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COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases.

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Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

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BACKGROUND: Socioeconomic inequality in antenatal care visits is a great concern in developing countries including Bangladesh; however, there is a scarcity of investigation to assess the factors of inequality and these changes over time. In this study, we investigated the trend of socioeconomic inequalities (2004-2017) in 1+ANC and 4+ANC visits, and extracted determinants contributions to the observed inequalities and urban-rural disparities in Bangladesh over the period from 2011 to 2017. METHODS: The data from the Bangladesh Demographic and Health Surveys (BDHS) conducted in 2004, 2007, 2011 and 2017 were analyzed in this study. The analysis began with exploratory and bivariate analysis, followed by the application of logistic regression models. To measure the inequalities, the Erreygers concentration index was used, and regression-based decomposition analyses were utilized to unravel the determinant's contribution to the observed inequalities. The Blinder-Oaxaca type decomposition is also used to decompose the urban-rural disparity into the factors. RESULTS: Our analysis results showed that the prevalence of 1+ANC and 4+ANC visits has increased across all the determinants, although the rate of 4+ANC visits remains notably low. The magnitudes of socioeconomic inequality in 4+ANC visits represented an irregular pattern at both the national and urban levels, whereas it increased gradually in rural Bangladesh. However, inequalities in 1+ANC visits declined substantially after 2011 across the national, rural and urban areas of Bangladesh. Decomposition analyses have suggested that wealth status, women's education, place of residence (only for 4+ANC visits), caesarean delivery, husband education, and watching television (TV) are the main determinants to attribute and changes in the level of inequality and urban-rural disparity between the years 2011 and 2017. CONCLUSIONS: According to the findings of our study, it is imperative for authorities to ensure antenatal care visits are more accessible for rural and underprivileged women. Additionally, should focus on delivering high-quality education, ensuring the completion of education, reducing income disparity as well as launching a program to enhance awareness about health facilities, and the impact of caesarean delivery.

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Carpal tunnel syndrome (CTS), the most common entrapment neuropathy, is compression of the median nerve deep to transverse carpal ligament at wrist. Ultrasonography and electrophysiological study are complementary in the diagnosis and grading of CTS in appropriate clinical settings. The initial management of patients with CTS is conservative with medical therapy and splinting. However, surgical interventions are indicated in patients in whom medical management has failed. With evolution of the concept of safe zone on ultrasonography and identification of the sonoanatomical landmarks of carpal tunnel in greater detail, Ultrasonography-guided interventions are safer and preferred over surgical management in CTS. The primary ultrasonography-guided interventions include perineural injection, perineural hydrodissection and ultrasonography-guided release of transverse carpal ligament. This review article presents the principles of ultrasonography-guided perineural injection, perineural hydrodissection in CTS, the merits and demerits of injectant used in perineural injection/ hydrodissection, and percutaneous ultrasonography-guided thread release of transverse carpal ligament utilizing the concept of safe zone of the ultrasonography-guided interventions for CTS.

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Macropattern analysis of traumatic brachial plexopathy (TBP) by Magnetic Resonance Imaging (MRI) encompasses localization of injured segments and determination of the severity of injury. The micropattern analysis implies the correlation of the MRI features of TBP with Sunderland's grading of the nerve injury, thereby guiding the management protocol. This review article presents a simplified novel pentavalent approach for the radiological anatomy of brachial plexus, MRI acquisition protocol for the evaluation of brachial plexus, cardinal imaging signs of TBP, and their correlation with Sunderland's microanatomical grading.

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OBJECTIVES: To assess the advantage of the addition of shear wave elastography (SWE) to gray-scale sonography in the diagnosis of plantar fasciitis. METHODS: 30 subjects between 18-60 years of age with unilateral heel pain who were clinically suspected of having plantar fasciitis were included in this study. Their affected feet were taken as cases; while their contralateral feet served as controls. On gray-scale ultrasound, the thickness of plantar fascia, its echopattern, presence of hypoechoic areas, and perifasicular collections were recorded. SWE was done by placing seven ROIs within the plantar fascia; and the mean of their Young's modulus was taken in kPa. RESULTS: Plantar fascial thickening more than 4 mm had 70% sensitivity and 66.7% specificity, echopattern had 90% sensitivity and 96.7% specificity, hypoechoic areas had 80% sensitivity and 96.7% specificity, and perifascial edema had 26.7% sensitivity and 100% specificity for diagnosing plantar fasciitis. Using the ROC curve, the cut-off value of Young's modulus for the diagnosis of plantar fasciitis was found to be ≤ 99.286 kPa. This predicted plantar fasciitis with 97% sensitivity and 100% specificity. The primary diagnostic feature of ultrasound of plantar fascia thickness more than 4 mm detected 21 out of 30 cases of plantar fasciitis; whereas elastography detected an additional 8 cases which would have been missed on B-mode ultrasound alone. CONCLUSIONS: SWE is a useful supplement and improves the diagnostic accuracy of gray-scale ultrasound in plantar fasciitis.

