk of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study.

BACKGROUND: Lateral lymph node metastasis (LLNM) is frequent in papillary thyroid carcinoma (PTC) and is associated with a poor prognosis. This study aimed to developed a clinical-ultrasound (Clin-US) nomogram to predict LLNM in patients with PTC. METHODS: In total, 2612 PTC patients from two hospitals (H1: 1732 patients in the training cohort and 578 patients in the internal testing cohort; H2: 302 patients in the external testing cohort) were retrospectively enrolled. The associations between LLNM and preoperative clinical and sonographic characteristics were evaluated by the univariable and multivariable logistic regression analysis. The Clin-US nomogram was built basing on multivariate logistic regression analysis. The predicting performance of Clin-US nomogram was evaluated by calibration, discrimination and clinical usefulness. RESULTS: The age, gender, maximum diameter of tumor (tumor size), tumor position, internal echo, microcalcification, vascularization, mulifocality, and ratio of abutment/perimeter (A/P) > 0.25 were independently associated with LLNM metastatic status. In the multivariate analysis, gender, tumor size, mulifocality, position, microcacification, and A/P > 0.25 were independent correlative factors. Comparing the Clin-US nomogram and US features, Clin-US nomogram had the highest AUC both in the training cohort and testing cohorts. The Clin­US model revealed good discrimination between PTC with LLNM and without LLNM in the training cohort (AUC = 0.813), internal testing cohort (AUC = 0.815) and external testing cohort (AUC = 0.870). CONCLUSION: Our findings suggest that the ClinUS nomogram we newly developed can effectively predict LLNM in PTC patients and could help clinicians choose appropriate surgical procedures.

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study.

Objective: Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM. Materials and methods: In this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients. Results: Multivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability. Conclusion: Our proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.

The influence of surface charge on the tumor-targeting behavior of Fe3O4 nanoparticles for MRI.

Nanomedicine-based tumor-targeted therapy has emerged as a promising strategy to overcome the lack of specificity of conventional chemotherapeutic agents. "Passive" targeting caused by the tumor EPR effect and "active" targeting endowed by the tumor-targeting moieties provide promising biomedical utilities and cancer therapy strategies for nanomedicine. However, as the nanoparticles are exposed to biological fluids, a large number of protein molecules will be adsorbed on their surface, known as protein corona, which may alter the targeting ability of the nanoparticles. The impact of different protein corona on the "passive" and "active" targeting behaviors is still ambiguous. Herein, three kinds of aqueous soluble Fe3O4 nanoparticles with different surface modifications were synthesized and applied to explore the correlation between their protein corona and passive/active tumor-targeting abilities. In the in vitro and in vivo studies, the protein corona exhibited completely different effects on the active and passive cancer-targeting capability of the particles. The particles presented active cancer-targeting ability if there was enough interaction time between the particles and cells. This was mainly due to the dynamic evolution of the protein corona, the proteins of which may be outcompeted by the cancer cell membrane and determine the targeting abilities. Unfortunately, the protein corona also inevitably accelerated RES/MPS uptake after the particles were injected into the body, which almost completely disabled the active targeting abilities of the particles. We believe that this in-depth understanding of protein corona will provide new ideas on the tumor-targeting mechanisms of nanoparticles and present a feasible approach to designing targeted drugs in the future.

Nanoparticle delivery of a triple-action Pt(IV) prodrug to overcome cisplatin resistance via synergistic effect.

Cisplatin is the most widely used chemotherapeutic agent due to its efficacy in the treatment of a broad range of cancer types; while the side effects and drug resistance of cisplatin limit its clincial application. Combination therapy, which contains several types of free drugs, exhibits promising potential in clinical practice. Nevertheless, current combination chemotherapy cannot accurately deliver different drug components into a single tumor cell at the same time. Herein, we report a triple-action nanoplatinum drug based on artesunate and cantharidin to overcome the influence of pharmacokinetics and distribution variation in different drugs. The results show that the triple action nanoplatinum drug enhances ROS generation, leads to DNA damage, and inhibits DNA repair. Therefore, a high-efficiency killing effect is achieved with a triple-action platinum drug in a single tumor cell.

A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer.

BACKGROUND: Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. METHODS: Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. RESULTS: Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. CONCLUSIONS: We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images.

