Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India.

BACKGROUND: Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely. METHODS: We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters. RESULT: The hybrid combination displayed significant reduction in RMSE (16.23%), MAE (37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries. CONCLUSION: Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

Application of Milan system for reporting of salivary gland pathology and risk stratification: An institutional experience.

BACKGROUND: Fine-needle aspiration cytology (FNAC) is a sensitive technique for diagnosing salivary gland pathologies. Milan system of reporting salivary gland cytopathology (MSRSGC) is an evidence-based system of reporting which has been introduced to improve reporting and communication between cytopathologist and clinician by introducing standardized categories with specified treatment protocols. AIMS AND OBJECTIVES: The aim of the present study is to find the diagnostic accuracy and risk of malignancy (ROM) in various categories when MSRSGC is applied. MATERIALS AND METHODS: A single-institute-based 3-year retrospective study was done. All salivary gland lesions were reclassified according to MSRSGC. ROM, diagnostic specificity, sensitivity and accuracy of FNAC of salivary gland lesions were calculated. RESULTS: A total of 133 cases were included in the study. Overall, the most common diagnosis was found to be pleomorphic adenoma comprising 61 (42.8%) of all cases. Adenoid cystic carcinoma was the most commonly diagnosed malignancy comprising of 6 (4.5%) of all lesions. Cases were further divided into Milan categories, namely nondiagnostic, nonneoplastic, atypia of undetermined significance, benign neoplasm, neoplasm of undetermined malignant potential, suspicious of malignancy and malignancy comprising 5 (3.7%), 29 (21.8%), 77 (57.8%), 4 (3%), 3 (2.2%), 1 (0.7%) and 14 (10.5%) cases, respectively, with ROM of 0, 14.28%, 33.33%, 5.71%, 66.66%, 100% and 80%, respectively. Sensitivity, specificity and diagnostic accuracy to separate benign from malignant lesions were 80%, 89.80% and 87.50%, respectively. CONCLUSION: FNAC of the salivary gland lesions is a safe and reliable diagnostic procedure. The Milan system of reporting is a risk stratification system which can improve the overall effectiveness of reporting and care of patients.

Implementation of Bagged SVM Ensemble Model for Classification of Epileptic States Using EEG.

BACKGROUND: To decipher EEG (Electroencephalography), intending to locate inter-ictal and ictal discharges for supporting the diagnoses of epilepsy and locating the seizure focus, is a critical task. The aim of this work was to find how the ensemble model distinguishes between two different sets of problems which are group 1: inter-ictal and ictal, group 2: controlled and inter-ictal using approximate entropy as a parameter. METHODS: This work addresses the classification problem for two groups; Group 1: "inter-ictal vs. ictal" for which case 1(C-E), and case 2(D-E) are included and Group 2; "activity from controlled vs. inter-ictal activity" considering four cases which are case 3 (A-C), case 4(B-C), case 5 (A-D) and case 6(B-D) respectively. To divide the EEG into sub-bands, DWT (Discrete Wavelet Transform) was used and approximate Entropy was extracted out of all the five sub-bands of EEG for each case. Bagged SVM was used to classify the different groups considered. RESULTS: The highest accuracy for Group 1 using Bagged SVM Ensemble model for case 1 was observed to be 96.83% with testing data; which was similar to 97% achieved by using training data. For case 2 (D-E) 93.92% accuracy with training and 84.83% with testing data were obtained. For Group 2, there was a large disparity between SVM and Bagged Ensemble model, where 76%, 81.66%, 72.835% and 71.16% for case 3, case 4, case 5 and case 6 were obtained. While for training data set, 92.87%, 91.74%, 92% and 92.64% accuracy was attained, respectively. The results obtained by SVM for Group 2 showed a huge difference from the highest accuracy achieved by bagged SVM for both the training and the test data. CONCLUSION: Bagged Ensemble model outperformed SVM model for every case with a huge difference with both training as well as test dataset for Group 2 and marginally better for Group 1.

