Chronic lymphocytic leukemia/small lymphocytic lymphoma complicated with skin Langerhans cell sarcoma:A case report

BACKGROUND Langerhans cell sarcoma(LCS)is a rare malignancy with poor prognosis.LCS and chronic lymphocytic leukemia(CLL)/small lymphocytic lymphoma(SLL)can occur in the same diseased tissues,such as lymph nodes or skin.CASE SUMMARY A 48-year-old female Han Chinese patient was admitted for generalized lymph node enlargement for 6 years and abdominal distension for 1 wk.She was diagnosed with small B-cell lymphoma(stage IV)/CLL(Benet stage B)and received chemotherapy.She started oral ibrutinib in February 2019.She was hospitalized on June 11,2019,and a 1.5 cm×1.5 cm dark-red nodule with ulceration scalp lesion was found.Biopsy revealed LCS but without CLL/SLL.She was diagnosed with CLL/SLL(Binet stage C,Rai stage IV)accompanied by secondary histiocytic sarcomas and skin LCS and received cyclophosphamide,doxorubicin,vincristine,dexamethasone,and etoposide but developed severe cytopenia.She ultimately refused treatments and discharged spontaneously.She died on September 12,2019.The literature review showed that in patients with CLL/SLL,skin lesions of LCS are accompanied by CLL/SLL.This patient was different from the previously reported cases of skin LCS in patients with CLL/SLL.CONCLUSION In this patient,the skin lesion of LCS showed no concomitant CLL/SLL.

基于MobileNetV3Small-ECA的水稻病害轻量级识别研究

为实现水稻病害的轻量化识别与检测,使用ECA注意力机制改进MobileNetV3Small模型,并使用共享参数迁移学习对水稻病害进行智能化轻量级识别和检测。在PlantVillage数据集上进行预训练,将预训练得到的共享参数迁移到对水稻病害识别模型上微调优化。在开源水稻病害数据集上进行试验测试,试验结果表明,在非迁移学习下,识别准确率达到97.47%,在迁移学习下识别准确率达到99.92%,同时参数量减少26.69%。其次,通过Grad-CAM进行可视化,本文方法与其他注意力机制CBAM和SENET相比,ECA模块生成的结果与图像中病斑的位置和颜色更加一致,表明网络可以更好地聚焦水稻病害的特征,并且通过可视化和各水稻病害分析了误分类原因。本文方法实现了水稻病害识别模型的轻量化,使其能够在移动设备等资源受限的场景中部署,达到快速、高效、便携的目的。同时开发了基于Android的水稻病害识别系统,方便于在边缘端进行水稻病害识别分析。