BACKGROUND: The aim of this study was to construct a deep convolutional neural network (CNN) model for localization and diagnosis of thyroid nodules on ultrasound and evaluate its diagnostic performance. METHODS: We developed and trained a deep CNN model called the Brief Efficient Thyroid Network (BETNET) using 16,401 ultrasound images. According to the parameters of the model, we developed a computer-aided diagnosis (CAD) system to localize and differentiate thyroid nodules. The validation dataset (1,000 images) was used to compare the diagnostic performance of the model using three state-of-the-art algorithms. We used an internal test set (300 images) to evaluate the BETNET model by comparing it with diagnoses from five radiologists with varying degrees of experience in thyroid nodule diagnosis. Lastly, we demonstrated the general applicability of our artificial intelligence (AI) system for diagnosing thyroid cancer in an external test set (1,032 images). RESULTS: The BETNET model accurately detected thyroid nodules in visualization experiments. The model demonstrated higher values for area under the receiver operating characteristic (AUC-ROC) curve [0.983, 95% confidence interval (CI): 0.973-0.990], sensitivity (99.19%), accuracy (98.30%), and Youden index (0.9663) than the three state-of-the-art algorithms (P<0.05). In the internal test dataset, the diagnostic accuracy of the BETNET model was 91.33%, which was markedly higher than the accuracy of one experienced (85.67%) and two less experienced radiologists (77.67% and 69.33%). The area under the ROC curve of the BETNET model (0.951) was similar to that of the two highly skilled radiologists (0.940 and 0.953) and significantly higher than that of one experienced and two less experienced radiologists (P<0.01). The kappa coefficient of the BETNET model and the pathology results showed good agreement (0.769). In addition, the BETNET model achieved an excellent diagnostic performance (AUC =0.970, 95% CI: 0.958-0.980) when applied to ultrasound images from another independent hospital. CONCLUSIONS: We developed a deep learning model which could accurately locate and automatically diagnose thyroid nodules on ultrasound images. The BETNET model exhibited better diagnostic performance than three state-of-the-art algorithms, which in turn performed similarly in diagnosis as the experienced radiologists. The BETNET model has the potential to be applied to ultrasound images from other hospitals.

Fully endoscopic versus microscopic vascular decompression for hemifacial spasm: a retrospective cohort study.

INTRODUCTION: Microvascular decompression (MVD) is the preferred surgical method for hemifacial spasm (HFS). The purpose of this study was to analyze the effectiveness and safety of fully endoscopic MVD for HFS relative to microscopic MVD. MATERIAL AND METHODS: The retrospective study was conducted on HFS patients who underwent microscopic or fully endoscopic MVD from January 2018 to March 2019. All patients were treated at a single institution and by a single surgeon. Patients were divided into two groups based on the surgical method, and clinical data were then compared between groups. RESULTS: A total of 116 patients, including 54 cases who received fully endoscopic MVD (E group) and 62 cases who received microscopic MVD (M group), were included in this study. Follow-up efficacy did not differ significantly between groups, with total effective rates of 88.9% in the E group and 90.3% in the M group. When postoperative complications were compared individually, there were no statistically significant differences between the two groups; however, the E group had a higher total incidence of complications than the M group (48.1% vs. 29.0%, P = 0.034). CONCLUSION: Although both fully endoscopic and microscopic MVD for HFS achieved good efficacy, the former method had a higher total incidence of complications. Based on the results of this study, there is no evidence that a microscope can be replaced by a full endoscope in MVD for HFS.

Climate change impacts on agriculture sector: A case study of Pakistan / Impactos das mudanças climáticas no setor agrícola: um estudo de caso do Paquistão

ABSTRACT: The current article looks at the effects of climate change on agriculture, especially crop production, and influence factors of agricultural development in terms of their rational use in Pakistan. Due to the dependence of economic development, and agriculture in the South Asian region on access to renewable national resources and the associated vulnerability to climate change, the limited financial and professional resources of the Islamic Republic of Pakistan require a clear definition of national priorities in this area. In the preparation of this article, general scientific cognition methods, in particular, empirical-theoretical methods were used. Grouping and classification methods have been used to process and systematize the data. The ability to change productivity, depending on the variation of the average annual air temperature and the average annual precipitation rate, was considered using a two-factor regression model. The main finding of the study is that temperature and precipitation have a negative impact on agricultural production. This study can provide a scientific justification for the specialization of agricultural production in the regions of Pakistan as well as the execution of the necessary agricultural activities.