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Background: With the development of coronavirus disease-2019 (COVID-19) pandemic, there is also increased risk of multiple secondary infections either disease- or drug-related. It includes many bacterial as well as invasive fungal infections. Patients and methods: There was suspicion of invasive pulmonary aspergillosis (IPA) infection in COVID-19 patients who were critically ill and had acute respiratory distress syndrome (ARDS). We did radiological evaluation and galactomannan assay in these patients. Result: We have diagnosed COVID-19-associated pulmonary aspergillosis (CAPA) in these patients and started antifungal treatment with voriconazole in all of these COVID-19 patients. Conclusion: It is very important to report such cases, so that healthcare professionals and authorities related to healthcare will be aware of and may also prepare for the increasing burden of this complication. We describe a case series of CAPA infection. How to cite this article: Sharma K, Kujur R, Sharma S, Kumar N, Ray MK. COVID-19-associated Pulmonary Aspergillosis: A Case Series. Indian J Crit Care Med 2022;26(9):1039-1041.

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Evolution and functional necessities have compelled the great toe of the foot and its embryological kin, thumb, to have some tendoligamentous differences with a similar basic anatomical structure. This provides biomechanical advantage to these joints: the thumb is apposable and more mobile, ensuring hand dexterity and tool-handling, whereas the great toe is less mobile and more stable, ensuring weight bearing, strength, and stability for bipedal locomotion. This pictorial review will methodically illustrate the similarities and dissimilarities of the joint morphology and its tendoligamentous attachments at the level of carpometacarpal joint, metacarpophalangeal joint, and interphalangeal joints of thumb compared with tarsometatarsal joint, metatarsophalangeal joint, and interphalangeal joints of great toe. It intends to provide a comprehensive understanding of the normal anatomy of great toe and thumb to the radiologists, enabling better interpretation of the pathologies.

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Targeted cannulation of the nidus and subsequent thermal ablation is the basis of CT-guided radiofrequency ablation (RFA) of osteoid osteoma, which is considered nowadays as the treatment of choice. The majority of complications during this procedure are due to thermal injury of adjacent structures. Specific measures as per the anatomical location of osteoid osteoma can avoid the majority of complications. This article enlists the possible complications and their necessary precautions and remedies to avoid these complications during CT-guided radiofrequency ablation of osteoid osteoma.

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BACKGROUND: We aimed to compare the rate of diagnostically successful vertebral biopsies using conventional bone biopsy needles versus those performed with bone biopsy needles with an acquisition cradle. METHODS: We retrospectively analyzed the data of patients who underwent CT-guided vertebral biopsy between December 2017 to December 2019 at our institute. From December 2017 to November 2018, the procedure was performed on 185 patients using an 11G conventional bone biopsy needle, Jamshidi needleTM "(group 1)". From December 2018 to December 2019, the procedure was performed on 242 patients using an 11G T-handle Jamshidi needle with an acquisition cradle "(group 2)". We reviewed their histopathological reports for both groups of patients to determine the rate of diagnostically successful biopsies. We also compared the crush artifact amongst the unsuccessful biopsy samples acquired by the two types of biopsy needles. RESULTS: 427 patients (270 male and 157 female patients; mean age, 46.4 years; age range, 25-67 years) who underwent CT-guided vertebral biopsy from December 2017 to December 2019 were included in our study. In group 1, diagnostic success was achieved in 136 out of 185 biopsies (73.5%); whereas in group 2, diagnostic success was achieved in 219 out of 242 biopsies (90.50%), p < 0.0001. Out of the diagnostically unsuccessful biopsies in Group 1, 36 out of 49 (73.5%) were due to crush artifact; whereas crush artifact accounted for only 3 out of 23 (13.0%) diagnostically unsuccessful biopsies in group 2, p < 0.0001. Other causes of unsuccessful biopsies (hemorrhagic contents or presence of normal osseous tissue and fibrin only) were statistically insignificant. CONCLUSION: The use of a T-handle Jamshidi needle with an acquisition cradle appears beneficial compared to the conventional Jamshidi needle in terms of the significantly higher rate of diagnostic success and a lower rate of crush artifact.