Diagnostic, Treatment, and System Challenges in the Management of Recurrent Neuroleptic Malignant Syndrome on a General Medical Service.

Neuroleptic malignant syndrome (NMS), an iatrogenic form of malignant catatonia, carries high morbidity and mortality rates especially in the context of delayed recognition and standard intervention protocol of lorazepam trial. However, there is limited guidance available through literature for further management if benzodiazepine treatment is ineffective and electroconvulsive therapy (ECT) is not readily accessible. This case report describes a multimodal approach to address the diagnostic, treatment, and logistical system challenges in an acute medical hospital through the case of a 69-year-old man with schizophrenia who represented from a psychiatric ward with neuroleptic malignant syndrome. We educated our inpatient colleagues for timely recognition of hyperexcited subtype of catatonia to avoid iatrogenic progression to neuroleptic malignant syndrome and our medical colleagues on the clinical course of catatonic symptoms to avoid any further disagreements and delays in treatment. We advocated for timely electroconvulsive therapy in the setting of limited access and utilized creative pharmacologic strategies such as N-methyl-D-aspartate (NMDA) receptor antagonists and longer acting benzodiazepines while managing medical complications.

Cost-minimization analysis of venous thromboembolism diagnosis: Comparison of standalone imaging with a strategy incorporating D-dimer for exclusion of venous thromboembolism.

BACKGROUND: The burden of healthcare costs has substantially risen in the last few decades. One possible contributing factor to this increase are the diagnostic approaches for venous thromboembolism (VTE) using only imaging to exclude a diagnosis of VTE. OBJECTIVE: To demonstrate cost minimization in the diagnosis of VTE by comparing standalone imaging (computed tomography pulmonary angiography and compression ultrasonography) to a published VTE diagnostic strategy incorporating assessment of pre-test probability and D-dimer testing. METHODS: We retrospectively reviewed data from a multicenter diagnostic accuracy study of a D-dimer reagent where consecutive outpatients (n=747) with suspected VTE, including both pulmonary embolism (n=346) and deep venous thrombosis (n=401) were evaluated. By applying a VTE diagnostic strategy and using the proportion of patients that were diagnosed as VTE-negative (n=137 for PE; n=120 for DVT), we developed a cost calculator to compare the average diagnostic test cost per suspected VTE patient, both before and after the implementation of the VTE diagnostic strategy. RESULTS: Implementation of the VTE diagnostic strategy reduced the average diagnostic test cost for a suspected PE patient by 38% and for a suspected DVT patient by 24%. Assuming the proportion of VTE suspected patients to be 30% PE and 70% DVT, the weighted average reduction in the diagnostic test cost per suspected VTE patient was 32%. CONCLUSION: Implementation of a VTE diagnostic strategy can allow hospitals to reduce costs without compromising patient safety.

Tankyrase inhibition promotes a stable human naïve pluripotent state with improved functionality.

The derivation and maintenance of human pluripotent stem cells (hPSCs) in stable naïve pluripotent states has a wide impact in human developmental biology. However, hPSCs are unstable in classical naïve mouse embryonic stem cell (ESC) WNT and MEK/ERK signal inhibition (2i) culture. We show that a broad repertoire of conventional hESC and transgene-independent human induced pluripotent stem cell (hiPSC) lines could be reverted to stable human preimplantation inner cell mass (ICM)-like naïve states with only WNT, MEK/ERK, and tankyrase inhibition (LIF-3i). LIF-3i-reverted hPSCs retained normal karyotypes and genomic imprints, and attained defining mouse ESC-like functional features, including high clonal self-renewal, independence from MEK/ERK signaling, dependence on JAK/STAT3 and BMP4 signaling, and naïve-specific transcriptional and epigenetic configurations. Tankyrase inhibition promoted a stable acquisition of a human preimplantation ICM-like ground state via modulation of WNT signaling, and was most efficacious in efficiently reprogrammed conventional hiPSCs. Importantly, naïve reversion of a broad repertoire of conventional hiPSCs reduced lineage-primed gene expression and significantly improved their multilineage differentiation capacities. Stable naïve hPSCs with reduced genetic variability and improved functional pluripotency will have great utility in regenerative medicine and human disease modeling.