RESUMO: O objetivo deste artigo é examinar os efeitos das mudanças climáticas na agricultura, especialmente a produção agrícola e os fatores de influência do desenvolvimento agrícola em termos de uso racional no Paquistão. Devido à dependência do desenvolvimento econômico e da agricultura na região do sul da Ásia do acesso a recursos nacionais renováveis ​​e à vulnerabilidade associada às mudanças climáticas, os recursos financeiros e profissionais limitados da República Islâmica do Paquistão exigem uma definição clara das prioridades nacionais nessa área. Na preparação deste artigo, foram utilizados métodos gerais de cognição científica, em particular métodos teórico-empíricos. Os métodos de agrupamento e classificação foram utilizados para processar e sistematizar os dados. A capacidade de alterar a produtividade, dependendo da variação da temperatura média anual do ar e da taxa média anual de precipitação, foi considerada usando um modelo de regressão de dois fatores. A principal descoberta do estudo é que a temperatura e a precipitação têm um impacto negativo na produção agrícola. Este estudo pode fornecer uma justificativa científica para a especialização da produção agrícola nas regiões do Paquistão, bem como a execução das atividades agrícolas necessárias.

Interleukin-35 promotes progression of prostate cancer and inhibits anti-tumour immunity.

BACKGROUND: Interleukin-35 (IL-35) has been reported to play an important role in the progression of cancers. The role of IL-35 in prostate cancer (PCA) is not well understood. In this study, we investigated the effects of IL-35 on PCA and its immunoregulatory effect on PCA. METHODS: The protein and mRNA expression of IL-35 in PCA cells was detected by western blot and RT-PCR. The invasion and migration of cells were detected using transwell and wound-healing assays. A CCK-8 assay was conducted to observe cell proliferation. In vivo, IL-35 plasma concentration was test by enzyme-linked immunosorbent assay. The role of IL-35 in tumour cell proliferation and angiogenesis of mice was detected by immunohistochemical stains. The mouse survival and tumour volumes were calculated, and lung metastasis rate was detected by HE staining. The modulatory effects of IL-35 on myeloid-derived inhibitory cells (MDSCs), regulatory T cells (Tregs), CD4+ T cells and CD8+ T cells from PCA mice were investigated by immunohistochemical stains and flow cytometry. RESULTS: High levels of IL-35 significantly promoted the migration, invasion and cell proliferation of PCA cells in vitro. IL-35 was associated with tumour growth, metastasis and poor prognosis in PCA mice. Additionally, high levels of IL-35 significantly increased the proportions of MDSCs and Tregs and decreased the proportions of CD4+ and CD8+ T cells in the spleen, blood and tumour microenvironment. The IL-35 neutralizing antibody played the opposite role. CONCLUSIONS: IL-35 contributed to the progression of PCA through promoting cell proliferation and tumour angiogenesis. IL-35 might limit the anti-tumour immune response by upregulating the proportions of Tregs and MDSCs and by reducing the proportions of CD4+ and CD8+ T cells. IL-35 might serve as a novel therapeutic target for PCA.

Thyroid nodules risk stratification through deep learning based on ultrasound images.

