An Integrated Machine Learning Approach for Congestive Heart Failure Prediction.

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features.

AIM OF THE STUDY: This research endeavours to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilizing longer electrocardiogram (ECG) signal events based on the Apnea ECG dataset. Apnea ECG is often used to detect sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification. Used method. To extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorizing ECG signals by utilizing several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6). Brief Description of Results. The results emphasize the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognizes 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy. FINDINGS: The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest wavelet families and machine learning can improve cardiac abnormality detection and categorization.

COVID-SegNet: encoder-decoder-based architecture for COVID-19 lesion segmentation in chest X-ray.

The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world's health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder-decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module.

Targeted silencing of the MCL-1 gene using multi-layered dendrimer-based nanoconstructs achieves efficient tumor regression in xenografted mice models.

The setback in the practical clinical use of RNA interference (RNAi)-based cancer treatment stems from the lack of targeted small interfering RNA (siRNA) delivery. Here, we show that luteinizing hormone-releasing hormone(LHRH) analog-tethered multi-layered polyamidoamine (PAMAM) nanoconstructs silence the anti-apoptotic MCL-1 gene in LHRH receptor overexpressing human breast (MCF-7) and prostate cancer (LNCaP) cells with 70.91 % and 74.10 % efficiency, respectively. These results were confirmed by RT-PCR. The Acridine orange/Ethidium bromide (AO/EB) dual staining revealed that the silencing of MCL-1 induced apoptosis in both the cell lines. In vivo tumor regression studies performed using MCF-7 and LNCaP xenografted severe combined immunodeficiency(SCID) mice demonstrated highly improved tumor regression in groups treated with targeted nanoconstructs complexed with MCL-1 siRNA (T + siMCL-1) compared to the other treatment groups. The quantitative RT-PCR results of tumor tissues demonstrated significant MCL-1 gene silencing, i.e., 73.76 % and 92.63 % in breast and prostate tumors, respectively, after T + siMCL-1 treatment. Reduction in MCL-1 protein expression as assessed by immunohistochemistry further confirmed these results. Furthermore, the caspase 3/7 assay demonstrated apoptosis in the MCL-1 silenced tissues. The study strongly suggests that targeted delivery of siRNAs using multi-layered dendrimer nanostructures could be an effective therapy for LHRH overexpressing cancers.

Segmentation and classification on chest radiography: a systematic survey.

Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. This leads to the need for computer-aided detection diagnosis. For decades, researchers were automatically detecting pulmonary disorders using the traditional computer vision (CV) methods. Now the availability of large annotated datasets and computing hardware has made it possible for deep learning to dominate the area. It is now the modus operandi for feature extraction, segmentation, detection, and classification tasks in medical imaging analysis. This paper focuses on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets. The studies performed using the Generative Adversarial Network (GAN) models for segmentation and classification on chest X-rays are also included in this study. GAN has gained the interest of the CV community as it can help with medical data scarcity. In this study, we have also included the research conducted before the popularity of deep learning models to have a clear picture of the field. Many surveys have been published, but none of them is dedicated to chest X-rays. This study will help the readers to know about the existing techniques, approaches, and their significance.

FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images.

COVID-19 is an acronym for coronavirus disease 2019. Initially, it was called 2019-nCoV, and later International Committee on Taxonomy of Viruses (ICTV) termed it SARS-CoV-2. On 30th January 2020, the World Health Organization (WHO) declared it a pandemic. With an increasing number of COVID-19 cases, the available medical infrastructure is essential to detect the suspected cases. Medical imaging techniques such as Computed Tomography (CT), chest radiography can play an important role in the early screening and detection of COVID-19 cases. It is important to identify and separate the cases to stop the further spread of the virus. Artificial Intelligence can play an important role in COVID-19 detection and decreases the workload on collapsing medical infrastructure. In this paper, a deep convolutional neural network-based architecture is proposed for the COVID-19 detection using chest radiographs. The dataset used to train and test the model is available on different public repositories. Despite having the high accuracy of the model, the decision on COVID-19 should be made in consultation with the trained medical clinician.

Endometrial DNA damage response is modulated in endometriosis.