miRNA对褐飞虱鞘脂质代谢基因表达的影响及沉默NlSPT1和Nl SMase4的small RNA分析

【背景】鞘脂质是第二大类膜脂,作为信号转导物质参与细胞生长发育、繁殖及凋亡等过程。鞘脂质代谢酶调控鞘脂质代谢以维持生物体内代谢的稳态。【目的】以重要水稻害虫褐飞虱(Nilaparvata lugens)为研究对象,通过RNA干扰(RNA interference,RNAi)技术检测微小RNA(microRNA,miRNA)生物合成通路核心组分NlAgo1、NlDicer1和NlDrosha沉默后褐飞虱鞘脂质代谢通路相关基因的相对转录水平,结合small RNA测序分析沉默丝氨酸棕榈酰转移酶1(serine palmitoyltransferase 1,SPT1)和鞘磷脂酶4(sphingomyelinase 4,SMase4)基因的差异miRNA,探究miRNA在褐飞虱鞘脂质代谢中的作用,为害虫防治提供新的分子靶标。【方法】利用RNAi技术,分别对羽化后第1天(1 PAE,post adult eclosion)的雌成虫NlAgo1、NlDicer1和NlDrosha进行dsRNA注射,以ds GFP为对照;分别解剖羽化后第5天的卵巢组织,以β-actin作为内参基因,采用实时荧光定量PCR(qRT-PCR)方法检测NlAgo1、NlDicer1和NlDrosha沉默后鞘脂质代谢通路相关基因的表达量变化;根据已有的小RNA文库联合miRNA-靶基因预测软件对可能调控NlSPT1和NlSMase4表达的miRNA进行预测;通过small RNA测序技术对沉默NlSPT1和NlSMase4的差异miRNA进行鉴定和靶基因富集性分析。【结果】与对照组相比,沉默NlAgo1、NlDicer1或NlDrosha显著上调卵巢中NlSPT1和NlSMase4等鞘脂质代谢通路相关基因的表达;靶基因预测结果显示,有6条miRNA能与NlSPT1结合,13条miRNA能与NlSMase4结合;沉默NlSPT1和NlSMase4的差异miRNA的靶基因显著富集在细胞核和蛋白质结合等生物学过程以及内吞作用、内质网加工、MAPK信号通路、TOR信号通路、凋亡、脂质代谢等代谢通路。【结论】NlAgo1、NlDicer1和NlDrosha依赖性的miRNA通过影响鞘脂质代谢相关基因的表达影响鞘脂质代谢。NlSPT1和NlSMase4沉默引起褐飞虱卵巢miRNA表达水平的改变。研究结果可为基于鞘脂质代谢基因为靶标的害虫防治提供理论依据。

Small nucleolar RNA and its potential role in the oncogenesis and development of colorectal cancer

Small nucleolar RNAs(snoRNAs)represent a class of non-coding RNAs that play pivotal roles in post-transcriptional RNA processing and modification,thereby contributing significantly to the maintenance of cellular functions related to protein synthesis.SnoRNAs have been discovered to possess the ability to influence cell fate and alter disease progression,holding immense potential in controlling human diseases.It is suggested that the dysregulation of snoRNAs in cancer exhibits differential expression across various cancer types,stages,metastasis,treatment response and/or prognosis in patients.On the other hand,colorectal cancer(CRC),a prevalent malignancy of the digestive system,is characterized by high incidence and mortality rates,ranking as the third most common cancer type.Recent research indicates that snoRNA dysregulation is associated with CRC,as snoRNA expression significantly differs between normal and cancerous conditions.Consequently,assessing snoRNA expression level and function holds promise for the prognosis and diagnosis of CRC.Nevertheless,current comprehension of the potential roles of snoRNAs in CRC remains limited.This review offers a comprehensive survey of the aberrant regulation of snoRNAs in CRC,providing valuable insights into the discovery of novel biomarkers,therapeutic targets,and potential tools for the diagnosis and treatment of CRC and furnishing critical cues for advancing research into CRC and the judicious selection of therapeutic targets.

Impact of comorbid subthreshold depressive symptoms on cancerrelated fatigue and complications in adults with leukemia