PURPOSE: Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) is a type of medical standard widely used in classification diagnosis. It divides the nodule's ACR TI-RADS level into five levels by quantitative scoring, from benign to high suspicion of malignancy. However, such assessment often relies on the radiologists' experience and is time consuming. So computer-aided diagnosis is necessary. But many deep learning (DL) models are difficult for doctors to understand, limiting their applicability in clinical practice. In this work, we mainly focus on how to achieve automatic thyroid nodules risk stratification based on deep integration of deep learning and clinical experience. METHODS: An automatic hierarchical method of thyroid nodules risk based on deep learning is proposed, called risk stratification network (RS-Net). It incorporates medical experience based on ACR TI-RADS. The convolutional neural network (CNN) is used to classify the five categories in ACR TI-RADS and assign their points respectively. According to the point totals, the level of risk can be obtained. In addition, a dataset involving 13 984 thyroid ultrasound images is established to develop and evaluate the proposed method. RESULTS: We have extensively compared the results of this paper with the evaluation results of sonographers. The accuracy of the risk stratification (TR1 to TR5) of the proposed method is 65%, and the mean absolute error (MAE) is 0.54. The MAE of the point totals (0 to 13 points) is 1.67. The Pearson's correlation between our method evaluation and doctor evaluation reached 0.84. For the benign and malignant classification, the performance indices accuracy, sensitivity, specificity, PPV, and NPV were 88.0%, 98.1%, 79.1%, 80.5%, and 97.9%, respectively. Our method's level of thyroid nodules risk stratification is comparable to that of a senior doctor. CONCLUSIONS: This work provides a way to automate the risk stratification of thyroid nodules. Our method can effectively avoid missed diagnosis and misdiagnosis caused by the difference of observers so as to assist doctors to improve efficiency and diagnosis rate. Compared with the previous benign and malignant classification, the proposed method incorporates clinical experience. So it can greatly increase the clinicians' trust in the DL model, thereby improving the applicability of the model in clinical practice.

Cascade marker removal algorithm for thyroid ultrasound images.

During thyroid ultrasound diagnosis, radiologists add markers such as pluses or crosses near a nodule's edge to indicate the location of a nodule. For computer-aided detection, deep learning models achieve classification, segmentation, and detection by learning the thyroid's texture in ultrasound images. Experiments show that manual markers are strong prior knowledge for data-driven deep learning models, which interferes with the judgment mechanism of computer-aided detection systems. Aiming at this problem, this paper proposes cascade marker removal algorithm for thyroid ultrasound images to eliminate the interference of manual markers. The algorithm consists of three parts. First, in order to highlight marked features, the algorithm extracts salient features in thyroid ultrasound images through feature extraction module. Secondly, mask correction module eliminates the interference of other features besides markers' features. Finally, the marker removal module removes markers without destroying the semantic information in thyroid ultrasound images. Experiments show that our algorithm enables classification, segmentation, and object detection models to focus on the learning of pathological tissue features. At the same time, compared with mainstream image inpainting algorithms, our algorithm shows better performance on thyroid ultrasound images. In summary, our algorithm is of great significance for improving the stability and performance of computer-aided detection systems. Graphical Abstract During thyroid ultrasound diagnosis, doctors add markers such as pluses or crosses near nodule's edge to indicate the location of nodule. Manual markers are strong prior knowledge for data-driven deep learning models, which interferes the judgment mechanism of computer-aided diagnosis system based on deep learning. Markers make models overfit the specific labeling forms easily, and performs poorly on unmarked thyroid ultrasound images. Aiming at this problem, this paper proposes a cascade marker removal algorithm to eliminate the interference of manual markers. Our algorithm make deep learning models pay attention on nodules' features of thyroid ultrasound images, which make computer-aided diagnosis system performs good in both marked imaging and unmarked imaging.

Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images.

BACKGROUND The number of studies on deep learning in artificial intelligence (AI)-assisted diagnosis of thyroid nodules is increasing. However, it is difficult to explain what the models actually learn in artificial intelligence-assisted medical research. Our aim is to investigate the visual interpretability of the computer-assisted diagnosis of malignant and benign thyroid nodules using ultrasound images. MATERIAL AND METHODS We designed and implemented 2 experiments to test whether our proposed model learned to interpret the ultrasound features used by ultrasound experts to diagnose thyroid nodules. First, in an anteroposterior/transverse (A/T) ratio experiment, multiple models were trained by changing the A/T ratio of the original nodules, and their classification, accuracy, sensitivity, and specificity were tested. Second, in a visualization experiment, class activation mapping used global average pooling and a fully connected layer to visualize the neural network to show the most important features. We also examined the importance of data preprocessing. RESULTS The A/T ratio experiment showed that after changing the A/T ratio of the nodules, the accuracy of the neural network model was reduced by 9.24-30.45%, indicating that our neural network model learned the A/T ratio information of the nodules. The visual experiment results showed that the nodule margins had a strong influence on the prediction of the neural network. CONCLUSIONS This study was an active exploration of interpretability in the deep learning classification of thyroid nodules. It demonstrated the neural network-visualized model focused on irregular nodule margins and the A/T ratio to classify thyroid nodules.