STUDY QUESTION: Is the DNA damage response (DDR) dysregulated in the eutopic endometrium of women with endometriosis? SUMMARY ANSWER: Endometrial expression of genes involved in DDR is modulated in women with endometriosis, compared to those without the disease. WHAT IS KNOWN ALREADY: Ectopic endometriotic lesions are reported to harbour somatic mutations, thereby hinting at dysregulation of DDR and DNA repair pathways. However, it remains inconclusive whether the eutopic endometrium also manifests dysregulated DDR in endometriosis. STUDY DESIGN, SIZE, DURATION: For this case-control study conducted between 2015 and 2019, eutopic endometrial (E) samples (EE- from women with endometriosis, CE- from women without endometriosis) were collected in either mid-proliferative (EE-MP, n = 23; CE-MP, n = 17) or mid-secretory (EE-MS, n = 17; CE-MS, n = 9) phases of the menstrual cycle. This study compares: (i) DNA damage marker localization, (ii) expression of DDR genes and (iii) expression of DNA repair genes in eutopic endometrial samples from women with and without endometriosis. PARTICIPANTS/MATERIALS, SETTING, METHODS: The study included (i) 40 women (aged 31.9 ± 0.81 years) with endometriosis and (ii) 26 control women (aged 31.4 ± 1.02 years) without endometriosis. Eutopic endometrial samples from the two groups were divided into different parts for histological analysis, immunohistochemistry, RNA extraction, protein extraction and comet assays. Eighty-four genes of relevance in the DNA damage signalling pathway were evaluated for their expression in eutopic endometrial samples, using RT2 Profiler PCR arrays. Validations of the expression of two GADD (Growth Arrest DNA Damage Inducible) proteins - GADD45A and GADD45G were carried out by immunoblotting. DNA damage was assessed by immunohistochemical localization of γ-H2AFX (a phosphorylated variant of histone H2AX) and 8-OHdG (8-hydroxy-2'-deoxyguanosine). RNA sequencing data from mid-proliferative (EE-MP, n = 4; CE-MP, n = 3) and mid-secretory phase (EE-MS and CE-MS, n = 4 each) endometrial samples were scanned to compare the expression status of all the genes implicated in human DNA repair. PCNA (Proliferating Cell Nuclear Antigen) expression was determined to assess endometrial proliferation. Residual DNA damage in primary endometrial cells was checked by comet assays. Public datasets were also scanned for the expression of DDR and DNA repair genes as our RNASeq data were limited by small sample size. All the comparisons were made between phase-matched endometrial samples from women with and without endometriosis. MAIN RESULTS AND THE ROLE OF CHANCE: Endometrial expression of DDR genes and intensity of immunolocalized γ-H2AFX were significantly (P < 0.05) higher in EE, compared to CE samples. DDR proteins, especially those belonging to the GADD family, were found to be differentially abundant in EE, as compared to CE. These patterns were evident in both mid-proliferative and mid-secretory phases. Intriguingly, higher DDR was associated with increased cell proliferation in EE-MP, compared to CE-MP. Furthermore, among the differentially expressed transcripts (DETs) encoded by DNA repair genes, the majority showed up-regulation in EE-MP, compared to CE-MP. Interestingly, CE-MP and EE-MP had a comparable percentage (P > 0.05) of cells with residual DNA damage. However, unlike the mid-proliferative phase data, many DETs encoded by DNA repair genes were down-regulated in EE-MS, compared to CE-MS. An analysis of the phase-matched control and endometriosis samples included in the GSE51981 dataset available in the Gene Expression Omnibus database also revealed significant (P < 0.05) alterations in the expression of DDR and DNA repair genes in EE, compared to CE. LARGE-SCALE DATA: N/A. LIMITATIONS, REASONS FOR CAUTION: The study was conducted on a limited number of endometrial samples. Also, the study does not reveal the causes underlying dysregulated DDR in the eutopic endometrium of women with endometriosis. WIDER IMPLICATIONS OF THE FINDINGS: Alterations in the expression of DDR and DNA repair genes indirectly suggest that eutopic endometrium, as compared to its healthy counterpart, encounters DNA damage-inducing stimuli, either of higher strength or for longer duration in endometriosis. It will be worthwhile to identify the nature of such stimuli and also explore the role of higher genomic insults and dysregulated DDR/DNA repair in the origin and/or progression of endometriosis. STUDY FUNDING/COMPETING INTEREST(S): The study was supported by the Department of Biotechnology and Indian Council of Medical Research, Government of India. No conflict of interest is declared.

Acute pancreatitis, ascites, and acute renal failure in Plasmodium vivax malaria infection, a rare complication.

A 22-year-old male presented with 6 days history of intermittent fever with chills, 2 days history of upper abdomen pain, distension of abdomen, and decreased urine output. He was diagnosed to have Plasmodium vivax malaria, acute pancreatitis, ascites, and acute renal failure. These constellations of complications in P. vivax infection have never been reported in the past. The patient responded to intravenous chloroquine and supportive treatment. For renal failure, he required hemodialysis. Acute pancreatitis, ascites, and acute renal failure form an unusual combination in P. vivax infection.

Retinal Hemorrhages in Severe Non-cerebral Plasmodium vivax Malaria in an Adult.

Malaria is the most important parasitic diseases of humans and one of the leading causes of morbidity and mortality in tropical countries. Earlier Plasmodium vivax was considered as a benign infection, but now it is recognized as a cause of severe malarial disease. It causes severe malarial disease similar to those as Plasmodium falciparum including cerebral malaria, severe anaemia, severe thrombocytopenia, hepatic dysfunction, shock, acute respiratory distress syndrome (ARDS), acute renal failure, and pulmonary oedema. Malarial retinopathy includes retinal whitening, vessel changes, retinal hemorrhages and papilledema. However, retinal hemorrhages are very rare in Plasmodium vivax infestation. Hereby, we report a case of 30-year-old man, who presented with fever with chills and diminution of vision. He was found to have Plasmodium vivax infection with retinal hemorrhages. He was treated successfully with artisunate, primaquine and doxycycline, completely recovered after one month.

Successful use of flupirtine in refractory neuropathic pain due to small fiber neuropathy.

Small fiber neuropathy typically involves the small diameter nerve fibers, is usually idiopathic, and presents with peripheral pain. It can be excruciatingly painful at times despite the best of treatments. We present the case of a 22-year-old postoperative case of right frontoparietal oligodendroglioma who received multiple drugs for his severe neuropathic pain without significant relief. However, the pain almost completely subsided once flupirtine was added and substituted for some of the currently recommended first-line drugs.

Radio Frequency Ablation in Drug Resistant Chemotherapy-induced Peripheral Neuropathy: A Case Report and Review of Literature.

Chemotherapy-induced peripheral neuropathy (CIPN) is a frequently encountered complication. It can result from a host of agents. Various modalities of treatment have been advocated, of which a novel method is radio frequency ablation. A 63-year-old male, a case of carcinoma prostrate with bone metastases, presented with tingling and numbness in right upper limb. He was given morphine, gabapentin and later switched to pregabalin, but medications provided only minor relief. Initially he was given stellate ganglion block, then radiofrequency ablation of dorsal root ganglion was done, but it failed to provide complete relief. Pulsed radiofrequency ablation (PRF) was then done for 90 seconds; two cycles each in both ulnar and median nerve. After the procedure the patient showed improvement in symptoms within four to five hours and 80% relief in symptoms. We conclude that PRF can be used for the treatment of drug resistant CIPN.

Prevalence of cataract type in relation to axial length in subjects with high myopia and emmetropia in an Indian population.

PURPOSE: To compare the prevalence of different types and densities of age-related cataract in subjects with high myopia and emmetropia in an Indian urban clinic-based population. DESIGN: Prospective, observational, clinic-based case-control study. METHODS: An observational case-control study of 800 healthy eyes was undertaken at Iladevi Cataract & IOL Research Center, Ahmedabad, India. Subjects with high myopia (axial length [AXL], > or = 26.0 mm; n = 400 eyes) and those with emmetropia (AXL, 21.0 to 23.99 mm) were examined (n = 400 eyes). The type of cataract was categorized as: nuclear, cortical, or posterior subcapsular cataract (PSC). Nuclear density was measured based on the Emery and Little classification. RESULTS: In all the age groups (40 years of age or more), nuclear cataract was encountered more often in subjects with high myopia (odds ratio, 3.8; 95% confidence interval, 2.9 to 5.2; P < .001); PSC and mixed cataracts were encountered frequently in subjects with emmetropia (P < .001). Prevalence of nuclear cataract was higher in subjects with high myopia with increasing AXL when compared with other types of cataract (P < .001). In all the age groups, the nuclear density was significantly higher than grade 3 in subjects with high myopia when compared with those with emmetropia (P < .001 in those younger than 70 years; P = .003 in those older than 70 years). CONCLUSIONS: Nuclear cataract was associated strongly with high axial myopia. The density of the cataract was higher in the high myopia group. No association was observed between PSC or cortical cataract and high axial myopia.