BACKGROUND Patients not only experience symptoms caused by cancer but also suffer from the accompanying psychological pain.Therefore,these patients do not have high quality of life.According to the World Health Organization,the incidence of leukemia in China in 2020 was 5.1/100000,the mortality rate was 3.3/100000,and the prevalence rate was 16.7/100000.Therefore,it is important to examine the influence of comorbid subthreshold depressive symptoms on leukemia patients.AIM To determine the impact of comorbid subthreshold depressive symptoms on cancer-related fatigue and complications in leukemia patients,thereby providing a basis for early diagnosis and treatment in clinical practice.METHODS A questionnaire survey was conducted among leukemia patients admitted to a tertiary hospital in Xi'an,Shaanxi Province,China,from August 2022 to December 2023.Patients with a score>16 on the Chinese Classification of Mental Disorders(CCMD-3)and a Hamilton Depression Rating Scale score of 8-17 were classified as the subthreshold depressive group(n=95),while 100 leukemia patients admitted during the same period were classified as the control group.Data were collected using Epidata 3.1 software,and comparisons were made between the two groups regarding general clinical data,the Piper Fatigue Scale(PFS),the Pittsburgh Sleep Quality Index(PSQI),the Numeric Rating Scale for pain assessment,laboratory indicators,and the occurrence of complications.RESULTS In this survey,120 leukemia patients with depression were preliminarily screened,95 patients with subthreshold depression were ultimately selected as the subthreshold depression group,and 100 leukemia patients admitted during the same period were enrolled as the normal group.Comparison of basic clinical data between the two groups revealed no significant differences in age,sex,body mass index,cognitive function,or comorbidity with other chronic diseases.However,there were statistically significant differences in the use of radiotherapy and regular exercise between the two groups(P<0.05).Comparisons of scales and laboratory indicators revealed no significant differences in albumin or PSQI scores between the two groups,but there were statistically significant differences in pain scores,PSQI scores,PFS scores,hemoglobin levels,and C-reactive protein levels(P<0.05).Spearman’s correlation analysis indicated that cancer-related fatigue was correlated with age,hemoglobin levels,C-reactive protein levels,pain,and regular exercise among leukemia patients with subthreshold depression.Multivariate regression analysis revealed that advanced age,combined radiotherapy,pain,and low hemoglobin levels were risk factors for cancer-related fatigue in leukemia patients with comorbid subthreshold depression,while regular exercise was a protective factor against cancer-related fatigue.Follow-up comparisons revealed a significantly lower overall incidence of complications in the control group(4%)than in the depressive group(24.21%;P<0.001).CONCLUSION Leukemia patients with comorbid subthreshold depressive symptoms experience more severe cancer-related fatigue and a higher incidence of complications.These findings may be related to advanced age,combined radiotherapy,pain,and low hemoglobin levels,while regular exercise may effectively alleviate symptoms.

Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis

Given the extremely high inter-patient heterogeneity of acute myeloid leukemia(AML),the identification of biomarkers for prognostic assessment and therapeutic guidance is critical.Cell surface markers(CSMs)have been shown to play an important role in AML leukemogenesis and progression.In the current study,we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas(TCGA)based on differential gene expression analysis and univariable Cox proportional hazards regression analysis.By using multi-model analysis,including Adaptive LASSO regression,LASSO regression,and Elastic Net,we constructed a 9-CSMs prognostic model for risk stratification of the AML patients.The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels.Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients.The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores.Notably,single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance.Furthermore,PI3K inhibitors were identified as potential treatments for these high-risk patients.In conclusion,we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.

Enhancing Dense Small Object Detection in UAV Images Based on Hybrid Transformer

Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.

Gastric metastasis of small cell lung carcinoma:Three case reports and review of literature

BACKGROUND Small cell lung carcinoma(SCLC)is highly susceptible to metastasis in the early stages of the disease.However,the stomach is an uncommon site of metastasis in SCLC,and only a few cases of this type of metastasis have been reported.Therefore,SCLC gastric metastases have not been systematically characterized and are easily missed and misdiagnosed.CASE SUMMARY We report three cases of gastric metastasis from SCLC in this article.The first patient presented primarily with cough,hemoptysis,and epigastric fullness.The other two patients presented primarily with abdominal discomfort,epigastric distension,and pain.All patients underwent gastroscopy and imaging examinations.Meanwhile,the immunohistochemical results of the lesions in three patients were suggestive of small cell carcinoma.Finally,the three patients were diagnosed with gastric metastasis of SCLC through a comprehensive analysis.The three patients did not receive appropriate treatment and died within a short time.CONCLUSION Here,we focused on summarizing the characteristics of gastric metastasis of SCLC to enhance clinicians'understanding of this disease.

Development of small molecule drugs targeting immune checkpoints

Immune checkpoint inhibitors(ICIs)are used to relieve and refuel anti-tumor immunity by blocking the interaction,transcription,and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune checkpoints.Thousands of small molecule drugs or biological materials,especially antibody-based ICIs,are actively being studied and antibodies are currently widely used.Limitations,such as anti-tumor efficacy,poor membrane permeability,and unneglected tolerance issues of antibody-based ICIs,remain evident but are thought to be overcome by small molecule drugs.Recent structural studies have broadened the scope of candidate immune checkpoint molecules,as well as innovative chemical inhibitors.By way of comparison,small molecule drug-based ICIs represent superior oral bioavailability and favorable pharmacokinetic features.Several ongoing clinical trials are exploring the synergetic effect of ICIs and other therapeutic strategies based on multiple ICI functions,including immune regulation,anti-angiogenesis,and cell cycle regulation.In this review we summarized the current progression of small molecule ICIs and the mechanism underlying immune checkpoint proteins,which will lay the foundation for further exploration.

Blood-brain barrier pathology in cerebral small vessel disease

Cerebral small vessel disease is a neurological disease that affects the brain microvasculature and which is commonly observed among the elderly.Although at first it was considered innocuous,small vessel disease is nowadays regarded as one of the major vascular causes of dementia.Radiological signs of small vessel disease include small subcortical infarcts,white matter magnetic resonance imaging hyperintensities,lacunes,enlarged perivascular spaces,cerebral microbleeds,and brain atrophy;however,great heterogeneity in clinical symptoms is observed in small vessel disease patients.The pathophysiology of these lesions has been linked to multiple processes,such as hypoperfusion,defective cerebrovascular reactivity,and blood-brain barrier dysfunction.Notably,studies on small vessel disease suggest that blood-brain barrier dysfunction is among the earliest mechanisms in small vessel disease and might contribute to the development of the hallmarks of small vessel disease.Therefore,the purpose of this review is to provide a new foundation in the study of small vessel disease pathology.First,we discuss the main structural domains and functions of the blood-brain barrier.Secondly,we review the most recent evidence on blood-brain barrier dysfunction linked to small vessel disease.Finally,we conclude with a discussion on future perspectives and propose potential treatment targets and interventions.

Cognitive impairment in cerebral small vessel disease induced by hypertension

Hypertension is a primary risk factor for the progression of cognitive impairment caused by cerebral small vessel disease,the most common cerebrovascular disease.Howeve r,the causal relationship between hypertension and cerebral small vessel disease remains unclear.Hypertension has substantial negative impacts on brain health and is recognized as a risk factor for cerebrovascular disease.Chronic hypertension and lifestyle factors are associated with risks for stro ke and dementia,and cerebral small vessel disease can cause dementia and stroke.Hypertension is the main driver of cerebral small vessel disease,which changes the structure and function of cerebral vessels via various mechanisms and leads to lacunar infarction,leukoaraiosis,white matter lesions,and intracerebral hemorrhage,ultimately res ulting in cognitive decline and demonstrating that the brain is the to rget organ of hypertension.This review updates our understanding of the pathogenesis of hypertensioninduced cerebral small vessel disease and the res ulting changes in brain structure and function and declines in cognitive ability.We also discuss drugs to treat cerebral small vessel disease and cognitive impairment.

Overview of novel nanobiosensors for electrochemical and optical diagnosis of leukemia:Challenge and opportunity

Leukemia is one of the ten types of cancer that causes the biggest death in the world.Compared to other types of cancer,leukemia has a low life expectancy,so an early diagnosis of the cancer is necessary.A new strategy has been developed to identify various leukemia biomarkers by making blood cancer biosensors,especially by developing nanomaterial applications so that they can improve the performance of the biosensor.Although many biosensors have been developed,the detection of leukemia by using nanomaterials with electrochemical and optical methods is still less carried out compare to other types of cancer biosensors.Even the acoustic and calorimetric testing methods for the detection of leukemia by utilizing nanomaterials have not yet been carried out.Most of the reviewed works reported the use of gold nanoparticles and electrochemical characterization methods for leukemia detection with the object of study being conventional cancer cells.In order to be used clinically by the community,future research must be carried out with a lot of patient blood objects,develop non-invasive leukemia detection,and be able to detect all types of blood cancer specifically with one biosensor.This can lead to a fast and accurate diagnosis thus allowing for early treatment and easy periodic condition monitoring for various types of leukemia based on its biomarker and future design controlable via internet of things(IoT)so that why would be monitoring real times.