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Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA .

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Osteoid osteoma is a painful benign bone tumour of children and young adults with characteristic clinico-radiological features depending upon the location of the lesion. Intraoperative visualisation of the nidus is difficult and therefore curative surgery is often associated with excessive bone removal, significant perioperative morbidity and potential need of bone grafting procedures. With advancement in cross-sectional imaging and radiofrequency ablation (RFA) technology, CT-guided RFA has emerged as the treatment of choice for the osteoid osteoma. This procedure involves accurate cannulation of the nidus and subsequent thermocoagulation-induced necrosis.Multidisciplinary management approach is the standard of care for patients with osteoid osteoma. Appropriate patient selection, identification of imaging pitfalls, pre-anaesthetic evaluation and a protocol-based interventional approach are the cornerstone for a favourable outcome. Comprehensive patient preparation with proper patient position and insulation is important to prevent complications. Use of spinal needle-guided placement of introducer needle, namely, "rail-road technique" is associated with fewer needle trajectory modifications, reduced radiation dose and patient morbidity and less intervention time. Certain other procedural modifications are employed in special situations, for example, intra-articular osteoid osteoma and osteoid osteoma of the subcutaneous bone in order to reduce complications. Treatment follow-up generally includes radiographic assessment and evaluation of pain score. Dynamic contrast-enhanced MRI has been recently found useful for demonstrating post-RFA healing.

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Magnetic resonance imaging (MRI) plays an important role in the characterization of vertebral lesions. Even if latest improvements in MRI permit to understand and suspect the nature of vertebral lesions and positron emission tomography computed tomography (PET-CT) gives information about lesion metabolism, biopsy is still needed in most cases. CT-guided percutaneous vertebral biopsy is a minimally invasive, safe and accurate procedure for definitive tissue diagnosis of a vertebral lesion. CT-guided vertebral biopsy is often the best alternative to a surgical biopsy. The purpose of this technical note is to discuss the approach-based techniques for CT-guided percutaneous vertebral biopsy.

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BACKGROUND: Iliolumbar syndrome is a frequent cause of chronic nonspecific low back pain. The cornerstone of its treatment lies upon the specific diagnosis of the iliolumbar syndrome. The ultrasound guided interventions have the potential for the specific diagnosis and treatment of the iliolumbar syndrome. OBJECTIVE: To assess the role of ultrasound-guided intervention for the diagnosis and treatment of the iliolumbar syndrome. MATERIALS AND METHODS: The study comprised of fifty-seven patients of nonspecific low back pain with the clinically suspected iliolumbar syndrome. Two-staged ultrasound-guided interventions were performed: Primary diagnostic and secondary therapeutic interventions. Favorable response after the injection of local anesthetic agent in iliolumbar ligament (defined as VAS score to ≥3) was classified as confirmed Ilio-lumbar syndrome. Clinico radiological efficacy after platelet-rich plasma (PRP) injection in confirmed iliolumbar syndrome patients was done. RESULTS: Out of 57 patients, 45 (78.95%) were diagnosed with confirmed Iliolumbar syndrome after primary diagnostic intervention. The mean value of VAS at presentation was 8.02 ± 0.72 which was decreased to 3.16 ± 1.63; P < 0.0001. All 45 patients underwent PRP injection in iliolumbar ligament and 42 patients (93.33%) showed reduction in mean VAS score from 8 ± 0.67 (at presentation) to 0.89 ± 1.23 after 6 weeks follow up; P < 0.0001. Iliolumbar ligament thickness was decreased from the day of presentation (2.66 ± 0.22) to 6 weeks after therapeutic intervention (0.91 ± 0.42); P < 0.0001. CONCLUSION: The ultrasound guided diagnostic and therapeutic intervention were found to result in a specific diagnosis and remarkable recovery in the iliolumbar syndrome group of nonspecific low back pain patients.