Growth factor-activated stem cell circuits and stromal signals cooperatively accelerate non-integrated iPSC reprogramming of human myeloid progenitors.

Nonviral conversion of skin or blood cells into clinically useful human induced pluripotent stem cells (hiPSC) occurs in only rare fractions (~0.001%-0.5%) of donor cells transfected with non-integrating reprogramming factors. Pluripotency induction of developmentally immature stem-progenitors is generally more efficient than differentiated somatic cell targets. However, the nature of augmented progenitor reprogramming remains obscure, and its potential has not been fully explored for improving the extremely slow pace of non-integrated reprogramming. Here, we report highly optimized four-factor reprogramming of lineage-committed cord blood (CB) myeloid progenitors with bulk efficiencies of ~50% in purified episome-expressing cells. Lineage-committed CD33(+)CD45(+)CD34(-) myeloid cells and not primitive hematopoietic stem-progenitors were the main targets of a rapid and nearly complete non-integrated reprogramming. The efficient conversion of mature myeloid populations into NANOG(+)TRA-1-81(+) hiPSC was mediated by synergies between hematopoietic growth factor (GF), stromal activation signals, and episomal Yamanaka factor expression. Using a modular bioinformatics approach, we demonstrated that efficient myeloid reprogramming correlated not to increased proliferation or endogenous Core factor expressions, but to poised expression of GF-activated transcriptional circuits that commonly regulate plasticity in both hematopoietic progenitors and embryonic stem cells (ESC). Factor-driven conversion of myeloid progenitors to a high-fidelity pluripotent state was further accelerated by soluble and contact-dependent stromal signals that included an implied and unexpected role for Toll receptor-NFκB signaling. These data provide a paradigm for understanding the augmented reprogramming capacity of somatic progenitors, and reveal that efficient induced pluripotency in other cell types may also require extrinsic activation of a molecular framework that commonly regulates self-renewal and differentiation in both hematopoietic progenitors and ESC.

Environmental and genetic factors that contribute to Escherichia coli K-12 biofilm formation.

Biofilms are communities of bacteria whose formation on surfaces requires a large portion of the bacteria's transcriptional network. To identify environmental conditions and transcriptional regulators that contribute to sensing these conditions, we used a high-throughput approach to monitor biofilm biomass produced by an isogenic set of Escherichia coli K-12 strains grown under combinations of environmental conditions. Of the environmental combinations, growth in tryptic soy broth at 37 degrees C supported the most biofilm production. To analyze the complex relationships between the diverse cell-surface organelles, transcriptional regulators, and metabolic enzymes represented by the tested mutant set, we used a novel vector-item pattern-mining algorithm. The algorithm related biofilm amounts to the functional annotations of each mutated protein. The pattern with the best statistical significance was the gene ontology 'pyruvate catabolic process,' which is associated with enzymes of acetate metabolism. Phenotype microarray experiments illustrated that carbon sources that are metabolized to acetyl-coenzyme A, acetyl phosphate, and acetate are particularly supportive of biofilm formation. Scanning electron microscopy revealed structural differences between mutants that lack acetate metabolism enzymes and their parent and confirmed the quantitative differences. We conclude that acetate metabolism functions as a metabolic sensor, transmitting changes in environmental conditions to biofilm biomass and structure.

Crystallization and preliminary X-ray diffraction analysis of the middle domain of Paip1.

The poly(A)-binding protein (PABP) simultaneously interacts with the poly(A) tail of mRNAs and the scaffolding protein eIF4G to mediate mRNA circularization, resulting in stimulation of protein translation. PABP is regulated by the PABP-interacting protein Paip1. Paip1 is thought to act as a translational activator in 5' cap-dependent translation by interacting with PABP and the initiation factors eIF4A and eIF3. Here, the crystallization and preliminary diffraction analysis of the middle domain of Paip1 (Paip1M), which produces crystals that diffract to a resolution of 2.2 A, are presented.