Carrimycin in the treatment of acute promyelocytic leukemia combined with pulmonary tuberculosis: A case report

BACKGROUND Pulmonary tuberculosis(PTB)is prevalent in immunocompromised populations,including patients with hematologic malignancies,human immunodeficiency virus infections,and chronic diseases.Effective treatment for acute promyelocytic leukemia(APL)combined with PTB is lacking.These patients show an extremely poor prognosis.Therefore,studies should establish efficient treatment options to improve patient survival and prognosis.CASE SUMMARY A 60-year-old male with pain in the right side of his chest and a fever for 4 d visited the outpatient department of our hospital.Peripheral blood smear revealed 54%blasts.Following bone marrow examinations,variant APL with TNRC18-RARA fusion gene was diagnosed.Chest computed tomography scan showed bilateral pneumonitis with bilateral pleural effusions,partial atelectasis in the lower lobes of both lungs,and the bronchoalveolar lavage fluid gene X-Pert test was positive,indicative of PTB.Carrimycin,ethambutol(EMB),and isoniazid(INH)were administered since he could not receive chemotherapy as the WBC count decreased continuously.After one week of treatment with carrimycin,the patient recovered from fever and received chemotherapy.Chemotherapy was very effective and his white blood cells counts got back to normal.After being given five months with rifampin,EMB and INH and chemotherapy,the patient showed complete remission from pneumonia and APL.CONCLUSION We report a case of PTB treated successfully with carrimycin with APL that requires chemotherapy.

Bone marrow microRNA-34a is a good indicator for response to treatment in acute myeloid leukemia

Background:microRNA 34a(miR 34a)had been reported to have a diagnostic role in acute myeloid leukemia(AML).However,its value in the bone marrow(BM)of AML patients,in addition to its role in response to therapy is still unclear.The current study was designed to assess the diagnostic,prognostic,and predictive significance of miR 34a in the BM of AML patients.Methods:The miR.34a was assed in BM aspirate of 82 AML patients in relation to 12 normal control subjects using qRT-PCR.The data were assessed for correlation with the relevant dinical critenia,response to therapy,disease-free survival(DFS),and overall survival(OS)rates.Results:miR.34a was significantly downregulated in AML patients[0.005(3.3×10^(-6)-1.32)],compared to the control subjects[0.108(3.2× 10^(-4)-1.64),p=0.021].The.median relative quantification(RQ)of miR-34a was 0.106(range;0-32.12).The specifaity,sensitivity,and area under the curve(AUC)for the diagnosis of AML were(58.3%,69.5%,0.707,respectively,p=0.021).patients with upregulated miR-34a showed decreased platelets count<34.5 × 10^(9)/L,and achieved early complete remission(CR,p=0.031,p=0.044,respectively).Similarly,patients who were refractory to therapy showed decreased miR 34a levels in comparison to those who achieved CR[0.002(0-0.01)and 0.12(0-32.12),respectively,p=0.002].Therefore,miR 34a could significantly identify patients with CR with a specificity of 75%and sensitivity of 100%at a cut-off of 0.014(AUC=0.927,p=0.005).There was no considerable association between miR-34a expression and survival rates of the induded AML patients.Condusion:miR-34a could be a beneficial diagnostic biomarker for AML patients.In addition,it serves as a good indicator for response to therapy,which could possibly identify patients who are refractory to treatment with 100%sensitivity and 75%specificity.

Synergistic Effects of Glutamine Deprivation and Metformin in Acute Myeloid Leukemia

Objective The metabolic reprogramming of acute myeloid leukemia(AML)cells is a compensatory adaptation to meet energy requirements for rapid proliferation.This study aimed to examine the synergistic effects of glutamine deprivation and metformin exposure on AML cells.Methods SKM-1 cells(an AML cell line)were subjected to glutamine deprivation and/or treatment with metformin or bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide(BPTES,a glutaminase inhibitor)or cytarabine.Cell viability was detected by Cell Counting Kit-8(CCK-8)assay,and cell apoptosis and reactive oxygen species(ROS)by flow cytometry.Western blotting was conducted to examine the levels of apoptotic proteins,including cleaved caspase-3 and poly(ADP-ribose)polymerase(PARP).Moreover,the human long noncoding RNA(lncRNA)microarray was used to analyze gene expression after glutamine deprivation,and results were confirmed with quantitative RT-PCR(qRT-PCR).The expression of metallothionein 2A(MT2A)was suppressed using siRNA.Cell growth and apoptosis were further detected by CCK-8 assay and flow cytometry,respectively,in cells with MT2A knockdown.Results Glutamine deprivation or treatment with BPTES inhibited cell growth and induced apoptosis in SKM-1 cells.The lncRNA microarray result showed that the expression of MT family genes was significantly upregulated after glutamine deprivation.MT2A knockdown increased apoptosis,while proliferation was not affected in SKM-1 cells.In addition,metformin inhibited cell growth and induced apoptosis in SKM-1 cells.Both glutamine deprivation and metformin enhanced the sensitivity of SKM-1 cells to cytarabine.Furthermore,the combination of glutamine deprivation with metformin exhibited synergistic antileukemia effects on SKM-1 cells.Conclusion Targeting glutamine metabolism in combination with metformin is a promising new therapeutic strategy for AML.

Two-Layer Attention Feature Pyramid Network for Small Object Detection

Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.

A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class classification accuracy of 97.08%.The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size(520 images).Hence,this method can be explored further for leukemia diagnosis having a limited number of dataset samples.

Damage Mechanism of CK2 and IKAROS in Philadelphia Like Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is characterized by immature and poorly differentiated B lymphocytes in large numbers in the blood. B cells are distinct from the cell types involved in their development (common lymphoid progenitor cells, pro-B cells, pre-B cells, and mature cells). The process of B cell maturation depends on precise communication within the cell: signals activate specific genes that are essential for proper development. Errors in this intricate signaling network can lead to issues with B cell function and contribute to disease. B-lineage acute lymphoid leukemias, malignancies of precursor-stage B lymphoid cells inhibit lymphoid differentiation, leading to abnormal cell proliferation and survival. The process of developing leukemia (leukemogenesis) can be triggered by an overproduction of both hematopoietic stem cells (the cells that form all blood cells) and the immature versions of white blood cells called lymphoblasts. Acute lymphoblastic leukemia (ALL) with the presence of the Philadelphia chromosome (ALL Ph) is classified as a high-risk manifestation of the disease, this chromosome is the product of the reciprocal translocation, whose product is a BCR-ABL fusion protein. It is a highly active tyrosine kinase that can transform hematopoietic cells into cytokine-independent. Hyperphosphorylation cascades inhibit the differentiating function of IKZF1 as a tumor suppressor gene which leads to an abnormal proliferation of B cells due to the presence of the Philadelphia chromosome;it inhibits the differentiating process, leukemogenesis involving immature B cells in the bloodstream can result from the uncontrolled growth and division of hematopoietic stem cells and immature lymphoblasts (the precursors to B cells).

Non-canonical BRAF variants and rearrangements in hairy cell leukemia

Hairy cell leukemia(HCL)is an uncommon mature B-cell malignancy characterized by a typical morphology,immunophenotype,and clinical profile.The vast majority of HCL patients harbor the canonical BRAF V600E mutation which has become a rationalized target of the subsequently deregulated RAS-RAF-MEK-MAPK signaling pathway in HCL patients who have relapsed or who are refractory to front-line therapy.However,several HCL patients with a classical phenotype display non-canonical BRAF mutations or rearrangements.These include sequence variants within alternative exons and an oncogenic fusion with the IGH gene.Care must be taken in the molecular diagnostic work-up of patients with typical HCL but without the BRAF V600E to include investigation of these uncommon mechanisms.Identification,functional characterization,and reporting of further such patients is likely to provide insights into the pathogenesis of HCL and enable rational selection of targeted inhibitors in such patients if required.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.