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BACKGROUND: Child and neonatal mortality is a serious problem in Bangladesh. The main objective of this study was to determine the most significant socio-economic factors (covariates) between the years 2011 and 2014 that influences on neonatal and child mortality and to further suggest the plausible policy proposals. METHODS: We modeled the neonatal and child mortality as categorical dependent variable (alive vs death of the child) while 16 covariates are used as independent variables using χ2 statistic and multiple logistic regression (MLR) based on maximum likelihood estimate. FINDINGS: Using the MLR, for neonatal mortality, diarrhea showed the highest positive coefficient (ß = 1.130; P < 0.010) leading to most significant covariate for both 2011 and 2014. The corresponding odds ratios were: 0.323 for both the years. The second most significant covariate in 2011 was birth order between 2-6 years (ß = 0.744; P < 0.001), while father's education was negative correlation (ß = -0.910; P < 0.050). In general, 10 covariates in 2011 and 5 covariates in 2014 were significant, so there was an improvement in socio-economic conditions for neonatal mortality. For child mortality, birth order between 2-6 years and 7 and above years showed the highest positive coefficients (ß = 1.042; P < 0.010) and (ß = 1.285; P < 0.050) for 2011. The corresponding odds ratios were: 2.835 and 3.614, respectively. Father's education showed the highest coefficient (ß = 0.770; P < 0.050) indicating the significant covariate for 2014 and the corresponding odds ratio was 2.160. In general, 6 covariates in 2011 and 4 covariates in 2014 were also significant, so there was also an improvement in socio-economic conditions for child mortality. This study allows policy makers to make appropriate decisions to reduce neonatal and child mortality in Bangladesh. CONCLUSIONS: In 2014, mother's age and father's education were also still significant covariates for child mortality. This study allows policy makers to make appropriate decisions to reduce neonatal and child mortality in Bangladesh.

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BACKGROUND: The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. RESULTS: We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites. CONCLUSION: Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

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Patient classification through feature selection (FS) based on gene expression data (GED) has already become popular to the research communities. T-test is the well-known statistical FS method in GED analysis. However, it produces higher false positives and lower accuracies for small sample sizes or in presence of outliers. To get rid from the shortcomings of t-test with small sample sizes, SAM has been applied in GED. But, it is highly sensitive to outliers. Recently, robust SAM using the minimum ß-divergence estimators has overcome all the problems of classical t-test & SAM and it has been successfully applied for identification of differentially expressed (DE) genes. But, it was not applied in classification. Therefore, in this paper, we employ robust SAM as a feature selection approach along with classifiers for patient classification. We demonstrate the performance of the robust SAM in a comparison of classical t-test and SAM along with four popular classifiers (LDA, KNN, SVM and naive Bayes) using both simulated and real gene expression datasets. The results obtained from simulation and real data analysis confirm that the performance of the four classifiers improve with robust SAM than the classical t-test and SAM. From a real Colon cancer dataset we identified 21 additional DE genes using robust SAM that were not identified by the classical t-test or SAM. To reveal the biological functions and pathways of these 21 genes, we perform KEGG pathway enrichment analysis and found that these genes are involved in some important pathways related to cancer disease.

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BACKGROUND AND OBJECTIVE: Diabetes is a silent killer. The main cause of this disease is the presence of excessive amounts of metabolites such as glucose. There were about 387 million diabetic people all over the world in 2014. The financial burden of this disease has been calculated to be about $13,700 per year. According to the World Health Organization (WHO), these figures will more than double by the year 2030. This cost will be reduced dramatically if someone can predict diabetes statistically on the basis of some covariates. Although several classification techniques are available, it is very difficult to classify diabetes. The main objectives of this paper are as follows: (i) Gaussian process classification (GPC), (ii) comparative classifier for diabetes data classification, (iii) data analysis using the cross-validation approach, (iv) interpretation of the data analysis and (v) benchmarking our method against others. METHODS: To classify diabetes, several classification techniques are used such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and Naive Bayes (NB). However, most of the medical data show non-normality, non-linearity and inherent correlation structure. So in this paper we adapted Gaussian process (GP)-based classification technique using three kernels namely: linear, polynomial and radial basis kernel. We also investigate the performance of a GP-based classification technique in comparison to existing techniques such as LDA, QDA and NB. Performances are evaluated by using the accuracy (ACC), sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and receiver-operating characteristic (ROC) curves. RESULTS: Pima Indian diabetes dataset is taken as part of the study. This consists of 768 patients, of which 268 patients are diabetic and 500 patients are controls. Our machine learning system shows the performance of GP-based model as: ACC 81.97%, SE 91.79%, SP 63.33%, PPV 84.91% and NPV 62.50% which are larger compared to other methods.

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Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum ß-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition.

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In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student׳s t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker.